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THREE ESSAYS ON THE TRADING BEHAVIOR OF
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THREE ESSAYS ON THE TRADING BEHAVIOR OF MARKET PARTICIPANTS
By

ORKUNT MESUT DALGIC

A DISSERTATION
Submitted to Michigan State University
in partial fulﬁllment of the requirements

for the degree of
DOCTOR OF PHILOSOPHY

Department of Finance

2003

ABSTRACT
THREE ESSAYS ON THE TRADING BEHAVIOR OF MARKET PARTICIPANTS
By

ORKUNT MESUT DALGIC

Grinblatt and Keloharju (2001) ﬁnd that investors prefer to trade and hold the stock
of ﬁrms that have the same national cultural origin as the investor. The second chapter
extends Grinblatt and Keloharju (2001) by studying brokerage houses’ choice of trade
counterparties in the upstairs market. Brokerage houses are found not to be more likely to
trade with counterparties having the same national culture. Market-making ﬁnancial
institutions are culturally unbiased in their trading behavior, which contradicts Grinblatt
and Keloharju’s (2001). There is no evidence to support an investor-level preference for
trading via like-culture brokerage houses. Brokerage houses are not more likely to trade
with like-culture counterparties when there is a greater chance of informed trading.
Finally, evidence supports the variable cost of upstairs searching for counterparties being
small.

The third chapter studies the existence and determinants of trading relationships
between investors and individual brokers and brokerage houses. It is found that when an
individual broker moves to a new brokerage house, customers shift a statistically
signiﬁcant proportion of their trade volumes to the new brokerage house. Customers of
the old brokerage house are more likely to switch when they have closer trading
relationships with the new brokerage house, consistent with the hypothesis that investors

incur costs when switching brokers. A new brokerage house attracts trading for stocks it

trades less actively, consistent with individual brokers being hired to increase a brokerage
house’s market Share. Results are mixed for more savvy investors’ likelihood of
switching. Female investors are more likely to switch than males, consistent with female
investors being more relationship-oriented.

The fourth chapter redevelops Grossman’s (1992) model, and discusses the eco-
nomic implications of intermediate results. Unexpressed demand of investors in the up-
stairs market is split into private and public components. It is found that as the public
Signal of unexpressed demand becomes less (more) noisy, the execution quality of the
upstairs market improves (worsens) relative to the downstairs market. Thus, exclusive or
close trading relationships between customers and market makers where signals for unex-

pressed demand remain private, can hurt upstairs market liquidity.

Acknowledgements

I am deeply indebted to my mentor and dissertation chairperson, Dr. G. Geoffrey
Booth, without whose generous advice, ﬁrm and patient guidance, and dedicated support,
I could not have written this dissertation. I am very grateful to Dr. S. Tamer Cavusgil for
guiding me and keeping me motivated throughout the doctoral program, as well as
supporting and advising me in writing this dissertation. I would like to thank Dr. Charles
Hadlock and Dr. Ted Fee for their advice and moral support. I would also like to thank
my wife, Kamilla, for her tremendous courage and patience during this challenging
period, and my parents, for their unwavering support. I especially would like to thank my
father, Dr. Tevﬁk Dalgic, without whose advice and support I would not have pursued a
PhD. degree in the ﬁrst place.

Additionally, I would like to thank Dr. Petri Sahlstrém, of Vaasa University,
Finland, for answering questions about the data set and for timely help during the running
of the required computer programs; Dr. Juha-Pekka Kallunki of 0qu University, Finland,
for invaluable discussions about the data sets; and brokers at the Finnish Stock Exchange
in Helsinki whose tremendous knowledge of the Finnish Stock Market has greatly

enriched this dissertation.

TABLE OF CONTENTS

LIST OF TABLES viii

CHAPTER 1: INTRODUCTION 1

CHAPTER 2: DOES THE NATIONAL CULTURAL ORIGIN OF BROKERS

AFFECT THEIR CHOICE OF TRADING PARTNERS? 6
2. 1 Introduction ................................. 7
2.2 Discussion of the Literature ......................... 11
2.3 The Data Set ................................ 17
2.4 Hypotheses to be Tested .......................... 21
2.5 Trading Behavior as a Function of the National Culture of Brokerage

Houses and Traded F inns .......................... 24

2.5.1 Ratios Showing the Effect of the National Culture of a Finn’s CEO
on Trading Intensity of the Firm’s Stock .............. 24

2.5.2 Ratios Showing the Effect of the National Culture of Brokerage

Houses on their Choice of Trading Partners ............ 27

2.6 A Multivariate Model to Study the Effect of National Culture on the Strength
of Trading Relationships between Brokerage Houses ........... 31
2.7 Results .................................... 35
2.8 Discussion and Interpretation of Results .................. 37
2.9 Conclusion ................................. 39
CHAPTER 3: HOW LOYAL ARE A BROKER’S CUSTOMERS? 42
3. 1 Introduction ................................. 43

3.2

3.3

3.4

3.5

3.6

3.7

3.8

Literature Review ..............................
3.2.1 Economic Relationships ......................
3.2.2 The Upstairs Market and Relationships ..............
3.2.3 Order Flow Preferencing and Intemalization ............
Research Questions (Hypotheses) .....................

Description of Trading on the HSE, the HETI and FCSD Data Sets, and
the Classiﬁcation of Individual Broker Switching Events .........

Descriptive Statistics ............................

An Analysis of the Effect of Individual Broker Switching on Customers’
Percentage of Trading with the Individual Brokers Old and New Brokerage
Houses ....................................

3.6.] Do Customers of the Individual Broker’s Old Brokerage House
Reduce Their Trading with the Old Brokerage House when the
Broker leaves? ...........................

3.6.2 Do Customers of the Individual Broker’s Old Brokerage House
Intensify Their Trading with the Broker’s New Brokerage House
when The Broker Moves to the New Brokerage House? ......

A Multivariate Model to Study the Effect of pre-existing business relation-
ships, and Investor Characteristics on Customer Trading Proportions when
Individual Brokers Switch Brokerage Houses ...............

Results ....................................

3.8.1 Hypothesis 1: Trading Relationships Aﬁect Investors’ Likelihood
of Switching ............................

3.8.2 Hypothesis 2: Upstairs Market Investors Develop Closer Busi-
ness Relationships with the Broker and are More Likely To Switch
With The Broker ..........................

3.8.3 Hypothesis 3: Brokers’ Market Dominance Affects Investors’ Like-
lihood of Switching .........................

vi

48

48

53

62

66

69

74

76

79

79

81

3.8.4 Hypotheses 4 / 5: Less Savvy Investors are Biased and More
Likely to Switch / Less Savvy Investors Have Simpler Trading
Preferences and Less Likely to Switch ............... 82

3.8.5 Hypotheses 6 / 7: Male Investors Have Stronger Relationships
with (Male) Brokers and More Likely to Switch / Female Investors
are More Relationship-Oriented and More Likely to Switch . . . 84

3.8.6 Hypothesis 8: Investors Prefer Brokerage Houses with Same Cul-
ture, and are Less Likely to Switch if New Brokerage House has

Different Culture .......................... 84
3.9 Discussion and Interpretation of Results .................. 85
3.10 Conclusions ................................. 88

CHAPTER 4: UNEXPRESSED DEMAND AND THE TRADING BEHAV-

IOR OF UPSTAIRS MARKET MAKERS 90
4.1 Introduction ................................. 91
4.2 Grossman’s (1992) Model ......................... 94
4.2.1 Downstairs Market Equilibrium .................. 97
4.2.2 Upstairs Market Equilibrium .................... 105
4.2.3 Relative Quality of Upstairs Market versus Downstairs Market . . 108
4.3 Grossman’s Model when Market Makers Know the Unexpressed Demand

4.4

4.5

Only of their Own Customers ........................ 109
4.3.1 Upstairs Market Equilibrium .................... 111
4.3.2 Endogenously Determined Market Makers ............ 115
Extension of Grossman’s Model of the Upstairs Market .......... 120

Comparison of Upstairs Market Equilibrium under Extended Model to the
Downstairs Market Equilibrium of Grossman ( 1992) ........... 125

vii

4.6 Conclusion ................................. 127

CHAPTER 5: CONCLUSION 128

REFERENCES 156

viii

List of Tables

2.1

2.2

2.3

2.4

2.4

2.5

2.6

2.6

3.1

3.1

3.2

3.3

3.4

3.4

3.5

3.5

3.6

76 share classes in the sample belonging to 61 ﬁrms ............ 130
23 Brokerage Houses in the Sample Grouped by Culture ......... 131

Ratios showing the effect of the Cultural Heritage of a Firm’s CEO on
Trading Intensity of the Firm’s Stock .................... 132

Ratios showing the effect of the Cultural Heritage of Brokerage Houses on
their Choice of Trading Partners ...................... 133

(cont’d) ................................... 134
Hypotheses Being Tested and the Corresponding Independent Variables . 135

A Multivariate Model to Study the Effect of National Culture on the Strength
of Trading Relationships between Brokerage Houses ........... 136

(cont’d) ................................... 137
Hypotheses Being Tested and the Corresponding Independent Variables . 138
(cont’d) ................................... 139

Percentage of the Number of All HETI Trades Matched Successfully with
Investor Records In the FCSD Data Set ................... 140

Comparison of Switching Brokers’ Presence in the Upstairs Market Before
the Switch to that of all Individual Brokers of the same Brokerage House . 141

Percentage of Customers’ Downstairs Trades via Old Brokerage House . 142
(cont’d) ................................... 143
Percentage of Customers’ Upstairs Trades via Old Brokerage House . . . 144
(cont’d) ................................... 145

Percentage of Customers’ Downstairs Trades via New Brokerage House . 146

3.6

3.7

3.7

3.8

3.9

3.10

3.11

3.12

3.13

(cont’d) ................................... 147

Percentage of Customers’ Upstairs Trades via New Brokerage House . . . 148
(cont’d) ................................... 149
Multivariate Model for the Change in Customers’ Proportion of Trading

By Value via Old Brokerage House (Month=—1 to Month=0) ...... 150

Multivariate Model for the Change in Customers’ Proportion of Trading
By Value via Old Brokerage House (Month=—l to Month=l) ....... 151

Multivariate Model for the Change in Customers’ Proportion of Trading
By Value via New Brokerage House (Month=—1 to Month=0) ...... 152

Multivariate Model for the Change in Customers’ Proportion of Trading

By Value via New Brokerage House (Month=—l to Month=1) ...... 153
Multivariate Simultaneous Equations Model for the Change in Customers’
Proportion of Trading By Value via Old and New Brokerage Houses (Month=-— l
to Month=0) ................................. 154
Multivariate Simultaneous Equations Model for the Change in Customers’

Proportion of Trading By Value via Old and New Brokerage Houses (Month=-— 1
to Month= l) ................................. 15 5

CHAPTER 1: INTRODUCTION

People are drawn to the familiar. People like to interact with familiar people. People
prefer ideas, objects, and behaviors already known to them, as opposed to ideas, objects
and behaviors unknown to them. In conventional economic theory, agents act only to
maximize their economic utility. It is unclear whether agents’ preference for the familiar
is a result of either personal biases or real world constraints (such as language barriers)
which limit the agent’s choices and reduce economic utility, or whether it is the result of
utility maximization, where a preference for the familiar provides more complete
information and allows agents to make more optimal decisions.l Merton (1987) shows
that investors’ unfamiliarity with a stock causes the stock to be underpriced in
equilibrium. Merton’s ﬁnding is similar that of Stapleton and Subrahmanyam (1977), and
Alexander, Eun, and Janakiraman (1987), who showed that internationally segmented
markets for a ﬁrm’s stock lead to an equilibrium underpricing of the stock which also
increase the ﬁrm’s equity cost of capital.

The ‘investor recognition’ bias of Merton (1987), refers to an investor’s familiarity
with only a limited number of securities. According to Merton (1987) this explains why
investors invest in securities that are (more) familiar to them, a phenomenon which is
extensively researched in the literature and referred to as ‘home bias’. In more recent
years, numerous empirical studies have used different dimensions of agents’ familiarity
with assets to explain ‘home bias’. Grinblatt and Keloharju (2001) use language culture,
language and geographical distance, to explain differences in investors’ propensity to buy

and hold stock within a country (Finland). Hau (2001) uses language and distance to

 

‘ Hirschleifer (2001) provides an extensive review of the psychological bias of investors and asset pricing.

explain the difference in trading proﬁts of German and foreign traders on the electronic
trading system of the German Security Exchange. Huberman (2001) ﬁnds that the
portfolios of the employees of a Regional Bell Operating Company are tilted towards that
particular company. Thus, the general thrust of the literature is that investors’ familiarity
with a security increases their likelihood of buying and holding that security, and also
increases their economic gain from doing so. Hau (2001) ﬁnds geographic and cultural
distance inversely related to trading proﬁts and argues that these measures of familiarity
may proxy for information advantage. However, Grinblatt and Keloharju (2001) do not
ﬁnd a correlation between proﬁts and familiarity, and between proﬁts and investor
sophistication, and argue that the familiarity bias exhibited by investors indicates a
psychological bias.

Although familiarity biases seem both pervasive and economically signiﬁcant
enough to affect the pricing of assets, the literature has thus far ignored the possibility of
a familiarity bias among economic agents in the ﬁnancial market itself, even though this
may also affect asset prices. Allen (2001) states that agency and information problems
between ﬁnancial institutions and investors can help explain asset-pricing anomalies such
as price-bubbles. Therefore, to the extent that familiarity among investors and ﬁnancial
institutions and among market makers themselves can reduce such agency and
information problems, familiarity may also help explain such asset-pricing anomalies.
Furthermore, the study of familiarity may also help in understanding why markets are
segmented. For example, if market makers in a ﬁnancial market prefer to transact with
familiar counterparties, then this may segment ﬁnancial markets, leading to an artiﬁcial

type of order-preferencing (without side-payments) that reduces trade execution quality

and increases transaction costs. On the other hand, in a segmented market, agency and
information asymmetry problems may be alleviated, and transaction costs reduced, by
trading with more familiar counterparties, so that a possible familiarity bias would not be
the cause but the symptom of a segmented market. Hansch, Naik and Viswanathan
(1999) examine the practices of preferencing and internalization on the London Stock
Exchange, and they interpret the absence of a relationship between order-ﬂow
preferencing/internalization and the cross-section of bid-ask spreads across stocks as not
being consistent with collusion among dealers but as being consistent with costly trading
relationships. Hence, a familiarity bias, if it existed among market makers, may be a
symptom of segmented markets rather than a cause.

The concept of familiarity has also played an important role in the banking and
relationship lending literature. The main thrust of this literature is that lending
relationships, in which lenders become familiar with their borrowers over repeated
business transactions, can alleviate asymmetric information problems and can facilitate a
more efﬁcient allocation of the lenders’ ﬁnancial resources. This relationship parallels the
relationship between investors and market makers in the market microstructure literature,
which also has been researched extensively. For example, the market maker can learn
whether or not an investor is trading on private information, which would inﬂict losses on
the market maker and on investors trading for liquidity reasons. Seppi (1990) argues that
such familiarity helps an upstairs market maker learn about his customers, and helps him
distinguish between informed and uninformed order-ﬂow, which can help reduce
transaction costs for uninformed investors. Benveniste, Marcus, and Wilhelm (1992)

suggest that professional relationships between brokers and Specialists and in the upstairs

market between market makers and investors can reduce asymmetric information
problems, a beneﬁt they argue is not easily achieved in anonymous (automated) trading
systems. Grossman (1992) argues that the business relationship of upstairs market
makers with their customers allows them to learn their customers’ unexpressed demand,
so that market makers can more accurately predict order ﬂow, and hence reduce the cost
of providing immediacy. The implication of this strand of the literature is that when
market participants are familiar with each other information problems are less likely,
which reduces transaction costs and increases market liquidity.

Chapters 2 and 3 of this dissertation study whether the existence of familiarity
affects the trading behavior of market makers and investors. Chapter 4 is also related to
the concept of familiarity as the chapter studies how the greater familiarity upstairs
market makers have with their customers, and with their customers’ trading behaviors,
can impact upstairs market liquidity.

Chapter 2 examines the concentration of brokerage house trading among different
counterparties in the upstairs market of the Helsinki Stock Exchange. Due to the ability
of upstairs brokers to decide on the trading venue and trading counterparty, and the
absence of explicit order-preferencing arrangements, the study permits the testing of
whether or not cultural familiarity between market makers increases the likelihood of a
trade. If cultural familiarity does matter, then this would support the costly search and
relationship hypothesis, implying that market makers may choose culturally familiar
counterparties to reduce the costs involved in searching for trade counterparties and in
maintaining a relationship with counterparties. It would also be consistent with the

existence of agency problems between market makers, which are reduced by trading with

culturally more familiar counterparties. Chapter 3 studies the changes in customers’
trading relationships with brokerage houses when individual brokers switch brokerage
houses. It uses different measures of customers’ familiarity with the old and new
brokerage houses, and the switching broker to explain the changes in these trading
relationships. Chapter 4 develops a model that captures the impact close business
relationships between upstairs market makers and their customers can have on upstairs

market liquidity. Finally, Chapter 5 presents the concluding remarks.

CHAPTER 2: DOES THE NATIONAL CULTURAL ORIGIN OF BROKERS
AFFECT THEIR CHOICE OF TRADING PARTNERS?

ABSTRACT

Grinblatt and Keloharju (2001) ﬁnd that investors prefer to trade and hold the stock of
ﬁrms that have the same national cultural origin as the investor. This chapter extends
Grinblatt and Keloharju (2001) by studying brokerage houses’ choice of trade
counterparties in the upstairs market. It is found that brokerage houses are not more
likely to trade with counterparties having the same national culture than they are to trade
with counterparties having a different national culture. Contrary to Grinblatt and
Keloharju’s (2001) ﬁnding that institutional investors show a cultural bias in their
stockholdings and trades, it is found that market-making ﬁnancial institutions, i.e.
brokerage houses, are culturally unbiased in their trading behavior. Furthermore, there is
no evidence to support an investor-level preference for trading via like-culture brokerage
houses. Brokerage houses are not more likely to trade with like-culture counterparties
when there is a greater chance of informed trading. Finally, evidence supports the

variable cost of upstairs searching for counterparties being small.

2.1 Introduction

In an ideal world, cultural differences between people should not matter. However,
in the real world cultural differences among people have always mattered, and the
identiﬁcation with a particular culture has always deﬁned societies and nations. But what
is culture? Most contemporary deﬁnitions of culture deﬁne it as the shared knowledge,
value or belief systems, and behaviors of a particular group of people. There are racial,
religious, tribal and national cultures. There are cultures of managers and employees
within organizations. There are the cultures of national fraternities, sororities, and student
cultures in schools, colleges, and the culture of criminal gangs.

In economics, culture is ignored in the classical rational expectations framework.
Recently, however, there has been a trend to understand if and how culture can affect the
behaviors of market agents. Grinblatt and Keloharju (2001) Show that investors prefer to
trade and hold the stock of ﬁrms more familiar to them, where familiarity is measured as
physical, cultural or linguistic closeness of investors to the ﬁrms in question. The authors
Show using a unique data set from Finland, a country whose citizens have either a Finnish
national cultural origin (native Finnish-speakers) or a Swedish national cultural origin
(native Swedish-speakers), that investors prefer to trade and hold the stock of ﬁrms
identiﬁed as having the same national cultural origin as the investor.2 Speciﬁcally, the
authors ﬁnd that investors prefer to trade and hold the stock of companies whose CEOs
have the same native language as the investor. The authors also ﬁnd that this cultural bias

is stronger for households than for institutional investors, and interpret the stronger effect

 

2 Although both the native Finnish-speaking and the native Swedish-speaking groups in Finland have differ-
ent national cultural origins, members of both groups nevertheless identify themselves as Finns.

of culture among households as implying that households are less sophisticated investors.
Bikhchandani, Hirshleifer and Welch (1992) Show how an informational cascade model
can replicate certain observed effects of culture, such as “the localized conformity of
behavior” which leads to the authors’ observation that “Americans act American,
Germans act German, and Indians act Indian” [page 992]. Lazear (1999) argues that
having a common culture and language facilitates trades between individuals, and
therefore that learning another culture or language brings additional economic beneﬁt to
individuals.

Despite the recent research interest in the effects of culture on the behavior of
investors and individuals, no research has been done on whether or not culture can affect
the behavior of agents in the ﬁnancial market itself. For example, cultural biases can exist
among the broker-dealers of an upstairs market, a decentralized trading environment
where these broker-dealers are responsible for facilitating the block trades of their
customers. As each broker-dealer in the upstairs market searches for trade counterparties
to a customer order among other broker dealers, he may concentrate his trading among
counterparties having the same cultural classiﬁcation. Consequently, such cultural biases
may segment the upstairs market along cultural lines, thereby reducing the liquidity
available in the upstairs market as a whole, and increasing transactions costs for
investors. On the other hand cultural biases among the broker-dealers of an upstairs
market may be just the consequence of a fragmented market in which there are fewer
agency and information problems (e. g. due to front-running) among broker-dealers
sharing a similar culture. In an upstairs market setting, a broker-dealer’s execution of its

own customer’s trades is referred to as internalization, whereas preferencing refers to the

practice of choosing the same dealer or counterparty regardless of price and time-priority.
Hansch, Naik and Viswanathan (1999) ﬁnd that preferencing and internalization in an
upstairs market reduce transaction costs for investors, which is consistent with trading
relationships and searches in the upstairs market being costly. Therefore, these upstairs
market costs may also be reduced when broker-dealers trade with similar culture
counterparties.

This paper extends the work of Grinblatt and Keloharju (2001) by examining
whether brokerage houses active on the upstairs market of the Helsinki Stock Exchange
prefer to trade with counterparty brokerage houses that have a Similar national cultural
origin to their own. If similarity of the national cultural origin among upstairs brokerage
houses can reduce the costs of maintaining trading relationships and search costs, then
brokerage houses may conduct a greater proportion of their trading with counterparties
sharing the same national cultural origin, ceteris paribus. Altemately, a cultural bias in
the trading behavior of brokers could mean that brokers are ignoring different culture
counterparties, which may adversely affect the purported liquidity beneﬁts of the upstairs
market as argued by extant research. There are two competing joint hypotheses which
can explain the possible ﬁnding of a cultural bias in the trading of upstairs brokers. One
hypothesis is that a cultural bias in the trading of upstairs brokers is good because brokers
having incomplete information use similar culture counterparties to reduce search and
relationship costs, which improves liquidity provision in an upstairs market. A competing
hypothesis is that a cultural bias in the trading of upstairs brokers is bad because it
segments the upstairs market along cultural lines reducing the upstairs market’s liquidity

beneﬁts. After controlling for a host of idiosyncratic brokerage house and stock

characteristics, the paper ﬁnds no evidence of a cultural preference in brokerage houses’
trading choices. Therefore as the paper ﬁnds that the trading choices of brokers in the
upstairs market are not divided along cultural lines, neither of the two competing
hypotheses is supported. Arguably, a more direct study of the effect of culture on the
liquidity provision in an upstairs market is not likely to provide signiﬁcant additional
insight. There is also no evidence of an investor-level preference for trading via
like-culture brokerage houses, such that while investor-level cultural biases affect which
stock investors choose to trade, as Grinblatt and Keloharju (2001) ﬁnd, such biases do not
affect how or with whom (broker) investors trade the stock.

Seppi (1990) argues that upstairs brokers can screen informed trades and thereby
reduce transaction costs. If information about the informativeness of the trades of
customers is shared among brokerage houses having the same culture (i.e. information
clienteles), such that there is a smaller adverse selection cost associated with trading with
like-culture counterparties, then brokerage houses should show an increasing preference
to trade with like-culture counterparties when a trade is more likely to contain
information, everything else remaining constant. The paper ﬁnds no evidence that a
greater potential for informed trading leads brokers to trade with like-culture
counterparties. This may imply, as Seppi (1990) suggests, that upstairs brokers
successfully screen out informed trades, such that brokers are indifferent to the culture of
counterparties. It may also imply that there is informed trading in the upstairs market and
that a broker is equally familiar with the trades of like-culture counterparties as with the

trades of unlike-culture counterparties. However, as a result of not screening informed

10

trades which may lead to losses by a counterparty broker, a broker is likely to suffer
adverse reputation effects. Hence, the former alternative seems more plausible.

The paper makes the following contributions to the literature. First, by studying
whether or not national culture affects the trading choices of brokers, the paper extends
the work of Grinblatt and Keloharju (2001) into the realm of ﬁnancial institutions.
Second, the paper contributes to the literature on upstairs markets by ﬁnding that the
cultural differences among brokers do not fragment upstairs markets. Third, by
documenting a lack of cultural bias in the trading behavior of brokers, the paper supports
the idea that brokers are members of a closely-knit professional community.

Section 2.2 reviews the literature on culture, upstairs markets and internalization.
Section 2.3 describes the features of the Helsinki Stock Exchange and introduces the data
set. Section 2.4 presents the hypotheses to be tested. Section 2.5 presents statistics that
examine the trading behavior of brokerage houses as a function of the national culture of
brokerage houses and traded ﬁrms. Section 2.6 introduces a multivariate model that
studies the effect of brokerage house national culture and the national culture of CEOs of
traded ﬁrms on the trading intensity between brokerage houses. Section 2.7 presents the
results of the multivariate analysis. Section 2.8 discusses and interprets the results.
Finally, Section 2.9 concludes.

2.2 Discussion of the Literature

The study examines whether national culture may inﬂuence the trading behavior of
brokerage houses in an upstairs market. Therefore, this section presents literature on
upstairs markets, including the practices order-ﬂow preferencing and internalization, and

discusses the role culture may play in an upstairs market.

11

The extant literature studies how certain features of upstairs markets that are
different from downstairs markets, such as lack of customer anonymity and market
fragmentation, can create information clienteles and counterparty search and trading
relationship costs. The contribution of upstairs markets to liquidity provision is also
explored. The literature argues that upstairs markets are special and that their role is not
duplicated by downstairs markets. This paper’s examination of the existence of a cultural
determinant in the trading behavior of upstairs market makers shows whether cultural
biases are another special feature of such markets. Seppi (1990) develops a multi-period
model of block trading and ﬁnds that traders who can credibly signal that their trades are
uninformed prefer to trade in the upstairs market. Grossman (1992) argues that the ’
upstairs market is a repository of information about the unexpressed demand of investors
and that this feature enhances the liquidity provided by the upstairs market relative to a
downstairs market. Using a data set of block transactions in Dow Jones stocks on the
NYSE, Madhavan and Cheng (1997) Show that the downstairs market contributes
signiﬁcantly to liquidity in these stocks, and that the upstairs market provides better
execution than the downstairs market, although this difference is not economically
signiﬁcant. However, the authors do ﬁnd support for Seppi’s (1990) hypothesis that
uninformed traders prefer to trade in the upstairs market. Keim and Madhavan (1996)
ﬁnd that information leakage occurs in the upstairs market prior to a trade, and that a
concave relationship exists between trade size and the temporary price impact of
liquidity. Booth, Lin, Martikainen and Tse (2002) ﬁnd that upstairs trades have less
information content and lower price impact than downstairs trades, consistent with Seppi

(1990) and Grossman (1992). The authors also ﬁnd that the upstairs market uses the

12

downstairs market to set prices, suggesting that price discovery occurs in the downstairs
market. Together these two ﬁndings are taken as evidence that the liquidity advantage of
an upstairs market depends on informed order trades being done in the downstairs market
where they contribute to the price discovery process. Smith, Tumbull and White ( 1999),
Show that brokers in the upstairs market successfully ﬁlter out information-based trades,
reducing the adverse selection costs relative to the downstairs market, which supports
Seppi (1990). It is also found that trading in the upstairs market is more likely during
times when liquidity is low in the downstairs market.

Studies of the upstairs market have also examined the practices of order
preferencing and internalization. Burdett and O’Hara (1987) develop a theoretical model
of the syndication process of block trading, and argue that in a search for syndication
counterparts, the dealer conducting the search must take into account the incentive that
potential counterpart dealers contacted during the search have to front-run the impending
block trade, thereby adversely affecting the price of the stock in the downstairs market,
and reducing the beneﬁts of syndication to the dealer if the dealer has quoted a
commitment price to the trader. This indicates optimal stopping rules for the search, such
that at some point the dealer stops the search and trades the remaining (unmatched) order
ﬂow against his own account. As a trade-initiating dealer knows the identities of each of
the potential trade counterparts in an upstairs market, he can exclude from the
syndication dealers who are likely to front-run the impending upstairs trade. If cultural
similarity is an important factor in brokers’ decision to choose trading counterparties,

such that having the same culture as the counterparty broker allows the trade initiator

l3

better to assess the likelihood that the counterparty will front-run, then brokers should
choose to trade with like-culture counterparties.3

Hansch, Naik and Viswanathan (1999) demonstrate that in an upstairs market the
broker practices of preferencing and internalization do not lead to higher spreads being
paid by investors and this contradicts the collusion hypothesis, but agrees with the
hypothesis that searching and trading relationships in the upstairs market are costly.
Therefore, the study refutes claims that preferencing and internalization indicate tacit
collusion among dealers. If there are cultural biases in the trading preferences of
brokerage houses, then the ﬁnding of Hansch, Naik and Viswanathan (1999) could imply
that cultural biases among brokers also reduce the costs associated with searching and
trading relationships in the upstairs market. This would also be consistent with Cornell
and Welch (l996)’s assertion that cultural similarity helps reduce the asymmetric
information problems between principal and agent. Battalio and Holden (1997) develop a
model of payment for order flow and internalization, and Show that when brokers know
more about certain characteristics of their customers than third-parties, they can proﬁt
from payment-for-order-ﬂow practices by selectively internalizing order ﬂow. This idea
also has implications about internalization in an upstairs market. As Seppi (1990) argues,
if a broker knows his own customers better than others, the broker can screen the orders
of an informed customer so that these orders go to the downstairs market while
uninformed customers’ orders are executed in the upstairs market. On the other hand,

potential counterparties not observing the characteristics of the broker’s customers will

 

3 This idea is consistent with Cornell and Welch’s (1996) assumption that an employer’s cultural similarity
with a job applicant allows the employer to learn the quality of the applicant.

l4

not know whether the broker’s customer really is uninformed. Therefore it will be
difficult for the broker to ﬁnd counterparties in the upstairs market, and the broker would
have to internalize the uninformed order. However, if the cultural Similarity of brokers
can reduce such asymmetric information problems among brokers, then brokers would be
expected to prefer trading with counterparties having the same culture, ceteris paribus.
Hence, the asymmetric information problem presented in Battalio and Holden (2001)
would increase the cost of trading with other brokers in the upstairs market, and make
internalization and trading with similar culture brokers more attractive. Therefore, if
upstairs market trading relationships are costly, then to the extent that trading with a
like-culture counterparty broker can reduce such costs, whether these are search costs as
in Burdett and O’Hara (1987), or asymmetric information costs as implied by Battalio
and Holden (1997), brokers should prefer trading with like- culture counterparties.

The work of economists and economic sociologists in the area of culture and
language is especially concerned with immigration/emigration and cultural or linguistic
assimilation. These papers build theoretical models based on the assumption that culture
and language differences among economic agents affect agents’ behaviors. However,
despite this argument, few studies actually test the relevance and importance of culture
and language to the behavior of market participants and to the functioning of markets in
general. Akerlof(1976) studies how the individuals’ decision of whether to behave
consistently with social norms and consequently to gain certain economic and social
beneﬁts, or to behave contrary to social norms and therefore be devoid of these beneﬁts,
can distort Arrow-Debreu’s general equilibrium model of perfect competition.

Bikhchandani, Hirshleifer, and Welch (1992) deﬁne an informational cascade as

15

occurring “when it is optimal for an individual, having observed the actions of those
ahead of him, to follow the behavior of the preceding individual without regard to his own
information”. The authors use informational cascades to generate localized conformity of
individual behavior, and interpret this as an explanation of how “fads, fashion, custom,
and cultural change” occur. Church and King (1993) examine a game-theoretic model
where a person’s utility depends positively on the number of other people the person can
communicate with, so that the popularity of a language induces a network extemality.
Katz and Matsui (1995) show that the standardization of language brings about beneﬁts
for trade. John and Yi (1995) study how the unequal distribution of language endowments
motivate individuals’ decision to learn a new language and the decision to emigrate.
Cornell and Welch (1996) assume that cultural similarity with a job applicant allows the
employer to learn the applicant’s quality, and show that employers tend to employ job
applicants with a Similar cultural background, referred to as ‘screening discrimination’,
even when employers are unbiased and the characteristics of job applicants are the same
across cultural groups. This discrimination is greater when quality is important but
difficult to observe, and screening is cheap. Lazear (1999) builds a model in which an
individual can only trade with others having a similar culture and language, and can learn
a new culture or language to increase the number of potential trading partners. Therefore,
the view that cultural differences represent barriers or additional costs to trade among
market agents, combined with the ﬁnding of Hansch, Naik and Viswanathan (1999) that
upstairs trading relationships are costly, may imply that cultural differences create
additional transaction costs in the upstairs market, and that culturally similar upstairs

brokers prefer to interact with each other in order to reduce transactions costs.

16

2.3 The Data Set

The Helsinki Stock Exchange is composed of two markets, an upstairs market and a
downstairs market, which are connected by an automated trading and information system
called the HETI. Trades are made during one of three periods each day, namely, the
pre-trading, free-trading and the after-market periods. The downstairs market of the
HETI is essentially an open electronic limit order book that is updated in real-time.
Trading members of the stock exchange enter market and limit orders into the HETI
where the orders are matched anonymously and executed into trades according to price
and time priority. In the upstairs market, trades are negotiated and then recorded in the
HETI system by the buyer in a timely manner after they occur. However, due to the
requirement that upstairs trades occur at prices no worse than the concurrent downstairs
book quotes, the reporting of upstairs trades may sometimes be delayed until the
after-market period, when the range of acceptable transaction prices is wider.4

During the pre-trading period, which starts at 8:30 am, traders announce bid and
offer prices and the HETI system matches orders and executes these as transactions. Any
unexecuted portions of orders are then transferred to the free-trading period, which is a
continuous trading session beginning at 10:00 am. and ending at 5:00 pm. During
free-trading, customers submit orders to their brokers, who typically make the decision
about whether to send the order directly to the HETI limit order book (downstairs
market), or to negotiate the trade with a counterparty (upstairs market), and then to report

the trade to the HETI. Conversations with HSE brokers suggest that while investors

 

" Trade prices reported during the after-market periods are required to occur inside the price range bracketed
by the greater of the highest transactions price of the trading day and the closing ask of the free-trading
session, and the smaller of the lowest transactions price of the trading day and the closing bid of the free-
trading session.

17

hypothetically can express a preference for trade venue, such as upstairs or downstairs,
the decision to choose either the upstairs or downstairs market is typically made by the
broker. However, even if the decision to execute an order in the upstairs market is made
by a customer, the choice of upstairs trade counterparty is made by the broker, and
therefore implies that the study in this paper of the upstairs brokers’ trading behavior is
free of a possible customer-level preference. Although large orders in less liquid stocks
are most often negotiated upstairs, for the most liquid stocks, notably Nokia, the broker
uses the downstairs market even for very large orders. Brokers receive these large orders
typically from big foreign institutions and break-up and trade the orders ‘over-the-day’ in
the downstairs market to obtain the best Volume Weighted Average Price (VWAP). The
after-market consists of two trading sessions, one from 5:05 pm. to 6:00 pm, and the
other from 8:00 am. to 8:25 am. of the following day. The after-market is distinguished
from the free-trading period by the fact that the HETI electronic limit order book system
is closed, and that all trades must therefore be negotiated and then reported to HETI.
However, anecdotal evidence suggests that most of the trades reported during the
after-market are in fact trades that have occurred earlier in the day.

As the paper investigates trading decisions made by brokerage houses, and
downstairs market trades in the pre-trading and free-trading periods involve automated
trade matching by the HETI system, the study uses upstairs market trades of the
free-trading and after-market periods to investigate the trading behavior of brokerage
houses. Brokerage houses often work over-the-day orders from large foreign customers.
An over-the-day order for a particular customer is executed as multiple trades in the

downstairs market, and the order is again reported during the aﬁer—market period as a

18

single pseudo-trade, usually at the request of the customer in order to simplify settlement.
A pseudo-trade report in the after-market is identiﬁed by the fact that the same brokerage
house is reported as both the buyer and seller, even though the actual downstairs trades
that make up the pseudo-trade are typically with numerous other brokerage houses.
Therefore, in order to exclude these pseudo-trades, the study disregards trades reported in
the after-market where the buyer and seller are the same. Upstairs trades with the same
buyer and seller reported during the free-market period are also disregarded, for
consistency. The paper uses data from 1997 and earlier, as the HSE stopped providing
broker codes after 1997. Therefore, the data set consists of all upstairs market trades of
ﬁrms occurring between different brokerage houses, and where the stock of the ﬁrm is
continuously listed on the Helsinki Stock Exchange from January 1, 1996 to December
31, 1997.

Although stocks of a ﬁrm listed on the HSE can belong to one of several categories
called share classes, which give different voting rights to shareholders, most ﬁrms have
only a single share class. As in Grinblatt and Keloharju (2000), this paper treats each
share class as a separate stock.5 After removing share classes not continuously listed on
the HSE during the sample period, and an additional eight stocks whose ﬁrms could not
be classiﬁed by culture, 76 share classes remained in the sample, corresponding to 61
unique ﬁrms. Table 2.1 shows the list of share classes in the sample, grouped by the
cultural classiﬁcation of the ﬁrm’s CEO. The classiﬁcation method for the ﬁrm’s cultural

heritage is similar to the method used by Grinblatt and Keloharju (2001). The national

 

5 Grinblatt and Keloharju (2000) point out that the different share classes of a particular company reﬂect
different voting rights, i.e. the more liquid share classes have fewer votes, and by Ilmanen and Keloharju
(1997) this gives rise to different clienteles of share ownership.

19

culture of the ﬁrm is classiﬁed according to the national culture associated with the name
of the ﬁrm’s CEO.6 There are 58 share classes where the ﬁrm’s culture is Finnish,
compared to 16 share classes of Swedish, and two ﬁrms having non-Finnish and
non-Swedish cultures. A unique feature of the HSE data set is that for each trade the
brokerage houses of the buyer and seller are identiﬁed. As of year-end 1997, there were
23 brokerage house members of the HSE.7

Table 2.2 lists according to cultural heritage, the brokerage houses that were active
in the after-market during the sample period. The cultural heritage of the brokerage house
is deﬁned as Finnish if either the cultural heritage of the name of the brokerage house is
Finnish, or the company that owns the brokerage house is headquartered in Finland. If the
brokerage house’s name is Swedish and the house is headquartered in Sweden, then the
house is classiﬁed as having a Swedish culture. If a brokerage house cannot be
categorized according to these rules, then it is given the cultural classiﬁcation ‘Other’.
The three Finnish brokerage houses that fall into the national culture category ‘Other’ are
headquartered in Switzerland, Denmark and the UK, respectively.8 According to this
classiﬁcation 15 brokerage houses have a Finnish culture, ﬁve have a Swedish culture,

and three have neither a Swedish nor a Finnish culture.

 

6 If both the ﬁrst and last names of the CEO are Finnish, then the ﬁrm’s culture is identiﬁed as Finnish. If
both the ﬁrst and last names of the CEO are Swedish, then Finnish biographical sources and the language
of the C EOs university education are used to identify the native language of the CEO and hence the CEO’s
national culture.

7 At the beginning of January 1996, there were 21 brokerage houses. During the sample two brokerage
houses stopped trading while four new brokerage houses started trading. so that 19 of the brokerage houses
that were trading at the start of the sample were also trading at the end of the sample.

8 The analysis in this paper is repeated with the brokerage house’s national culture deﬁned as the national
culture of the brokerage house’s managing director. The qualitative results of the paper were robust to this
reclassiﬁcation.

20

Table 2.2 shows 25,812 after-market trades in the sample, where 15,914 trades are
conducted by the 15 Finnish culture brokerage houses, and 9,233 trades are conducted by
the ﬁve Swedish culture brokerage houses. Although Swedish culture houses trade more
actively in the after-market on average than the average Finnish culture house, the
difference is not statistically signiﬁcant. Furthermore, more active brokerage houses
conduct a smaller percentage of their trades with other houses than less active brokerage
houses. Therefore it is likely that the difference between the medians of the average
percentage of extemalized buys and sells (Table 2.2, fourth column), for Finnish culture
houses and Swedish culture houses of 22.2, and 17.4, respectively, is due to the difference
in trade activity between the brokerage houses.

2.4 Hypotheses to be Tested

Traditional models of market microstructure treat market makers as agents whose
primary function is to match the supply and demand curves of traders, while being
adequately compensated for their services. Grinblatt and Keloharju (2001) ﬁnd that
investors’ national culture affect their preferences for trading and shareownership, and
interpret this as a lack of sophistication, consistent with investors’ lack of familiarity. If
there is also a lack of familiarity in the upstairs market among brokerage houses, then this
may imply that brokerage houses prefer to trade with counterparties having the same
culture. This leads to the following hypothesis:

Hypothesis I = A brokerage house prefers to trade with other brokerage houses
having the same national culture, ceteris paribus.

Grinblatt and Keloharju (2001) ﬁnd that investors prefer to trade stocks of ﬁrms

having the same culture as the investor. If a brokerage house also prefers to trade in

21

stocks of ﬁrms sharing the same culture as itself, then it should have a closer trading
relationship with other brokerage houses that have the same culture as the stocks of ﬁrms
it wishes to trade. However, a brokerage house’s specialization in trading the stock of a
ﬁrm with the same culture as itself may not necessarily indicate a bias on the part of the
brokerage house. The bias may originate from the investor level because an investor who
trades stocks of ﬁrms with the same culture may also prefer to do business with
brokerage houses with the same culture. AS Grinblatt and Keloharju (2001) showed that
investors prefer to trade stocks of ﬁrms similar in culture to the investor, a cultural bias in
investors’ choice of brokerage house when both buying and selling a stock may lead to
like-culture brokerage houses having a closer trading relationship in trading the stock of a
like-culture ﬁrm, than in trading the stock of an unlike-culture ﬁrm. For example, a
Swedish culture brokerage house trading with other Swedish culture brokerage house
would prefer to trade stocks of Swedish culture ﬁrms to trading stocks of ﬁrms with
non-Swedish cultures. Hence:

Hypothesis [1 = An upstairs brokerage house has a closer trading relationship with
a like-culture brokerage house when trading the stock of a like-culture ﬁrm than when
trading the stock of an unlike-culture ﬁrm.

Battalio and Holden (1997) argue that brokers observing characteristics of their own
customers can distinguish between uninformed and informed order ﬂow. The upstairs
market requires a broker receiving an order from a customer to shop around among
different counterparty brokers. As counterparty brokers do not directly observe
characteristics of the order submitting customer, it is likely for there to be an asymmetric

information problem, such that a counterparty broker does not know whether the order

22

being shopped is informed. Burdett and O’Hara (1987) simulate an asymmetric
information problem with their model, and argue that searching for trade counterparts
gives other brokers the opportunity to front-run the impending trade, which limits the
number of counterparties the broker contacts during the search. Therefore to the extent
that having the same national culture can reduce such information asymmetries, the order
receiving broker would be expected to concentrate the upstairs market search among
brokers having the same national culture.

Therefore, asymmetric information problems may contribute to the inﬂuence of
culture on brokers’ trading preferences:

Hypothesis [II = A brokerage house trades more often with brokerage houses having
the same culture when there is a greater likelihood of asymmetric information with
counterparty brokers about the transaction, ceteris paribus.

Hansch, Naik and Viswanathan (1999) found that trading relationships in the
upstairs market were costly. Therefore, upstairs brokers should limit their search and
trading of a particular stock to a few counterparties that are most active in trading that
stock and hence most likely to be counterparties to trades of that stock. This leads to the
hypothesis:

Hypothesis IV = In the trading of any given stock, an upstairs brokerage house
prefers to buy from brokerage houses that are more active than itself in selling that stock,

ceteris paribus.

23

2.5 Trading Behavior as a Function of the National Culture of Brokerage Houses
and Traded Firms

2.5.1 Ratios Showing the Effect of the National Culture of a Firm’s CEO on Trading
Intensity of the Firm’s Stock

Grinblatt and Keloharju (2001) ﬁnd that investors prefer to hold, and trade the stock
of ﬁrms with CEOS that Share the same national culture as the investor. This section uses
some ratios to examine whether brokerage houses trade in stocks of ﬁrms with CEOS that
share the same national culture as the brokerage house. AS the data set does not uniquely
identify the order submitter, i.e. either agency trades from a customer, or proprietary
trades of the brokerage house, one cannot distinguish between cultural biases caused by
investors’ preference of trading stock of like-culture ﬁrms with like-culture brokerage
houses, and the brokerage house’s preference for trading like-culture ﬁrms ﬁ'om its
portfolio. However, anecdotal evidence from interviews with brokers suggests that
brokerage houses carried very small inventories and there was also little proprietary
trading in the sample period. Furthermore much of inventory and proprietary trading was
limited to a few highly liquid stocks. Therefore, any investor level cultural bias for
trading stock of like-culture ﬁrms with like-culture brokerage houses would be picked up
as a brokerage house level cultural bias for trading stock of like-culture ﬁrms.

Table 2.3 shows the intensity of the trading of brokerage houses in stocks of ﬁrms
whose CEOS belong to one of three different national culture groups, Finnish, Swedish
and Other (neither Finnish nor Swedish). Panel A shows the intensity of trading of
Finnish culture brokerage houses in stocks of ﬁrms with CEOS having Finnish, Swedish
and Other national culture. Panel B shows the same information for brokerage houses

having a Swedish national culture, and Panel C shows the information for brokerage

24

houses having the national culture classiﬁcation of Other, i.e. neither Swedish nor
Finnish.
The trading intensity ratio of a brokerage house 2' in the stock of ﬁrms with CEOS

having a Finnish national culture is deﬁned as:

Brokerage house i’s proportion of trading in stocks of ﬁrms
with Finnish national culture CEOS

Brokerage house i’s proportion of trading in all stocks

 

(2.1)

The numerator in the above ratio is the number of trades in stocks of all ﬁrms with CEOS
having a Finnish culture where brokerage house 2' was the buyer (seller), divided by the
number of trades in stocks of all ﬁrms with CEOS having a Finnish culture executed by all
brokerage houses. The denominator is the number of trades in all stocks where brokerage
house 2' was the buyer (seller), divided by the number of trades in all stocks executed by
all brokerage houses. Taking the brokerage house Evli as an example, the number trades
in stocks of ﬁrms with CEOS having Finnish national culture where Evli was the buyer, is
1,577, and the number of buy trades all brokerage houses conduct in Finnish stocks of
ﬁrms with CEOS having Finnish national culture is 23,823. Therefore, the numerator, or
Evli’s trading proportion in stocks of ﬁrms with CEOS having a Finnish national culture
where Evli was the buyer is 0.066. The number of trades in all stocks where Evli was the
buyer is 1,637, and the total number of trades conducted by all brokerage houses in all
stocks is 25,817. Therefore, the denominator, or Evli’s trading proportion in all stocks
where Evli was the buyer, is 0.063. The trading intensity ratio for Evli in stocks of ﬁrms

with CEOS having a Finnish national culture is therefore, 0066/0063, or 1.044.

25

The trading intensity ratios of Swedish, and Other (neither Finnish nor Swedish)
national culture brokerage houses are calculated in a similar manner. If the trading
intensity ratio for a brokerage house i is greater (less) than one, then the brokerage house
i trades more (less) intensely in stocks of ﬁrms with CEOS having a Finnish culture than
it does in all stocks. In other words, the ratio measures the brokerage house i’s
after-market specialization in stocks of ﬁrms with CEOS having a Finnish culture. Table
2.3, Panel A shows that the median trading intensity ratio of brokerage houses in each of
the three ﬁrm CEO culture categories, Finnish, Swedish and Other, are all similar and
close to l, for both buys and sells columns. Also, the fractions of the number of
brokerage houses that have trading intensities larger than 1, are similar and close to 0.5,
which indicates that for stocks of ﬁrms with CEOS having a Finnish culture, the trading
intensity ratios are evenly distributed within each brokerage house national culture
category, and that the distributions of this ratio among brokerage house national culture
categories are also similar.

Panel B shows that the median trading intensity ratio of Finnish culture brokerage
houses in stocks of ﬁrms with Swedish culture CEOS, is about one, but the median
trading intensity ratios for Swedish and Other culture brokerage houses in stocks of ﬁrms
with Swedish culture CEOS are signiﬁcantly less than 1. Also, the fraction of trading
intensity ratios greater than one under the buyer column for Finnish culture brokerage
houses is 0.6, whereas for Swedish culture brokerage houses, this ﬁgure is 0.17, which is
statistically signiﬁcantly less than 0.6. Although the fraction under the buyer column for
Other culture brokerage houses is 0.33, the standard error is too big to reject the null that

it is equal to the ﬁgure for Finnish culture brokerage houses of 0.6. Therefore, Finnish

26

culture brokerage houses are more active than Swedish culture brokerage houses in
trading stocks of ﬁrms with Swedish culture CEOS.

Table 2.3, Panel C Shows that the median trading intensity ratios of all brokerage
house culture categories are less than one in stocks of ﬁrms with CEOS having the culture
Other. Also, the fractions of trading intensity ratios in each brokerage house culture
category are less than 0.5. The median brokerage house in each cultural category trades
less intensely in stocks of ﬁrms with CEOS having ‘Other’ national culture than in all
stocks. Hence trading in stocks of ﬁrms with CEOS having the national culture
classiﬁcation ‘Other’, is concentrated in a very few brokerage houses in each brokerage
house culture category.

2.5.2 Ratios Showing the Effect of the National Culture of Brokerage Houses on
their Choice of Trading Partners

Table 2.4 shows for stocks of ﬁrms with CEOS having Finnish, Swedish and Other
national culture, the intensity of trading between brokerage houses on both sides of a
trade, i.e. trade-initiating and trade-counterparty brokerage houses, belonging to Finnish,
Swedish and Other culture categories. Panel A shows for all ﬁrms with CEOS having
Finnish national culture, the median and the fraction greater than one for trade-initiating
brokerage house cultures of the median trade intensity ratio with cultures of
trade-counterparty brokerage houses. Panels B and C Show this information for ﬁrms

with CEOS having Swedish and Other national culture, respectively. The trading intensity

27

ratio between a trade-initiating brokerage house i and a trade-counterparty brokerage

house j, where z' ¢ j, in stocks of all ﬁrms having a: culture CEOS, is deﬁned as:

Trade-initiating brokerage house i’s trading weight with counterparty
j in stocks of ﬁrms having a: culture CEOS (2 2)

 

Trade-initiating brokerage house i’s trading weight with all other
counterparties in stocks of ﬁrms having a: culture CEOS

The numerator is the number of trades brokerage house 2' conducts with brokerage house
j in stock of ﬁrms with CEOS having culture :16, divided by the number of trades all
brokerage houses except j conduct with brokerage house j in stock of ﬁrms with CEOS
having culture at. The denominator is the number of trades brokerage house i conducts
with all brokerage houses except i in stock of ﬁrms with CEOS having culture 2:, divided
by the number of trades all brokerage houses conduct with all other brokerage houses

(2' ¢ j) in stock of ﬁrms with CEOS having culture :r.

Panel A Shows that for stocks of ﬁrms with CEOS having Finnish national culture
that the median among trade-initiating Finnish brokerage houses of the median trade
intensity ratios with Finnish, Swedish, and Other culture trade-counterparty brokerage
houses are 0.85, 0.70 and 0.28 respectively. The ﬁgures for the fraction of trade intensity
ratios greater than one for the Finnish trade-initiating brokerage house culture category,
are 0.27, 0.13 and 0.33, for Finnish, Swedish and Other trade-counterparty brokerage
house culture categories, respectively. The fact that all median ratios are less than one
and the fractions are less than 0.5, means that for the Finnish trade-initiating brokerage
house culture category, the trading with each trade-counterparty culture category is

concentrated between a small number of brokerage house trading pairs. The largest

28

median trade intensity ratio for Finnish trade-initiating brokerage house culture category
of 0.85 occurs when the trade-counterparty culture category is Finnish. The median trade
intensity ratio when the trade-counterparty culture category is Swedish is 0.7, which is
somewhat less than the ratio for the Finnish trade-counterparty culture category of 0.85
but larger than the ratio for Other trade-counterparty culture category of 0.28, although
these differences are not statistically signiﬁcant. The median trade-intensity ratios with
each of the trade-counterparty culture categories, Finnish, Swedish and Other, when the
trade-initiating culture category is Swedish, are 0.97, 1.07, and 0.67, respectively. The
fraction of ratios greater than one for each trade-counterparty culture, Finnish, Swedish,
and Other, when the trade-initiating culture category is Swedish, are 0.40, 0.60, and 0.20,
respectively. The largest trade intensity ratio of 1.07 occurs with the Swedish
trade-counterparty culture category, implying that Swedish brokerage houses trade most
intensively with other Swedish brokerage houses. As the median ratio for each
trade-counterpart culture category is greater in the Swedish trade-initiating culture
category than the corresponding ratio in the Finnish trade-initiating culture category, the
trades of Swedish culture brokerage houses are more evenly distributed within each
trade-counterpart category than are the trades of Finnish culture brokerage houses. This
may be due to Swedish brokerage houses being larger than Finnish brokerage houses on
average and being more likely to trade with a greater number of brokerage houses. For
the trade-initiating culture category ‘Other’, the medians of trade intensity ratios with
trade-counterparty culture categories are all zero except with the Swedish

trade-counterparty culture category.

29

Panel B examines the trading intensity ratios among brokerage house culture
categories for stocks of ﬁrms with CEOS having Swedish national culture. The median
ratios are zero for all trade-initiating and trade-counterparty cultural category
combinations, indicating that these stocks are not traded between most of the
trade-initiating and trade-counterparty brokerage house pairs. The fraction of ratios
greater than one also are all zero except when the trade-initiating brokerage house culture
is Finnish and the trade-counterparty culture is either Finnish, where the fraction is 0.08,
or Swedish, where the fraction is 0.23, and when the trade-initiating and
trade-counterparty cultures are both Swedish, where the fraction is 0.40. A noteworthy
difference between the results in Panel A and Panel B is that for Panel B the fraction of
ratios greater than one for the Finnish trade-initiating culture category is greater when the
trade-counterparty culture category is Swedish than when it is Finnish, whereas for Panel
A, the fraction is greater when the counterparty culture category is Finnish. This indicates
that Finnish culture brokerage houses trade more intensively with other Finnish culture
brokerage houses than they do with Swedish culture brokerage houses when the stocks
belong to ﬁrms with CEOS having Finnish national culture, but Finnish culture brokerage
houses trade less intensively with other Finnish culture brokerage houses than they do
with Swedish culture brokerage houses when the stocks belong to ﬁrms with CEOS
having Swedish national culture. However, the differences among the fractions are not
statistically signiﬁcant.

Panel C examines the trading intensity ratios between trade-initiating and
trade-counterparty brokerage house culture categories for stocks of ﬁrms with CEOS

having national culture of Other. The results are very Similar qualitatively to Panel B. The

30

medians of all trade-initiating and trade-counterparty culture combinations are zero
except for trades occurring between brokerage houses that both belong to a Swedish
culture category, where the median is 0.37. Also, the fractions greater than one are all
zero except when the trade-initiating culture is Finnish, and the trade-counterparty culture
is either Swedish or Other, where the fractions for both are 0.08, or when the
trade-initiating and trade-counterparty cultures are both Swedish, where the fraction is
0.25.

Overall, the results of Table 2.4 are consistent with the hypothesis that brokerage
houses prefer to trade with brokerage houses that share the same culture, although many
of the statistics are not statistically signiﬁcant.

2.6 A Multivariate Model to Study the Effect of National Culture on the Strength
of Trading Relationships between Brokerage Houses

The previous section presented some statistics examining the effect of national
culture on the trading intensities of brokerage houses. This section presents a multivariate
regression model in which to test the hypotheses presented earlier. The model’s
dependent variable Dug, is a measure of the strength of the trading relationship in stock
:1: between buying (selling) brokerage house 2' and selling (buying) partner 3' and is
deﬁned as the difference between i’s trading weight with j ’s in stock 2:, W,‘ 1.2:, where
2' 74 j , and i’s trading weight with all brokerage houses in stock 1:, W”. The dependent
variable is therefore the difference of these trading weights which are expressed in
equation (2.2) as a ratio. Using the difference of the two trading weights instead of their
ratio means that unlike the ratio variable, which is truncated at zero, the dependent

variable can take on negative values as well, so that weaker than expected trading

31

relationships, i.e. negative values of the dependent variable, are accurately represented.
This was not a consideration when using equation (2.2), the ratio, as a ‘relative’ measure
of the trading relationship, i.e. the median of the ratio of weights, is more intuitive a

descriptive statistic, than the difference of the weights. Therefore,

Di.j.r = Wi,j,:r: _ Win: (23)

The trading weight of 2' with j in stock 1:, Wu; is deﬁned as the number of trades in
stock at where brokerage house i is the buyer (seller) and brokerage house j is the seller
(buyer), divided by the number of trades in stock at where brokerage house j is the seller
(buyer) and any other brokerage house is the buyer (seller), and the trading weight of 2'
with all brokerage houses, W”, is deﬁned as the number of trades in stock a: where i is
the buyer (seller), divided by the number of trades in stock :2: occurring between different
brokerage houses. For example, during the sample period, Alfred Berg bought Nokia
stock on 18 occasions (trades) from Arctos, and on 60 occasions Arctos sold Nokia to all
brokerage houses, so that the trading weight of Alfred Berg with Arctos for Nokia stock
is 18/60, or 0.3. Alfred Berg bought Nokia stock on 47 occasions from all brokerage
houses and all brokerage houses bought Nokia stock on 479 occasions from all other
brokerage houses, so that the trading weight of Alfred Berg with all brokerage houses for
Nokia stock is 47/479, or 0.1. Therefore the difference between these ﬁgures is 03-01,
or 0.2. As this ﬁgure is positive, the buying relationship of Alfred Berg with Arctos for

Nokia stock is stronger than Alfred Berg’s buying relationship with all brokerage houses.

32

The multivariate ordinary least squares regression method is used to estimate the
determinants of trading behavior among brokers. Each data point in the data set is
composed of the buying brokerage house, the selling brokerage house and the stock
traded by the brokerage houses. There are 14,027 observations. The following
independent variables are used to test the hypotheses introduced earlier, as indicated in
Table 2.5: dummy variables Swedish Buyer, Other Buyer, Swedish Seller, Other Seller,
indicating the national cultures of the buying and selling brokerage houses, where the
Finnish buyers represent the base case for buyer dummies and Finnish sellers represent
the base case for seller dummies; dummy variables Finnish BuyerxSwedish Seller,
Finnish Buyerx Other Seller, Swedish Buyerx Finnish Seller, Swedish Buyerx Swedish
Seller, Swedish Buyerx Other Seller, Other Buyerx Finnish Seller, Other BuyerxSwedish
Seller, Other Buyerx Other Seller, indicating the buyer and seller brokerage house
combinations, where Finnish Buyeerinnish Seller is the base case; dummy variables
Traded Firm ’5 CEO has Swedish culture, Traded Firm '3 CEO has Other Culture,
indicating the national culture of the CEO of the ﬁrm whose stock is traded, the base case
being stocks of ﬁrms with CEOS having a Finnish culture; dummy variables for each
stock but one; standard deviation of the average daily return for the stock over the whole
sample period Average Daily Return Volatility, proxying for information asymmetry
among brokers; the natural log of the number of all trades in a stock where the buying
brokerage house was the seller divided by the number of trades in that stock where the
selling brokerage house was the seller, Log(No of Seller Sells/No of Buyer Sells),
proxying for search costs in the upstairs market. The regression also uses over 600

dummies to control for spurious signiﬁcance in the right hand side variables, i.e.

33

dummies are used for all stocks but one, and for all buying and selling brokerage house
combinations but one. Stock dummies control for whether a stock is widely held and
hence widely traded among brokers, or narrowly held and hence traded by a few brokers.
Buying and selling brokerage house combination dummies control for idiosyncratic
trading relationships between brokerage houses that cannot otherwise be proxied and that
may lead to spurious statistical signiﬁcance in the brokerage house culture combination
dummies.

As the broker’s choice of counterparty for an extemalized small trade should be less
constrained by the liquidity that can be provided by the counterparty, measures of trading
relationships using small trades should be more indicative of brokers’ personal trading
preferences than similar measures using medium or large trades. Therefore, any cultural
bias among broker trading behavior should be stronger for smaller trades. Therefore the
regression is run separately for dependent variables constructed using trades from
different trade size categories. The trade size categories are constructed with the same
trade size cutoffs used in Barclay and Warner (1993). Barclay and Warner (1993) use
these trade size cutoffs when analyzing the information content of different trade Size
categories for stocks traded on the NYSE. Thus the small trade size category is deﬁned as
all extemalized trades with volume less than 500 shares, the medium size category is
deﬁned as all extemalized trades where the volume is between 500 and 10,000 shares,
and the large trade size category as all extemalized trades where the volume is greater
than 10,000 shares. The distribution of the number of trades in each of the size categories

is not much different from Barclay and Warner (1993), implying that the trade size

34

cutoffs used for the NYSE are also appropriate for the HSE.9 According to Barclay and
Warner (1993), the small trade size category is the least likely to contain informed
trading. However, as this paper studies the upstairs market which according to Seppi
(1990) ﬁlters out informed trades, the size categories used in this paper do not separate
trades based on the likelihood of information content, as in Barclay and Smith (1993) but
on the demand they place on the liquidity of the upstairs market. Hence, the cutoffs of
Barclay and Warner (1993) are used here merely for consistency and do not affect
qualitatively the results obtained in this paper.
2.7 Results

Table 2.6 shows the results of the various multivariate regression models. In the ﬁrst
column the dependent variable is constructed using all extemalized trades. In the second,
third and fourth columns, the dependent variable, trading intensity between the buying
and selling brokers for a given stock, is constructed using trades from the small, medium
and large trade size categories. The p-values are presented in parentheses, next to each
point estimate. The adjusted R2 are 0.013 for MODEL I which uses all available
extemalized upstairs trades, 0.0045 for the model that uses small trades, -—-0.0008 for the
model that uses medium trades, and 0.074 for the model that uses large trades.

In MODEL 1, for the explanatory variables used to test Hypothesis 1, i.e. that
brokerage houses prefer to trade with others having the same culture, namely Finnish
Buyer x Swedish Seller, Finnish Buyer x Other Seller, (Swedish Buyer X Swedish Seller

— Swedish Buyer x Finnish Seller), (Swedish Buyer x Swedish Seller — Swedish Buyer

 

9 For example, in Barclay and Warner (1993), the median of the percentage of trades in each trade cate-
gory were 59.6%, 38.6%, 0.8% for the small, medium and large trade size categories, respectively. The
corresponding percentages in the HSE sample used in this paper are 49.8%, 45.1% and 3.1%, respectively.

35

X Other Seller), (Other Buyer X Other Seller -— Other Buyer X Finnish Seller) and
(Other Buyer X Other Seller — Other Buyer >< Swedish Seller), the point estimates are
0.062, —0.003, 0.065 (=0.096—0.031), 0.091 (=0.096—(—0.005)), —0.095
(=—0.075—0.020) and ——0.01 (=—0.075-—0.085), and the p-values are all greater than 0.1.
Furthermore, only two of the six point estimates have the expected Sign. For the models
using the different trade size categories, the p-values of all corresponding point estimates
are also greater than 0.1. Thus the ﬁndings do not support Hypothesis 1. The explanatory
variables used in MODEL I to test Hypothesis II, i.e. that when brokerage houses trade
with others having the same culture, they are more likely to trade stock of like-culture
ﬁrms than that of unlike-culture ﬁrms, namely ((Swedish Buyer X Swedish Seller) X
Traded Firm ’s CEO has Swedish Culture —Swedish Buyer X Swedish Seller), and
((Swedish Buyer X Swedish Seller) X Traded Firm is CEO has Swedish
Culture—(Swedish Buyer X Swedish Seller) X Traded Firm is CEO has Other Culture),
the point estimates are —0.1 (=—0.004—0.096) and —0.023 (=—0.004——0.019), and the
p-values are greater than 0.1. Neither of the two point estimates have the expected value.
The corresponding point estimates from the other models also have p-values greater than
0.1. Thus, the ﬁndings do not support Hypothesis II. The explanatory variables used in
MODEL I to test Hypothesis III, that there is more cultural bias in trading when there is
more chance of asymmetric information, namely Average Daily Return Volatility, X
Finnish Buyer X Swedish Seller, X Finnish Buyer X Other Seller, X Swedish Buyer X
Finnish Seller, X Swedish Buyer X Swedish Seller, X Swedish Buyer X Other Seller, X
Other Buyer X Finnish Seller, and x Other Buyer x Swedish Seller, are 3.446, —0.115,

—0.375, —0.675, 0.754, 0.735, 0.247, 0.109, and 0.240, and the p-values are all greater

36

than 0.1. However, the ﬁrst six of the nine point estimates have the expected value. The
corresponding point estimates in other models have p-values greater than 0.1. Finally, the
explanatory variable used in MODEL I to test Hypothesis IV, that stock brokerage houses
prefer to buy from others that are more active sellers than itself for a given stock, LogﬂVo
of Seller Sells / No of Buyer Sells), the point estimate is 0.001, and has the expected sign
but the estimate’s p-value is greater than 0.1. The p-values of the corresponding estimates
in other models are also greater than 0.1. Hence the results do not support Hypothesis IV.

Therefore, the results of the multivariate analysis do not support any of the
hypotheses being tested. The overall thrust of the results are consistent with broker
trading behavior in the upstairs market being determined by idiosyncratic brokerage
house speciﬁc, and stock speciﬁc factors. After controlling for these idiosyncratic factors,
the study ﬁnds that brokerage house culture, and the culture of the traded ﬁrm has no
effect on brokerage houses’ trading behaviors. Thus the cultural effect Grinblatt and
Keloharju (2001) document in the trading preference of investors does not exist in the
trading preference of brokerage houses.
2.8 Discussion and Interpretation of Results

The lack of a culture effect in the trading behavior of brokers may be interpreted as
being consistent with the assumption that brokers are professional market participants.
The study supports the idea that cultural differences among brokers do not present an
extra trading cost for brokers that would lead brokers to prefer. trading with like-culture
counterparties. The lack of a cultural preference may also be due to the close physical
proximity of brokers to one another, as in the Helsinki Stock Exchange. In fact all

brokerage houses in Finland are within walking distance of each other in the downtown

37

area of Helsinki. This physical proximity may mean that brokers are already familiar
with each other and that culture has no additional explanatory power in determining
broker trading behavior. Moreover, despite the fact that brokers are in competition with
each other for customers, brokers are also dependent on one other when making markets,
i.e. in the upstairs market brokers need to shop around among other brokers in order to
ﬁnd the best price for their own customers. This business-related interdependence as well
as brokers’ personal familiarity with each other in a community of professionals may also
explain the lack of a cultural bias observed in brokers’ trading preferences.

The lack of a preference for trading the stock of a like-culture ﬁrm between two
like-culture brokers implies two things, namely that investors are indifferent about the
culture of the brokerage house they place their orders with, and that brokerage houses do
not have a preference for trading like—culture ﬁrms with like-culture counterparties. Of
course, if there had been a cultural bias, then it would be incumbent upon the study to
differentiate between possible sources of this bias. Although it would be interesting to
study whether or not there was such a bias at an investor level, instead of inferring this
bias or lack thereof from the trading behavior of brokerage houses, it can be argued that
such a study would yield no signiﬁcant additional insight.

There is also evidence that potential information asymmetry between brokers for a
particular stock does not affect brokers’ likelihood of trading with like-culture
counterparties. This is consistent with brokers not using the cultural identity of a
counterparty to reduce potential adverse selection costs due to asymmetric information
about the future price of a stock. The paper does not distinguish between causes of a

change in the price of a stock. Such a price change can be due to a lack of liquidity or the

38

release of new information. As the presence of asymmetric information imparts no
cultural bias in a broker’s choice of trade counterparty, it can be argued that neither of the
possible causes of a price change have an effect. Hence, the lack of a broker’s bias for
like-culture counterparties when trading stock with a highly uncertain future price, can
either mean that there are no information asymmetries that create adverse selection costs
for the broker that could be lessened by trading with like-culture counterparties, or that
cultural differences between the broker and counterparty brokers do not indicate the
presence of inter-broker information asymmetry regarding the future price of the stock.

Brokers do not prefer to buy from others that are more active sellers in a particular
stock than the broker itself. After controlling for effects of idiosyncratic trading activity
between the buyer and seller, i.e. trading relationships, brokers do not concentrate their
upstairs trading among a small number of highly active sellers. This is consistent with the
average marginal cost of searching for an additional upstairs counterparty being small,
and consistent with upstairs trading costs not having a signiﬁcant variable component.
2.9 Conclusion

The paper examines cultural and other determinants of brokerage houses’ choice of
trading counterparties. The main result is that brokerage houses are indifferent to trading
with like-culture counterparties. This is contrary to Grinblatt and Keloharju’s (2001)
ﬁnding that investors prefer to trade the stock of like-culture ﬁrms. Furthermore, there
does not seem to be an investor level bias for placing orders with like culture brokerage
houses, or a brokerage house level bias that makes brokers concentrate the trading of the
stock of like—culture ﬁrms with like-culture counterparties. This ﬁnding is also contrary

to investors’ cultural bias as documented by Grinblatt and Keloharju (2001). There is no

39

cultural bias in brokers’ trading preferences caused by possible asymmetric information
about the informativeness of order ﬂow. And lastly, upstairs brokers do not concentrate
their trading among a few large counterparties, which is consistent with the marginal cost
of searching for additional counterparties being small.

This paper studied the effect of national culture on the trading relationships in the
upstairs market on the Helsinki Stock Exchange, and found that such differences did not
affect the trading relationships of upstairs market makers. Therefore this result can be
interpreted to imply that cultural heterogeneity among broker-dealers of a country’s
upstairs market does not affect the trading relationships in that market. ‘0 More generally,
this result implies that cultural ‘home bias’ for assets documented by papers such as
Grinblatt and Keloharju (2001), and Han (2001), seems not to occur in the trading
relationships between market makers. Hence, while ‘home bias’ in general, and cultural
‘home bias’ in particular exist and seem to be relevant for the asset trading and holding
behaviors of investors, they do not seem to exist and hence seem not to be relevant for the
trading behaviors of the market making middlemen of the upstairs market. Another way
to interpret the irrelevance of culture is in terms of the fragmentation of the upstairs
market, so that while the upstairs market is fragmented in that market makers need to
search for the best price, this fragmentation does not appear to have a national cultural
dimension. Therefore differences in the national culture of upstairs market makers seem
neither to increase the fragmentation of the upstairs market by increasing the search and

trading relationship costs of the upstairs market, nor do they appear to reduce the

 

’0 Other examples of culturally heterogeneous stock markets include Toronto and Brussels.

40

fragmentation of the upstairs market such that upstairs market makers try to reduce
agency costs (e. g. related to front-running) by trading with similar culture counterparties.
While this paper examined the trading behavior of brokerage houses based on the
national culture of these brokerage houses, the determination of the national culture was
done using the cultural origin of the name of the brokerage houses, and the country of the
headquarters or parent company of the brokerage house, instead of the native language of
the CEO or director of the brokerage house which would have been more consistent with
the classiﬁcation used in Grinblatt and Keloharju (2001). However, any misclassiﬁcation
is unlikely to be systematic and unlikely to affect materially the result that culture is
irrelevant. Furthermore, idiosyncratic misclassiﬁcations are not likely to obtain the result

that culture is irrelevant.

41

CHAPTER 3: HOW LOYAL ARE A BROKER’S CUSTOMERS?

ABSTRACT

This paper studies the existence and determinants of trading relationships between
investors and individual brokers by analyzing events in which individual brokers moved
from one brokerage house to another. The paper also studies the extent to which ex-ante
measures of the strength of a trading relationship between the investor and his brokerage
house, or individual broker, affect the investor’s trading relationships with the old and
new brokerage houses of the individual broker. The study also uses various investor
characteristics, such as investor type and investor national culture as well as
characteristics of the traded ﬁrm and the brokerage houses. It is found that customers do
shift a statistically signiﬁcant proportion of their trades to the new brokerage house when
the individual broker switches. Existing relationships with the new brokerage house make
it more likely that the customer will switch, supporting the hypothesis that investors incur
costs when switching brokers. The new brokerage house attracts trading in stocks in
which it is less active, consistent with the idea that the individual brokers are lured away
by brokerage houses for their ability to increase their market share. There are mixed
results on whether more savvy investors are more or less likely to switch to the new
brokerage house. Female investors are more likely than male investors to switch to the
new brokerage house, consistent with the hypothesis that female investors are more
relationship—oriented. Finally, there is some evidence, although statistically insigniﬁcant,

supporting the hypothesis that investors prefer to trade with like culture brokerage houses.

42

3.1 Introduction

How important are personal relationships? More precisely, how does the existence
of personal relationships between market agents affect the agents’ decisions to conduct
business with each other? In order to study the importance of ‘relationships’, one must
ﬁrst deﬁne what is meant by the word ‘relationship’. Webster’s deﬁnes the word
‘relationship’ as “a state of being related”. The American Heritage Dictionary deﬁnes a
‘relationship’ as “.. a condition or fact of being related”, as “a connection or association”,
and more speciﬁcally as “.. a particular type of connection existing between people
related to or having dealings with each other.” While historically the study of
relationships between people has been central to disciplines such as psychology,
sociology, and even to business disciplines such as organizational behavior and human
resources, and to marketing, the relationship between market agents as the term applies to
the ﬁelds of economics and ﬁnance has not been investigated in its own right, but rather it
has been used to motivate certain assumptions in theoretical settings and the term
‘relationship’ has been used to describe speciﬁc arrangements, for example the close
business relationship a bank has with the individuals and institutions it lends to, is
referred to as a ‘lending relationship’ or as ‘relationship banking’. In this setting the
assumption of the existence of a relationship motivates the widely accepted belief that
banks learn about the type of their customers by lending to them, monitoring their actions
and updating their prior beliefs based on what they observe.

The effect social or human capital may have on the theory of the ﬁrm has also been
acknowledged by Rajan and Zingales (2000) as well as Zingales (2000). Zingales (2000)

points out that the traditional theory of the ﬁrm needs to be revised to recognize the fact

43

that many of today’s ﬁrms, including but by no means limited to those in the high-tech
industry, are more aptly deﬁned in terms of the human capital they possess than in terms
of their physical assets, which unlike the latter are diﬂicult to control by traditional
methods of corporate governance. The author gives the advertising ﬁrm Saatchi and
Saatchi as an example of what can happen when such traditional governance methods are
used. Namely, in 1994 the US fund manager equity holders of this company voted against
the proposal of an arguably undeserved but generous option package for the management,
which resulted in the departure of the chairman and top executives of the ﬁrm, who
subsequently regrouped under a new name, and captured some of the most important
clients of their former company. In this example, it can easily be argued that the close
business relationships the Saatchi and Saatchi managers had with the ﬁrm’s clients as
well as the expertise of the managers contributed to the latter groups’ ability to take a
sizeable part of the ﬁrm value away from the ﬁrrn’s owners.

In the ﬁelds of business, sociology and political science the concept of ‘social
capital’ has attracted a great deal of attention lately. Burt (2000) deﬁnes social capital as
“.. the contextual complement to human capital”, and continues “... Certain people or
certain groups are connected to certain others, trusting certain others, obligated to support
certain others, dependent on exchange with certain others. Holding a certain position in
the structure of these exchanges can be an asset in its own right.” DeMarzo, Vayanos and
Zwiebel (2001) have formalized in a bounded-rationality framework, the concept of
social inﬂuence and social networks and their inﬂuence on the opinion formation of
agents. The authors show that under the assumption that agents cannot tell whether they

have been exposed to a particular opinion before, due to their bounded rationality, their

44

posterior beliefs are biased toward these ‘double-counted’ opinions. This is deﬁned by
authors as ‘persuasion bias’ that arises in their model from the social network hierarchies
in which the opinions of agents higher up in the network are multiplied further down the
hierarchy. The deﬁnition of persuasion used in DeMarzo, Vayanos and Zwiebel (2001),
that repeated exposure to an opinion biases the beliefs of the listener toward the opinion,
is also consistent with the concepts of trust and trusting business relationships among
economic agents.

This paper uses a unique data set to study the strength of the business relationship
between a brokerage house, and its customers. Speciﬁcally, it studies the determinants of
a change in a customer’s trading relationship with a brokerage house when an individual
broker of the brokerage house changes jobs, i.e. moves to another brokerage house. The
paper investigates whether various characteristics of the customer, the old brokerage
house, the new brokerage house, the job-switching individual broker, the stock, trade
direction, and market venue (upstairs or downstairs) affect the customer’s proportion of
trading with the brokerage house.

Studies on the switching costs of economic agents, as reviewed by Klemperer
(1995), imply that a business relationship with one’s broker generates costs for an investor
if the investor decides to use another broker. Thus, a utility maximizing investor should
prefer to trade with a broker already familiar to him or with whom the investor already
has a business relationship. In the scope of the interaction between an investor and his
broker, switching costs can arise for the investor from the need to communicate his
trading preferences to the broker. Grossman (1992) argues that it is prohibitively costly

for an investor to reveal his demand for a stock to the market by continuously trading, and

45

that an investor instead can communicate (costlessly) his demand schedules for the stock
to an upstairs market broker, who acts as a repository of information about the investor’s
unexpressed demand. However, due to an unavailability of investor and broker level data,
the nature and strength of the business relationship between investors and their brokers
has not yet been studied empirically. Furthermore it is unclear if the business relationship
exists at an individual broker level or at a brokerage house level. In theoretical models of
the upstairs market, where brokers are assumed to have a relationship with their
customers, no distinction is made between individual brokers and brokerage houses.

This study ﬁnds that a customer’s pre-existing relationship with the new brokerage
house increases (decreases) the customer’s trading with the new brokerage house (old
brokerage house) after the individual broker moves to the new brokerage house. This
ﬁnding is consistent with the existence of switching costs for a customer, such that a
customer’s existing relationship with the new brokerage house reduces the costs of
switching for the customer, and makes it more likely that he will switch his business
away from the old brokerage house to the new brokerage house, when the individual
broker moves. Interestingly, the proportion of a customer’s trading done through the
individual broker prior to the broker’s move to the new brokerage house, does not appear
to affect the trading proportions of the customer after the individual broker moves. This
implies that the proportion of a customer’s trading done by the individual broker is
perhaps not an adequate measure of the business relationship with the individual broker.
In fact, individual brokers that have the closest relationships with customers tend the be
sales brokers who often delegate the execution of their customers’ trades to other brokers.

The ﬁnding that an investor’s switch to the new brokerage house may have less to do with

46

maintaining the relationship with the switching broker than with increasing an existing
relationship with the new brokerage house, is consistent with the ﬁnding of Anand (2002)
that specialist ﬁrms can provide a uniform quality of execution out of the extremely
diverse quoting behavior of individual specialists.

This study analyzes the changes in the proportion of a customer’s trading business
received by both the old and new brokerage houses, i.e. both the house which loses the
individual broker dealer and the house that gains the individual broker. If there is a strong
business relationship between a customer of the brokerage house and the switching
individual broker, then the customer’s proportion of trading conducted by the (old)
brokerage house should decrease after the individual broker leaves, whereas the
customer’s proportion of trading conducted by the new brokerage house should increase
after the individual broker begins working for the new brokerage house. If relationships
with the individual broker are weak or non-existent, then a customer’s proportion of
trading should not change for either the old or the new brokerage house. The study uses a
unique data sample during which 11 individual brokers stopped trading at one brokerage
house and began to trade at another, i.e. switched from one brokerage house to another.
The study identiﬁes the customers of switching brokers and their old brokerage houses,
and estimates multivariate models to explain changes in the proportion of a customer’s
trading after the switch. The study uses various cross-sectional characteristics of
investors, characteristics of the old and new brokerage houses and the individual broker
involved in the switch, as well as characteristics of the market and the traded stock, to
explain changes in customers’ proportions of trading with the old and new brokerage

houses.

47

The rest of the paper is organized as follows. Section 3.2 reviews the literature.
Section 3.3 introduces the research questions and the hypotheses to be tested. Section 3.4
describes trading on the HSE, the HETI and FCSD Data Sets, and the classiﬁcation of
individual broker switching events. Section 3.5 presents descriptive statistics. Section 3.6
analyzes the effect of individual broker switching on the customers’ percentage of trading
with the individual brokers old and new brokerage houses. Section 3.7 introduces a
multivariate model framework to study the effect of pre-existing business relationships,
and investor characteristics on customer trading proportions when individual brokers
switch brokerage houses, and deﬁnes the proxy variables being used. Section 3.8
introduces the results. Section 3.9 interprets the results and discusses the implications for
the theory. Finally, Section 3.10 concludes.

3.2 Literature Review

The literature review is organized into three main subject areas, economic
relationships, the role of an upstairs market, and order ﬂow internalization and
preferencing. Overlaps of subject matter among these different areas are also highlighted.
3.2.1 Economic Relationships

This section reviews the literature on the treatment of relationships between
economic agents, and the types of information and other problems these relationships are
presumed to alleviate or cause.

Intermediary Relationships (Banking)

Studies involving banking relationships in the banking and ﬁnancial intermediation

literature have recently become more and more common. Relationship banking can be

deﬁned as the provision of lending, and other services to customers by ﬁnancial

48

intermediaries, where the intermediary obtains proprietary borrower-speciﬁc information
during repeated transactions with the borrower. Such borrower-speciﬁc information is
typically assumed to include the proﬁtability of a borrower’s projects, and the effort of
the borrowing ﬁrm’s entrepreneur, etc. The ability of banks and other ﬁnancial
intermediaries to collect information that is not publicly available, i.e. proprietary, is
often cited as one of the major differences between ﬁnancial intermediaries such as banks
and the public debt markets. Boot (2000) mentions that relationship banking is a response
to problems of asymmetric information in ﬁnancial intermediation.

Diamond’s (1984) seminal paper on ﬁnancial intermediation involves an
intermediary that raises funds from depositors and uses these funds to make loans to
entrepreneurs. The model relies on the assumption of costly information and the ability
of banks to monitor the loans given to entrepreneurs and to learn project cash ﬂows
otherwise known only to entrepreneurs. This gathering of proprietary information is
therefore accomplished through costly monitoring, which underlies relationship banking.
The paper shows that a world with ﬁnancial intermediation reduces the expected costs of
monitoring relative to a world without such intermediation. Without ﬁnancial
intermediaries, each lender must either monitor the entrepreneurs and their projects, or be
faced with a free-rider problem where they don’t make loans in equilibrium. Thus,
ﬁnancial intermediaries eliminate the duplication of effort in monitoring, and facilitate
the funding of proﬁtable projects.

Diamond (1991) develops a repeated game model of reputation and monitoring and
shows that bank lending can solve moral hazard problems of project choice. The main

result of the paper is that bank monitoring can reduce borrower moral hazard by allowing

49

borrowers (entrepreneurs) to develop reputations of creditworthiness, which can reduce
the cost of borrowing. Thus, ﬁrms that develop higher credit ratings prefer to borrow
from banks as opposed to the publicly placed debt market. In Allen (1990) and
Rarnakrishnan and Thakor (1984), banks collect proprietary information from borrowers
when screening low quality borrowers. However, lending relationships in banking, while
they can alleviate asymmetric information problems, can also create other types of
problems. Rajan (1992) uses the assumption that a bank lending relationship allows the
bank to learn about the cash ﬂows from a ﬁrm’s project, which reduces the asymmetric
information problem the bank faces relative to other lenders. However, this means that
the bank can extract rents from the borrower to allow the continuation of a positive NPV
project, which creates a moral hazard problem, in that the borrower exerts less than the
maximum effort. Therefore, lending relationships can reduce asymmetric information
problems for the bank but in doing so can cause moral hazard problems.

The importance of intermediary relationships is not conﬁned to the banking industry,
however. Private equity markets, such as venture capital markets also rely heavily on
relationships between the venture capitalist and entrepreneur. A venture capitalist learns
about the entrepreneur and his project on an ongoing basis and continually updates the
payoffs expected from the project, and if necessary abandons the project. Gompers
(1995) ﬁnds empirical evidence suggesting that the staging of capital allows the venture
capitalist to learn about the entrepreneur and his project. Hence, the role of staging in
venture capital is Similar to the role of lending in the banking literature, in that potential

information asymmetries about the entrepreneur’s or borrower’s project are resolved by

50

the repeated interactions of the capital staging or lending relationship. F enn, Liang and
Prowse (1997) provide a comprehensive survey of the venture capital market.
Dealer and Broker Relationships (Market Microstructure)

The market microstructure literature has used assumptions about relationships
between brokers and dealers and their customers in various theoretical models.

Benveniste, Marcus and Wilhelm (1992) study the importance of professional
relationships between ﬂoor brokers who act on behalf of traders, and the specialists who
conduct the trades. They show that these relationships, by allowing specialists to identify
the ﬂoor brokers with whom they trade, can alleviate the asymmetric information problem
between these market participants, thereby reducing the costs to trade for uninformed
traders and under certain conditions that of the informed as well. The authors’ basic
intuition involves the fact that ﬂoor brokers such as exist on the NYSE trading ﬂoor can
have relationships with specialists, much the same way that dealers in an upstairs market
knowing the identities of their customers can screen informed trades. This would indicate
that professional relationships among the dealers of an upstairs market could also serve
an information screening purpose. However, since the upstairs market already screens
informed traders, due to the fact that informed traders are known to the upstairs dealers
and therefore prefer to trade anonymously in the downstairs market, it is not clear
whether an asymmetric information problem such as the one in Benveniste et al. (1992)
would exist among upstairs market dealers. Benveniste et a1. (1992) also state that
reputation effects among the brokerage houses and the dealers of a brokerage ﬁrm would

limit the dealers’ incentives to conceal private information when trading with each other.

51

This would imply that a professional relationship if it did exist among dealers of an
upstairs market would not be justiﬁable on the basis of certifying trades as uninformed.

Hansch, Naik and Viswanathan (1998) use the data available on the inventories of
the dealers operating on the London Stock Exchange, to test the inventory control
hypothesis of Ho and Stoll (1983), and to study the effect of dealer inventory levels on
interdealer trading behavior. The authors ﬁnd strong support for Ho and Stoll (1983), and
ﬁnd that inventory imbalances motivate interdealer trading, which emphasizes the
importance of interdealer trading in the management of large dealer inventories, such as
exist for the dealers active on the LSE.
3.2.2 The Upstairs Market and Relationships

The term ‘upstairs market’ refers to the decentralized trading environment created
among broker-dealers where block trades are negotiated on behalf of counterparties
known to the brokers. A few theoretical and some empirical papers study the various
features of upstairs markets. Seppi (1990) develops a multi-period model of block trading
and ﬁnds that traders who can credibly Signal that their trades are not information based
prefer to trade in the upstairs market. Grossman (1992) argues that the upstairs market is
a repository of information about the unexpressed demand of investors and that this
feature enhances the liquidity provided by the upstairs market. Using a dataset of block
transactions in Dow Jones stocks on the NYSE, Madhavan and Cheng (1997) Show that
the downstairs market contributes signiﬁcantly to liquidity in these stocks, but that the
upstairs market provides better execution than the downstairs market, although the
difference is not economically signiﬁcant. However, the authors do ﬁnd support for

Seppi’s (1990) hypothesis that uninformed traders prefer to trade in the upstairs market.

52

Keim and Madhavan (1996) ﬁnd that information leakage occurs in the upstairs market
prior to a trade, and that a concave relationship exists between trade size and the
temporary price impact of liquidity. Booth, Lin, Martikainen and Tse (2001) ﬁnd that
upstairs trades have less information content and lower price impact than downstairs
trades, which is consistent with Seppi (1990) and Grossman (1992). The authors also ﬁnd
that price discovery occurs in the downstairs market, and that the downstairs market is
used to set trade prices in the upstairs market. Smith, Turnbull and White (1999), show
that brokers in the upstairs market successfully ﬁlter out information-based trades,
reducing the adverse selection costs relative to the downstairs market, which supports
Seppi (1990). It is also found that trading in the upstairs market is more likely during
times when liquidity is low in the downstairs market.

Therefore the extant literature supports the hypotheses that trading relationships
between the upstairs market makers and traders give market makers the ability to improve
liquidity by screening informed trades and that such relationships also allow market
makers to learn about the unexpressed demand schedules of investors who use the
upstairs market.

3.2.3 Order Flow Preferencing and Intemalization

Hansch, Naik and Viswanathan (1999) demonstrate that the broker practices of
preferencing and internalization do not lead to higher spreads being paid by investors and
this contradicts the collusion hypothesis, but agrees with the hypothesis that searching
and trading relationships in the upstairs market are costly. Therefore, the study refutes

claims that preferencing and internalization indicate tacit collusion among dealers.

53

3.3 Research Questions (Hypotheses)

How strong is the business relationship between an investor and his broker?
Business relationships may develop between investors and their brokers, both at the
brokerage house and individual broker level, as a result of the cost of communicating an
investor’s trading preferences to a new broker. Grossman (1992) argues that upstairs
brokers through their continuing relationship learn the trading preferences of their
customers, deﬁned by the author as the customers’ unexpressed demand. This reduces the
costs of trading in the upstairs market for the broker who can pass the savings on to the
customer. Another reason for an investor to develop business relationships with a broker
is to reduce agency costs associated with the investor’s inability costlessly to monitor the
broker and to verify that the broker is ﬁnding the best price. Therefore if a pre-existing
business relationship with a broker reduces such costs, an investor is more likely to
continue to maintain these relationships. Hence if an investor has a close business
relationship with an individual broker at a brokerage house, the investor can be expected
to continue the relationship even after the broker quits and moves to another house.
Similarly, a close business relationship with the switching broker’s old brokerage house
can make it less attractive for an investor to continue the relationship with his individual
broker after the broker switches to the new brokerage house. Also, a pre-existing
relationship with the switching broker’s new brokerage house may make it more likely
that the investor will follow his broker to the new brokerage house.

The proportion of an investor’s trading during the estimation period (i.e. 30 calendar
days of trades 60 days before the broker leaves) by trade value via the old brokerage

house, excluding the trades of the switching broker, via the new brokerage house, and via

54

the switching broker, proxy for the ex-ante relationships between the investor and other
brokers at the old brokerage house, the investor and the new brokerage house, and the
investor and the switching broker, respectively.

Therefore the ﬁrst hypothesis is: Hypothesis 1 = Trading relationships aﬂect
investors ' likelihood of switching.

Hypothesis 1 is split into three sub-hypotheses, deﬁned as follows.

After the broker leaves the old brokerage house, an investor having a higher ex-ante
proportion of trading with the old brokerage house (excluding switching broker) should
show a smaller decrease in his proportion of trading with the old brokerage house, and a
greater increase in his proportion of trading with the new brokerage house. For example,
this is consistent with an investor who does 30 percent and ﬁve percent of ex-ante trades
(by volume) via the old and new brokerage houses, respectively, shifting a substantial 20
percent of his ex-post proportion of trades from the old to the new brokerage house, and
another investor who does 80 percent and ﬁve percent of ex-ante trades via the old and
new brokerage houses, respectively, shifting only three percent of his ex-post proportion
of trades from the old to the new brokerage house. Therefore:

Sub-hypothesis 1a = Investors having stronger ex-ante trading relationships with
other brokers in the old brokerage house are less likely to switch with broker (higher
switching cost).

The greater is the investor’s ex-ante trading relationship with either the new
brokerage house, or the switching broker, the lower should be the cost of shifting order
ﬂow to this new brokerage house. Hence, an investor having a higher ex-ante proportion

of trading with either the new brokerage house or the switching broker, however, should

55

show a larger decrease in his proportion of trading via the old brokerage house after the
broker leaves. This is consistent with an investor who does 20 percent of his ex-ante
trades (by volume) via the new brokerage house, increasing his ex-post proportion of
trades through the old brokerage house a lot to 30 percent, and another investor who does
ten percent of his ex-ante trades via the new brokerage house, increasing his ex-post
proportion of trades through the new brokerage house only slightly to 11 percent.

The ex-ante measures of an investor’s relationships should also affect the proportion
of trading done by the new brokerage house after the broker starts working at the new
brokerage house. Therefore, after the broker starts working at the new brokerage house,
an investor having a higher ex-ante proportion of trading with the old brokerage house
(excluding the switching broker) should Show a smaller increase in his proportion of
trading with the new brokerage house. This is consistent with an investor who does 30
percent of his ex-ante trades via the old brokerage house and ten percent of his ex-ante
trades (by volume) via the new brokerage house, increasing his ex-post proportion of
trades through the new brokerage house a lot to 20 percent, and another investor who
does 80 percent of his ex-ante trades via the old brokerage house and also ten percent of
his ex-ante trades via the new brokerage house, increasing his ex-post proportion of
trades through the new brokerage house only Slightly to 11 percent. There are therefore
two additional hypotheses:

Sub-hypothesis 1b = Investors having (stronger) ex-ante trading relationships with
switching broker are more likely to switch with the broker (lower switching cost),

and, also:

56

Sub-hypothesis IC = Investors having stronger ex-ante trading relationships with
brokers in the new brokerage house are more likely to switch with the broker (lower
switching cost).

If the upstairs market is more likely than the downstairs market to foster a trading
relationship, as the literature argues, then investors trading mainly in the upstairs market
Should be more likely to continue the relationship than investors trading mainly in the
downstairs market. At the HSE, however, all customer orders to trade in the sample
period under study are received by brokers, who then decide on the venue for the trade
(upstairs or downstairs). This means that brokers always know the identities of investors
who place orders through the broker. However, brokers typically spend less time trading
in the downstairs market than ﬁnding a counterparty in the upstairs market, and still
should be able to develop closer relationships with investors whose orders are worked
more frequently in the upstairs market. Therefore:

Hypothesis 2 = Upstairs market investors develop closer business relationships with
the broker: and are more likely to switch with the broker.

The ability of a broker to provide liquidity affects his ability to attract order ﬂow.
Hence, a brokerage house or individual broker that is dominant in the trading of a stock in
the market, and is dominant in providing liquidity in that stock, Should be more likely to
attract order ﬂow from investors. This is also consistent with the idea that markets (and
brokers) are networks having positive extemalities, such that the beneﬁt for an investor of
trading in a market (or via a broker) goes up with the ntunber of investors using the
market (or the broker). Furthermore, a broker that is dominant in the trading for a stock is

also more visible to investors, and is likely to have a high degree of investor recognition

57

and therefore likely to attract a lot more order ﬂow. This is similar to the investor
recognition theory of Merton (198 7), that investors invest only in stocks they have heard
about. Therefore, the main hypothesis is:

Hypothesis 3 = Brokers ’ market dominance aﬂects investors ' likelihood of switching.

Hypothesis 3 is Split into three sub-hypotheses as follows:

Hypothesis 3a = Investors are less likely to switch with the broker if the old
brokerage house (excluding the switching broker), is more dominant in the stock,

Hypothesis 3b = Investors are more likely to switch with the broker if the broker is
more dominant in the stock,
and, also:

Hypothesis 3c = Investors are more likely to switch with the broker if the new
brokerage house is more dominant in the stock.

The data set identiﬁes six different classes of investors, namely, households, and
ﬁnancial, non-ﬁnancial, governmental, non-proﬁt, and foreign institutions. In Grinblatt
and Keloharju (2001), ﬁnancial and non-ﬁnancial institutions are assumed to be savvy, or
sophisticated, while governmental, and non-proﬁt institutions and households are
assumed to be unsavvy, or unsophisticated investors. Institutional investors are generally
assumed to be more sophisticated than individual (household) investors. In theoretical
models of market microstructure institutional traders are assumed to be informed traders,
whereas individual traders are the liquidity or uninformed traders. If institutional traders
have more complicated trading preferences than individual traders, they are more likely
to develop close relationships with their brokers, and given the greater cost of conveying

these preferences to a new broker, they should prefer to continue such relationships.

58

Institutional investors such as mutual funds that rebalance their portfolios due to
liquidity needs and not due to private information may also be considered
unsophisticated, at least for liquidity motivated trades. However, it is possible that
institutional investors while not being informed for every trade, may still be more
sophisticated on average than individual investors. In fact, Grinblatt and Keloharju (2000)
ﬁnd strong evidence that Finnish households are less sophisticated investors than are
Finnish institutions, where the performance of an investor’s stock portfolio after
controlling for the investment strategy (i.e. momentum or contrarian) is used as a
measure of investor sophistication.

Hence, a savvy investor is likely to have more complicated trading preferences than
an unsavvy one, and hence likely to develop closer business relationships with brokers in
order to reduce the cost of being present in the market, which is consistent with
Grossman’s (1991) assertion that (upstairs) market makers learn the unexpressed demand
of customers. A savvy investor may also be less dependent on relationships with
particular individual brokers, and likely to have relationships with many individual
brokers at a brokerage house. Hence more (less) sawy investors may be less (more)
biased toward the (switching) broker and therefore less (more) likely to switch with a
particular individual broker to a new brokerage house.

Hence two non-mutually exclusive and competing hypotheses can be formed
regarding how savviness affects investors’ switching behavior:

Hypothesis 4: Less savvy investors are (more) biased toward the broker; and are

more likely to switch with the broker;

59

and, Hypothesis 5 .' Less savvy investors have simpler trading preferences, and are less
likely to switch with the broker.

These non-mutually exclusive and competing hypotheses are further subdivided into
non-mutually exclusive and competing sub-hypotheses, each of which tests the main
hypotheses in each of the different investor classes.

Grinblatt and Keloharju (2001) argue that household investors are less savvy than
institutional investors, in that they are biased toward the familiar. Therefore, households
should be more attached to their brokers and hence more likely to switch with their
brokers than institutions. Hence:

Hypothesis 4a = Households are less savvy than institutions and more biased
towards the broker; and are more likely to switch with the broker:

However, as households are likely to have less complicated trading preferences than
institutions, they are less dependent on their brokers and therefore are less likely to
switch with their brokers. The competing hypothesis is:

Hypothesis 5a = Households have less complicated trading preferences than
institutional investors, and are less likely to switch with the broker.

Grinblatt and Keloharju (2001) regard ﬁnancial and non-ﬁnancial institutions as
savvy and households as unsavvy. Thus, savvy institutions should be less biased toward
their brokers and hence less likely to switch with their brokers than household investors.
Hence:

Hypothesis 4b = Financial and non-ﬁnancial institutions are more savvy (than
households), and less biased toward the switching broker: and are less likely to switch

with the broker.

60

Also, as ﬁnancial and non-ﬁnancial institutions are savvy, they are likely to have
more complicated trading preferences than households, and are more dependent on the
Switching brokers and are more likely to switch with the brokers. The competing
hypothesis is:

Hypothesis 5b = Financial and non-ﬁnancial institutions are more savvy (than
households) and have more complicated trading preferences, and are more likely to
switch with the broker.

The paper also tests the differences between savvy and unsavvy institutions
regarding their tendencies to switch with brokers. Thus, savvy institutions should be less
biased toward their brokers and hence less likely to switch with their brokers than
unsavvy institutions. Hence:

Hypothesis 4c = Governmental and non-proﬁt institutions are less savry than
ﬁnancial and non-ﬁnancial institutions and more biased toward trading with the broker:
and are more likely to switch with the broker:

As governmental and non-proﬁt institutions are unsavvy, they are likely to have less
complicated trading preferences than households, and are less dependent on their brokers
and are less likely to switch with their brokers. Hence, the competing hypothesis is:

Hypothesis 5c = Governmental and non-proﬁt institutions are less savvy than
ﬁnancial and noncﬁnancial institutions and have less complicated trading preferences,
and are less likely to switch with the broker:

Investors’ biases toward brokers may also be gender based. Therefore, male
investors may have stronger relationships with individual brokers who are typically male,

than female investors do. Thus:

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Hypothesis 6 = Male investors have stronger relationships with (male) brokers than
female investors (i. e. gender bias) do, and are more likely to switch with the broker

It is often argued that females in general tend to be more emotional than males, and
that females are more relationship oriented than males. If so, then male investors should
be less likely to switch when the broker switches than female investors do. Hence, the
hypothesis that is non-mutually exclusive and competing with Hypothesis 6 is:

Hypothesis 7 = Male investors are less relationship-oriented and have weaker
relationships with brokers than female investors do, and are less likely to switch with the
broker:

Cultural familiarity with a brokerage house may also affect an investor’s decision to
do business with a brokerage house. Brokerage houses in the sample are identiﬁed by
name and headquarters location as being of Finnish or Swedish cultural origin. If
investors prefer same culture brokerage houses, an investor having the same culture as an
old brokerage house is less likely to switch to a different culture new brokerage house
than an investor that has the same culture as the new brokerage house. The last
hypothesis to be tested therefore is:

Hypothesis 8 = A Finnish customer of a Finnish culture old brokerage house, is less
likely to switch with the broker to a Swedish culture new brokerage house, than a Swedish
culture customer of the old brokerage house.

3.4 Description of Trading on the HSE, the HETI and FCSD Data Sets, and the
Classiﬁcation of Individual Broker Switching Events

The data consists of all trades of ﬁrms continuously and publicly listed on the

Helsinki Stock Exchange during the period Jan 1, 1996 to December 31, 1997. In 1990,

62

the HSE adopted the Helsinki Stock Exchange Automated Trading and Information
Systems, otherwise known as the HETI. In the years subsequent to its adoption of the
HETI, the HSE grew both in the number and volume of its listings. The downstairs and
upstairs markets at the HSE are linked to the HETI system, in that all trades are recorded
on the HETI system and the information is made available instantly to the public. The
downstairs market of the HSE, which is a part of HETI, is essentially an open electronic
limit order book that is continuously updated with the limit orders of customers who
retain their anonymity in the HETI system, and where orders are matched according to
price and time priority. The upstairs market consists of authorized broker dealers of
brokerage houses who upon receiving a customer order to trade look for trade
counterparties, ﬁnding them either among their own customers or among the customers
of other broker dealers either in the same or in a different brokerage house. “ Trading on
the HSE occurs during one of three trading periods each day, namely, the pre-trading,
free-trading and the after-market trading periods. During the ﬁrst twenty minutes of the
pre-trading period, beginning at 8:30 am. each day, traders announce bid and offer
prices, after which the HETI system matches orders and executes transactions from 9:50
am. to 10:00 am. Any unexecuted portions of orders are then transferred to the
free-trading period. During “free-trading”, a continuous trading session beginning at
10:00 am, customers can submit their orders via the brokers. If the customer does not
express a preference for the trading venue, i.e. upstairs or downstairs, then the broker
chooses the venue at his discretion. The upstairs trade occurs in the office of one of the

authorized brokerage ﬁrms and is reported in a timely manner to the HETI system. The

 

” There were 25 brokerage houses actively trading on the HSE as of the end of 1997.

63

free-trading round lasts until 5 pm. each day, after which there are two after-market
trading sessions, one from 5:05 pm. to 6:00 pm, and the other from 8:00 am. to 8:25
am. of the following day. The after-market is distinguished from the free-trading period
by the fact that the HETI system is closed, and that all trades must therefore occur in the
upstairs market. Trades during the after-market periods are required to occur inside the
price range bracketed by the greater of the highest transactions price of the trading day
and the closing ask of the free-trading session, and the smaller of the lowest transactions
price of the trading day and the closing bid of the free-trading session. Appendix A
shows the list of share classes, 85 in total corresponding to 67 ﬁrms in the sample. As in
Grinblatt and Keloharju (2000), the share classes are treated as separate stocks. Grinblatt
and Keloharju (2000) point out that the different share classes of a particular company
reﬂect different voting rights, i.e. the more liquid share class have fewer votes, and by
Ilmanen and Keloharju (1997) this gives rise to different clienteles of share ownership.
The FCSD, or Finnish Central Securities Depository keeps an electronic database of
investor share ownership and trading, called the Book Entry System, which contains
records of each investor’s initial shareholdings of HEX listed (public) companies, as of
January 1, 1995, and all changes of Share ownership that occur between January 1 1995,
and May 31, 2000. The FCSD’S Book Entry System contains investor ID numbers which
identify uniquely Finnish individual and institutional investors as these investors are
required to register their holdings under their own names. AS the registration for
foreigners is less strict, and can be done in a street name, a single ID number typically
represents many foreign individuals. The Book Entry System also contains share class,

which identiﬁes both the share type and the company whose shares are being traded, the

64

number of shares, and type of ownership of which only private (Finnish) and nominee
registered (foreign) ownership types are meaningful. The data set used in the study is
composed of two data sets, the HETI transactions data set for the years 1996 to 1997,
inclusive, which contains codes for brokerage houses and individual brokers, and trading
venue (upstairs and downstairs markets), and the FCSD’s Book Entry System. The
matching algorithm is rather complicated, where for each trading day, and in each stock,
one or more buyers of a stock are paired with one or more sellers, such that the buyers’
total trade quantity and the average transaction price equal the sellers’ total trade quantity
and average transaction price, which must also equal the trade volume and price of a
transaction reported in the HETI transactions data set. As not all entries in the F CSD data
set could be unambiguously (uniquely) matched with counterparts in the HETI data set,
about half of the HETI transactions were discarded, leaving over 420,000 transactions for
the years 1996 and 1997. An analysis of the matching results, as shown in Table 3.2,
indicates that trades of more active stocks were less likely to be matched successfully.
For example, on average, 74.4% of the trades of the nine least active stocks and 34.6% of
the trades of the four most active stocks were matched successfully. Furthermore trades
of stocks on more active days were also less likely to be matched successfully. This is
because an increasing number of trades per day in the HETI data set increases the number
of possible combinations of FCSD records that match each HETI trade, and thus reduces
the likelihood of a unique match.

In the sample data set, the broker code of each of 20 individual brokers became
disassociated with one brokerage house code and later became associated with another

brokerage house code. In the case of brokerage house ABB, all seven of the house’s

65

individual broker codes abruptly became disassociated with the house’s code, one of
which became associated with the house code ALF ﬁve months later, while the remaining
six became associated with the house code ARO a few days later, which prior to the
association of the ﬁrst individual broker, did not exist in the data. This systematic
reassociation of individual broker codes is consistent with a name change of the old
brokerage house subsequent to a merger or a takeover, and these events are thus excluded
from this study. Additionally, all three of the remaining individual broker codes of the
house SEL became disassociated with the house code SEL and became associated with
the codes for houses CAR, and ARC, both of which existed in the data set prior to the
switching of the three individual broker codes. After the three switching events, the house
code SEL ceased to exist in the data set. This would be consistent with brokerage house
SEL terminating its operations and the individual brokers ﬁnding employment at the
brokerage houses CAR and ARC. Of the remaining 11 individual brokers, two left
brokerage house SEL six months before and one left SEL a year before the house
terminated its operations. As six months is more than suﬂicient for the purpose of the
study, these switching events are not discarded from the data set. The old brokerage
houses of the remaining seven switching brokers on the other hand continued their
operations until the end of the sample period. Therefore, the study considers these 11
cases of reassociation of individual broker codes with new house codes, i.e. individual
broker switching events.
3.5 Descriptive Statistics

Panel A in Tables 3.4 and 3.5 (Tables 3.6 and 3.7) Show the percentages by value of

customers’ downstairs and upstairs trades, respectively, done by the old brokerage house

66

(new brokerage house) for each switching event and for each relative period, t, where

t = —i, is the 2th month before the broker’s last trade at his old brokerage house, and

t = j is the jth month after the broker’s ﬁrst trade at his new brokerage house. Panel B in
Tables 3.4 to 3.7 Show the means of the percentages by value of the trades and the change
in the means of the percentages after a broker leaves the old brokerage house (t=0) and
after the broker begins to work at the new brokerage house (t>0), relative to the periods
when the broker is still working at his old brokerage house (t<0). The data sample used
for Tables 3.4 to 3.7 is restricted to the three most active stocks in the data set, as
customers do not trade less active stocks each month. The customers in Tables 3.4 to 3.7
are deﬁned as investors who have traded at least once in the trading venue (upstairs or
downstairs) and via the brokerage house (old or new) in question during the earliest
trading month for the brokerage house, i.e. January 1996. Therefore Tables 3.4 to 3.7
examine the mean trading percentages and changes in mean trading percentages for the
same group of customers throughout the entire sample. In Table 3.4 Panel B all of the
changes in the percentage mean are negative. In the All Events section of Table 3.4 Panel
A, the negative change in the mean percentage from t=-I,-2 to t=1,2, of 14.9% and from
t<0 to t> 0, of 5.7% are both signiﬁcant at the 0.05 level, implying that on average
customers reduce their proportion of downstairs trades done through the old brokerage
house, when the individual broker switches. Similarly, in Table 3.5 Panel B, all changes
in the mean percentage are negative, and in the Events with at least 4 months of
observations section, the negative change in the mean percentage from t=-4 to -1 to t=1
to 4, of 12.9% is signiﬁcant at the 0.05 level, implying that on average customers also

reduce their proportion of upstairs trades done through the old brokerage house.

67

Furthermore in the All Events section of Table 3.6 Panel B, the positive change in the
mean percentage from t<0 to t> 0, of 3.1% is signiﬁcant at the 0.05 level, implying that
on average customers increase their proportion of downstairs trading done through the
new brokerage house, when the broker switches. The corresponding statistic in Table 3.6
Panel B is 1.5% and signiﬁcant at the 0.1 level, implying that for upstairs trades, the
average increase is half as much as that for the downstairs trades. This appears to
contradict the theory that investors trading in the upstairs market form closer
relationships with brokers. However, it is also possible that investors using the upstairs
market have weaker relationships on average with switching brokers, than investors using
the downstairs market. This may be due to switching brokers being less active in the
upstairs market, and hence forming weaker relationships with customers using the
upstairs market, but this appears not to be the case. Table 3.3 shows a comparison of
statistics measuring the presence of individual switching brokers in the upstairs market to
the respective statistics for the median individual broker of the switching broker’s old
brokerage house, prior to the broker’s switch. As can be seen, eight out of eleven of the
switching brokers, are more active than the median in terms of the average daily volume
traded in the upstairs market (fourth column), but only six out of the eleven are more
active than the median in terms of the average daily number of trades (third column). As
they are among the more active upstairs brokers, switching brokers are likely to be sales
brokers and have close relationships with customers. However, in terms of the presence
in the upstairs market relative to the entire market as measured by the average percentage
of daily number of trades, and average percentage of daily volume, only four, and two of

the eleven switching brokers are more active than the median, respectively. This means

68

that while switching brokers are more active than the median in absolute terms, in
percentage terms they tend to be less active than the median. This means that switching
brokers are also more active in the downstairs market in absolute terms than the median
broker, and as the average shift in the proportion of a customer’s business done via the
upstairs market is only half that via the downstairs market, this may mean that brokers in
general and switching brokers in particular develop closer relationships with customers
using the downstairs market. Although this appears to contradict the theory of Seppi
(1990) and Grossman (1992) that relationships develop in the upstairs market but not the
downstairs market, it is reasonable in the context of the customer-broker relationship on
the HSE. This is because brokers active on the HSE know the identities of all customers,
as most customers telephone in their orders and typically leave the decision of market
venue up to the broker. Therefore, in both the upstairs and downstairs markets of the
HSE, brokers know the identities of customers and can develop relationships with them.

3.6 An Analysis of the Effect of Individual Broker Switching on Customers’ Per-
centage of Trading with the Individual Brokers Old and New Brokerage Houses

This section analyzes the effect on the trading activity of a brokerage house’s
customers when an individual broker of the brokerage house leaves to work at another
brokerage house. If the relationship between customers and their brokers is at the
brokerage house level, then the leaving of an individual broker should not affect the
business that the brokerage house receives from its customers. If on the other hand, some
customers prefer to do business with a brokerage ﬁrm at least partly because of their
relationship with an individual broker, then one should expect these customers to have a

weakened preference for doing business with the brokerage house of their preferred

69

individual broker if the broker quits this brokerage house. Moreover, if the broker
subsequently begins to work for another brokerage house, his customers should then
show a stronger preference for trading with the new brokerage house of the broker. This
implies that from a switching costs perspective, a customer of a switching individual
broker faces a higher utility cost when switching to a new brokerage house than when
switching to a different individual broker at the same brokerage house. Anecdotal
evidence from brokers at the HSE suggests that an individual broker is likely to be hired
away from his brokerage house by a competing brokerage house hoping to attract the
business of the broker’s customers. Although it is known that such predatory hiring
practices occurred among HSE brokerage houses during the sample period used in the
study, only 11 switching brokers were identiﬁed in the two year sample used in this study.
However, as the study examines the determinants of the customers’ trading behavior after
the switch, and the switching brokers had a total of 362 customers prior to the switch, the
size of the sample size is suﬂ‘iciently large. Furthermore, the ﬁnding of 11 brokers who
switch in non-overlapping periods means that each broker switch is likely to be
independent of cross-sectional effects from other broker switches. For example, if two
brokers leave two different brokerage houses and move to the same new brokerage house,
then it would not be possible to attribute an increase in an investor’s proportion of trading
with the new brokerage house uniquely to a single switching broker.

3.6.1 Do Customers of the Individual Broker’s Old Brokerage House Reduce Their
Trading with the Old Brokerage House when the Broker leaves?

If customers of a brokerage house have a strong business relationship with a

particular individual broker then their orders may follow the broker to a new brokerage

70

house rather than stay with a different individual broker at the broker’s old house. The
study uses different proxies for business relationships to explain the change in an existing
customer’s trade concentration with a brokerage house. After an individual broker has
switched to a new brokerage house, one would expect to observe a drop in the proportion
of a customer’s trading by value done via the broker’s old brokerage house. This
expectation depends on the assumption that the percentage by value, or equivalently, the
percentage volume of a customer’s trades in a particular stock made by a brokerage house
is constant in the short term, for example a month before and after the individual broker
leaves the old brokerage house. Although for a given stock, a constant proportion by
value more or less implies a constant proportion by volume, the use of proportion by
value makes it easier to aggregate trades of all stocks when creating a value-weighted
measure of the concentration of a customer’s trades (across all stocks) with a particular
broker or brokerage house. This distinction is important, because having a large
proportion of one’s trade volume for a low-priced stock with a broker arguably is not as
indicative of an important trading relationship as having a similarly high proportion of
trade volume for a high-priced stock with that broker. Therefore, the analysis weights
trading relationships involving high-priced stock more heavily than those involving
low-priced stock.

The change in the percentage value of customer c’s trades in stock an executed

through the old brokerage house 0 of an individual broker after the individual broker

71

leaves, A(% Value After Broker Leaves Old Job)?”M is therefore expected to be negative

on average, and is deﬁned as:

 

Value After Broker Leaves Old Jobcw, _ Value Before Broker Leaves Old John“,
Total Value After Broker Leaves Old Job“. Total Value Before Broker Leaves Old Job”
(3.1)

where Value After Broker Leaves Old Jobc,“ and Total Value After Broker Leaves Old
Job“ are the values of customer c’s trades in stock 1:, executed by the old brokerage
house 0, and by all brokerage houses after the individual broker leaves the old brokerage
house, respectively. Similarly, Value Before Broker Leaves Old Jobaw and Total Value
Before Broker Leaves Old Job” are the values of customer c’s trades in stock X
executed by the old brokerage house 0, and by all brokerage houses before the individual
broker leaves the old brokerage house, respectively.

Most of the individual brokers in the sample of 11 switching brokers execute their
ﬁrst trade at their new positions at least one month after the last trade at their old
positions. While this does not mean that brokers stop working during these periods, it
may mean they are less active than when executing trades and are not as involved in
generating business for either their new or their old brokerage houses. Perhaps brokers
spend this time familiarizing themselves with their positions at their new brokerage
house. Given that a switching broker is not as active during these periods, it is possible
that his customers use either another broker at the old brokerage house of the broker, or a
broker at another brokerage house with which they have an existing business relationship,
or both. To the extent that other brokers at the old brokerage house can take over the role

of the switching broker, at least until the switching broker starts working again at the new

72

brokerage house, the leaving of a broker should not instantly shift all of the broker’s order
ﬂow away from the old brokerage house. However, the full effect of the leaving of a
broker may not be felt until the broker begins his new job at the new brokerage house. As
periods of a broker’s trading inactivity between jobs may lead to temporary changes in
the order placement behavior of the broker’s customers, these periods are studied
separately from periods when the broker is actively trading.

Therefore, the trading preferences of the customers of the switching broker and the
switching broker’s old brokerage house, after the broker starts to trade at the new
brokerage house, are also examined. The change in the percentage value of customer c’s
trades in stock :I: executed through the old brokerage house 0 of an individual broker after
the broker starts working at the new brokerage house, A% Value After Broker Begins New

Jobaw is expected to be negative on average, and is deﬁned as:

 

Value After Broker Begins New Jobqw _ Value Before Broker Leaves Old Jobcmo
Total Value After Broker Begins New Job” Total Value Before Broker Leaves Old Job”
(3 .2)

where Value After Broker Begins New JobCW, and Total Value After Broker Begins New
John are the values of customer c’s trades in stock :5, executed by the old brokerage
house 0, and by all brokerage houses after the individual broker’s ﬁrst trade at the new
brokerage house, respectively. Similarly, Value Before Broker Leaves Old Jobcw, and
Total Value Before Broker Leaves Old Job”, are the values of customer c’s trades in stock
a: executed by the old brokerage house 0, and by all brokerage houses, before the

individual broker leaves the old brokerage house, respectively.

73

3.6.2 Do Customers of the Individual Broker’s Old Brokerage House Intensify Their
'Itading with the Broker’s New Brokerage House when The Broker Moves to
the New Brokerage House?

The previous section described the construction of a proxy for measuring the loss in
customer business when an individual broker stops working at a brokerage house. If
customer business follows the individual broker to the new brokerage house, then an
increase in customer business with the new brokerage house should be observed. In other
words, there should be an increase in the proportion of their business customers of the
individual broker’s old brokerage house conduct with the new brokerage house after the
individual broker relocates. As it is not known if the broker begins to generate business
immediately at his new position after leaving his old position, the study examines the
changes in the customers’ proportion of business with the new brokerage house both after
the broker’s last trade at his old position and after the broker’s ﬁrst trade at his new
position. For example, it is possible that the broker starts his new job immediately after
leaving his old position, but may not yet be trading actively. In fact in most of the broker
switching events in the sample there was approximately a thirty day delay between the
broker’s last trade at his old position and his ﬁrst trade at his new position. Hence, if the
broker receives orders from his loyal customer base during this time, he may delegate
trade execution to other brokers in the new brokerage house. This practice is supported
by strong anecdotal evidence which suggests that switching brokers tend to be sales
brokers, who generate business for the house from a personal network of clients and tend
to delegate order execution in-house to brokers specializing in trade execution. Trade

executing brokers then either enter the order in the HETI limit order book for automatic

execution or ﬁnd counterparties for negotiated (upstairs) trading. As in the previous

74

section, a measure of the change in an investor’s trade concentration, this time with the
broker’s new house, is constructed. If customers of the switching broker’s old house
prefer to continue their trading relationship with the broker at the new house, then one
would expect an increase in the proportion of the trading by value of these investors made
via the new house to increase after the broker switches. The change in the percentage
value of old brokerage house customer c’s trades in stock at executed through the new
brokerage house n of an individual broker after the individual broker leaves, A(% Value

After Broker Leaves Old Job)c,m is therefore expected to be positive on average, and is

 

deﬁned as:
Value After Broker Leaves Old Jobam _ Value Before Broker Leaves Old Jobm'n
Total Value After Broker Leaves Old Job“, Total Value Before Broker Leaves Old Job”

(3.3)
where Value After Broker Leaves Old Jobcw, and Total Value After Broker Leaves Old
Job” are the values of customer c’s trades in stock 3:, executed by the new brokerage
house It, and by all brokerage houses after the individual broker leaves the old brokerage
house, respectively. Similarly, Value Before Broker Leaves Old Johan, and Total Value
Before Broker Leaves Old Jobm are the values of customer c’s trades in stock X
executed by the new brokerage house n, and by all brokerage houses before the
individual broker leaves the old brokerage house, respectively.

As a broker begins to trade at his new brokerage house at least one-month after the
last trade at his old brokerage house, it is likely that loyal customers of the broker hold off
on placing their orders with the broker before he starts trading actively at his new

position. Thus, as in the previous section, the change in customers’ proportion of trading

75

variable is again constructed after the broker begins to trade actively at the new house.
The change in the proportion by value of customer c’s trades in stock a: executed through
the new brokerage house n of an individual broker after the broker starts working at the
new brokerage house, A% Value After Broker Begins New Jobm," is expected to be

positive on average, and is deﬁned as:

 

 

Value After Broker Begins New Jobcm, _ Value Before Broker Leaves Old Jobcmn
Total Value After Broker Begins New Job” Total Value Before Broker Leaves Old Job”
(3.4)

where Value After Broker Begins New Jobmm and Total Value After Broker Begins New

John are the values of customer c’s trades in stock as, executed by the new brokerage

house It, and by all brokerage houses after the individual broker’s ﬁrst trade at the new

brokerage house, respectively. Similarly, Value Before Broker Leaves Old Jobam and

Total Value Before Broker Leaves Old Job” are the values of customer c’s trades in stock

:I: executed by the new brokerage house n, and by all brokerage houses, before the

individual broker leaves the old brokerage house, respectively.

3.7 A Multivariate Model to Study the Effect of pre-existing business relationships,
and Investor Characteristics on Customer 'D'ading Proportions when Individ-
ual Brokers Switch Brokerage Houses

This section presents a series of multivariate regression models to test the
hypotheses presented earlier. Speciﬁcally, investor and stock characteristics are used to
explain the changes in a customer’s proportion of trading done by a brokerage house
when an individual broker either leaves or begins working at the brokerage house. Each

of the change in proportion variables deﬁned in (3.1), (3.2), (3.3) and (3.4) represented by

A(% Value) is used in turn as the dependent variable in three model speciﬁcations. Trade

76

value proportion and market share related independent variables are constructed using
trades in the estimation period, which is deﬁned as a 30 calendar day period beginning 60
calendar days prior to the last trade of each switching broker at his old brokerage house.
Each observation in the data set represents the trades for a particular investor, in a
particular stock, on both the buy and sell sides, and executed through either the upstairs
or downstairs market.

Independent variables used to test the hypotheses presented earlier are shown in
Table 3.1, along with the expected signs of the coefﬁcients. The variables Prop. of
Investor Trading Via Old B. House, Prop. of Investor Trading Via Switching Broker, and
Prop. of Investor Trading Via New B. House, are proportions of the value of investor
trading in stock done by the switching broker, old brokerage house (excluding switching
broker) and new brokerage house, respectively; Customer of Switching Broker is a
dummy equal to one if the investor traded via the switching broker in the estimation
period, and zero otherwise; Upstairs Market is a dummy equal to one if the observation is
constructed using trades in the upstairs market; Log(Total Value Traded in Stock), is the
log of total value traded in a stock; Old B. House is Market Share in Stock, Switching
Broker 's Market Share in Stock, and New B. House ’s Market Share in Stock, are the
market shares by value in the stock for the old brokerage house (excluding the switching
broker), the switching broker, and new brokerage house; Household is a dummy equal to
one if the investor is a household and zero otherwise; Financial Institution, Non-ﬁnancial
Institution, Governmental Institution, and Non-proﬁt Institution, are dummies equal to
one if the investor is a ﬁnancial, non-ﬁnancial, governmental or non-proﬁt institution,

respectively, and zero otherwise; Investor is Male, and Investor is Female are dummies

77

equal to one if the investor is male, or female, respectively, and zero otherwise; Investor
and Old House = Finnish, New House = Swedish, is a dummy equal to one if the culture
of the investor and the old brokerage house is Finnish and the culture of the new
brokerage house is Swedish, and zero otherwise, and where the base is that the old
brokerage house is Finnish, and the investor and new brokerage house are Swedish. Other
variables used, but not shown in Table 3.1, are, a dummy indicating whether the
observation is constructed using the buy sides of transactions, dummies for all but one of
the switching events, and dummies for each stock but one.

As the decrease in a customer’s proportion of trading via the old brokerage house
and its increase in the new brokerage house of the investor occur contemporaneously,
each of the two variables for the change in the proportion of investor trading are likely to
be determined endogenously to the other. Hence estimation of each variable separately
using single stage multivariate regressions, as was done earlier, are likely to yield
coefficients that are inconsistent, as each variable will be correlated with the stochastic
error term of the other’s model, i.e. yl will be correlated with the error term 6 in the
regression equation: y2 = a + .1311: + 7323/1 + e. To alleviate this potential problem, both
equations are estimated simultaneously using two-stage least squares (ZSLS) estimation.
Tables 3.12 and 3.13 Show the results of the 2SLS estimation for the relative periods t=-I
to t=0 and t=-1 to t=1, respectively. In Tables 3.12 and 3.13, the change in the
customers’ proportion of trading via the old brokerage house is deﬁned as dependent
variable (1), the left hand side variable of the ﬁrst Simultaneous equation. The change in
the customers’ proportion of trading via the new brokerage house is deﬁned as dependent

variable (2), and is used as a right hand side variable in the ﬁrst Simultaneous equation.

78

Other right hand side variables of the ﬁrst simultaneous equation include all exogenous
variables used in the earlier single stage models. The dependent variable (2) is the left
hand side variable of the second simultaneous equation, whereas the dependent variable
( l) is a right hand side variable of this equation.
3.8 Results

Tables 3.8, 3.9, 3.10, and 3.11, show the results of estimation of models having
change in proportion variables deﬁned in (3.1), (3.2), (3.3) and (3 .4), respectively,
whereas Tables 3.12 and 3.13 show the results for two simultaneous equations models,
one having (3.1) and (3.2), and the other having (3.3) and (3.4), as the dependent
variables, respectively. Each of the following subsections interprets the results presented
in these tables in the light of one of the hypotheses presented in Table 3.1.

3.8.1 Hypothesis 1: TI'ading Relationships Affect Investors’ Likelihood of Switch-
ing

In Table 3.8, the coefﬁcient of the variable Prop. of Investor Trading Via Old B.
House in all three models is positive and Signiﬁcant, and in Table 3.12 the coefﬁcient of
the third model is positive and signiﬁcant. This is consistent with sub-hypothesis la, that
investors having stronger relationships with the old brokerage house, will be less likely to
switch to the new brokerage house when the broker leaves. The corresponding
coefﬁcients of this variable for the models in Table 3.9 and Table 3.13 are statistically
insigniﬁcant, although in two of the three models in both tables, the coefﬁcient has the
expected Sign, consistent with the hypothesis. However, the coefﬁcients of this variable in
Table 3.10 and in Table 3.11 are insigniﬁcant for all models. Thus, a stronger relationship

with the brokers of the old brokerage house increases the proportion of an investor’s

79

trading via the old brokerage house in the ﬁrst month after the broker leaves, (Table 3.8),
but not after he starts trading at the new brokerage house (Table 3.9). Furthermore, the
insigniﬁcance of the coefficients in Tables 3.10 and 3.11 implies that an investor’s past
relationship with the old brokerage house does not affect the likelihood of his switching
to the new brokerage house. Therefore, the results indicate weak support at best for
sub-hypothesis 1a.

The coeﬂicients of Customer of Switching Broker are insigniﬁcant in all models and
in all tables. However, in Tables 3.9, 3.10 and 3.11, the corresponding coeﬂicients in all
models have the expected Sign. The coefﬁcients of Prop. of Investor Trading Via
Switching Broker are signiﬁcant in Table 3.8 but do not have the expected Sign. In Tables
3.9, 3.10, and 3.13, the corresponding coefficients in all models have the expected Sign
but are insigniﬁcant. Therefore, the thrust of the evidence does not support
sub-hypothesis lb, that investors having a (stronger) ex-ante relationship with switching
brokers are more likely to switch with the broker.

The coefﬁcients of Prop. of Investor Trading Via New B. House are signiﬁcant in
Table 3.8 but none have the expected Sign. In Table 3.9 the coefﬁcients in all three
models have the expected Sign and in two of the three cases they are statistically
signiﬁcant. In Table 3.10 the coeﬂicients do not have the expected sign and are
insigniﬁcant. However, in Table 3.11 all three coeﬂ‘icients have the expected sign and are
signiﬁcant. The results most supportive of the sub-hypothesis 1c comes from Tables 3.9
and 3.1 1 which use the change in an investors proportion of trading via the old and new
brokerage houses after the broker starts trading at the new brokerage house. Hence an

ex-ante relationship with the new brokerage appears to affect trading of investors after the

80

broker starts working at the new brokerage house. In the simultaneous equation
regressions shown in Table 3.13, all coefficients have the expected Sign but are not
signiﬁcant. Therefore, there is moderate support for sub-hypothesis 1c.

Therefore, the weak support for sub-hypothesis la and moderate support for
sub-hypothesis 1c, imply that there is little support for Hypothesis 1, that trading
relationships affect the investors’ likelihood of switching.

3.8.2 Hypothesis 2: Upstairs Market Investors Develop Closer Business Relation-
ships with the Broker and are More Likely To Switch With The Broker

The coefficients of Upstairs Market in all model speciﬁcations except for those in
Table 3.13 have the expected Signs. However, despite the fact that the signs of the
coeﬂicients imply overwhelming support for Hypothesis 2, none of these coefficients are
statistically signiﬁcant. Hence, there is no statistical evidence supporting Hypothesis 2,
that upstairs market investors develop closer business relationships.

3.8.3 Hypothesis 3: Brokers’ Market Dominance Affects Investors’ Likelihood of
Switching

In Tables 3.8, and 3.10, 3.11, and 3.12, the coefficients of Old House is Market Share
in Stock in all model speciﬁcations have the expected Sign but are all insigniﬁcant. In
Tables 3.9 and 3.13, the coefﬁcients do not have the expected signs and are also
insigniﬁcant. Therefore, there is no statistically signiﬁcant evidence supporting
sub-hypothesis 3a, that investors are less likely to switch if the old brokerage house is
more dominant in a stock.

In Tables 3.8, 3.11, and3.12, the coefﬁcients of Switching Broker ’s Market Share in

Stock in all model Speciﬁcations have the expected signs but are all insigniﬁcant. In

81

Tables 3.9, 3.10, and 3.13, the coefﬁcients do not have the expected signs and are also

insigniﬁcant. Sub-hypothesis 3b is therefore not supported.

In Tables 3.8, 3.10, and in Model 3, Table 3.13, the coefficients of New House 's
Market Share in Stock have the expected signs but are statistically insigniﬁcant. In Tables
3.9, and 3.11, the coefficients are signiﬁcant but do not have the expected sign. Therefore,
in Table 3.9, all three coeﬂicients of the variable are signiﬁcantly positive, implying that
customers of the old brokerage house reduce their trading with the old brokerage house
when the new brokerage house is less active in the stock. In Table 3.11, all three
coefficients are signiﬁcantly negative, implying that customers of the old brokerage house
increase (decrease) their trading with the new brokerage house when the new brokerage
house is less (more) active in the stock. These two results imply that investors are more
(less) likely to switch in stocks where the new brokerage house is less (more) active.

Overall, although there is strong support that a new brokerage house’s market
dominance affects investors’ likelihood of switching, this effect is opposite in direction to
what was hypothesized. Hence, there is little support for Hypothesis 3.

3.8.4 Hypotheses 4 / 5: Less Savvy Investors are Biased and More Likely to Switch
/ Less Savvy Investors Have Simpler Trading Preferences and Less Likely to
Switch

Although not statistically signiﬁcant, the coefficients of Household in Tables 3.8,
3.9, 3.10 and 3.11 have signs consistent with sub-hypothesis 5a, that households have less
complicated trading preferences and are therefore less likely to switch with the broker.

The coeﬂicients in Tables 3.12 and 3.13 support the sub-hypothesis that households are

biased and therefore more likely to switch, although the coefficients are not statistically

82

signiﬁcant. Therefore, the result is on the balance consistent with sub-hypothesis 5a
rather than sub-hypothesis 4a, but not signiﬁcantly so.

In Table 3.8, the coefficients of both Financial Institution and Non-ﬁnancial
Institution are insigniﬁcant but their signs being positive is consistent with
sub-hypothesis 4b. In Table 3.10, the coefficients of both variables are insigniﬁcant but
their signs are both negative and also consistent with sub-hypothesis 4b. In Table 3.9, the
Sign of Financial Institution is insigniﬁcant and negative, which is consistent with
sub-hypothesis 5b, and the sign of Non-ﬁnancial Institution is insigniﬁcant and positive,
which is consistent with sub-hypothesis 4b. Finally, in Table 3.11, the signs of both
coefficients are insigniﬁcant and positive, which is consistent with sub-hypothesis 5b.
Overall, the signs of the coefficients are not consistent with either of the sub-hypotheses
4b and 5b. Furthermore, none of the coefficients of either variable is signiﬁcant.

The coeﬂ‘icients of Governmental Institution — Financial Institution, and
Governmental Institution — Non-ﬁnancial Institution, are insigniﬁcant and consistent
with sub-hypothesis Sc in Table 3.8, and signiﬁcant and consistent with sub-hypothesis
Sc in Table 3.11. The coefficients of these variables are insigniﬁcant and consistent with
sub-hypothesis 4c in Tables 3.9, and 3.11. Thus, in the case of governmental institutions
there is some evidence to support sub-hypothesis 5c, the hypothesis that governmental
institutions have less complicated trading preferences than ﬁnancial and non-ﬁnancial
institutions and are therefore less likely to switch. On the other hand the coeﬂicients of
Non-proﬁt Institution — Financial Institution and Non-proﬁt Institution — Non-ﬁnancial
Institution are insigniﬁcant and consistent with sub—hypothesis 4c in Table 3.8, and

signiﬁcant and consistent with sub-hypothesis 4c in Table 3.10. The coefficients are

83

insigniﬁcant and consistent with sub-hypothesis 5c in Tables 3.9 and 3.11. Thus, in the

case of non-proﬁt institutions there is some evidence to support sub-hypothesis 4c, the

hypothesis that non-proﬁt institutions are less sawy than ﬁnancial and non-ﬁnancial

institutions and more biased toward the broker and therefore more likely to switch.
Although there is some evidence to support sub-hypothesis 4c and So, there is

insufficient evidence to reject Hypothesis 4 in favor of Hypothesis 5, or vice versa.

3.8.5 Hypotheses 6 / 7: Male Investors Have Stronger Relationships with (Male)
Brokers and More Likely to Switch / Female Investors are More Relationship-
Oriented and More Likely to Switch

In all model estimations, the coefficients of Investor is Male -— Investor is Female,
have signs consistent with Hypothesis 7, and in the case of Tables 3.8, 3.11 and 3.13, the
coefficients are also statistically signiﬁcant, implying that female investors are more
likely than their male counterparts to leave the old brokerage house and move to the new
brokerage house after the broker switches. Hence there is strong evidence to support the
hypothesis, Hypothesis 7, that female investors are more relationship-oriented than male

investors, and are more likely to switch with the broker.

3.8.6 Hypothesis 8: Investors Prefer Brokerage Houses with Same Culture, and are
Less Likely to Switch if New Brokerage House has Different Culture

In Tables 3.8, 3.9, and 3.12 and two model speciﬁcations in 3.13, the coefficients of
Investor and Old House = Finnish, New House = Swedish have a positive Sign which is
consistent with Hypothesis 8, although they are insigniﬁcant. The coefficients have a
positive Sign in Table 3.9 which is inconsistent with the hypothesis, but have a negative
Sign in Table 3.11 which is consistent with the hypothesis. On the balance, the signs of

the coefficients appear to support the hypothesis that Finnish culture investors are less

84

likely to switch to a Swedish culture new brokerage house from a Finnish culture old
brokerage house than a Swedish culture investor. However, this support is not statistically
signiﬁcant.

In Table 3.12, for all three speciﬁcations, the coefficients of dependent variable (2)
in the second simultaneous equation are negative, and for the third model it is also
signiﬁcant. This is consistent with the idea of changes in trading proportions of the old
and the new brokerage houses being negatively correlated. For example, a decrease in the
proportion of investor’s trading in stock done by the old brokerage house of 50% is
accompanied by a contemporaneous increase in the proportion of trading in stock done
by the new brokerage house of 0.5 x 27.2% or 13.6%. This negative relation is even
more apparent in Table 3.13, where the coefficients of both Dependent Variable (1 ) and
Dependent Variable (2), are negative, and statistically signiﬁcant in two of the three
model speciﬁcations for the ﬁrst variable, and signiﬁcant in all three model speciﬁcations
for the latter variable.

3.9 Discussion and Interpretation of Results

The hypothesis that trading relationships with brokers affect investors’ likelihood of
switching, namely Hypothesis 1, was not supported. However, there is some evidence
that having a stronger pre-existing relationship with the new brokerage house increases
the proportion of an investor’s trading with the new brokerage house after the broker
switches. Hence, trade relationships with the new brokerage house appear to lower the
cost of switching for an investor, such that it becomes ‘cheaper’ for the investor to follow
the individual broker to the new brokerage house, and shift a greater proportion of trading

to the new brokerage house.

85

The hypothesis that the upstairs market is more conducive to the development of
business relationships between investors and the switching broker, namely Hypothesis 2,
was not supported. This may be due to the fact that brokers can develop equally close
relationships with investors using either the upstairs or the downstairs markets because
brokers get to know all investors personally regardless of where their orders eventually
end up, i.e. either in the upstairs or the downstairs market. Therefore, in the case of the
HSE, there may be less of a distinction between the strength of relationships that develop
in the upstairs and the downstairs markets. However, in markets where downstairs trades
are entered directly by investors, such as via an electronic order placement system, it
would be more reasonable to expect there to be a difference between upstairs and
downstairs markets.

It is found that new brokerage houses that are less dominant in a stock attract a
greater proportion of investor trading when the broker switches. This contradicts the
hypothesis, namely sub-hypothesis 3c, that new brokerage houses more dominant in a
stock and therefore better able to provide liquidity, attract a greater proportion of investor
trading when the broker switches. One reason for this result could be that when the new
brokerage house is less active in the stock, investors who wish to trade this stock can have
a closer and more valuable relationship with the new brokerage house. Another
explanation is that the new brokerage houses try to attract order ﬂow from the old
brokerage house of the switching broker for stocks in which the new brokerage house is
not dominant, in order to increase their market share for these stocks. Additionally, the
study did not ﬁnd evidence to support the hypotheses, sub-hypotheses 3a and 3b, so that

old brokerage houses more dominant in a stock do not retain a greater proportion of

86

investor trading when the broker leaves, and switching brokers more dominant in a stock
do not attract a greater proportion of investor trading away from the old brokerage house
when they leave.

There is some evidence albeit statistically insigniﬁcant, that households have less
complicated trading preferences than institutions and are therefore less likely to switch,
i.e. sub-hypothesis 5a. There is no statistically signiﬁcant evidence for either
sub-hypothesis 4b, the hypothesis that ﬁnancial and non-ﬁnancial institutions are either
less biased than households and less likely to switch, or sub-hypothesis 5b, the hypothesis
that ﬁnancial and non-ﬁnancial institutions have more complicated trading preferences
and are more likely to switch. With regard to the switching behavior of savvy versus
unsavvy institutional investors, the results are mixed. While there is evidence that
governmental institutions, assumed to be unsawy, are less likely to switch than sawy
investors, including both ﬁnancial and non-ﬁnancial institutions, there is also some
evidence that non—proﬁt institutions, also assumed to be unsavvy are also more likely-to
switch to the new brokerage house. This means that there is some support for both of the
competing hypotheses. Hence the evidence supports both the hypothesis that less savvy
investors have simpler trading preferences and are more likely to switch with the broker
as well as the hypothesis that less savvy investors are biased toward the broker and more
likely to switch.

There is very strong evidence that male investors are less likely to switch with the
broker than female investors. This is consistent with the hypothesis that female investors
are more relationship-oriented and are more likely to switch in order to continue their

relationship with the broker.

87

Finally, there was weak evidence to support the hypothesis that Finnish culture
investors were less likely than Swedish culture investors to switch to new brokerage
houses having a Swedish culture.

3.10 Conclusions

This paper studied the determinants of customer switching from one brokerage
house to another, when an individual broker of the ﬁrst house switches to the second
house. There is signiﬁcant evidence to. support that customers of a brokerage house do
indeed switch their trading to a new brokerage house when an individual broker switches.
A main ﬁnding of the paper is that customers’ pre-existing relationships with the new
brokerage house make it more likely that they will switch to the new brokerage house
after the individual broker switches. This is consistent with the hypothesis that there are
costs of switching between brokerage houses, and that already existing trading
relationships can reduce such switching costs. Another interesting ﬁnding, albeit
inconsistent with one of the hypotheses, is that new brokerage houses that are less active
in a particular stock are more likely to attract the customers from old brokerage houses of
the switching broker. This is consistent with the idea that brokerage houses attempt to
increase their market share in a stock by attracting brokers away from other houses.
Interestingly, however, customers are not lured away from the old brokerage house by
switching brokers having a greater presence in the market for trading the stock. There
were mixed results for the hypotheses related to whether savvy or unsavvy investors were
more likely to switch. While governmental institutions were more likely to switch than
ﬁnancial and non-ﬁnancial institutions, non-proﬁt institutions were less likely to switch.

Thus, it is not clear whether unsavvy investors are less likely to switch because they have

88

less complicated trading preferences, or whether unsavvy investors are more likely to
switch because they are biased toward the switching broker. There is strong evidence
supporting the hypothesis that female investors are more likely to switch with the broker
than male investors, consistent with the hypothesis that female investors are more
relationship oriented. Finally there is some evidence albeit statistically insigniﬁcant, that

investors prefer to trade with similar culture brokerage houses.

89

CHAPTER 4: UNEXPRESSED DEMAND AND THE TRADING BEHAVIOR OF
UPSTAIRS MARKET MAKERS

ABSTRACT

Due to the possibility of front-running, an upstairs market maker is unlikely to share with
others information on his customers’ likelihood of trading, called unexpressed demand by
Grossman (1992). This is because a market maker more knowledgeable about the
likelihood of future trading can more accurately predict price movements, and can
front-run other market makers more successfully. As costs of switching lead investors to
concentrate their business with a limited number of upstairs market makers, this implies
that market makers do not learn the unexpressed demand of all upstairs market investors.
In this chapter, Grossman’s (1992) model is redeveloped, and the economic implications
of intermediate results are discussed. An extension of Grossman’s (1992) model is made
where the unexpressed demand in the upstairs market is split into private and public
components. It is found that as the public signal of the unexpressed demand in the
upstairs market becomes less (more) noisy, the execution quality of the upstairs market
improves (worsens) relative to the downstairs market. Thus, exclusive or close trading
relationships between customers and market makers where signals for unexpressed

demand remain private, can be detrimental to upstairs market liquidity.

90

4.1 Introduction

Upstairs markets have certain unique properties, which authors such as Seppi (1990)
and Grossman ( 1992) have argued improve liquidity provision in such markets. One of
these unique properties is that upstairs market makers know the identities of their
customers in each trade and can become familiar to some extent with these customers’
trading preferences and their willingness to trade in certain states of the world. For
example, a customer may ask the upstairs broker to buy or sell a certain quantity of stock
when the price is within a pre-speciﬁed range, or to buy or sell a certain quantity of stock
continuously in pre-speciﬁed intervals. It is also possible that the customer wishes to buy
or sell a large enough number of shares of a relatively illiquid stock that the order needs
to be executed in multiple transactions over many days, weeks, or even months. In such
cases, it is quite obvious that upstairs market makers can become privy to information
that will affect the future price of a security. Grossman (1992) refers to this as ‘a
repository of information about the unexpressed demand of customers’. However,
Grossman’s (1992) model makes the assumption that the upstairs market is composed of
a number of identical market makers who observe the expressed and unexpressed
demands of the whole upstairs market. This assumption implies that the expressed and
unexpressed elements of the total order ﬂow in the upstairs market are observed by each
market maker. '2

However, since the upstairs market is fragmented, and there are possibly many

upstairs market prices for a security at any given time, it is unlikely that each market

 

'2 Expressed order ﬂow refers to the aggregate of all orders currently arriving in the upstairs market. Un-
expressed order ﬂow refers to an aggregate of the unexpressed demand schedules of the customers who
are expected to trade in the upstairs market at same later date, and whose willingness to trade is taken
exogenously.

91

maker knows the expressed and unexpressed demands of all upstairs market customers.
But as each upstairs market maker knows the identities of its customers, it can be argued
that it is not the whole upstairs market but each upstairs market maker that is a repository
of information about the unexpressed demands of upstairs market customers.
Furthermore, in the upstairs market, market makers can be brokers as well as dealers.
This means that upstairs market makers can serve a dual role as both broker to their
customers by ﬁnding counterparties for their customers’ orders, and as a dealer who takes
the opposite side of a trade, i.e. intemalizes the order. The analysis of market makers in
Grossman (1992) and also in this paper, take into account this dual role of market makers
in the upstairs market.

The development of trading and professional relationships with other market
makers, as well as with the customers of other market makers can allow an upstairs
market maker to become more informed about the trading habits of other market makers’
customers. However, due to the possibility of front-running, the development of trading
or professional relationships among upstairs market makers would not necessarily
increase the market makers’ knowledgeability about the trading preferences of each
others’ customers. A market maker’s knowledgeability is more likely to increase through
professional relationships with customers who also use other market makers. For
example, if an individual broker switches to another brokerage house, he may take with
him the knowledge of the unexpressed demand of his old brokerage house’s customers.
As studied in the previous chapter, a customer of the individual broker’s old brokerage
house may also switch some of his trading to the new brokerage house. Thus depending

on the predictability of the customer’s trading habits, the old brokerage house is likely to

92

retain some or all of the knowledge about that customer’s unexpressed demand. Also, the
new brokerage house is likely to increase its knowledge of this customer’s trading habits,
and therefore the customer’s unexpressed demand. Investors in need of liquidity, i.e. for
an illiquid stock, may also shop around in the upstairs market among different brokerage
houses, and over time reveal their trading preferences to brokerage houses they may not
transact with. Thus upstairs market makers are likely to have some information about the
unexpressed demand of other market makers’ customers. Thus, the insight that trading
relationships help market makers become more informed about the unexpressed demand
of other market makers’ customers, is also incorporated into the Grossman (1992) model
in this paper.

First, the paper redevelops Grossman’s (1992) original model, presents a setup of
the model, derives the model including intermediate steps left out of Grossman (1992).
Economic implications of some intermediate ﬁndings are also discussed. Next, the
general Grossman (1992) model is revised under the assumption that upstairs market
makers only observe the unexpressed demand of their own customers, but not that of
other market makers. Upper and lower bounds on the informativeness of order ﬂow are
identiﬁed in this general model and are compared to the ﬁndings of Grossman. A
simpliﬁed but tractable model incorporates the more plausible assumption that allows for
varying levels of inforrnedness about the unexpressed demand of other market makers’
customers. The paper then develops a more general model of the upstairs market where
market makers know their own customers’ unexpressed demand with certainty, but the
unexpressed demand of other market makers’ customers with a probability. The paper

ﬁnds a parsimonious expression for the upstairs equilibrium price of the security, and the

93

volatility of the change in upstairs market price, which corresponds to execution quality
in the upstairs market. The execution quality in the upstairs market under Grossman’s
(1992) assumptions is compared to its counterpart under the general model derived here.
4.2 Grossman’s (1992) Model

This section derives Grossman’s (1992) model of the upstairs and downstairs
markets, explaining clearly the intermediate steps in the derivation. The sequence of
events in Grossman’s (1992) model is also presented.

The model assumes, without loss of generality, that there are two time periods. At
time 1, the following events take place:

1) Each investor decides whether to use the upstairs or the downstairs market.

2) Each investor experiences an exogenous liquidity shock, and either places an order
to trade in the appropriate market (expressed demand) or informs all upstairs
market makers of his willingness to trade at some future period (unexpressed
demand), where the proportion of the demand in the upstairs versus the downstairs

market is taken exogenously.

3) All My upstairs market makers observe all of the expressed and unexpressed
demands of upstairs market customers, but not the expressed demand of downstairs

market customers.

4) All M D downstairs market makers observe all of the expressed demands of
downstairs customers but neither the expressed nor the unexpressed demands of the

upstairs market customers.

94

5) Upstairs market makers clear the upstairs market by choosing their demand to
maximize the utility of their future (time 2) wealth. An average upstairs clearing
price at time 1, Ply, is established. There are as many upstairs market prices as
upstairs market makers, but each deviates from the average price by an amount 5,.

6) Downstairs market makers clear the downstairs market by choosing their demand to
maximize the utility of their future (time 2) wealth. A single downstairs clearing

price at time 1, P10, is established.
Finally, at time 2, the following event occurs:

7) The upstairs and downstairs market prices of the asset converge to a single price,
P2, reﬂecting all public information about the asset, minus the cost of providing

liquidity in both the upstairs and downstairs markets at time 1.

The model also assumes that the asset being traded is a forward or a futures contract
and is in zero net supply. It is immaterial to the analysis whether or not the asset is
assumed to be in zero net supply, except that it simpliﬁes the exposition.” The price at
time 2, P2, can therefore be viewed as the settlement price of a forward or a futures
contract:

E=@—w2 an

where 92 is the random payoff of the contract’s underlying asset, the coefficient b
represents the sensitivity of the contract’s settlement price to 522, which is the total order

ﬂow at dates 1 and 2, expressed and unexpressed, in both the upstairs and downstairs

 

'3 If assets were not assumed to be in zero net supply, then the supply side of the market clearing equation
would contain an exogenous constant, and this would not affect the analysis qualitatively.

95

markets. Therefore —b:22 is the liquidity cost incurred by the holder of the contract due to
the trading of liquidity motivated traders. The price P2 is viewed as a ‘long-run’
equilibrium price.

The model deﬁnes :52 as a combination of the total expressed customer order ﬂow in
both the upstairs and downstairs markets at time 1, :51, and the total unexpressed
customer order ﬂow in the upstairs market, 3]}. Total time 1 order ﬂow is in turn
expressed as a combination of the total upstairs and downstairs market expressed

demands, yij, and y'b, respectively. Hence:

5131: ﬂux/5 + 37m/1- 4 (4.2)

and,

5:2 =5:I\/7+I72\/1—f (4.3)

where ﬂ is the proportion of the total time 1 (expressed) order ﬂow that is sent to the
downstairs market, andx/fr is the proportion of the total demand that is expressed at time
1. The ﬂ and m are used to reallocate the variance of expressed order ﬂow
between the upstairs and downstairs markets so that the variance of expressed order ﬂow
remains constant. The J? and \/1_—_7 are used to reallocate the variance of total order
ﬂow between expressed and unexpressed components, so that the variance of total order
ﬂow is constant. Although the proportions ﬂ and F7 add to more than one except
when f = 1 or f = 0, this is necessary to satisfy the model’s assumption that the total

variance of order ﬂow stays constant. The scaling up of the order ﬂow proportion to

96

possibly greater than one, does not impact the analysis, however, since the analysis is
concerned with the quality of the market, which is found to be a function not of the order
ﬂow, but its volatility.

It is also assumed that 2711. go, and g2, are independent and identically distributed
normal random variables with zero mean and variance 0%.

The model assumes that market makers in a particular market can only trade in that
market. Therefore, downstairs (upstairs) market makers, observing the order ﬂow only
from downstairs (upstairs) market customers, trade only in the downstairs (upstairs)
market. Hence, two separate market equilibria arise as a result of the disconnect between
trading in the upstairs and downstairs markets. These two market equilibria are now

derived.

4.2.1 Downstairs Market Equilibrium

After observing the order ﬂow from the downstairs market traders, downstairs
market makers determine the amount they wish to trade, Z ,3, in order to maximize the
utility of their future (time 2) wealth, W2. Therefore, conditional on their observation of
the order ﬂow of all downstairs market customers, y D, the downstairs market makers

choose Z D to maximize:
E [U (We) lye] 2 —exp [—aWQ] * (4.4)

where W, = W1 + (132 — P10) 20, and W1 is the initial wealth of each market maker,

and P10 is the date 1 price of the asset in the downstairs market. Since the risk aversion

97

coefficient a in equation (4.4) is independent of market maker, the model assumes all
market makers are equally risk averse.

The optimal demand of downstairs market makers, Z D is found to be equal to:

E [1321110] — PID
a{Var [PglyD] +03}

20—

 

(4.5)

There are M D identical downstairs market makers, each of which independently choose
the same optimal demand, Z D. To clear the market, the total market maker demand must
equal the order ﬂow of all customers in the downstairs market at time 1. Therefore, the

market clearing condition is:

Mng = ypﬁ (4.6)

After rearranging the market clearing equation (4.6), the downstairs equilibrium

price at time 1 is:

 

P11) 2 E [F2 IyD] — (LIE/1:?) Var [132 lyoij- (4.7)

Equation (4.7) shows that the time 1 price is negatively related to the customer
supply in the downstairs market. Hence, customer sell orders push down the time 1 price,
whereas customer buy orders push the price up. Furthermore, as in Grossman and Miller
(1988), the impact of the liquidity demanded by customers at time 1 on the price at time
1, is inversely proportional to the number of market makers in the downstairs market.
Hence, in a highly competitive market with lots of market makers, the impact of the

liquidity demand is reduced. The number of market makers trading in the downstairs

98

market, however, depends on how easily agents can become market makers, i.e. on
whether or not the beneﬁts of market making exceed the costs.
Endogenously Determined Market Makers

As in Grossman and Miller (1988), it is assumed that each potential market maker
must incur a cost of cD at time 0, in order to become a market maker and observe the
expressed order ﬂow in the downstairs market at time 1. Each potential market maker
must at least break even when he decides to pay cD to enter the market and proﬁt from
market making, as opposed to staying out of the market. Thus, the individual rationality
condition for becoming a market maker must be exactly satisﬁed at date 0. This means
that in equilibrium, the unconditional expectation of the utility of becoming a downstairs
market maker must equal the unconditional expectation of the utility of non-participation

in the downstairs market. In other words:
E0 [U (W0 — e0 + (P, — 13m) 20)] = E0 [U (W0)] (4.8)

where W0 is the initial endowment of each market maker, i.e. at date 0, Z D is the optimal
demand of each downstairs market maker as shown in equation (4.5).
Now, by using the Law of Iterated Expectations, the left hand side of equation (4.8) can

be expressed as an expectation of a conditional expectation, so that:

E0 [E1 [U (W0 — CD + (H — 13m) 2e) [gel] = E0 [U (Woll (4.9)

99

Therefore, after substituting for Z D from equation (4.6), using the negative utility
function, i.e. equation (4.8), the conditional expectation expression on the left hand side

of equation (4.9) becomes:

E1[U(VV0 — CD + (P2 — P10) 20) lyd] =

WI (4.10)
—€’IP(—0W0) 833190100) E1 [8331? (—a (P2 — P10) 37017) lilo]
D
Given a constant a, a normally distributed variable X, has the following property:
_ a2
E [exp(aX)] E exp (aX + 303'), (4.11)

where 7 and aﬁ are the mean and variance of X, respectively. Using the property in

(4.11), equation (4.10) becomes:

ErlU(-)I-l =—exp<—awo)exp<aco>exp< -a(E1 [Blvd-P10) 90%;

2 ...
+03 (Vail [P2 'yDl) 92” AS113" )

 

(4.12)
Now, substituting for E1 [P2 lyD] — P10 from equation (4.7), and simplifying the
resulting expression, one obtains:
E [U( )l l = -eep<—aw > eprac )exp —“—2(q> (Var [15 ly ]) (ﬂ)
1 - - o D 2 - 1 2 D A [D
(4.13)

100

Now, after substituting for the conditional expectation on left hand Side of equation (4.9)
from equation (4.13), substituting for the right hand side of equation (4.9) from equation
(4.8), cancelling the exp(—aW0) terms from both sides, and moving the erp(acD) term

from the left to the right hand side, the following is obtained:

an

E. [exp (3328) (Van [P2 lyn]) (1%) ﬂ = era—ace) (4.14)

 

Now, let:
a20y2lq) (Van [132 1310])
to = Me" (4.15)
and
Q2
22 = 0—03 (4.16)
Y

where 370 is normally distributed with mean [.ty and variance ay2, and 2:2 has a

A-

M
0Y

non-central X2 distribution. Now, let X = , so that X has the standard normal

distribution.

Then, the moment generating function of 22, MAT) is given by:

Eerp(z2T) = E(ea:p (X + Elf T) (4.17)

which equals:

NIH

 

ezrp( “’2T )> (1—2T)‘ (4.18)

0'y2(1— 2T

101

t
Now, for equation (4.18), let T = ——22, and substitute for to from (4.15), to obtain:

NIP-

2 ( “Ill/2 t0) —
E[ea:p(z T)] = exp —————— (l + tD) (4.19)

0y2(1+tD) 2

It is assumed that at date 0, the expected magnitude of the liquidity event occurring at
date 1 is equal to zero, so that py = 0.
Therefore, by letting ,uy = 0, equation (4. 19) reduces to (1 + t D)-’:’ which is equal

to the right hand side of equation (4.14), i.e.:

rel—-

(1+tD)_ = exp(—a.cD) (4.20)

Now, using equation (4.20) to substitute for t D into (4.15), the equilibrium number of

market makers can be obtained for the downstairs market:

 

M — Gay‘ﬁl/Vm (’5’ '3’”) + ‘7’”? (4 21)
D \/e:1:p(2acD) — l .

 

Thus, it can be seen from equation (4.21) that the number of market makers entering
the downstairs market increases with the uncertainty of the time 2 price given that the
market maker observes the order ﬂow of downstairs customers, y D. Increasing the cost of
downstairs market making, CD on the other hand, reduces the number of market makers
that enter the market in equilibrium, as smaller market making proﬁts support fewer

market makers.

102

A Measure of Execution Quality in the Downstairs Market

Although, the time 1 price and the number of equilibrium market makers have been
found, nevertheless the liquidity demanded by customers has been assumed to be
exogenous. However, it is more natural to assume that an investor receives a random
liquidity shock before it observes the time 1 or time 2 prices in the downstairs market. It
is further assumed that the investor decides to trade in the downstairs market, even before
it learns its liquidity shock, at. Supposing that an investor sells :1: contracts in the
downstairs market at a price P11) and holds the position until time 2 when the price

becomes P2, the expected utility of the investor’s wealth can be expressed as:
EU. [r (1311) — 152)] (4.22)

where 5: is normally distributed and independent of (P1 D — P2), g2, 00, ﬂu and g2, and
has mean zero and variance 0.2x-

The utility function of the customer is negative exponential, same as the market
maker. However, the customer is allowed to have a different risk aversion than the market
maker. The following is the form of the customer’s utility, where h is the customer’s risk
aversion coeﬂicient:

UC(W) = —e:rp(—hW') (4.23)

Using the law of iterated expectations, the equation (4.22) can also be written as:

EUC [.r-(PJD — 152)] = E (E(U [.f(P1D — 132)] |r)) (4.24)

103

Now, the distribution of the price change from time 1 to time 2, i.e. P1D — P2 is
found as follows. First, E [P2 lyD] is moved to the left hand side of equation (4.7) and
expectations are taken of both Sides. Since by iterated expectations

E [E [E2 IyDH = E [132], it obtains that:

E [PlD — P2] = — (if) E [Var [152 [yDHEyD (4.25)

 

Now, since E g D = 0, the expected value (or mean) of P1D — P2 is also zero. It is

also obvious that P1 D —— P2 is normally distributed. Its variance is thus:

2

Jim E Var (P1D — P2) = —§—1% (Var [P2 [yD])2a$. + Var [P2 IyD[. (4.26)

The conditional expectation on the right hand side of equation (4.24) becomes:

E (U [515(131D — [32)] [23) = —e:rp [7122;034 (4.27)

After substituting the right hand side of equation (4.27) for the conditional
expectation expression in equation (4.24), and evaluating the expectations operator for a

)8 distribution, the following is obtained:

] 1/ 2 (4.28)

E (U [r (1510 — P2)[) = — [1 — magma}

. . . *1 . . .
Provrded that the condItIon aﬁ < (712 aim) 15 met, re. the variance of the

customer’s demand is small enough, then equation (4.28) will be valid. Hence it can be

104

seen that the customer’s expected utility is inversely proportional to the variance of the
price change from time 1 to time 2, i.e. 0% Thus, a customer will be better off when the
variance of the price change, 04%, is lower. Therefore, a} can be viewed as a cost arising
from the liquidity demanded in the downstairs market at time 1. After substituting for
Mg in equation (4.26) from equation (4.15), the following is obtained for the variance of

the price change in the downstairs market:
03“., = Var [152 [3,0] exp(2acD) (4.29)

This means that the customer is better off when the expressed demand in the downstairs
market at time 1 is more informative about the time 2 price, and when the downstairs
market makers ﬁnd it less costly (CD) to enter the market. Also, increasing the market
maker cost of entry for the downstairs market, 60, increases 02”, the ’downstairs trading
cost’, exponentially. Thus even a small reduction in the cost of entry can dramatically
improve market liquidity.
4.2.2 Upstairs Market Equilibrium

After observing the order ﬂow from the upstairs market traders, upstairs market
makers determine the amount they wish to trade, Z U, in order to maximize the utility of
their future (time 2) wealth, W2. Therefore, conditional on their observation of the
expressed and unexpressed demand of all upstairs market customers, W, and y2,

respectively, the upstairs market makers choose Z U to maximize:
E [U (W2) my, 3),] s — exp [—0.143] (4.30)

105

where W2 = W1 + (132 — Ply) ZU, and W1 is the initial wealth of each market maker,
and P10 is the average date 1 price of the asset in the upstairs market. Since the risk
aversion coefﬁcient a in equation (4.30) is independent of market maker, the model
assumes all market makers are equally risk averse.

As mentioned by Grossman (1992), the price traded at by each market maker 2', can
be deﬁned as being equal to the average upstairs price Pw plus a perturbation 5,- which
averages to zero across all My upstairs market makers, and which causes additional

volatility of 0,2] in the date 1 upstairs price. Thus,

PM“ = Pu] + 51' (4.31)

Since simultaneous upstairs trades can take place at different prices, one can view
each upstairs trade as taking place at the average upstairs market price plus a random
error term. Hence, in the Grossman (1992) model, upstairs market makers are identically
informed about the upstairs order ﬂow, and random errors therefore cancel out in the
expression for the average price in the upstairs market at time 1, i.e. Pw. In other words,
the average of P1 (1,,- across 2’ upstairs market makers is simply Ply, the average price in
the upstairs market at date 1.

However, the random errors create additional volatility in the average upstairs

market price, which is denoted by of].

106

Therefore, optimal demand of upstairs market makers, ZU is found in the same way

as the downstairs market maker demand in the previous section, and is equal to:

E [132 mutt/2] — PlU
a{Var [152 lyuty2] +03}

ZU—

 

(4.32)

There are M U identical upstairs market makers, each of which independently choose the
same optimal demand, ZU. To clear the market, the total market maker demand must
equal the expressed order ﬂow of all customers in the upstairs market at time 1.

Therefore, the market clearing condition is:

MUZU = yUl/l — q (4.33)

Aﬁer rearranging the market clearing equation (4.33), the average upstairs equilibrium
price at time 1 is:

p1,) = E[132lyu,y2] — (Ll—#Var [P2 lyuay2lyU. (4.34)

The equilibrium number of upstairs market makers, MU can be derived in the same way
as in the previous section, equations (4.8) to (4.21), where CU represents each upstairs

market maker’s cost of entry. Hence:

 

0(7le — Q\/Va7‘1(132lyu,yz) + (7Y2

MU =
ﬁrspﬂacu) — 1

 

(4.35)

107

Thus, it can be seen from equation (4.35) that the number of market makers entering the
upstairs market increases with the uncertainty of the time 2 price given that the market
maker observes the expressed and unexpressed order ﬂows of all upstairs customers, 31;),
and y2. Increasing the cost of upstairs market making, CU on the other hand, reduces the
number of market makers that enter the market in equilibrium, as smaller market making
proﬁts support fewer market makers.

The expression for execution quality in the upstairs market is also derived analogous
to the equations (4.22) to (4.29) in the previous section. Therefore, the variance of the
price change in the upstairs market which represents the ‘upstairs market trading cost’ for

an upstairs market customer, is given by:

agpu = (Var [152 IyU, yg] + 05) exp(2acU) (4.36)

This means that the customer is better off when the expressed demand in the upstairs
market at time 1 is more informative about the time 2 price, and when the upstairs market
makers ﬁnd it less costly (cu) to enter the market. Also, increasing the market maker cost
of entry for the upstairs market, CU, increases 02,)“, the ‘upstairs market trading cost’,
exponentially. Thus even a small reduction in the cost of entry can dramatically improve
market liquidity.
4.2.3 Relative Quality of Upstairs Market versus Downstairs Market

From equations for the trading cost in the upstairs and downstairs markets, (4.3 6)
and (4.29), respectively, it is obvious that a lower market entry cost for the downstairs

(upstairs) market, CD(CU) will beneﬁt the customers of the downstairs (upstairs) market.

108

Thus, in order to place the two markets on an equal footing the same cost of market entry
is used for both upstairs and downstairs market makers, i.e. CU = cD = c. Hence, the
difference of equations (4.36) and (4.29) will indicate whether the upstairs or the

downstairs market has better quality. Thus:

A(D —- U) E exp(—2ac) (02AM — aim) (4.37)

which equals:

= Var [152 IyU, 3,12] + 0(2) — Var [152 |yD] (4.38)

By substituting for P2 into Var [I32 lyu, yg] and Var [152 lyD] from equation (4.1),

and simplifying gives:

= A(D — U)(q) = (220,2, [2fq — 11+ 0,21. (4.39)

4.3 Grossman’s Model when Market Makers Know the Unexpressed Demand Only

of their Own Customers

This section develops Grossman’s model under the assumption that individual
market makers are repositories of information about the expressed and unexpressed
demand of their own customers, but not that of other market makers. Although the model
makes the assumption that a market maker knows both expressed and unexpressed
demands of his own customers but not that of other market makers’ customers, it is likely
that at least some of the expressed demands (orders) may be made available to all upstairs

market participants. Therefore, large orders may be displayed to all upstairs market

109

makers, instead of only being known by the market maker who receives the order. The
main difference with this assumption, and the one in Grossman (1992) is that market
makers are not assumed to be identical, which does not lead to a tractable expression for
the price of the security at market clearing, as in Grossman (1992). In order to simplify
the analysis, upper and lower bounds for the informativeness of order ﬂow are developed,
and compared with the results of Grossman (1992). The setup is similar to Grossman
(1992). There are My market makers in the upstairs market. However, each of the market
makers is assumed to know only the expressed and unexpressed demand of her own
customers. At time 1, there is an exogenous liquidity event that changes the customers’
optimal portfolio holdings of an asset from their current portfolio holdings of the asset.
This change motivates the customers to trade. For expositional consistency, the paper
also makes the same assumption as in Grossman (1992) that the assets being traded are in
zero net supply (e. g. futures or forwards contracts). Therefore, these contracts are traded
at date 1 and settled at date 2.

In this paper, it is further assumed that each upstairs market maker 2' receives at time

1 an exogenous expressed order ﬂow of 37w, such that:

Ala

.1711 = Z gun/Er".- (4.40)
1'21

and

Mu
Za, = 1 (4.41)
1'21

110

where \/cT.- is the proportion of the total expressed order ﬂow at time 1 that upstairs
market maker 2' receives.

This paper makes a further assumption that the total unexpressed demand in the
upstairs market, 372, is composed of exogenous unexpressed demands of each upstairs
market maker.

Therefore, 372,,- is the exogenous unexpressed demand of the customers of market

maker 2', such that:

A! u

392 = Z rim/l3:- (4.42)
i=1

and
A! 11

2 ﬂ, = 1 (4.43)
i=1
where V3; is the proportion of the unexpressed demand that upstairs market maker 2'
receives. Further, it is assumed that Q2,“ 3302:", and g0 are independently and identically
normally distributed with zero mean and variance of».
4.3.1 Upstairs Market Equilibrium
At date 1, each upstairs market maker, 2', chooses his demand ZUJ- to maximize his
expected utility of ﬁnal wealth. As in Grossman (1992), it is assumed that the market
maker agents have negative exponential utility, and market makers are unable to trade
simultaneously in both upstairs and downstairs markets, so that they are forced to carry

the position they acquire at time 1 through to time 2, when they can unwind their position

111

by selling the asset at a price of 152. ‘4 Also, as in Grossman (1992), it is assumed that the
upstairs market makers do not immediately observe the arrival of orders in the downstairs
market. This paper makes the additional assumption that the upstairs market makers only
learn the expressed and unexpressed demands of their own customers, and not of other
market makers. Therefore, each upstairs market maker maximizes his expected utility of
wealth conditional on the expressed and unexpressed demands of his own customers.

Thus,

E [U (W...) m... 312.] a — exp [—aW2,.-] (4.44)

where W2,- = W1, + (132 — PM“) ZU,,-, and W1,- is the initial wealth of market maker 2',
and P1 U,- is the date 1 price of the asset traded by upstairs market maker 2'. Since the risk
aversion coefﬁcient a in equation (4.44) is independent of the subscript 2', this means that
the model assumes all dealers are equally risk averse (risk tolerant).

Then, using the normality assumption, for any market maker 2', the optimal demand

Z03,- is given by:

E [132 lyw, 312,2] — P1,,-

 

ZUJ' 2' * ., ' (4.45)
a {Var [P2 IyU,,-, yzﬂ'] + 0(2)}
The market clearing condition therefore implies that:
11421
2 Zn. = yU\/1 — q (4.46)
i=1

 

‘4 The negative exponential utility implies that dealers have constant absolute risk aversion. This means that
agents’ absolute risk aversion is unaffected by wealth. An extra dollar of wealth gives rise to a similar
increase in the utility of an agent’s wealth regardless of the agent’s wealth level. This is arguably counter-
intuitive. However, despite this drawback, the negative exponential utility leads to tractable solutions in
utility maximization problems, which explains the popularity of its use in the asset pricing and market
microstructure literature.

112

After substituting for ZU,,- above from equation (4.45) and for P103,- from equation (4.31),

and some algebraic manipulation, the following expression is obtained for the average

upstairs market price, P1 U:

 

 

 

FIl/lu —1
1
P10 = - 2
i=1 Var [P2 Ilium/2,2] + 0U
: [ ~ ] (4.47)
Mu E P2 lyU,iay2,i - 52'
2: ~ * 2 — (ax/1 ‘ (1)3111
_z=1 Var [P2 Iyut, .42.] + 0U
Now, in order to continue the analysis, one needs to simplify this expression. First,
choose ya.)- 6 (yu.1.yu,2, - - ‘yU,Mu) and 322,2 6 (y2,1, 212,2, - - - 92,3422) such that:
i 2' Var [132 lying yak] S Var [132 [yum 312,2] for all 2' = 1,. ..MU. (4.48)

This means that 12,2 is the minimum variance of the date 2 price of the asset given that
the expressed order ﬂow is as informative as that of the market maker with the most
informative expressed order ﬂow, and the unexpressed order ﬂow is as informative as that
of the market maker with the most informative unexpressed order ﬂow. Note that j and k
can be different, meaning that oi); could be smaller than any market maker’s observed
variance of the date 2 price. Therefore, one can see that no single market maker observes
a variance of date 2 price that is less than £1212 . Thus, if one assumes that all market
makers observe order ﬂows that lead to the minimum variance of the date 2 price, the

expression for the date 1 average price, equation (4.47), becomes:

 

 

M" E[132lyv,j,y2.k] — 51
Z ( YE‘F 0‘2}- -— (04/1 — q) yU (4.49)

113

which reduces to:

(am) { 2

£1_U_ = E [Eli/(14,923] '— MU 0_pg + 0121} ya.” (4.50)

Therefore, given market makers observe the order ﬂows that give rise to minimum

volatility in price changes, the optimal demand of each dealer 2' becomes:

E [132 lyUJtyZJc] — Pw
Z = ' .
a {02.2 + 0(2)}

 

‘6 (4.51)

Similarly, choose ya... 6 (yu,1,yu,2,---yu,MU) and 312,... 6 (312,1. 312,2, - - - yam.) such that:
37p; = Var [132 lyU‘v, yaw] 2 Var [132 [yum 312,-] for all 2' = 1, . . . MU. (4.52)

This means that 31;; is the maximum variance of the date 2 price of the asset given that
the expressed order ﬂow is as informative as that of the market maker with the least
informative expressed order ﬂow, and the unexpressed order ﬂow is as informative as that
of the market maker with the least informative unexpressed order flow. Note that as
above, v and w can be different, meaning that F21; could be larger than any market
maker’s observed variance of the date 2 price. Therefore, one can see that no single

market maker observes a variance of date 2 price that is greater than 0?.2.

 

‘5 Note that when summed over all market makers, the sis of equation (4.49) cancel out.

“5 Note that in the market clearing condition of equation (1 1), using E instead of the actual optimal demand
ZUJ- will lead to a cancellation of the 5,’s from the expression for Pm,” as deﬁned in equation (4.31).
Therefore equation ( 16) shows Pw instead of Pu)...

114

Thus, if one assumes that all market makers observe order ﬂows that lead to the
maximum variance of the date 2 price, the expression for the date 1 average price,

equation (4.45), becomes:

—-1

 

:3 (E WWW-w] * ) _ (am) ya] (4.53)

2 2
221 0P2 +UU

 

_ i 1
= l (——=---)l
2:1 02ch ‘l' 0?]

which reduces to:

E5 : E [132 Iyu... 212...] — L314 {3%} + 0221}3/U (454)

Therefore, given that market makers observe the order ﬂows that give rise to maximum

volatility in price changes, the optimal demand of each dealer 2' becomes:

_ E [P2 lyUma 312,20] _ PIU

U = —
a {0%2 + 0%,}

 

(4.55)

4.3.2 Endogenously Determined Market Makers

In this section, as in Grossman (1992), the equilibrium value of the number of
upstairs market makers, MU is determined, consistent with the upper and lower bounds of
the informativeness of the expressed and unexpressed order ﬂows of each market maker,
as obtained in the previous section.

First, it is assumed that both the expressed and unexpressed order ﬂows of each
upstairs market maker are as informative as that of the market maker having the most

informative order ﬂows. This is the upper bound for the informativeness of order ﬂows in

115

the upstairs market, and therefore the upper bound for the quality of execution in the
upstairs market. Therefore each market maker observes 310,1 and yak, subject to condition
(4.48). It is further assumed that 330,]- and 92,1. are normally distributed and uncorrelated
with 132, meaning that the liquidity event is exogenous to the realization of 132. Each agent
spends an amounth in order to become a market maker. An agent only observes the
order ﬂows yUJ and y” at the time of the liquidity event at date 1, if he has already spent
CU and become a market maker at date 0.

Therefore, in equilibrium, the number of market makers will be such that any
potential agent is indifferent between paying cu and becoming a market maker and not
paying cu and not participating in the market. Thus, the individual rationality condition
for becoming a market maker must be exactly satisﬁed at date 0. This means that in
equilibrium, the unconditional expectation of the utility of becoming a market maker
must equal the unconditional expectation of the utility of non-participation in the market.

In other words:
E0 [U (W0 — cu + (132 — ﬁg) Q)] = E0 [U (W0)] (4.56)

where W0 is the initial endowment of each dealer, i.e. at date 0, £1. is the optimal demand
of each dealer 2' given the dealer observes the most informative order flows, as deﬁned in
equation (4.51).

Now, by using the Law of Iterated Expectations, the left hand side of equation (4.56) can

be expressed as an expectation of a conditional expectation, so that:

116

E0 [E1 [U (wO — cu + (E — £33.) _Z_t;) lyu,,-. 2122]] = E0 [U (W0)] (4.57)

Since each of the MU market makers, 2', has the same optimal demand, 3&1, the market

clearing condition, (i.e. equation (4.46)) becomes:
Muﬁ = gu 1 — q. (4.58)

Therefore, after substituting for _Z_g_ from equation (4.58), using the negative utility
function, i.e. equation (4.44), the conditional expectation expression on the left hand side

of equation (4.57) becomes:

E1 [U (W0 — CU + (152 — ﬂ) i) lyUJv Um] =

 

(4.59)
- - ~ , /"—1 _
_e:cp(—aW0) exp(acU)E1 [clip (-—a (P2 — ELI/f) yU MU q) 131w, 3121:]
Given a constant a, a normally distributed variable X, has the following property:
E [eatp(a.X)] E exp (a7 + gaﬁ), (4.60)

where 7 and air are the mean and variance of X, respectively.

Using the property in (4.60), equation (4.59) becomes:

117

E1 1U (.) I1 = —6:rp(-aWo) e:rp(acU)

exp (—a (E1 [E2 lyUJa yak] — ﬂ) 5U 11W;q (4-61)

2 (I—Q))

+£2- (Van [1321310333123] + 0U2)37(21ET

 

Now, substituting for E1 [I32 [yum 172,16] — 51;, from equation (4.50), and simplifying the

resulting expression, one obtains:

E1 [U (.) |.] = —e:1:p(—aW0) exp(acu)

~

(22 ~ y 2 (4.62)
exp (“‘2—(1 ‘ (1)(V07“1 [P2lyU,jsy2.k] + 0U2) (vi) )

Now, after substituting for the conditional expectation on leﬁ hand side of equation (4.5 7)
from equation (4.62), substituting for the right hand side of equation (4.57) from equation
(4.44), cancelling the ezp(—aW0) terms from both sides, and moving the exp(acu) term

from the left to the right hand side, the following is obtained:

E0

 

2 ~ ~ 2
6X1) (-%(1- (1) (Van [P2 lyw, 112,2] + 0212) (Ag/[3) H = exp(—a6u)
(4.63)

Now, let:

118

a20y2(1 — q) (VGTI [132 lyU,j? y2,k] + 0U2)

 

t = . , 4.64
U ﬂit) ( )
and
~2
Z2 = .222 (4.65)
0y

where gu is normally distributed with mean ﬂy and variance 0'y2, and 22 has a

~

yU —#Y
UY

 

non-central x2 distribution. Now, let X = , so that X has the standard normal
distribution.

Then, the moment generating function of 22, Mx(T) is given by:

Ee$p(z2T) = E(e:cp (X + Z—YY T) (4.66)
Y

which equals:

rob-

 

HYQT —
exp(UY2(1- 2:”) (1 - 2T) (4.67)

t
Now, for equation (4.67), let T = —?U, and substitute for tU from (4.64), to obtain:

Nit-

E .1 2 _ ( —#Y2 tU) -
ezp(z T) — 6:13p —— (1 + tu) (4.68)

0y2(1+ty) 2

As in Grossman (1992), it is assumed that at date 0, the expected magnitude of the

liquidity event occurring at date 1 is equal to zero, so that ﬂy = O.

119

Therefore, by letting [.ty = 0, equation (4.68) reduces to (1 + tU)—% which is equal

to the right hand side of equation (4.63), i.e.:

NIH

(1 + tU)— = exp(—acu) (4.69)

Now, using equation (4.69) to substitute for tU into (4.64), the equilibrium number of
market makers can be obtained for the upper bound of the informativeness of the

expressed and unexpressed order flow:

 

210va — (I\/VGT1 (132 lyU,jay2,k) + 01/2

My 2
\/ea:p(2acu) — 1

 

(4.70)

By a similar method, the equilibrium number of marker makers can be obtained for

the lower bound of order flow informativeness, so that:

 

(10le — q\/Var1 (Ell/0.633121») + 0112

m =
\/e:1:p(2acu) — 1

 

(4.71)

It is evident from equations (4.48) and (4.52) that
Var] (132 [yum 3121,) < Van (I32 13121.63 312.16). Therefore, equations (4.70) and (4.71)
imply that :Mﬂ < m.
4.4 Extension of Grossman’s Model of the Upstairs Market

This section outlines an extension of Grossman’s (1992) model (for the upstairs
market) by relaxing the assumption that upstairs market makers know the unexpressed

demand of all upstairs customers.

120

In this model, it is assumed that unexpressed demands of upstairs market customers
are made up of two components. The ﬁrst component, called private unexpressed
demand, is unexpressed demand that is known by just one upstairs market maker, and
represents the portion of customers’ trading preferences known only by that market
maker. The second component, called public unexpressed demand, is unexpressed
demand that is known by all upstairs market makers. For customers that have trading
relationships with many upstairs market makers, a market maker can learn public
unexpressed demand directly through trading or business relationships with customers, or
through professional relationships with other market makers. Therefore, at any given
time in the upstairs market, some portion of the unexpressed demand is privately known
and the remaining unexpressed demand is known publicly. As customers make trading
relationships with new market makers, the public proportion of unexpressed demand is
likely to increase over time, and the private proportion is likely to decrease
proportionately. For example, in the case of a brokerage house that hires an individual
broker from another brokerage house, the hiring brokerage house can learn the trading
preferences of other brokerage house’s customers, which increases the proportion of
unexpressed demand that is known publicly. However, new customers entering and
leaving the market, as well as customers changing their trading preferences, mean that it
probably is not possible to make all unexpressed demand public.

Hence, the model assumes that an upstairs market maker observes the private

unexpressed demand of its own customers as well as the public unexpressed demand of

121

 

either its own or other market makers’ customers. The total unexpressed demand 2123-,

observed by a single upstairs market maker 2' is therefore:

172,2 = ﬁghyubz + (1 “ P)372,Prtv,t‘ (4-72)

where 3129,,“ is the public unexpressed demand, and 372mm- is the private unexpressed
demand of market maker 2’s customers. Furthermore, it is assumed that gg'publ and
331mm, 2' = 1 - . - MU, are mutually independently and identically normally distributed
variables with mean zero and variances 0,24%“ and (If/pm, respectively. The total
unexpressed demand in the upstairs market, 312(1)) is therefore just the sum of the public
and the individual private unexpressed demand components of all upstairs market makers:
Mu
02(1)) = Vim.“ + 1/ (1 — p) 21 22.13....- (4.73)
z:
where the proportions ﬂ and m are used to ensure that the variance of total
unexpressed demand is constant. Therefore p and (1 — p) can be viewed as the proportion
of the variance of total unexpressed order ﬂow made up of public and private unexpressed
demands, respectively. It is also clear that when p = 1, the expression for the unexpressed
demand in the whole upstairs market reduces to that used in Grossman’s (1992) model, as
unexpressed demands of all upstairs customers in Grossman’s (1992) model are by
assumption publicly known to the entire upstairs market, and therefore known by all

market makers. When p = 0, all unexpressed demand is private.

122

 

The events assumed to take place in this model are similar to the events described
for the Grossman (1992) model, in section (4.2), with the exception that each of the MU
upstairs market makers observes all of the expressed upstairs demand, public unexpressed
demand of upstairs market customers, and private unexpressed demand of his own
customers, but not the expressed demand of downstairs market customers. This model
also uses the same deﬁnitions used in Grossman’s (1992) derivation of the upstairs
market equilibrium for P2, Plug, and W.

Therefore, the objective function of each upstairs market maker 2 becomes:
E [U (Vi/2,2) lin 3122] E “ exp [—GW2,i(P)] (4-74)

where W2,,(p) = W1, + (152 — PM) ZU,,(p), and W1, is the initial wealth ofmarket
maker 2', and P1 (1,,- is the date 1 price of the asset traded by upstairs market maker 2'. This
expression of the expected utility is similar to the one in Grossman’s (1992) model for the
upstairs market, (4.30), except that the market maker demand ZUJ- and hence the market
maker wealth at time 2, W2,“ is a function of the proportion p, which is an exogenous

variable. The Optimal demand for market maker 2' is therefore:

E 132 lin 3121‘] —;P1U,i
a {Var [152 Iin y2,i] + 012/}

 

 

Z1), 2 (4.75)

There are MU identical upstairs market makers, each of which independently choose the

same optimal demand, Z U. To clear the market, the total market maker demand must

123

equal the expressed order ﬂow of all customers in the upstairs market at time 1.

Therefore, the market clearing condition is:

NIUZU = ym/l -— q (4.76)

After rearranging the market clearing equation (4.76), the upstairs equilibrium price at
time 1 is:

-a———q)-Var [132 lyu, 112.1] 110- (477)

P10 = E [Eli/0,112.1] — Mu

The equilibrium number of upstairs market makers, MU can be derived in the same
way as in the previous section, equations (4.8) to (4.21), where cu represents each
upstairs market maker’s cost of entry.

Hence:

 

(101/VI- q\/Var1 (13213/Uay2,i) + 0Y2

My =
\/ea:p(2acU) — 1

 

(4.78)

Thus, it can be seen from equation (4.78) that the number of market makers entering the
upstairs market increases with the uncertainty of the time 2 price given that the market
maker observes the expressed and unexpressed order ﬂows of upstairs customers, W, and
y2,,. Increasing the cost of upstairs market making, cu on the other hand, reduces the
number of market makers that enter the market in equilibrium, as smaller market making
proﬁts support fewer market makers.

The expression for execution quality in the upstairs market is also derived analogous

to the equations (4.22) to (4.29) in the section on the downstairs market equilibrium.

124

Therefore, the variance of the price change in the upstairs market which represents the

‘upstairs market trading cost’ for an upstairs market customer, is given by:
2 _ * 2
cap. — (Var [P2 lye, 212:] + 0v) exp<2acu) (4.79)

This means that the customer is better off when the expressed demand in the upstairs
market at time 1 is more informative about the time 2 price, and when the upstairs market
makers ﬁnd it less costly (cu) to enter the market.

4.5 Comparison of Upstairs Market Equilibrium under Extended Model to the
Downstairs Market Equilibrium of Grossman (1992)

The downstairs market has better quality execution compared to the upstairs market
if the following expression is positive, where it is assumed that the upstairs and

downstairs markets have the same cost of entry, i.e. cu 2 CD = c:

A(D — U) E exp(—2ac) (03,,” — 0.23.104) (4.80)

which equals:

= VG’I‘ [p2 lva ygd‘] ‘i‘ 012] — VGT‘ [152 IUD] (4.81)

By substituting for P2 and yg‘, into Var [152 [2);], 312,-] and Var [152 lyD] from

equations (4.1) and (4.72) one obtains:

= 4(1) — U)(q) = b? [as 1274 — fl — <1 — f) [<1 — meta... — animal] +0.5. (4.82)

125

It is clear from equation (4.82) that when the term [(1 — p)012/,Pr2v — P0371161] is equal to
one, equation (4.82) reduces to the relative market quality equation of Grossman (1992),
as derived in (4.39). Hence, if this term is less than one, then the quality advantage of the
downstairs (upstairs) market increases (decreases) relative to Grossman’s (1992) model.
Therefore, in order to ensure that the upstairs market’s quality relative to the downstairs
market, is no worse than in the Grossman (1992) model, the following must hold:

= p < ”3’2”": _ 1 (4.83)

2 2 '
0Y,Priv + UY,Publ

 

This makes sense because if the volatility of private unexpressed demand, (If/fr,” is
high, and keeping volatility of the total unexpressed demand constant, this causes the
volatility of public unexpressed demand, dam“, to fall, and hence the public signal
becomes less noisy, and this improves the overall efﬁciency and hence the liquidity of the
upstairs market. Therefore reducing the public component of the unexpressed demand
volatility and increasing the private component, is good for the upstairs market. Hence
keeping p very low implies that the public signal of unexpressed demand becomes very
informative, which improves the liquidity in the upstairs market.

Therefore, if the unexpressed demand of customers in the upstairs market is not
sufﬁciently public, such that equation (4.83) does not hold, then the upstairs market’s
execution quality will be reduced. For example, if customers’ switching costs are too
high, such that customers concentrate their trade with a small number of market makers

(broker dealers), and the upstairs market as a whole is not sufﬁciently informed about

126

customers’ unexpressed demands, i.e. if there is little public unexpressed demand, the
upstairs market’s liquidity beneﬁts will be adversely affected.

Finally, exclusive or close trading relationships between customers and their
brokers, in which unexpressed demands of such customers are not communicated to other
market makers but kept private, may reduce the liquidity beneﬁts of the upstairs market.
4.6 Conclusion

The paper redevelops Grossman’s (1992) model for clearer exposition and economic
intuition. A more realistic model of unexpressed demand in the upstairs market is
developed, where the unexpressed demand of Grossman (1992) is broken into private and
public components, such that in the ﬁrst case each market maker observes a unique
private signal, and in the second all market makers observe a common public signal. It is
found that when public signals of unexpressed demand are less noisy, this has a beneﬁcial
effect on the liquidity provision of an upstairs market, whereas when public signals are
noisier, this reduces the beneﬁts of the upstairs market relative to the downstairs market.
This ﬁnding underlies possible detrimental effects noisy public signals of customers’
unexpressed demand can have on the upstairs market. Therefore, close trading
relationships among customers and market makers, where unexpressed demand of the
customer is held privately by a small number of market makers, can hurt upstairs market
liquidity, whereas an open sharing of information about the unexpressed demand of a
market maker’s customers with other market makers, can improve upstairs market

liquidity.

127

CHAPTER 5: CONCLUSION

The ﬁrst two chapters studied empirically the existence and the determinants of
trading relationships among brokerage houses, and between brokerage houses, individual
brokers and their customers. The general thrust of the ﬁndings is that whereas brokerage
houses do not display a preference or a bias for trading with another brokerage house,
such as a cultural bias, customers of individual brokers and brokerage houses appear to
display such preferences or biases. Proxies for familiarity among brokerage houses do
not explain the trading relationships between brokerage houses, but measures of
familiarity among brokers and their customers do explain the trading choices of
customers. This suggests that brokerage houses are already familiar enough with each
other that measures of familiarity do not explain their trading relationships. Furthermore
brokerage houses may not show any biases in their trading because they are unbiased.
However, perhaps due to switching costs, or due to psychological biases, investors can
develop close relationships with their brokers.

However, familiarity with their customers and the customers of other brokerage
houses, can help brokers better provide liquidity in a market. The third essay ﬁnds that
when market makers learn about the trading habits of their customers, such as how likely
they are to trade at some time in the future, and if this information about their customers
remains private to each market maker, then this can adversely affect upstairs market trade
execution quality. Trading and business relationships with customers increase market
makers’ awareness about their trading habits which can help improve (upstairs) market

liquidity, especially if such information can be shared with other market makers. This

128

implies that the trading preferences and biases shown by customers of brokerage houses

may also hinder the quality of an upstairs market.

129

 

Table 2.1: 76 share classes in the sample belonging to 61 ﬁrms

 

 

Share Class Ticker Industry
CEO Culture = Finnish
Aamulehti AAMZS Media
Atria Oy' ATRI V Food
Citycon y: CTYlS Investment
Espoon 85 k6 ESSlV Energy
Finnair Oyj FIAI S Transport
Finvest 0y) A FINAS Investment
Finvest Oy B FINBS Investment
Finnlines yj FLGl S Transport
Hackman 0 j A HACAS Multi-Bus.
Oy' Hartwa l A HARAS Food
Hu tamaki Oyj HUHIV Food
Huhtaméiki Oyj HUHKV Food
lnstrumentarium lNSAS Other
lnstrumentarium INSBV Other
Interavanti Oyj INT 1 S Investment
J Tallberg-Kiint. B JTKBS Investment
Kesko KES 1 S Trade
Kone 0% B KONBS Metal & Eng.
Kemira yt! KRAIV Chemicals
Lassila & ikanoja LASlS Multi-Bus.
Lemminkéiinen LEM l S Construction
Leo Lon life A LEOAS Other
Lannen ehtaat LTEIS Food
Lansivoima Oyj LVOIV Enerjgy
Metsa-Serla A MESAS Foo
Metsa-Serla B MESBS Food
Merita MTAAV Banks & Fin.
Merita MTABV Banks & Fin.
Neste NESIV Energy
Nokia A NOKAV Telecom & Elec.
Nokia K NOKKV Telecom & Elec.
Nokian Renkaat NORIV Other
OKO Bank A OKOAS Banks & Fin.
Orion—yhtymii A ORIAS Chemicals
Orion-yhtymﬁ B ORIBS Chemicals
Outokumpu Oyj OUTAS Metal & Eng.
Pohjola A POHAS Insurance
Pohjola B POHBS Insurance
Polar Kiinteistéit POLKS Investment
Raisio Yhtym RAIVV oo
Rauma RAMlV Metal & Eng.
Rautakirja A RTKl S Trade
Rautakirja B RTKBS Trade
Rautaruukki K RTRKS Metal & Eng.
Raute Oyj A RUTAV Metal & Eng.

 

 

Share Class Ticker Industry
CEO Culture = Finnish
Santosalo-IOT SAJ IV Metal & Eng.
Yrityspankki A SCAAS Banks & Fin.
Starckjohann Steel STABS Metal & Eng.

Stockmann Oyj A STOAS Trade
Stockmann 0y] B STOBV Trade
Tamfelt Kama TAF KS Other
Tamfelt Etu TAFPS Other
Tulikivi Oyj A TULAV Construction
Vaisala Oyj A VAIAS Telecom
Valmet VALAS Metal & Eng.
Sanoma WSOAS Media
Sanoma WSOBS Media
YIT-Yhtyméi Oyj YTY l V Construction

CEO Culture = Swedish
Arctos Capital ACAlV Banks & Fin.
Aspoyhtyma ASYl V Multi-Bus.
Birka Line Abp BIRKS Transport
Birka Line Abp BIRPS Trans ort
F iskars Oyj Abp A FISAS Meta & Eng.
F iskars 0y] Abp K FISKS Metal & Eng.
Metra 0y) Abp A METAS Metal & Eng.
Metra Oyj Abp A METBS Metal & Eng.
Norvestia 0.11) NVABV Investment
Panek Oyj p PARIS Metal & Eng.
Sampo A SAMAS Banks & Fin.
Sil'a Line SLJAV Transport
Si Ja Line SLJKV Transport
Stromsdal Oyj B STMBS Forestry
Tamro Oyj TROlV Trade
Viking Line Abp VIKIV Transport

C E0 Culture = Other

Amer— htyma" A AMEAS Other
Suuntoy SUUIV Metal & Eng.

 

130

 

 

Table 2.2: 23 Brokerage Houses in the Sample Grouped by Culture

 

 

 

 

 

 

 

Brok. Avg. '/o of
Culture of House Avg. No. of Externalized
Brok. House Brok. House Name Code Buys & Sells* Buys & Sells"
Finnish Ane G llenberg AG 1,769.5 19.9
Aktia avings ank Ltd AKT 2,735.5 4.3
Arctos Capital Oy ARC 951 28.0
Aros ARO 1,316 16.9
Evli Securities Ltd EVL 1,593 16.8
F [M Securities Ltd F [M 456 22.2
FSB Securities FSB 125.5 39.4
Hiisi HII 70.5 22.8
Interbank IBA 376 25.8
Merita Securities Ltd. MER 3,505 18.8
Opstock Securities Ltd. OPS 848.5 29.9
Postipankki PSP 421 42.4
Protos Davy Securities PTS 844 16.6
Soﬁ Securities, Inc. SOF 363 21.7
United Bankers Securities UB 539.5 23.8
Subtotal: 15,914 Median: 22.2
Swedish Alfred Berg Finland Oyj Ab ALF 3,230 15.6
D. Came 16, Finland Branch CAR 1,959 16.9
Svenska andelsbanken HAN 1 769 17.4
Skandinaviska Enskilda Bank SEB 1,704.5 20.5
Erik Selin Securities SEL 570.5 31.2
Subtotal: 9,233 Median: 17.4
Other ABB Financial Services ABB 346.5 21.7
Danske Bank Securities DDB 298.5 9.5
Williams de Broé WDB 20.5 50.5
Subtotal: 665.5 Median: 21.7

Total: 25,8125

Median: 20.5

‘This is the average of the number of buys and the number of sells conducted in the after-market period by
the brokerage house.

"This is the average of the percentage of buys and the percentage of sells conducted by the brokerage house
during the after-market period where another brokerage house is the counterparty.

131

Table 2.3: Ratios showing the effect of the Cultural Heritage of a Firm’s CEO on
Trading Intensity of the Firm’s Stock

Panel A shows for ﬁrms with CEOS having Finnish cultural heritage, the medians of the trading intensity
ratios of Finnish, Swedish and Other culture brokerage houses. Panels B and C show the same data for ﬁrms
with CEOS having Swedish and Other cultural heritage, respectively. The trading intensity of a brokerage
house in stocks of ﬁrms having Finnish, Swedish and Other culture CEOS is deﬁned as the brokerage house’s
trading weight in stocks of ﬁrms having Finnish, Swedish and Other Culture CEOS, respectively, divided by
the brokerage house’s trading weight in all stocks. The trades used in calculating the ratios took place in the
after-market of the Helsinki Stock Exchange, between January 1996 and December 1997. Standard errors
are shown in parentheses, and denoted by ‘na’ where missing.

 

 

Trading Weights where Brokergge House is
Buyer Seller

 

 

 

Panel A: Stocks of Firms with Finnish Culture CEOs_
Summag Statistics for the Rat1o: Brokerage House 2’s Tradrng Weight 1n Stocks of Firms
with Finnish ultural Heritage CEOs Divided by Brokerage House i’s Trading Weight in All Stocks.

 

Median for brokerage houses having:

Finnish cultural heritage 22 = 15 1.01 I. 1
Swedish cultural heritage 11 = 5 1.01 1.01
Other cultural heritage 72 = 3 1.05 1.06

Fraction >1 for brokerage houses having:
Finnish cultural heritage n = 15 0.53 0.13 0.53 (0.13)
Swedish cultural heritage 72 = 5 0.67 0.19 0.60 0.22
Other cultural heritage 72 = 3 0.67 0.27 1.00 (na)

 

Panel B: Stocks of Firms with Swedish Culture CEOS
Summary Statistics for the Ratio: Brokerage House i’s Trading Weight in Stocks of Firms with
Swedish Cultural Heritage CEOS Divided by Brokerage House i’s Trading Weight in All Stocks.

 

Median for brokerage houses having:

Finnish cultural heritage 72 = 15 1.10 0.97
Swedish cultural heritage 72 = 5 0.58 0.45
Other cultural heritage n = 3 0.45 0.31

Fraction >1 for brokerage houses having:
Finnish cultural heritage 72 = 15 0.60 0.13 0.46 (0.13)
Swedish cultural heritage n = 5 0.17 0.15 0 (m)
Other cultural heritage 72 = 3 0.33 0.27 0 na

 

Panel C: Stocks of Firms with Other Culture CEOS .
Summary Statistics for the Ratio: Brokerage House 2’s Tradin Wei ht in Stocks of Firms with
Other Cultural Heritage CEOS Divided by Brokerage House 2' s Tra ing Weight in All Stocks.

Median for brokerage houses having:

Finnish cultural heritage 72 = 15 0.67 0.80
Swedish cultural heritage 72 = 5 0.41 0.51
Other cultural heritage 72 = 3 0.17 0.17

Fraction >1 for brokerage houses having:
Finnish cultural heritage 72 = 15 0.33 [0.12) 0.40 0.13)
Swedish cultural heritage 72 = 5 0.33 0.19 0.40 0.22
Other cultural heritage 72 = 3 0 (na) 0 411a)

 

132

 

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139

Table 3.2: Percentage of the Number of All HETI Trades Matched Successfully with
Investor Records in the F CSD Data Set

The table shows the mean and median within each stock activity decile of the percentage of HETI trades
by number matched with investor records in the FCSD data set. The most active decile is ten, and the least
active decile is one.

 

 

Activity Decile No. Median °/o Mean % No. of Stocks
1 82.9 74.4 9
2 78.7 76.5 8
3 72.5 64.2 9
4 71.1 66.8 9
5 68.1 65.5 9
6 65.6 65.0 8
7 55.1 56.1 9
8 59.2 55.1 9
9 51.4 50.8 9
10 32.1 34.6 4
A11 Stocks 66.9 62. 3 83

 

140

Table 3.3: Comparison of Switching Brokers’ Presence in the Upstairs Market Before
the Switch to that of all Individual Brokers of the same Brokerage House

The table shows descriptive statistics of the switching broker in the upstairs market during the free-trading
period. For example, # Days Traded is the number of days traded by the switching broker in the upstairs
market before the switch, Avg Daily # Trades is the average daily number of trades conducted by the switch-
ing broker in the upstairs market before the switch, and Avg % of Daily # Trades is the average of the daily
percentage of the number switching broker’s trades conducted in the upstairs market. The median of the cor-
responding values of all individual brokers in the same brokerage house, and in the same period, are given
in parentheses below each statistic. The value of each statistic greater than or equal to the corresponding
median value for all individual brokers of the brokerage house, is highlighted in bold font.

 

 

Avg Avg Avg “/o Avg '/o No of
Switching # Days Daily # Daily of Daily of Daily Brokers
Event Traded Trades Volume # Trades Volga-he in B. House

1 65 48.0 184,372 17.4 41.1
(63) (30.4) (138,310) (24.1) (46.2) 4

2 78 47.8 40,352 12.9 1 1.3
(78) (16.3) (31,604) (16.0) (30.3) 5

3 225 34.6 48,943 13.6 14.7
(240) (29. 6) (47,114) (13.9) (20.6) 6

4 72 25.6 37,838 10.6 11.6
(241) (30.1) (4 7, 103) (13.9) (20. 6) 6

5 327 18.2 95,971 13.7 32.4
(78) (25. 6) (40,352) (13. 7) (16.3) 9

6 148 23.1 64,445 14.0 32.5
(161) (12.2) (61,146) (22.4) (47.5) 4

7 306 12.1 69,121 25.0 49.1
(22 7) (15. 7) (66, 783) (21. 9) (46.1) 4

8 10 2.9 3,360 0.0 0.0
(60) (19.7) (8 7,300) (14. 0) (28.6) 4

9 165 18.4 66,120 15.1 30.4
(165) (16.5) (63,181) (15.1) (30.4) 5

10 232 17.2 64,731 20.7 44.1
(165) (1 7. 2) (64, 731) (14.6) (30. 4) 5

11 292 32.9 110,121 15.2 34.0
(164) (14.5) (50,268) (15.2) (30. 4) 7

 

141

Table 3.4: Percentage of Customers’ Downstairs Hades via Old Brokerage House

The table shows the average percentage of customer trading by value done in the downstairs market by
the switching broker’s old brokerage house, for each switching event and period relative to broker’s last
trade at the old house (t). Note: t=-l represents the switching broker’s last month of trading at the old
house, and t=l represents the switching broker’s ﬁrst month of trading at the new brokerage house. In this
table, customers are deﬁned as investors who have traded at least once in the downstairs market via the old
brokerage house during the earliest month in the sample, i.e. January 1996. Data sample is restricted to the
three most active stocks.

 

Panel A
Switching Event

t 1 2 3 4 5 6 7 8 9 10 11 Mean

— 17 70.0 55.0 62.5
— 16 58.2 67.2 49.8 58.4
--15 44.0 60.0 51.7 51.9
-— 14 75.0 66.7 39.9 60.5
— 13 66.6 58.3 39.8 66.7 73.8 61.0
-— 12 54.2 46.4 57.1 54.8 40.6 50. 6
— 1 1 56.2 56.2 53.4 50.0 49.3 25.0 48.3
— 10 83.5 83.5 59.3 33.3 57.1 64.4 63.5
-—9 66.3 66.3 51.1 42.9 32.8 45.9 40.0 63.5 51.1
—8 71.5 80.0 32.6 48.6 17.4 47.5 77.4 35.7 51.3
—7 61.1 57.1 41.2 50.0 16.6 42.9 33.3 74.8 47.1
—6 73.2 76.9 50.7 50.0 22.6 68.8 46.0 50.5 54.8
— 5 58.7 69.9 53.6 66.7 33.3 40.0 76.4 48.4 55.9
—4 48.5 62.0 55.3 36.0 50.0 25.0 95.2 60.0 54.0
—3 55.7 45.2 50.4 64.0 62.2 25.0 64.4 100.0 25.0 54. 7

—2 91.3 59.8 52.6 58.3 78.1 66.7 40.0 48.0 80.1 99.4 44.0 65.3
100.0 40.7 57.1 57.1 66.7 29.8 33.3 47.1 50.0 100.0 65.6 58.9

_

0 57.6 66.7 66.4 71.4 73.5 24.6 33.3 54.6 66.5 63.5 26.8 55. 0
I 74.6 42.2 48.6 48.9 100.0 29.0 20.0 35.6 50.0 60.0 0.0 46.3
2 49.8 69.1 50.0 100.0 56.2 33.3 40.0 32.6 47.6 50.0 0.0 48.1
3 71.3 55.9 45.6 90.7 57.3 54.0 25.0 28.6 60.0 74.5 0.0 51.2
4 59.4 57.4 85.9 98.1 80.4 29.2 50.0 0.0 50.0 64.1 0.0 52.2
5 73.8 51.1 100.0 75.0 33.3 0.0 74.5 0.0 51.0
6 64.4 32.8 72.5 65.3 33.3 0.0 63.9 0.0 41.5
7 66.7 37.0 72.9 78.2 33.3 0.0 33.3 0.0 40.2
8 65.3 47.2 81.5 77.5 25.0 0.0 0.0 0.0 37.1
9 67.2 59.0 25.0 0.0 37.8
10 74.9 55.3 25.0 0.0 38.8
11 97.9 64.0 80.9
12 66.7 78.1 72.4
13 41.0 66.7 53.8
14 74.8 63.9 69.4
15 75.0 100.0 87.5
16 75.0 66.7 70.8
17 73.9 55.3 64.6
18 75.0 85.9 80.4
Mean

t<0 95.6 52.1 61.1 63.3 55.9 50.3 42.6 47.5 51.6 70.4 50.8 58.3
t>0 69.3 60.4 69.6 79.2 73.5 32.1 33.8 12.1 37.9 31.1 0.0 45.4

142

 

 

 

 

 

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143

Table 3.5: Percentage of Customers’ Upstairs Trades via Old Brokerage House

The table shows the average percentage of customer trading by value done in the upstairs market by the
switching broker’s old brokerage house, for each switching event and period relative to broker’s last trade
at the old house (t). Note: t=—l represents the switching broker’s last month of trading at the old house,
and t=1 represents the switching broker’s ﬁrst month of trading at the new brokerage house. In this table,
customers are deﬁned as investors who have traded at least once in the upstairs market via the old brokerage
house during the earliest month in the sample, i.e. January 1996. Data sample is restricted to the three most

active stocks.

 

 

Panel A
Switching Event

t 1 2 3 4 5 6 7 8 9 10 11 Mean

— 17 36.6 71.0 53.8
16 55.5 39.4 76.0 57.0
— 15 100.0 40.6 50.0 63. 5
— 14 100.0 40.0 69.7 69. 9
-— 13 79.9 85.0 100.0 46.7 40.3 70.4
— 12 93.3 93.3 100.0 30.4 25.0 68.4
—1 1 50.0 94.9 61.2 25.0 72.4 49.8 58.9
— 10 79.1 68.9 68.0 74.3 66.7 25.0 63.7
—9 100.0 100.0 63.0 57.5 50.0 83.6 49.4 25.0 66.1
—8 97.3 100.0 86.0 40.0 33.3 66.7 36.2 25.0 60.6
—7 67.2 67.4 67.8 52.1 20.0 67.0 49.8 66.7 5 7.3
—6 77.8 81.5 69.1 33.3 25.0 40.0 25.0 25.0 47.1
—5 88.6 88.6 46.3 38.9 49.2 25.0 33.3 49.4 52.4
—4 72.0 71.5 58.0 0.0 50.0 40.4 33.3 50.0 46.9
—3 19.5 75.0 75.0 95.0 35.5 100.0 33.3 50.0 50.0 59.3
——2 87.3 83.2 80.5 57.4 42.1 36.3 66.7 78.4 33.3 50.0 60.0 61.4
— 1 75.0 100.0 80.3 77.5 100.0 49.0 50.0 100.0 20.0 25.0 82.3 69.0
0 67.7 95.7 88.1 100.0 50.6 40.2 33.3 44.8 26.5 33.0 27.2 55.2

1 100.0 63.3 96.6 96.5 50.0 32.8 35.8 51 4 25.0 33.3 0.0 53.2

2 83.2 100.0 50.0 79.7 78.5 25.0 20.0 34 5 33.0 33.3 0.0 48.8

3 100.0 66.6 88.2 68.2 50.0 33.3 27.9 0 0 33.3 51.7 0.0 47.2

4 100.0 37.5 64.1 67.2 78.4 40.0 33.3 0 0 33.3 27.6 0.0 43.8

5 100.0 63.0 100.0 75.0 50.0 0 0 61.3 0.0 56.2

6 100.0 90.6 100.0 85.1 20.0 0 0 34.5 0.0 53.8

7 99.9 75.9 66.7 82.5 22.1 0 0 0.0 0.0 43.4

8 78.4 52.8 89.6 100.0 20.0 0 0 0.0 0.0 42.6

9 99.8 44.7 38.7 0.0 0.0 36.6

10 99.7 77.8 33.3 0.0 0.0 42.2

1 1 100.0 95.0 0.0 65.0
12 100.0 42.1 0.0 47.4
13 100.0 100.0 100.0
14 82.2 55.1 68. 7
15 100.0 50.0 75.0
16 100.0 75.4 87. 7
17 93.8 0.0 46.9
18 100.0 75.9 87.9

Mean

t<0 81.2 67.6 80.1 81.6 73.4 38.1 47.7 89.2 45.5 44.6 48.1 63.4
t>0 96.5 64.8 81.9 81.8 64.2 31.5 29.3 10.7 18.4 14.6 0.0 44.9

 

144

 

 

 

 

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145

Table 3.6: Percentage of Customers’ Downstairs Trades via New Brokerage House

The table shows the average percentage of customer trading by value done in the downstairs market by
the switching broker’s new brokerage house, for each switching event and period relative to broker’s last
trade at the old house (t). Note: t=—1 represents the switching broker’s last month of trading at the old
house, and t=l represents the switching broker’s ﬁrst month of trading at the new brokerage house. In this
table, customers are deﬁned as investors who have traded at least once in the downstairs market via the old
brokerage house during the earliest month in the sample, i.e. January 1996. Data sample is restricted to the
three most active stocks.

 

Panel A
Switching Event

t 1 2 3 4 5 6 7 8 9 10 11 Mean
-17 0.0 13.5 6. 7
-16 4.7 0.0 1.8 2.2
-15 0.0 0.0 0.3 0.1
-14 0.0 0.0 0.0 0.0
-13 0.0 0.0 10.2 0.0 0.0 2.0
-12 0.0 0.0 0.0 0.0 0.0 0.0
-1 1 0.0 0.0 0.0 0.0 1.7 0.0 0. 3
-10 0.0 4.0 0.0 0.0 0.0 0.0 0.7
-9 00 00 00 00 0.0 82 00 00 10
-8 00 00 00 00 0.0 140 00 00 18
-7 83 00 00 0.0 0.0 167 00 00 31
-6 00 00 00 0.0 0.0 111 00 00 14
-5 00 143 27 00 0.0 202 00 00 46
-4 00 00 00 00 0.0 250 00 00 31
-3 0 0 0 O 2.9 0 0 0 0 0.0 20.0 0.0 25.0 5.3
-2 0 O O O 0 0 0.0 O O 0 O 0 0 14.9 16.7 0.0 25.0 5.1
-1 O O 0 0 O 0 0.0 O O 0 O O 0 20.4 50.0 0.0 0.0 6.4
0 6 1 0 0 0 0 0.0 0 0 O 0 0 0 17.9 33.3 0.0 4.6 5.6
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 27.7 50.0 0.0 12.3 8.2

2 0.0 0.0 0.0 0.0 0.0 0.0 20.0 33.3 24.9 50.0 0.0 11.7
3 0.0 4.7 0.0 0.0 16.7 0.0 0.0 23.4 0.0 0.0 33.3 7.1
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 33.2 0.0 0.0 0.0 3.0
5 0.0 0.0 0.0 0.0 0.0 40.0 0.0 0.0 5.0
6 0.0 0.0 0.0 0.0 0.0 69.0 1.2 0.0 8. 8

7 0.0 0.0 0.0 0.0 0.0 65.5 14.8 25.6 13.2
8 0.0 0.0 0.0 4.3 0.0 50.0 1.8 0.0 7.0
9 0.0 0 0 0.0 0 0 0 0
10 0 0 0.0 0.0 O 0 0 0
1 1 O 0 0.0 0 0
12 0 0 0.0 0 0
13 4 8 0.0 2 4
14 O 2 0.0 0 1
15 0 0 0.0 0 0
16 0 0 0.0 0 0
17 O 0 0.0 0 0
18 O 0 0.0 0 0

KO 0.0 0.0 0.6 1.6 1.0 0.0 0.8 17.7 20.2 0.2 3.3 4.1
t>0 0.3 0.3 0.0 0.5 4.2 0.0 5.0 42.8 9.3 9.4 0.0 6.5

146

 

 

 

 

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147

Table 3.7: Percentage of Customers’ Upstairs Trades via New Brokerage House

The table shows the average percentage of customer trading by value done in the upstairs market by the
switching broker’s new brokerage house, for each switching event and period relative to broker’s last trade
at the old house (t). Note: t=—l represents the switching broker’s last month of trading at the old house,
and t=1 represents the switching broker’s ﬁrst month of trading at the new brokerage house. In this table,
customers are deﬁned as investors who have traded at least once in the upstairs market via the old brokerage
house during the earliest month in the sample, i.e. January 1996. Data sample is restricted to the three most
active stocks.

 

 

PanelA
Switching Event
t l 2 3 4 5 6 7 8 9 10 11 Mean
—17 0.0 8.0 4.0
-—l6 0.0 0.0 0.0 0.0
-—15 0.0 2.2 0.0 0.7
—14 0.0 15.0 00 50
—13 00 00 0.0 271 00 54
—12 00 00 0.0 13.7 00 2 7
—ll 00 00 109 0.0 00 00 18
—10 00 00 00 0.0 00 00 00
—9 00 00 265 00 0.0 00 00 00 33
—8 00 00 00 00 0.0 00 00 00 00
—7 26 131 00 00 16.3 00 00 00 40
——6 00 00 00 00 11.7 200 00 00 40
—5 00 00 00 00 0.0 00 00 00 00
—4 00 00 00 0.0 0.0 00 00 00 00
—3 00 47 00 00 0.0 00 00 00 00 05
-2 00 OO 00 58 00 0.0 0.0 00 00 00 21 07
—l 00 00 47 00 00 0.0 0.0 00 200 00 00 23
0 00 00 0.0 00 00 0.0 0.0 12 252 00 103 33
1 OO 00 0.0 00 00 00 14.2 00 04 00 00 13
2 168 00 0.0 00 00 00 7.0 00 172 00 472 80
3 00 26 0.0 00 00 0.0 20.3 500 00 31 00 69
4 00 333 8.2 00 00 4.7 00 00 00 00 00 42
5 00 00 0.0 00 0.0 00 00 00 00
6 00 00 0.0 00 0.0 00 00 00 00
7 00 00 0.0 27 0.0 160 00 146 42
8 00 00 0.0 00 3.2 125 0.0 371 66
9 00 00 0.0 0.0 00 00
10 00 6.4 0.0 209 00 55
ll 00 0.0 00 00
12 00 0.0 00 00
13 00 0.0 00
14 00 2.5 12
15 00 0.0 00
16 00 0.0 00
17 00 0.0 00
18 00 5.4 27

KO 0.0 0.0 0.9 1.5 2.2 0.0 5.5 0.0 4.4 0.0 0.1 1.3
t>0 0.9 2.8 1.0 0.3 0.0 0.8 10.4 9.8 3.2 5.5 0.0 3.2

148

 

 

 

 

 

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