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This is to certify that the
dissertation entitled

Essays on Corporate Investment Decisions

presented by

Zhikun Li

has been accepted towards fulﬁllment
of the requirements for the

PhD degree in Business Administration

 

 

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DATE DUE DATE DUE DATE DUE

 

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6/01 c-JCIFIC/DateDquSS-sz

ESSAYS ON CORPORATE INVESTMENT DECISIONS

By

Zhikun Li

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Finance

2004

ABSTRACT

ESSAYS ON CORPORATE INVESTMENT DECISIONS

By

Zhikun Li

It is well known that there should not be any link between ﬁnancial market and
corporate investment after controlling for Q variables. Thus, there should not be any link
between the equity liquidity and corporate investment. However, once the assumptions of
a perfect market and rational decisions of all market participants are violated or relaxed,
the equity liquidity can actually affect the corporate investment in different ways. The
ﬁrst essay of this dissertation investigates how stock liquidity affects corporate
investment. We ﬁnd that corporate investment is signiﬁcantly and negatively related to
stock liquidity after controlling for Q and various variables, both at aggregate and
individual ﬁrm level. Such a negative relationship holds even after extensive robustness
checks. The negative relationship is consistent with the “market sentiment hypothesis”
that managers interpret low stock returns following high liquidity as a consequence of
investor sentiment, instead of cheap cost of ﬁnancing. They thus reduce investment due
to worse-than-expected business conditions and lack of knowledge about the fundamental
value of the risky assets.

The second essay ﬁnds the evidence that primary seasoned equity offering (SEO)
ﬁrms and seasoned debt offering (SDO) ﬁrms signiﬁcantly underperform their stylized

matches in investment growth after offering. We carefully examine ﬁve hypotheses and

 

 

 

 

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ﬁnd that the evidence is attributable to the market sentiment hypothesis documented in
the ﬁrst essay. That is, high liquidity followed by low stock returns signals investor
sentiment as suggested in Baker and Stein (2003), then for the same reasons, managers
also interpret low stock returns as a consequence of investor sentiment. Thus, both
individual investors and managers tend to hold cash and reduce their investment due to
worse-than-expected business conditions and lack of knowledge about the fundamental
value of the risky assets. Extensive robustness checks and a regression analysis conﬁrm
our conclusions.

This dissertation contributes to the literature in the way that it establishes the
direction and statistical signiﬁcance of the relationship between equity liquidity and
corporate investment, and explores the economic intuition behind it. This dissertation
also makes an attempt to examine several potential explanations to “New Issues Puzzle”.
To the extent that a valid explanation should be able to explain both the
underperformance in equity returns and the underperformance in investment growth
together, the market sentiment hypothesis is at least the dominant explanation in this

context.

For the long nights, and even longer weekends,
of solicitude while I worked on this dissertation,
I dedicate it to my loving wife, Songling,
my parents, and my son.

iv

 

 

 

 

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ACKNOWLEDGEMENTS

I would like to express my appreciation to the members of my dissertation
committee — Dr. Zsuzsanna Fluck, Dr. Long Chen, and Dr. Charles Hadlock for their
insightful guidance and support. Especially, I thank Dr. Fluck and Dr. Chen for serving as
my committee co-chairs and guiding me through the completion of my doctoral program.
Their continuous support and encouragement have been invaluable. I am also grateful to
Dr. Hadlock for the time he spent with me helping me better understand the fundamental

nature of corporate ﬁnance.

I would like to thank Dr. Geoffrey Booth and Dr. Jun-Koo Kang for providing me
with an opportunity to obtain my doctorate. I acknowledge ﬁnancial assistance from the
Financial Management Association International (EMA), the Southwest Finance
Association (SWFA), and the Graduate School at Michigan State University. In
particular, I thank SWFA for giving the ﬁrst essay in this dissertation the 2004 Best
Doctoral Student Paper Award, and the Graduate School at MSU, for giving me 2004

Graduate Student Research Enhancement Award.

Finally, I would like to thank all of my family for their love and support. In
particular, I thank my wife for the sacriﬁces she made in order to facilitate the completion
of my doctoral dissertation, and my parents, who gave me continuous encouragement and

mental support from halfway around the world.

LIST OF TABLES ............................................................................ viii
LIST OF FIGURES ........................................................................... x
INTRODUCTION ............................................................................ 1
CHAPTER 1 LIQUIDITY, FINANCIAL MARKET SENTIMENT,
AND CORPROATE INVESTMENT
Introduction .................................................................. 3
Previous Research ........................................................... 10
1.2.1 Financial Market is Important for Corporate
Investment Decisions .............................................. 10
1.2.2 Liquidity is an Important Market Factor ........................ 15
1.2.3 Summary of Hypotheses .......................................... 16
1.3 Data ........................................................................... 17
1.4 Methodologies and Results ................................................ 19
1.4.1 Dependent and Independent Variables .......................... 19
1.4.2 Summary Statistics ................................................ 22
1.4.3 Relationship between Liquidity and Corporate
Investment at Aggregate Level .................................. 23
1.4.4 Relationship between Firm Liquidity and
Corporate Investment at Firm Level ............................ 25
1.4.5 Business Cycle ..................................................... 28
1.4.6 Relationship between Firm Liquidity and Corporate
Investment When Different Firm Characteristics
Are Considered .................................................... 31
1.4.6.1 Relative Liquidity ........................................ 31
1.4.6.2 Financial Constraint ...................................... 32
1.4.6.3 Leverage ................................................... 34
1.4.6.4 Income Performance ..................................... 35
1.4.6.5 Firm Size .................................................. 36
1.4.6.6 Book-to-Market (BE/ME) Ratio ........................ 37
1.4.7 Tests of Joint Effects ............................................. 38
1.4.8 Portfolio Approach with Different Benchmark ............... 40
1.4.9 Considering All Relevant Variables in One Regression. . 42
1.5 Conclusions ................................................................. 43

APPENDIX 1A

APPENDIX 1 B

TABLE OF CONTENTS

TABLES AND FIGURES FOR CHAPTER 1 ................ 47

The Difference between Dollar Liquidity
and Stock Turnover ................................................ 74

vi

 

 

 

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REFERENCE

Reference for Chapter 1 ........................................... 76

CHAPTER 2 WHY DO SEASONED OFFERING FIRMS

2. 1 Introduction .................................................................. 80
2.2 Hypotheses ................................................................... 84
2.3 Data ........................................................................... 86
2.3.1 Sample Selection ................................................... 87
2.3.2 Sample Characteristics ............................................. 88
2.4 Methodologies ............................................................... 89
2.4.1 Matching Technique ............................................... 89
2.4.2 Fiscal Year-End Month and Calendar
Year-End Month ................................................... 90
2.4.3 The Calculation of Buy-and-Hold Returns (BHRs) ........... 92
2.5 Results ........................................................................ 93
2.5.1 Replicating BHRs .................................................. 93
2.5.2 Main Results ........................................................ 94
2.5.2.1 Primary Seasoned Equity Issues ........................ 95
2.5.2.1.1 The Underperformance in
Investment Growth .......................... 95
2.5.2. 1.2 Cash, Sales, and Expenses .................. 99
2.5.2.1.3 Liquidity and Market Sentiment .......... 102
2.5.2.2 Secondary Seasoned Equity Issues ..................... 105
2.5.2.3 Seasoned Debt Issues .................................... 108
2.5.3 Robustness Checks ................................................ 111
2.5.3.1 Different Matching Benchmarks ....................... 111
2.5.3.2 Regression Analysis ...................................... 113
2.6 Conclusions .................................................................. 1 15
APPENDIX 2 TABLES AND FIGURES FOR CHAPTER 2 ................. 118
REFERENCE Reference for Chapter 2 .......................................... 139

UNDERPERFORM IN INVESTMENT GROWTH?

vii

 

LIST OF TABLES

TABLES FOR CHAPTER 1
Table 1 Summary of Hypotheses ................................................... 17
Table 2 Summary Statistics of Variables .......................................... 48
Table 3 Aggregate Level Liquidity and Aggregate Level Investment ......... 49
Table 4 The Relationship between Liquidity and Investment at Firm Level..50
Table 5 The Relationship between Liquidity and Investment at

Firm Level (Manufacturing ﬁrms) ........................................ 53
Table 6 Relative Liquidity ........................................................... 55
Table 7 Financial Constraint: KZ Index ........................................... 56
Table 8 Leverage ..................................................................... 57
Table 9 Income Performance ........................................................ 58
Table 10 Firm Size ..................................................................... 59
Table 11 Book-to-Market Ratio (BE/ME) .......................................... 60
Table 12 Joint Effect of Relative Liquidity and Leverage ........................ 61
Table 13 Joint Effect of Relative Liquidity and BE/ME .......................... 62
Table 14 Joint Effect of Relative Liquidity and Income Performance .......... 63
Table 15 Joint Effect of Relative Liquidity and Financial Constraint ........... 64
Table 16 Joint Effect of BE/ME and Leverage ..................................... 65
Table 17 Joint Effect of Firm Size and Income Performance .................... 66
Table 18 Portfolio Approach ......................................................... 67
Table 19 The Regression with All Relevant Variables ............................ 70
TABLES FOR CHAPTER 2
Table 0 Year Selection Procedures ................................................. 92
Table 1 Number of Seasoned Offering Years by Year and Industry ........... 1 19
Table 2 Replicating the Evidence on the Underperformance in

Stock Returns for Seasoned Offering ﬁrms ............................. 120
Table 3 The Evidence on the Underperformance in

Investment Growth (Primary SEOs) ..................................... 123
Table 4 The Evidence of Cash, Sales, and Expenses (Primary SEOs) ........ 124
Table 5 The Indicator of Investor Sentiment:

Liquidity Run-Up (Primary SEOs) ....................................... 125
Table 6 The Evidence on the Underperformance in

Investment Growth (Secondary SEOs) .................................. 126
Table 7 The Indicator of Investor Sentiment:

Liquidity Run-Up (Secondary SEOs) ..................................... 127
Table 8 The Evidence on the Underperformance in

Investment Growth (SDOs) ............................................... 128
Table 9 The Indicator of Investor Sentiment:

viii

Liquidity Run-Up (SDOS) ................................................ 129
Table 10 Robustness Check: Difference Matching Benchmarks ................ 130
Table 11 Regression Analysis (Primary SEOs) .................................... 132

ix

 

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Fig

LIST OF FIGURES

FIGURES FOR CHAPTER 1

Figure l

The Difference between Dollar Liquidity and Stock Turnover ...... 71

Figure 2 The Difference between Real Investment Growth

and R&D Growth .......................................................... 72
Figure 3 Liquidity Growth and Investment Growth .............................. 73
FIGURES FOR CHAPTER 2
Figure 1 The Underperformance in Investment Growth (Primary SEOs). . 133
Figure 2 The Evidence of Cash, Sales, and Expenses (Primary SEOs). . . . 134
Figure 3 The Underperformance in Investment Growth (Secondary SEOs)... 137
Figure 4 The Underperformance in Investment Growth (SDOs) ............... 138

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INTRODUCTION

This dissertation contains two chapters that address issues in the areas of
corporate investment and ﬁnancial market. The ﬁrst chapter examines the relationship
between stock liquidity and corporate investment. The signiﬁcant negative relationship is
supported by the market sentiment hypothesis, that is, high liquidity followed by low
stock returns signals investor sentiment, as a consequence, managers tend to reduce
corporate investment and prefer to hold cash, due to worse-than-expected business

conditions and uncertainty in fundamental value of risky assets.

Chapter 1 proposes a new puzzle because the ﬁnancial constraint literature, on the
contrary, predicts a positive link between market valuation and corporate investment. The
possible explanation is that the stock liquidity is used primarily as a proxy for investor
sentiment. It does not necessarily contain all relevant information of market valuation.
Thus, our results show a relationship between investor sentiment, especially investor
optimism, and corporate investment. On the other hand, the ﬁnancial constraint literature
matters especially when investors are pessimistic (e.g. Barberis and Thaler (2003)). In
addition, there is still no direct empirical evidence showing a positive relationship
between ﬁnancial constraint and corporate investment, due to the lack of appropriate

proxy for ﬁnancial constraint.

The second chapter is an application of the ﬁrst chapter. We ﬁnd that primary
seasoned equity offering (SEO) ﬁrms and seasoned debt offering (SDO) ﬁrms

signiﬁcantly underperform their stylized matches in investment growth after offering,

while secondary seasoned equity offering (SEO) ﬁrms do not. We carefully examine ﬁve
hypotheses and ﬁnd that the evidence is attributable to the market sentiment hypothesis

documented in the ﬁrst chapter.

Our results allow us to reconsider some of the possible solutions to “New Issues
Puzzle”. To the extent that a valid explanation should be able to explain both the
underperformance in equity returns and the underperformance in investment growth

together, the market sentiment hypothesis is the only one that can solve the puzzle.

Our extensive robustness checks conﬁrm our conclusions in both chapters.

 

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CHAPTER I

LIQUIDITY, FINANCIAL MARKET SENTIMENT,

AND CORPROATE INVESTMENT

1.1 Introduction

In a perfect world, the standard Q theory, pioneered by Tobin (1969) and Hayashi (1982),
suggests that corporate investment opportunities are summarized by market valuation of
ﬁrms’ capital stock. Therefore, the theory predicts no link between ﬁnancial market and
corporate investment after controlling for Q variables. Having said this, stock liquidity is

supposed to have no effect on corporate investment after controlling for Q variables.

However, a great deal of literature suggests that this may not be true. The key argument is
that the conclusion of no relationship between stock liquidity and corporate investment
relies heavily on the assumptions of a perfect market and rational decisions of all
participants in economy. Thus, stock liquidity itself can actually represent different things
and can affect corporate investment in different ways, when these assumptions are
violated or relaxed. For example, stock liquidity is no longer a priced risk factor in
equilibrium, instead it signals investor sentiment when some market participants become
overconﬁdent about the fundamental value of the risky assets (e.g. Baker and Stein

(2003)).'

 

' Baker and Stein (2003) argue that irrational investors under-react to the information in
the trading because they have different likelihood functions when updating probabilities.
Thus, the differences in opinion become bigger, and the trading increases. This is the case
especially in a hot market when more investors tend to be overconﬁdent about the

The above brief discussion indicates that the relationship between stock liquidity and
corporate investment is an open empirical issue. To the best of our knowledge, this is the
ﬁrst study that intends to establish the direction and statistical signiﬁcance of this
relationship, and explore the economic intuition behind it. Indeed, there does not appear
to be a consensus in the literature regarding the impact of stock liquidity on corporate
investment. We can summarize at least ﬁve hypotheses as follows, according to the

different assumptions of the ﬁnancial market and market participants’ decisions.

“Rational pricing hypothesis”: If the ﬁnancial market is perfect and all market
participants rationally make their decisions, then stock liquidity is simply a priced risk
factor in equilibrium. And then, higher liquidity implies lower expected future return,
hence lower cost of equity ﬁnancing (e.g., Pastor and Stambaugh (2001), and Gibson and
Mougeot (2001)). As the lower cost of capital facilitates equity ﬁnancing, this hypothesis

implies a positive relationship between stock liquidity and corporate investment.

Our next four hypotheses are all related to the market irregularities. After all, we are not
living in a perfect world. The recent development of the imperfect capital market theory
discovers a different meaning of stock liquidity. Baker and Stein (2003) show that stock
liquidity is a signal of investor sentiment because trading increases when more investors
disagree on the true value of the risky assets. What makes things worse is that more
investors become overconﬁdent about business conditions and their ability of selecting

good stocks when the ﬁnancial market becomes over-heated. The aggressive trading

 

business conditions and their abilities to pick good stocks. Also see Shiller (1999),
Barberis and Thaler (2003), and Hong and Stein (2003).

 

 

 

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further boosts trading volume or stock liquidity. Odean (1998b) called this pattern “the
most robust effect of overconﬁdence”. The empirical ﬁndings are documented and
conﬁrmed by several studies. Glaser and Weber (2003), and Statman, Thorley, and
Vorkink (2004) both ﬁnd a strong positive link between trading volume and lagged stock
returns. They all ascribe this evidence to the investor overconﬁdence. Baker and Stein
(2003) regress equity returns on lagged liquidity and ﬁnd a negative relationship. The
interpretation is that there will be a negative equity adjustment after high market
sentiment because investors realize that the business conditions are not as rosy as they
were thought to be, and the fundamental value of the risky assets were not correctly
understood.2 Actually, in connection with the above results, the fact that high equity
returns are followed by a negative equity adjustment is consistent with the nature of the

business cycle at a general market level.

Despite the increasing interest shown by ﬁnancial economists, relatively little effort has
been spent on establishing the direct empirical link between ﬁnancial market sentiment
and corporate investment. This motivates us to study the relationship between stock
liquidity and corporate investment. If stock liquidity is a signal of investor sentiment, and
sentiment can affect corporate investment, then it is a meaningful strategy to investigate
how stock liquidity, both at aggregate and ﬁrm level, affects corporate investment. Based

on the above reasoning, our next four hypotheses are as follows.

 

2 Business conditions in this situation include both economic conditions and ﬁrm
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“Investor sentiment hjpothesis": According to the empirical implication in Daniel,
Hirshleifer and Subrahmanyan (1998)3 and the empirical ﬁnding in Baker and Stein
(2003), high liquidity tends to be followed by low equity returns as the ﬁnancial market
gradually corrects from investor sentiment. However, on the other hand, managers still
interpret low equity retums as cheap cost of ﬁnancing and raise corporate investment.
Thus, one would observe that high liquidity leads to high investment. Although this
hypothesis also predicts a positive relationship between liquidity and corporate
investment, it is different from rational pricing hypothesis because, in this hypothesis, the

low equity returns are caused directly by investor sentiment.

“Market sentiment hypothesis ”: As we mentioned above, the high liquidity followed by
low equity returns signals market sentiment, as suggested by Baker and Stein (2003). For
the similar reasons, managers can also interpret low stock returns as a consequence of
investor sentiment. To the extent that managers themselves are also investors in real
economy, it is thus natural to conjecture that they tend to reduce corporate investment
due to worse-than-expected business conditions, and lack of certain knowledge about the
fundamental value of the risky assets.4 This hypothesis predicts a negative relationship
between stock liquidity and corporate investment. This negative relationship can hold at

aggregate as well as individual ﬁrm level.

 

3 They assume overconﬁdent investors overreact to private information, and thus lead to
lower stock returns.

4 It would be helpful to think that both individual investors and managers are investors.
The individual investors are those investors in ﬁnancial markets, while managers are the
investors in real economy. All of them can be affected by the similar things.

 

 

 

 

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What is worth the whistle is the difference between this hypothesis and the investor
sentiment hypothesis. In this hypothesis, managers and investors all treat low equity
returns as a consequence of investor sentiment. In the investor sentiment hypothesis,
investors treat low equity returns as a consequence of investor sentiment, while managers
treat low equity returns as cheap cost of ﬁnancing. The different treatment of the low
equity returns following high liquidity distinguishes the predictions between the above

two hypotheses.

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“Agency hypothesis . When investor sentiment is high in the ﬁnancial market, even if
managers are rational, they may not necessarily choose to maximize ﬁrm value. Instead,
agency theories argue that some managers may want to enhance their own prestige. Thus,
they try to use investor exuberance as a cover for doing “empire building” investments
otherwise they cannot do under other situations. This hypothesis predicts a positive

relationship between liquidity and corporate investment.

“Managerial sentiment hypothesis”: As Barberis and Thaler (2003) suggest, “investor
sentiment can also affect investment if managers put some weight on investors’ opinions,
perhaps because they think investors know something they do not”. Thus, it is likely that
investor overconﬁdence can be transferred to managers. This is because managers may
think their ﬁrms have reached a higher level in the life cycle, and accordingly expand
investment to a higher level. This hypothesis predicts a positive relationship between

stock liquidity and corporate investment.

The main ﬁndings of this article can be summarized as follows. The corporate investment

is signiﬁcantly and negatively related to the lagged stock liquidity, both at aggregate and

 

 

 

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individual ﬁrm level. This is consistent with our “market sentiment hypothesis”. The
evidence indicates that managers actually interpret low stock returns following high
liquidity as a consequence of investor sentiment, instead of cheap cost of ﬁnancing. They
reduce investment because they are not comfortable about the business conditions and the
fundamental value of the risky assets following high investor sentiment. We do extensive
robustness checks by trying different stock liquidity measures and splitting the sample
into subsamples according to business cycle, relative liquidity, leverage, ﬁrm size, book-
to-market (BE/ME) ratio, income performance, and ﬁnancial constraint. We ﬁnd that the
negative relationship between stock liquidity and corporate investment is more signiﬁcant
during the recession periods, and for the ﬁrms with higher relative liquidity (liquidity that
is higher than market level liquidity). The bigger ﬁrms or the ﬁrms with lower book-to-
market (BE/ME) ratios have more signiﬁcant relationship too. Also noteworthy is that the
ﬁrms with lower leverage ratios exhibit more signiﬁcant relationship. This is consistent
with the theory in Stein (1996) - ﬁrms that are in need of external equity ﬁnance will
have investment that is especially sensitive to non-fundamental components of stock
valuation. A different approach (portfolio approach) with different benchmarks further
conﬁrms our results. Finally, to make sure our stock liquidity measures do not capture
other things such as income performance, size, etc., we consider all relevant variables in
one regression. The negative relationship between stock liquidity and corporate
investment holds even when we control for different lagged Q variables, different lagged
cash ﬂow variables, ﬁnancial slack, business cycle, income performance, leverage, size,
BE/ME, and ﬁnancial constraint. Once again, overall, our results support the “market

sentiment hypothesis”.

 

 

 

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In the literature, it is not uncommon that the ﬁnancial market valuation provides an
impact on corporate investment even when Q variables are taken into account. Due to
asymmetric information, corporate investment is ﬁnancially constrained by the
availability of internal funds, and the market valuation of ﬁrms as collateral value.
Among the large body of ﬁnancial constraint studies, Fazzari, Hubbard, and Petersen
(1988a) provide evidence-that corporate investment is sensitive to ﬁrm internal cash.
Baker, Stein, and Wurgler (2003) show that more equity-dependent ﬁrms’ investment is

more sensitive to market valuation.5

While our new ﬁndings contribute to the debate that links the ﬁnancial market to
corporate investment, it is distinctively different from the ﬁnancial constraint literature
because the latter would naturally predict a positive relationship between stock liquidity
and investment.6 In sharp contrast, our evidence implies low corporate investment
following high stock liquidity. This intuition, seemingly surprising, is consistent with

Baker and Stein (2002): stock liquidity represents investor sentiment.

The conﬂicting results offer a new puzzle in the literature of corporate investment. Our
explanation is based on the representation of stock liquidity. In our article, stock liquidity

is used primarily as a proxy for investor sentiment. It does not necessarily contain all

 

5 The equity dependence channel is actually ﬁnancial constraint channel, especially when
investors are too pessimistic. Managers may have to forgo good investment opportunities
because it is too costly to ﬁnance them with undervalued equity. Nevertheless, for ﬁrms
with ample internal funds, there is no such problem.

6 For example, Baker, Stein and Wurgler (2003) ﬁnd a positive relationship between
investment and stock valuation (proxied by Q), especially for those ﬁrms that are highly
equity-dependent. Their proxy for equity-dependence is KZ index, which is also widely
used as a proxy for ﬁnancial constraints.

relevant information of market valuation. Thus, our results show a relationship between
investor sentiment, especially investor optimism, and corporate investment. On the other
hand, the ﬁnancial constraint literature matters especially when investors are pessimistic
(e.g. Barberis and Thaler (2003)). In addition, there is still no direct empirical evidence
showing a positive relationship between ﬁnancial constraint and corporate investment,
due to the lack of appropriate proxy for ﬁnancial constraint.7 Although the overall
relationship between ﬁnancial market valuation and corporate investment remains
unclear, our empirical evidence casts the ﬁrst stone on the relationship between market
overvaluation and corporate investment. We thus think this is the main contribution of

our article to the literature.

The rest of the article is organized as follows. Section 2 brieﬂy reviews previous
research. Section 3 describes the data and Section 4 conducts regression analysis. We

provide a conclusion in Section 5.

1.2 Previous Research
1.2.1 Financial Market is Important for Corporate Investment Decisions

Standard Q theory predicts that investment opportunities are summarized by market
valuation of capital stock (marginal Q), and thus, there should be no link between the
ﬁnancial market and corporate investment when Q variables are controlled for. To see it,
following the standard investment literature (e. g., Cochrane (1996) and Hubbard ( 1998)),

we assume that a ﬁrm maximizes its present value,

 

7 The use of K2 index looks like a way out but is still very controversial.

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k,+1 = (1—6)(k, +i,) (3)

where ytis an output function of production f (k,,6,)and adjustment cost function
C(i, ,k,). k, is capital stock, i, is investment, 9, is an exogenous shock to the production
function, and 6 is depreciation rate. m, is the stochastic discount factor that prices asset

return. We assume there is no exogenous shock to the cost adjustment function.

The ﬁrst order condition is thus

 

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this is equivalent to
1+Ci(’)=9t (5)
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1+Ci(iit~kit) = qir (7)

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As Hubbard (1998) suggests, a conventional cost adjustment function in Q literature is

given by

cam/cit) = (grl’f’ — aiizkit (9)
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Substituting this cost adjustment function into the ﬁrst order condition generates the

following investment equation, where a” is optimization error.

i l
(—)n = at + ‘— qit + 5i: (10)
k a

Based on the above analysis, q,, is the marginal Q and deﬁned as the present value of

proﬁts from new investments. Equation (10) says that investment can be completely
predicted by marginal Q. In the empirical literature, average Q constructed from ﬁnancial

market can usually be used as a proxy for marginal Q under certain assumptions.8

Evidence supporting this view can be found in as early as Tobin (1969). Fama and
Gibbons (1982), and Cochrane (1991, 1996) suggest corporate investment responds to
changes in risk prernia that the empirical ﬁnance literature has found to dominate changes
in expected returns. Thus, the market valuation of the assets in the equity market can be

used to predict corporate investment.

 

8 The assumptions include perfect competition, linear homogeneity of technologies for
production, and ﬁnancing and investment decisions are independent.

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The growing ﬁnancial constraint literature, on the other hand, argues that corporate
investment is affected by the availability of internal funds and market valuation. For
example, F azzari, Hubbard and Petersen (1988a) show that internal cash ﬂow is another
explanatory variable, in addition to Q, in determining corporate investment. Baker, Stein
and Wurgler (2003) follow a model developed by Stein (1996), and show the equity-
dependent ﬁrms or the ﬁrms that are more likely to be ﬁnancially constrained, are more

sensitive to stock valuation.

Another line of literature suggests ﬁnancial market is an important factor for corporate
investment decisions through the inefﬁciency of the market. Researchers conclude that
stock prices contain an important element of misvaluation or sentiment. Thus, cost of
capital can deviate from its expected value, this in turn affects corporate investment
decisions. Using the above framework, in an imperfect market, since market valuation
per se may be biased by the misvaluation, clearly Q is no longer sufﬁcient to proxy for all
available investment opportunities. In this case, it is possible that misvaluation can
directly affect managers and thus corporate investment decisions. Evidence supporting
this view can be found as early as in Keynes (1936). Chirinko and Schaller (2001)
recently show that even if cash ﬂow variables, such as corporate proﬁts, are considered,
the stock market variables retain signiﬁcant predictive power for corporate investment.
Stein (1996) claims, “those ﬁrms that are in need of external equity ﬁnance will have
investment that is especially sensitive to the non-fundamental components of stock

prices.” Polk and Sapienza (2002) ﬁnd the ﬁrms that are classiﬁed as overvalued seem to

13

 

 

 

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invest more than other ﬁrms.9 Their evidence indicates a link between sentiment and
corporate investment. Finally, Baker, Stein and Wurgler (2003) show empirical evidence
that, “corporate investment will be sensitive to non-fundamental movements in stock
prices, ..., stock prices (they use Q as a proxy) will have a stronger impact on the
investment of ﬁrms that are ‘equity dependent’, which implies ﬁrms need more outside

- - ”10
equity to ﬁnance investment.

Like many authors in the behavioral ﬁnance literature, we relax the assumption of
individual rationality in this article. However, note that an alternative departure from
rational expectation equilibrium is to retain individual rationality but relax the consistent
beliefs assumption. In this case, investors are rational but will not have enough
information to ﬁgure out the correct distribution for the variables of interest. For
example, Leahy and Whited (1996) show uncertainty, instead of sentiment, can
negatively affect corporate investment mainly through Q. Other related research in this
area also includes a decomposition of Q into fundamental and non-fundamental parts. For
example, see Blanchard, Rhee and Summers (1993), Galeotti and Schiantarelli (1994),
and Goyal and Yamada (2000). Our research is different because ﬁrst, we investigate the
impact of stock liquidity on corporate investment. Second, if stock liquidity signals

investor sentiment, then we want to see whether there exists a direct link between

 

9 They use accounting accruals and issuance of equity, which are both based on
managers’ decisions, as proxies for investor sentiment. Arguably, good proxies for
investor sentiment should be more related to ﬁnancial market or investors’ decisions,
instead of managers’ decisions.

'0 Baker, Stein and Wurgler (2003) only show more equity dependent ﬁrms’ investment
is more sensitive to market valuation (proxied by Q). However, we know Q includes
information about both fundamentals and non-fundamentals. Because misvaluation or
sentiment is more related to non-fundamentals, the relationship between investment and
Q is thus an indirect link between investment and sentiment.

14

ﬁnancial market valuation and corporate investment through investor sentiment. Most

importantly, we try to explore the economic intuition behind this link.

1.2.2 Liquidity is an Important Market Factor

We now turn to review some recent developments in liquidity literature. As we discussed
above, the different representations of stock liquidity motivate us to investigate whether
there is a link between stock liquidity and corporate investment. Stock liquidity gains a
great deal of attention recently. First, it represents a priced risk factor in equilibrium.
Pastor and Stambaugh (2001) document the links between liquidity and stock returns.
Jones (2001) shows that liquidity and transaction cost variables have more predictive
power than dividend yields for US stock returns. Chordia, Shivakumar and
Subrahmanyam (2000) show that absolute stock returns and volatility are linked to stock
liquidity. Amihud (2001) introduces a broadly-deﬁned liquidity that can capture the

combined effect of price and volume.

Second, stock liquidity signals investor sentiment. Odean (1998b) theoretically shows
overconﬁdence boosts trading volume. Baker and Stein (2002) use a model featuring a
class of irrational investors who under-react to the information contained in trading, and
Baker and Stein claim high market liquidity is a mirror of investor sentiment.
Empirically, Glaser and Weber (2003), and Statman, Thorley, and Vorkink (2004) both
ﬁnd there is a positive relationship between overconﬁdence and trading volume or stock

turnover.

15

The newly found evidence seems to support that both market level stock liquidity and
ﬁrrn-level stock liquidity are related to stock returns and have meaningful information
content. The evidence also suggests that liquidity may reﬂect investors’ opinions toward
ﬁrm speciﬁc information and could thus signal investor sentiment. Overall, stock
liquidity should be an important aspect of the ﬁnancial market. As we noted before, if
there exists a link between ﬁnancial market and corporate investment, then it is inevitable
to investigate whether there is a link between stock liquidity and corporate investment, or

whether stock liquidity can predict corporate investment.

1.2.3 Summary of Hypotheses

The main goal of this article is to directly test how stock liquidity affects corporate
investment and explore the economic intuition behind it. We summarize ﬁve hypotheses
about the effect of stock liquidity on corporate investment. The “rational pricing
hypothesis” suggests that stock liquidity is a risk factor, and there exists a rational pricing
model that can explain the relationships among returns, risk and investment. This
hypothesis predicts a positive relationship between liquidity and corporate investment.
The “investor sentiment hypothesis” suggests rational managers tend to increase
investment according to the low cost of capital, which is however generated by investor
sentiment. This hypothesis also predicts a positive relationship between liquidity and
investment. The “market sentiment hypothesis” implies that, if high liquidity followed by
low stock returns signals investor sentiment, then managers treat low stock returns as a
consequence of investor sentiment. As a result, managers reduce investment due to lack

of certain knowledge about business conditions and the true value of the risky assets.

16

 

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This hypothesis suggests a negative relationship between liquidity and corporate
investment. The “agency hypothesis” assumes managers are rational, but they try to use
investor sentiment as a cover to build their own “empires”. This hypothesis also predicts
a positive relationship between liquidity and corporate investment. Finally, the
“managerial sentiment hypothesis” supports the idea that investor sentiment can be
transferred to managers, and hence affects corporate investment positively. We list these
hypotheses in the following table.

Table 1: Summary of Hypotheses

 

 

 

Hypotheses Corporate Investment
Rational Pricing Hypothesis +
Investor Sentiment Hypothesis +

 

Market sentiment Hypothesis ——

 

Agency Hypothesis +

 

Managerial Sentiment Hypothesis +

 

 

 

 

1.3 Data

For the purpose of this analysis, two different datasets are utilized. We collect liquidity
related data (e.g. returns and volume) from CRSP and ﬁrms’ ﬁnancial and accounting
information from COMPUSTAT. The data from COMPUSTAT are based on the
following criteria. First, we choose all ﬁrms that have valid ﬁnancial and accounting
numbers. We ignore those ﬁrms with negative accounting numbers for book assets,
capital, or investment. We also drop ﬁrms with assets less than 0.5 million, and extreme

observations. When considering investment, we include only ﬁrms with December as

17

 

 

  
  

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ﬁscal year-end to eliminate the usual problems caused by the use of overlapping

observations.

Second, because assets in utilities, ﬁnancial institutions, investment funds and REITS
have different trading characteristics from other ordinary equities, we exclude all of them
from the sample by deleting observations with SIC code between 4911 and 4941
(utilities), between 6000 and 6081 (ﬁnancial institutions), and 6722, 6726, 6792
(investment funds and RElTs). Above procedures yield 105,016 observations over 42

years, average number of ﬁrms per year is 2,500.

Third, we extract data from CRSP by using the ﬁrms we obtain from COMPUSTAT. The
data include daily returns, daily trading volume, capitalization, price, and total shares
outstanding at each year end between 1962 and 2001. Daily variables are used to
calculate annual variables. For example, annual stock liquidity is calculated by taking
average of all daily stock liquidity during the year. This dataset has smaller number of
observations than those in the dataset we obtain from COMPUSTAT, because some ﬁrms
do not start trading in equity market until recent years, and some ﬁrms were delisted after
being traded for a while. We also delete some observations with either missing or zero
daily volume, because one of the stock liquidity measures is to divide dollar volume by
absolute return. To avoid the effect of special stock events such as stock split or
repurchase, daily returns greater than 100% or less than —100% are treated as outliers and
deleted. The ﬁnal sample contains 26,966 observations. The average number of ﬁrms

used per year is 710.

18

Aggregate real investment data are obtained from Federal Reserve Economic Data
(FRED) database. The data include quarterly real gross private domestic investment level
between 1947 and 2001. All numbers are seasonally adjusted and scaled by 1996 dollar
value. The business cycle data are obtained from National Bureau of Economic Research
(NBER). Since NBER offers quarterly data, we treat one speciﬁc year as “recession” if at
least two quarters are classiﬁed as “trough” by NBER. Otherwise, we treat the year as
“normal” or “boom”. There are 39 years in aggregate data, and nine of them are
“recession” years. Alternatively, we identify recession years by using BAA-AAA bond
default spread. If the spread in one year is higher than the average spread in past 40 years,
then this year is classiﬁed as a recession year. This procedure generates very similar

business cycle years as does the other approach.

1.4 Methodologies and Results

1.4.1 Dependent and Independent Variables

It is useful to deﬁne some basic variables ﬁrst. The variables used in this article are
calculated using merged data described above. We use three measures of corporate
investment. Variable IN V1 is a ﬁrm’s real investment (calculated by dividing capital
expenditure (COMPUSTAT data 128) by year beginning total assets (lagged data 6)).
Variable RD represents a ﬁrm’s research and development investment (calculated by
dividing a ﬁrm’s research and development (R&D) expense (data 46) by year beginning
total assets (lagged data 6)). Variable IN V2 is a ﬁrm’s total investment (deﬁned as ratio

of the sum of capital expenditure and ﬁrm’s research and development (R&D) expense

l9

(data 46) over year beginning total assets (lagged data 6)). We use three investment
variables because there is no consensus that gross investment or net investment is deﬁned
as corporate investment. We include net investment (IN V] ), gross investment (1N V2) and
their difference (R&D investment) to identify which one is the dominant variable in this

context.

Two liquidity measures are used in this article: a broadly-deﬁned liquidity measure (see
the following for the reason why we want to use a broadly-deﬁned measure) introduced
by Amihud (2001), and stock turnover. Variable DLIQ is a measure of broadly-deﬁned
annual ﬁrm level stock liquidity. It is obtained by applying following procedures: we ﬁrst
ﬁnd ratio of daily volume (dollar volume) to daily absolute return, and then average all

ratios for each year and each ﬁrm.

Dollar Volume”

 

T
DLIQ,- = z . (ll)
i=1 i’itl

According to Amihud (2001), if ratio of absolute return to volume is a measure of
illiquidity, then the inverse should be a measure of liquidity. Dollar liquidity (DLIQ)

represents how many dollars are needed if stock return is driven up or down by 1 percent.

Variable T0 is stock turnover. Daily stock turnover is the ratio of the number of shares
being traded per day to total shares outstanding on that day. Annual stock turnover is

calculated by averaging all ratios for each year and each ﬁrm.

T 0i 2 1 Share Volume”
T I

 

IMH

. (12)
1 Outstanding"

20

Controversies abound over what variables can best proxy for stock liquidity. While stock
turnover is a widely used measure of liquidity, there are two reasons why we want to use
a broadly-deﬁned liquidity measure. First, the broadly-deﬁned liquidity is deﬁned as ratio
of “daily volume” to “absolute value of daily return”. It combines both price effect and
volume effect. In the literature, many authors often consider two important market
factors: price effect (retums, transaction costs, bid-ask spread) or volume effect (trading
volume, stock turnover) separately. Thus their methodologies may be ﬂawed. Second,
some traditionally-deﬁned liquidity measures focus on intra-daily data, which is only
available for less than one decade. As Gibson and Mougeot (2001) suggest, traditionally-
deﬁned liquidity is primarily suited to study the cross-sectional and time-series
determinants of liquidity over short-term horizon. However, long-term features are
ignored. Stock turnover is also considered in this article for two reasons. First, because
stock turnover is a widely accepted measure of liquidity, we want to use it as a robustness
check.ll Second, although our different liquidity measures share similar aspects of
liquidity, they might emphasize different things. According to their deﬁnitions, dollar
liquidity may be more related to transaction cost feature of liquidity. Stock turnover
represents more about level of trading activity. We demonstrate how these two liquidity

measures respond differently to economic shocks in the Appendix.

Following the literature, we consider two more explanatory variables: Tobin’s Q and cash
ﬂow (CF). Brainard and Tobin (1968), and Tobin (1969) argue that a ﬁrm should invest

when Q value is equal to or above 1, where Q ratio is deﬁned as the ratio between the

 

H Baker and Stein (2002) use stock turnover as a proxy for liquidity.

21

value of ﬁrm’s assets in capital market and their replacement cost.'2 A ﬁrm’s investment
decision can also be sensitive to ﬁrm’s cash ﬂow. Cash ﬂow should thus be controlled
for. We deﬁne Q as the market value of equity plus assets minus book value of equity
over assets, that is, market value of equity plus assets (data 6) minus the sum of common
equity (data 60) and deferred taxes (data 74) over assets (data 6). Firm’s cash ﬂow (CF)
equals the sum of earnings before extraordinary items (data 18) and depreciation (data
14) over year beginning assets (lagged data6). Moreover, ﬁrm’s internal cash availability
should have an effect on investment. For those ﬁrms that have high ﬁnancial slack, they
probably can invest more. We use two measures of ﬁnancial slack, SLA CKI is the ratio
of cash and short-term investments (data 1) and sales (data 12); the CASH is the ratio of

cash and short-term investments (data 1) and lagged assets (data 6).

As noted before, some authors have already documented a link between ﬁnancial market
and corporate investment through ﬁnancial constraint. Thus, to make sure we are

investigating a different link, we also control for ﬁnancial constraint in this article.

1.4.2 Summary Statistics

Before going any further with the discussion, we summarize all independent and
dependent variables in Table 2. Overall, ﬁrms with higher dollar liquidity usually have
higher stock turnover. This conﬁrms that dollar liquidity and stock turnover share similar
aspects of liquidity, although they may emphasize different things. They have higher real

investment, higher R&D investment, and thus higher total investment. They also have

 

'2 Some authors use Q as a measure of growth opportunity. For example, see recent
works by Bae, Kang, and Lim (2001) and Graham (2000).

22

better income performance, lower leverage, bigger size, and lower book-market-ratio
(BE/ME). Their much lower KZ index suggests they are less likely to be ﬁnancially

constrained.

Firms with higher stock turnover usually have higher dollar liquidity. They have higher
real investment, higher R&D investment, and higher total investment. They also have
higher leverage, lower book-market-ratio (BE/ME), and higher ﬁnancial slack. It seems

income performance and K2 index are not very sensitive to stock turnover.

1.4.3 Relationship between Liquidity and Corporate Investment at Aggregate
Level

We ﬁrst look at the relationship between liquidity and corporate investment at aggregate

level. Because the aggregate investment data from the Federal Reserve only represent

real investment, the relationship between liquidity and R&D investment, and the

relationship between liquidity and total investment will not be studied at aggregate level.

If liquidity does have explanatory power in corporate investment, we should observe such

relationship at aggregate level throughout time.

Using average DLIQ as a measure of market liquidity, we ﬁnd a negative link between
aggregate real investment and variable DLIQ. This link is statistically signiﬁcant at 10%
signiﬁcance level. The t-statistic is —1.78 and R_squared is 85 percent. Using stock
turnover T0 as measure of market liquidity yields same negative relationship but
coefﬁcient of liquidity is more statistically signiﬁcant as t—statistic becomes —2.57.

R_squared increases to 86 percent. This indicates a negative relationship between market

23

liquidity and corporate real investment. At aggregate level, the “market sentiment
hypothesis” is supported because there is a negative investment adjustment after high

liquidity.

To show it graphically, we plot the relationship between liquidity growth rates and
investment growth rates. The pattern is illustrated in Figure 3. As liquidity growth
increases, market level investment growth decreases. This conﬁrms the story suggested at
aggregate level. If liquidity is an indicator of investor sentiment as in the Baker and Stein
(2002), when liquidity increases, managers tend to reduce investment. This suggests that
evidence at aggregate level supports our market sentiment hypothesis. Namely, managers
interpret low stock returns following high liquidity as a consequence of investor
sentiment, and thus tend to reduce investment due to the worse-than-expected business

conditions and lack of certain knowledge about the fundamental value of the risky assets.

Because different economic circumstances provide different investment opportunities,
ﬁrms’ investment decisions could be different throughout business cycles. Accordingly,
we divide sample by business cycles. The results are reported in Table 3. During
recession periods, the coefﬁcients of two liquidity measures are both statistically
insigniﬁcant. During boom periods, however, coefﬁcients of liquidity measures are
negative and statistically signiﬁcant. For example, the t-statistics for coefﬁcients of dollar
liquidity and stock turnover are —2.14 and —2.50, respectively. This seems to imply the
liquidity negatively affects real investment when economy is booming, but has no impact
on real investment when economy is in recession. However, our aggregate results may be

driven by sample bias. The reason is that we have only nine observations for recession

24

years but 30 observations for boom years. In addition, the effect of liquidity on real
investment is currently studied at aggregate level. The true effect at ﬁrm level is still
unclear. We will now turn to an examination of the relationship between liquidity and

corporate investment at ﬁrm level.

1.4.4 Relationship between Firm Liquidity and Corporate Investment at Firm
Level

Looking at relationship between stock liquidity and corporate investment at ﬁrm level,

we ﬁrst study the relationship between dollar liquidity and corporate investment. Later,

we also consider stock turnover as a proxy for liquidity. As previously analyzed, although

the main interest is the inﬂuence of liquidity on investment, other variables are also

included in the regression analysis to make sure the link between liquidity and corporate

investment is robust. The regression speciﬁcation is the following:

3
INVESTMENT}, = C, + f,- + bOLIQUIDITYi,_I + z anit—n +
n=l
13
3 ( )
Z amCF},_m + b4SlackI-t_1 + t + 8,7
m=l

Where INVESTMENT can be [NV], RD or INVZ. LIQUIDITY can be DLIQ or T0. c, is a
time-varying intercept, f, is an individual ﬁxed effect. Because Q and cash ﬂow (CF)
may be persistent throughout time, we also include Q“, Q“, and CF“, CF“,

although many other authors include only one lagged Q variable and one lagged cash

ﬂow variable in their regression models in the literature.

25

Table 4 shows the regression results. The results indicate that, as dollar liquidity
increases, the real investment decreases. The relationship is negative and statistically
signiﬁcant at 1 percent signiﬁcance level (t-statistic is —4.51). All Q variables and cash
ﬂow variables are signiﬁcant, but SLACK variables are not. We also try another liquidity
measure T 0 (stock turnover). The results are similar to the case when the variable DLIQ
is used. Although the magnitude of the t-statistic on coefﬁcient of T 0 drops, it is still
very signiﬁcant (t—statistic=-2.34). In all cases, the evidence suggests a negative
relationship between stock liquidity and real investment. This is consistent with the
market sentiment hypothesis. Firms with high Q values and cash ﬂows tend to invest
more controlling for liquidity. This is consistent with Hubbard (1998) that both Q and
cash ﬂow have explanatory power for corporate investment in an imperfect capital
market. It seems that ﬁnancial slack is not important for real investment when liquidity,

Q and cash ﬂow are all accounted for.

We also study the effect of liquidity on R&D investment. Many authors ﬁnd ﬁrrns’
capital expenditure and R&D expense represent different characteristics of corporate
investment decisions. Basically, real investment represents investment mostly in physical
assets. R&D expense is the investment mostly in intangible assets, thus it is riskier. R&D
investment also implies long-term commitment of a ﬁrm’s growth. For example, see
Griliches (1979), Chan, Martin and Kensinger (1990), Hall (1993). To test the link
between R&D investment and liquidity, we use R&D expense as dependent variable and
run above regression again. The results shown in Table 4A indicate the relationship

between dollar liquidity and R&D is negative, although this relationship is marginally

26

signiﬁcant (t-statistic is -l.68). However, the coefﬁcient of stock turnover is statistically
insigniﬁcant (t-statistic is 0.25, see Table 4B). This shows a weak link between R&D
investment and liquidity. One possible explanation is that R&D investment is usually
ﬁnanced by internal funds, thus the link between R&D investment and market valuation
is relatively weak. For example, see Himmelberg and Petersen (1994), and Hall (1992).'3
Therefore, the link between ﬁnancial market and corporate investment should have no

role of the R&D investment.

Replacing investment IN V 1 with [N V2 generates very similar results. The relationship
between liquidity and total investment is negative and the magnitude of t-statistic of
liquidity increases. For example, the t-statistic of coefﬁcient of DLIQ becomes —5.24,
compared to —4.51 in the case of IN V1 . This shows that as liquidity increases, the total
investment level will decrease. Once again, the market sentiment hypothesis is supported
because managers interpret low stock returns following high liquidity as a consequence
of investor sentiment rather than cheap cost of ﬁnancing, and thus tend to reduce
investment due to the worse-than-expected business conditions and lack of certain
knowledge about the true value of the risky assets. Low-dividend manufacturing ﬁrms
are of special interest for ﬁnancial economists. We restrict to the subsample similar to the
low-dividend manufacturing sample used by Kaplan and Zingales (1997). That is, we use
manufacturing ﬁrms in SIC 2000 to 3999. The results in Table 5A and Table 5B show

that using manufacturing ﬁrms does not change our results, and that the effect of

 

'3 Hirnmelberg and Petersen (1994) show ﬁrms’ R&D spending changes are explained
substantially by internal ﬁnance changes. Using a panel of R&D spending in US.
manufacturing ﬁrms, Hall (1992) concludes that internal funds affect R&D.

27

sentiment is even stronger. For example, the coefﬁcient of dollar liquidity is more

statistically signiﬁcant.

The well-known story of standard Q theory is clearly not supported by our analysis. Our
results report that, in addition to Q, cash ﬂow, and ﬁnancial slack, misvaluation also has
explanatory power for corporate investment. This precisely underlies the empirical
ﬁndings in F erderers (1993) that Q is not the only channel that has explanatory power for
corporate investment decisions. The alternative hypotheses (rational pricing hypothesis,
investor sentiment hypothesis, agency hypothesis and managerial sentiment hypothesis)
are not supported because most of the coefﬁcients of liquidity variables are not positive.
Overall, our results show market valuation plays an important role in corporate
investment, and thus there exists a direct link between ﬁnancial market and corporate
investment through investor sentiment. We also use share liquidity and trading volumes
as robustness checks. Trying different liquidity proxies, however, does not quantitatively

change our results, thus we do not report those results here.

1.4.5 Business Cycle

To investigate the effect of liquidity on investment under different economic
circumstance, we group ﬁrms into two categories: recession and boom. Notice that we
include only one lag for Q or cash ﬂow (CF) because we do not have many consecutive
recession years. The results are provided in Table 4A and Table 48. We ﬁnd that, during
recession periods, the coefﬁcient of dollar liquidity is statistically signiﬁcant for real

investment and the total investment. The coefﬁcients of stock turnover for real

28

investment and total investment are also statistically signiﬁcant (t=-3.01, -2.80,
respectively). Our results suggest that ﬁrm level sentiment can emerge even during
recession periods. To conﬁrm this possibility, we use manufacturing ﬁrms again. We ﬁnd
that, although the sentiment effect is not signiﬁcant for R&D investment, it is very
signiﬁcant for both real investment and total investment during recession period (t=-5.49,
-3.22, respectively). This evidence is further conﬁrmed by trying stock turnover as

liquidity measure.

During boom periods, the relationship between liquidity and real investment is also
negative and statistically signiﬁcant. This is again consistent with our market sentiment
hypothesis. The dollar liquidity has a signiﬁcantly negative impact on real investment but
the effect on R&D is not statistically signiﬁcant. The t-statistics are 41.93 and -l.47,
respectively. The negative coefﬁcient of dollar liquidity for IN V2 (total investment, t=-
4.23) conﬁrms that managers want to reduce total investment when liquidity becomes
high. When using stock turnover, the coefﬁcient of liquidity for real investment becomes
insigniﬁcant (t=—l.34). Using manufacturing subsample generates stronger results. The
coefﬁcients of two liquidity measures are more statistically signiﬁcant (t=-5.70, -2.66,
respectively) for real investment. Again, the coefﬁcients of two liquidity measures are
statistically insigniﬁcant for R&D investment. It is worth noting that the sentiment effect
is stronger when the economy is poor than when it is good. It appears that market
irregularity draws more concern from managers when the economy is had. We propose a
possible explanation for this. The ﬁrm level sentiment may be masked by whole market
sentiment level when overall economy is hot. Nevertheless, the investor sentiment can

gain more attention when economy is cold. This induces managers to believe investor

29

sentiment is more credible in this situation, the consumption and demand would soon

decline to fundamental levels. Hence, managers reduce real investment.

However, we ﬁnd weak evidence showing the relationship between liquidity and R&D
investment is positive during recession period and no relationship between liquidity and
R&D expense during boom period. This may be because managers try to use R&D
investment as long-term commitment of growth during recession periods when they think
the actual business conditions may be worse than expected. In addition, ﬁrms’ R&D
investment is usually ﬁnanced by internal funds; this causes a weak link between R&D
investment and market valuation. From Figure 2, we can observe that the growth of R&D
investment behaves quite differently from that of real investment. This can explain why

the pattern of R&D is different from the pattern of real investment.

The well-known story of standard Q theory is clearly not supported by our analysis. Our
results report that, in addition to Q, cash ﬂow, and ﬁnancial slack, misvaluation also has
explanatory power for corporate investment. This precisely underlies the empirical

ﬁndings in F erderers (1993) that Q is not the only channel that has explanatory power for
corporate investment decisions. The alternative hypotheses (rational pricing hypothesis,
investor sentiment hypothesis, agency hypothesis and managerial sentiment hypothesis)
are not supported because most of the coefﬁcients of liquidity variables are not positive.
Overall, our results show market valuation plays an important role in corporate
investment, and thus there exists a direct link between ﬁnancial market and corporate

investment through investor sentiment. We also use share liquidity and trading volumes

30

 

 

as robustness checks. Trying different liquidity proxies, however, does not quantitatively

change our results, thus we do not report those results here.

1.4.6 Relationship between Firm Liquidity and Corporate Investment When
Different Firm Characteristics Are Considered

We study how different characteristics of ﬁrms affect the investor sentiment effect on

corporate investment. If the effect of market sentiment is particularly important for some

ﬁrms, we would expect it to be more pronounced for the subsample that includes those

ﬁrms. We add more ﬂavors into this article by sorting our data into subsamples based on

relative liquidity, ﬁnancial constraint, leverage, ﬁrm size, BE/ME, and income

performance. After sorting, then we run above regression analysis by using these

subsamples accordingly.

1.4.6.1 Relative Liquidity

Firm level liquidity may be masked by market-level liquidity. If liquidity is a signal of
investment sentiment, then investor sentiment should be stronger for ﬁrms that have
liquidity higher than market-level liquidity. We thus expect that ﬁrms with relatively
higher than market-level liquidity have stronger sentiment effect on corporate investment.
To test this issue, we ﬁrst make a market liquidity index. The market liquidity index is
formed by taking value-weighted average of all ﬁrms in the sample for each year. The
reason why we use value-weighted average is that big ﬁrms should provide more
liquidity to the whole market. The relative liquidity is then computed by dividing each

ﬁrm’s liquidity by market liquidity index. The ﬁrms with a relative liquidity greater than

31

 

 

 

one are thus firms with high liquidity. We sort the ﬁrms by relative liquidity. That is, we
split the sample into two subsamples, one has the top 50 percent relative liquidity, the

other has the bottom 50 percent relative liquidity, and then we run above regressions

again.

Using the top 50 percent relative liquidity, we ﬁnd ﬁrms tend to reduce real investment as

liquidity increases. Table 6 shows that all coefﬁcients of liquidity measures for real
investment (IN V 1 ) are negative and statistically signiﬁcant. The market sentiment effect

is not statistically signiﬁcant for R&D expense regardless which liquidity measure is used

as a proxy for liquidity. Using the bottom 50 percent relative liquidity (results shown in
Table 6), the coefﬁcient of dollar liquidity for real investment is no longer signiﬁcant, but

the coefﬁcient of stock turnover is still signiﬁcant. However, it is not as signiﬁcant as in

the top 50 percent relative liquidity subsample. The results suggest that ﬁrms with high
relative liquidity have stronger sentiment effect. The t-statistic of dollar liquidity for real
investment is —5.00 for ﬁrms with high relative liquidity, but only 0.82 for ﬁrms with low
relative liquidity. The t-statistic of stock turnover for real investment is —3.24 for ﬁrms
with high relative liquidity, and —2.13 for ﬁrms with low relative liquidity. Since some of
the liquidity coefﬁcients are still statistically signiﬁcant for ﬁrms with low relative
liquidity, even if a ﬁrm’s liquidity is relatively lower, managers may want to take market
sentiment into account when they make investment decisions. The evidence shown above

veriﬁes that individual ﬁrm level sentiment is important for corporate decisions.

1 .4.6.2 Financial Constraint

32

 

 

 

As we discussed above, a link between ﬁnancial market and corporate investment has
been established through ﬁnancial constraint. We control for this link to make sure the
link we are investigating is a new one. Our proxy for ﬁnancial constraint is KZ index
based on Kaplan and Zingales (1997). They run a probit regression which models the
probability of ﬁnancial constraint as a function of a ﬁrm’s cash ﬂow, Q, leverage,
dividend payout and cash balance. The ﬁrms with high KZ index are more likely to be
ﬁnancially constrained.M See Lamont et al. (2001), Baker, Stein and Wurgler (2003),
Polk and Sapienza (2002), or Alrneida, Campello and Weisbach (2002) for a similar
approach. We split the sample into two subsamples according to this index (KZ index):
the top 50 percent KZ ﬁrms and the bottom 50 percent KZ ﬁrms. Note that the top 50

percent KZ ﬁrms are more likely to be ﬁnancially constrained.

The results are presented in Table 7. We ﬁnd the coefﬁcients of dollar liquidity or stock
turnover are statistically signiﬁcant for real investment regardless of ﬁnancial constraint.
The t-statistic of dollar liquidity is —3.80 for the subsample with bottom 50 percent KZ
index, and it is —3.68 for the subsample with top 50 percent KZ index. The t-statistic of
stock turnover is —3.95 for the subsample with bottom 50 percent [(2 index, and it is —
2.12 for the subsample with top 50 percent KZ index. Our results show investment-
liquidity sensitivities are strong regardless of presence of ﬁnancial constraints. However,
the evidence on R&D investment is mixed for different liquidity proxies. Using dollar

liquidity, we ﬁnd no effect on R&D investment. Using stock turnover, for the low KZ

 

'4 The KZ index model and coefﬁcients are as follows,

[(22 —l .002 x CashFlow + 0.2826 x Q + 3.14 x Leverage — 39.37 x Dividends — 1.3 15 x CashBalance

33

 

 

index ﬁrms, there is evidence supporting sentiment effect; but for the high KZ index
ﬁrms, we ﬁnd the relationship between R&D investment and liquidity is positive.
Actually, the question whether KZ index is a legitimate proxy for ﬁnancial constraint
itself is very controversial. Finally, trying total investment (INV2) also generates
statistically signiﬁcant sentiment effect. Our results conﬁrm a new link between ﬁnancial

market and corporate investment through sentiment, especially when investors are too

optimistic.

1.4.6.3 Leverage

How can a ﬁrm’s leverage affect our results? Usually, ﬁrms with low leverage have
relatively high portion of equity in the capital structure. Do ﬁrms with more equity care

more about sentiment effect? We want to answer this question in this section. The total

sample is split into two subsamples according to leverage ratio.

Consistent with Stein (1996) and Baker, Stein and Wurgler (2003) --- ﬁrms that are more

dependent on external equity ﬁnance will have investment more sensitive to non-

fundamental components of stock prices, evidence shown in Table 8 conﬁrms that ﬁrms
with low leverage will consider more about market sentiment effect. For the ﬁrms with
low 50 percent leverage, managers tend to reduce real investment when liquidity is high.
The coefﬁcients of dollar liquidity and turnover are both statistically signiﬁcant and
negative (t=~3.90 and t=-2.95, respectively). The coefﬁcients of all liquidity measures for
total investment (IN V2) are also statistically signiﬁcant and negative. However, for the

high 50 percent leverage ﬁrms. the coefﬁcient of stock turnover for real investment is

34

 

 

insigniﬁcant, but the coefﬁcient of dollar liquidity for real investment is still statistically
signiﬁcant. The results conﬁrm that ﬁrms with high portion of equity (low leverage) will
consider sentiment effect when making investment decisions. But there is weak evidence
indicating even ﬁrms with low portion of equity will also consider sentiment in
investment decisions. The evidence for R&D investment is mixed. Using dollar liquidity,
we ﬁnd sentiment effect only for high leverage ﬁrms (t=-3.44). Using stock turnover, we
ﬁnd no sentiment effect on R&D investment. Again, mixed evidence on R&D investment

suggests real investment is the dominant investment variable that is affected by investor

sentiment.

1.4.6.4 Income Performance

Does a ﬁrm’s current income performance affect the effect of sentiment on corporate
investment? Intuitively, if a ﬁrm’s current performance is good, managers may think the
current investment is good, and try to expand output and investment until, at the margin,
earnings on investment return to competitive-equilibrium level. Or, at least, they do not
want to reduce investment, even if the sentiment is high. On the other hand, managers
themselves may not know when earnings on the investment return to competitive-
equilibrium level. Since good performance has induced more investment in the past, they
thus reduce investment whenever sentiment indicates worse-than-expected business
conditions. We test these two views in this section. Our income performance is deﬁned

by dividing ﬁrms’ net income by lagged total assets. We group the sample into two

subsamples by income performance.

35

 

 

 

 

In Table 9, we ﬁnd strong evidence showing ﬁrms with better income performance
actually reduce real investment more signiﬁcantly. The t-statistic of dollar liquidity for
the high 50 percent income performance is —4.45, compared to -3.16 in the case of the
low 50 income performance. The t-statistic of stock turnover is —2.25 for ﬁrms with high
50 percent income performance, compared to —0.79 in the case of the low 50 income
performance. This may be explained by the second view that ﬁrms with better past
income performance are easier to generate investor sentiment in the capital market. In
addition, these ﬁrms have already had more investment in place. In this sense, only ﬁrms
have been doing more investment can reduce investment when sentiment is high.
However, we also ﬁnd evidence that ﬁrms with better income performance are not
willing to reduce R&D investment when sentiment is high. For example, only the
coefﬁcient of dollar liquidity for low performance ﬁrms is signiﬁcantly negative. This
suggests that ﬁrms, which have being doing well, are willing to keep R&D investment

level for long-term growth commitment even sentiment is high.

1.4.6.5 Firm Size

Firm size may also play a role in this context. Big ﬁrms usually are able to do more real
investment, but small ﬁrms usually have to do more R&D investment to gain market
shares. This can be seen in our sample. Overall, big ﬁrms have average of 0.098 in real
investment and 0.038 in R&D investment. Small ﬁrms have average of 0.083 in real
investment but 0.042 in R&D investment. As a result, if market sentiment really matters

for both types of investment, it would affect big ﬁrms more on real investment but small

ﬁrms more on R&D investment.

36

 

The results shown in Table 10 support above supposition. Regardless which liquidity
measure we use, big ﬁrms try to reduce real investment but small ﬁrms try to reduce
R&D investment when investor sentiment becomes high. For big ﬁrms, the coefﬁcients
of liquidity for real investment are all negative and statistically signiﬁcant (t=-5.59, t=-
2.55, dollar liquidity and turnover, respectively). For small ﬁrms, the coefﬁcients of
liquidity for R&D investment are all negative and statistically signiﬁcant (t=-3.25, t=~
2.28, dollar liquidity and turnover, respectively). Overall, the sentiment effect is stronger

for ﬁrms with bigger size.

The statistically signiﬁcant relationship between R&D investment and liquidity should
not be surprising for small ﬁrms, although we usually document a weak link between
R&D investment and market valuation in other cases. The reason for this is that small
ﬁrms try to use R&D investment to gain market shares, since their internally generated
fund (cash ﬂow) is usually relatively small (in our sample, small ﬁrms’ cash ﬂow is
0.080, compared to big ﬁrms’ 0.126), thus they need to rely on external funds to ﬁnance
R&D investment. Therefore, the relationship between R&D investment and market

valuation for small ﬁrms are relatively strong.

1.4.6.6 Book-to—Market (BE/ME) Ratio

Finally, we study how book-to-market (BE/ME) ratio affects our hypothesis. We ﬁnd
ﬁrms with low BE/ME are more signiﬁcantly affected by sentiment effect (See Table 11).

For low BE/ME ﬁrms, the t-statistic of dollar liquidity is —5.40, but it is only —l.34 for

37

high BE/ME ﬁrms. Using stock turnover conﬁrms our conclusion. The t-statistic is —3.98
for low BE/ME ﬁrms but —0.71 for high BE/ME ﬁrms. This is because that low BE/ME
ﬁrms usually have better growth opportunities and thus have been investing more. We
ﬁnd no evidence that sentiment effect affects ﬁrms’ R&D investment for either high

BE/ME ﬁrms or low BE/ME ﬁrms.

1.4.7 Tests of Joint Effects

From the above analysis, we know relative liquidity, leverage, income performance, ﬁrm
size, and book-to-market ratio all play important roles. They may interact with each
other. Some joint effects related to relative liquidity are analyzed a examples. We study
how relative liquidity interacts with leverage, BE/ME, income performance and ﬁnancial
constraints on the market sentiment hypothesis. Then we brieﬂy study joint effect of
leverage and BE/ME, and joint effect of ﬁrm size and income performance. Other
combinations of variables can also be tested. For simplicity, those results are not reported

in the article.

For each joint effect, we split the sample into four subsamples based on variables of
interest. For example, to test the joint effect of relative liquidity and leverage, we ﬁrst
split the sample into two subsamples according to relative liquidity (top 50 percent vs.
bottom 50 percent), then for each subsample, we further split it into two subsamples
according to leverage. Therefore, in total we use four subsamples: the bottom 50 percent
relative liquidity and the bottom 50 percent leverage, the bottom 50 percent relative

liquidity and the top 50 percent leverage, the top 50 percent relative liquidity and the

38

bottom 50 percent leverage, the top 50 percent relative liquidity and the top 50 percent

leverage.

We ﬁnd the ﬁrms with high relative liquidity and low leverage have strongest market
sentiment effect. The results are reported in Table 12. The t—statistic of dollar liquidity is
—3.75 and that of turnover is —3.39. This is consistent with our previous results. Because
the coefﬁcient of dollar liquidity is always statistically signiﬁcant and negative for the
ﬁrms with high relative liquidity regardless of the leverage, it seems ﬁrms consider
relative liquidity more than leverage when sentiment is high. However, this result is not
supported by stock turnover. We think this may be due to the difference in our liquidity
measures. The results about R&D investment is mixed. The joint effect of relative
liquidity and income performance is more interesting. For real investment, 'when we use
dollar liquidity, there is strong sentiment effect for the ﬁrms with high relative liquidity
regardless of income performance (t=-3.02, t=-5.09, low income performance and high
income performance, respectively). But when we use stock turnover, we ﬁnd strong
sentiment effect for the ﬁrms with high income performance regardless of relative
liquidity (t=-2.29, t=-2.3 8, low relative liquidity and high relative liquidity, respectively).
It appears that managers who consider transaction cost feature of liquidity would consider
relative liquidity more than income performance when sentiment is high. While managers
who consider trading activity feature of liquidity would consider income performance
more than relative liquidity. Our results also show managers try to consider relative
liquidity more than ﬁnancial constraint when sentiment is high. Because no matter which
liquidity measure we use and no matter ﬁnancial constraint is high or low, the

coefﬁcients of liquidity for real investment are always signiﬁcantly negative for the ﬁrms

39

with high relative liquidity. Finally, we ﬁnd strong market sentiment effect for the ﬁrms
with high relative liquidity and low BE/ME. The evidence is supported by both dollar

liquidity and stock turnover. See Table 13, Table 14 and Table 15 for the results.

Table 16 shows the relationship between liquidity and real investment is negative and
statistically signiﬁcant for subsample with low leverage and low BE/ME. Using dollar
liquidity, the t-statistic of the coefﬁcient is —3.72. Using stock turnover, the t-statistic is —
4.35. We also ﬁnd the joint effect of income performance and BE/ME is the most
signiﬁcant for subsample with high income performance and low BE/ME. When dollar
liquidity is used, only ﬁrms with high income performance and high BE/ME will
signiﬁcantly reduce R&D investment when sentiment is high. Table 17 reports that the
ﬁrms with better income performance and bigger ﬁrm size will signiﬁcantly reduce the
real investment when sentiment is high. However, for most of the joint effect regressions,
the effect of investor sentiment on R&D investment is weak. Above results are all
conﬁrmed by both liquidity measures. Similarly, the other joint effects can also be
analyzed. Overall, we ﬁnd the evidence that is consistent with our previous results in

subsamples. We conﬁrm that different variables do offset with each other.

1.4.8 Portfolio Approach with Different Benchmark

We use a portfolio approach to further conﬁrm that the market overvaluation does have
effect on corporate investment decisions. We want to use the portfolio approach because
it is less likely to be affected by outliers. For example, if only a few ﬁrms have very high

liquidity and consequently reduce investment signiﬁcantly, it is possible that our results

40

are mainly driven by those ﬁrms. In addition, we want to try different benchmarks for
subsample selections. Previously, we simply split the whole sample into two subsamples
(top 50 percent and bottom 50 percent). Now we form portfolios based on NYSE stocks.
Like many other authors, we only consider the portfolio formed based on size and book-
to-market (BE/ME) ratio in this article. The portfolios based on other variables should
exhibit similar patterns. As we mentioned above, four portfolios (S/L, S/H, B/L and B/H)
are formed yearly from a simple sort of ﬁrms into two group on market equity value
(ME) and another sort into two groups on BE/ME. Two BE/ME groups are based on the
breakpoints for the bottom 50 percent (low) and the top 50 percent (high). We regress
investment variables on liquidity variables, Q variables, cash ﬂow variables, and ﬁnancial
slack. Because there is no clear time trend in investment variables, we do not include a

time variable in the time-series regressions using portfolios.

The results are presented in Table 18A and Table 188. We ﬁnd sentiment effect is not
trivial because more than two portfolios (out of four portfolios) exhibit strong sentiment
effect. Using dollar liquidity, we ﬁnd three portfolios S/L, B/L and B/H have strong
market sentiment effect but portfolio B/L has the strongest. For example, the coefﬁcient
of dollar liquidity for real investment is —6.04 for the portfolio B/L. This is consistent
with our previous results. Namely, ﬁrms in the portfolio with big size and low BE/ME
are most likely to reduce real investment when sentiment is high. Signiﬁcant sentiment
effect in portfolios 5/1. and B/H may be due to either big size or low BE/ME. Using stock
turnover as liquidity measure, we also ﬁnd the strongest market sentiment effect in big

ﬁrms with low BE/ME. The t-statistic for real investment is —3.05 for the portfolio B/L.

41

Overall, our portfolio results support our market sentiment hypothesis for ﬁrms with big

size and low BE/ME.

However, we do not ﬁnd evidence showing there exists sentiment effect on ﬁrms’ R&D
investment. We ﬁnd ﬁrms with small size and low BE/ME tend to increase R&D
investment. This may be explained by the fact that most of the R&D investment is
ﬁnanced by internal funds, thus the relationship between R&D investment and valuation
is relatively weak. We also use manufacturing ﬁrms to form portfolios based on ﬁrm size
and BE/ME. Our results show even stronger sentiment effect for the portfolio with big
ﬁrms and low BE/ME. For dollar liquidity, the t-statistic is —7.49, compared to —6.04 in
previous case. For stock turnover, t-statistic is —5.03, compared to —3.05 in previous case.
Again, we ﬁnd no evidence supporting the existence of market sentiment effect on

manufacturing ﬁrms’ R&D investment. The results are not reported in this article.

1.4.9 Considering All Relevant Variables in One Regression

Finally, we consider all relevant variables in one regression speciﬁcation. The purpose of
doing this is to assure our stock liquidity measures do not capture other things such as
income performance, size, etc. The dependent variables are three investment measures.
The independent variables include lagged liquidity, Q variables, cash ﬂow variables,
lagged ﬁnancial slack, lagged business cycle, lagged income performance, lagged
leverage, lagged size, lagged BE/ME, lagged KZ index, and a time trend. The regression

model is the following.

42

3
INVESTMENT” = c, + f,— + bOLIQUIDITI’i,_1 + Z b,,Q,-,_,, +

n2]
3 14
Z email—m + b4Slack,-,_1 + dlcrc,,_1 + ( )
m=l

dZINCPP},_1 + d3LEVR,~,_1 + d4SIZE,-,_1 +
dsBEMEi,_1-t- d6KZit—l + t + 8,"

Table 19 shows the results. Using dollar liquidity as a measure of liquidity, we ﬁnd
signiﬁcant negative relationship (t=-6.l6) between real investment and stock liquidity
after controlling for business cycle, income performance, leverage, size, BE/ME, and K2
index. Using stock turnover yields identical results (t=-2.79 for the coefﬁcient of stock
turnover). The evidence suggests that liquidity measures do not capture other variables
such as income performance, size, leverage, BE/ME, or KZ index. This implies that the
link we documented in this article is the link between market sentiment and corporate

investment.

1.5 Conclusions

In this article, we ﬁnd a signiﬁcant negative relationship between stock liquidity and
corporate investment. The negative relationship is consistent with the “market sentiment
hypothesis” that managers tend to interpret low stock returns following high liquidity as a
consequence of investor sentiment, they thus reduce investment due to worse-than-
expected business conditions and lack of certain knowledge about the fundamental value
of the risky assets. Our results are consistent with the ﬁndings in Baker and Stein, Glaser

and Weber (2003), and Statman, Thorley, and Vorkink (2004). All of them ﬁnd a positive

43

link between sentiment and trading volume or stock liquidity. Other hypotheses that
predict a positive relationship between stock liquidity and corporate investment are not

supported by our data.

The evidence shown in this article indicates a direct link between ﬁnancial market
valuation and corporate investment through sentiment channel. However, this channel is
different from the one documented in ﬁnancial constraint literature (e. g. Baker, Stein, and
Wurgler (2003)), in which the ﬁnancial constraint positively links market valuation to
corporate investment when managers have to forego some investments due to low
valuation, otherwise it would be too costly for ﬁrms to raise funds in ﬁnancial market.
However, when market valuation improves, corporate investment also increases. Our
results indicate misvaluation, especially optimistic investor sentiment, can negatively link

market valuation to corporate investment.

Further, we split sample into subsamples according to business cycle, relative liquidity,
ﬁrm size, book-to-market, income performance, ﬁnancial constraint, and leverage. We
ﬁnd that the negative relationship between stock liquidity and corporate investment is
more signiﬁcant during the recession periods, and for the ﬁrms with higher relative
liquidity (liquidity that is higher than market level liquidity). The bigger ﬁrms or the
ﬁrms with lower book-to-market (BE/ME) ratios have more signiﬁcant relationship too.
Also noteworthy is that the ﬁrms with lower leverage ratios exhibit more signiﬁcant

relationship.

However, our results appear to suggest that the relationship between liquidity and R&D

investment is not signiﬁcant. This can be explained by the fact that most of the R&D

44

investment is usually ﬁnanced by internal funds, thus the relationship between R&D
investment and ﬁnancial market valuation is relatively weak. In addition, because ﬁrms
tend to use R&D investment to make long-term commitment of growth, this also makes
the impact of sentiment on R&D investment small. Finally, Chordia, Shivakumar and
Subrahmanyam (2000) ﬁnd there exists a channel whereby stock returns are inﬂuenced
by lagged information: through the inﬂuence of information on liquidity. We document
the evidence of a similar channel whereby investment growth can also be affected by
lagged information through the inﬂuence of information on liquidity.l5 This is consistent
with the ﬁndings in Fama and Gibbons (1982), and Cochrane (1991) that equity returns
and investment growth behave alike. Li (2004) conﬁrms this argument by documenting a
signiﬁcant decline in investment growth following seasoned equity or debt offerings,

which are usually associated with a signiﬁcant liquidity run-up prior to offerings.

Even if it were possible to conclusively establish a link between stock liquidity and
corporate investment, whether these two phenomena are causally linked would remain a
matter of speculation. However, should the investors be incapable to identify the actual
purpose of managers, or should the decisions be considered necessary by the managers in
the name of investor interest, there exists evidence saying that managers tend to be
cautious when market becomes hot, especially given the irreversible nature of most

corporate investment.

 

'5 Our channel is different from the one in Chordia, Shivakumar and Subrahmanyam
(2000). They focus on the compensation demanded by investors for illiquidity. We focus
on the consequence of investor sentiment on managers’ investment decisions. However,
both channels are through stock liquidity.

45

Finally, our results are different from the evidence shown in Polk and Sapienza (2002).
Often research opinion may be ambiguous or divided on issues, while differences in
research opinions and empirical evidence may exist concerning the appropriate standards
of testing methods and suitable proxies. Polk and Sapienza use three proxies of investor
sentiment to show a positive relationship between sentiment and corporate investment.
However, their two proxies -- discretionary accruals and new equity issues — are both
based on managers’ decisions instead of investors’ decisions. Arguably, since investor
sentiment is directly reﬂected in the capital market, good proxies should be the ones that
are closely related to the market factors. In this sense, Polk and Sapienza might actually
test the relationship between managerial sentiment and corporate investment. Their
results could thus be interpreted to be consistent with our “managerial sentiment
hypothesis”. But, even though, it is still possible that they documented a link between
corporate investment and other things, because there is no theory explaining discretionary
accruals and new equity issues are the proxies for managerial sentiment, although they

are both related to managers’ decisions.

46

APPENDIX IA

Tables and Figures

47

Table 2: Summary statistics of variables

Q is deﬁned as the market value of equity plus assets minus book value of equity over assets, that is, CRSP
market value of equity plus COMPUSTAT data 6 minus the sum of data 60 and data 74 over data 6, where
data 60 is common equity and data 74 is deferred taxes on balance sheet. F inn’s cash ﬂow (CF) equals the
sum of earnings before extraordinary items (data 18) and depreciation (data 14) over year beginning assets
(lagged data6). There are two measures of liquidity: DLIQ and T0. Variable DLIQ is obtained by taking
average of ratio of daily volume (dollar volume) to daily absolute return for each year and each ﬁrm. DLIQ
is scaled by 1,000,000. T0 is stock turnover. There are three measures of corporate investment. Variable
IN V] is calculated by dividing capital expenditure (COMPUSTAT data 128) by year beginning total assets
(lagged data 6). IN V2 is calculated by dividing sum of capital expenditure and ﬁrm’s research and
development expense (data 46) by year beginning total assets (lagged data 6). RD is research and
development (data 46) divided by total assets (data 6). ME is the market value of equity. BE/ME is common
equity (data 60) plus deferred taxes (data 74) and then divided by market value of equity. SLA CK]
(ﬁnancial slack) is the ratio of cash and short-term investments (data 1) and sales (data 12). INCPF is a
ﬁrm’s income performance, which is deﬁned as net income (data 13) divided by year beginning assets (data
6). LEV is a ﬁrrn’s leverage ratio, it is sum of a ﬁrm’s debt (data 9 plus data 34) divided by sum of debt and
shareholders’ equity (data 216). KZ index is calculated by using following equation,

KZ= —l .002 x CashFlow + 0.2826 x Q + 3.14 x Leverage — 39.37 x Dividends — 1.315 x CashBaIance

 

 

 

ALL SAMPLE
Variable Obs Mean Std. Dev. Min Max
inv1 26346 0.08797 0.10775 0 3.016742
rd 12529 0.040812 0.062897 0 1.776369
inv2 12327 0.11972 0.112322 0 3.215579
dliq 25846 646.4528 4949.075 0.001842 2439648
to 25846 2.277863 3.324645 0.006217 304.0041
q 26966 1.379733 0.916868 0.156812 23.54966
cf 26966 0.09615 0.113317 -1.78163 5.742172
kz 26146 -2.49106 6.006054 -75.4523 8.479383
incpf 26963 0.153526 0.130414 -1.17572 3.373444
Iev 26152 0.355987 0.248919 0 2.447893
me 26966 1761.49 10691.25 0.625446 5190388
BE/ME 26106 0.964574 0.701311 0.000917 9.659767
slack1 26430 0.143673 0.322187 0 6.319299

 

48

Table 3: Aggregate level liquidity and aggregate level investment

Aggregate level investment is obtained from Federal Reserve Economic Data (FRED) database. The data include
quarterly Real Gross Private Domestic Investment level between 1947 and 2001, all numbers are seasonally adjusted
and scaled by 1996 dollar value. The business cycle data are obtained from National Bureau of Economic Research
(NBER). Market liquidity is obtained by taking average of all ﬁrm’s liquidity for each year. We consider two liquidity
measures: dollar liquidity and stock turnover. See Table l for more variable deﬁnition. Robust T-statistic is in the
parenthesis. ‘, ”, and "‘" represent signiﬁcance at the 1 percent, 5 percent and 10 percent levels respectively.

Aggregate Real Investment

All Recession Boom

 

dollar liquidity -0.00018 dollar liquidity 0.00016 dollar liquidity -0.00029

 

 

 

 

(-1.78)' (1 .72)‘ (-2.14)**
t -0.062 1 -0.0978 t -0.0563
(-7.78) (-8.22) (45.58)
intercept 6.694 intercept 9.57 intercept 6.217
(10.58) (9.84) (9.48)
Obs 39 Obs 9 Obs 30
R_sq rd 85% R_sq rd 93% R_sq rd 85%
Aggregate Real Investment
All Recession Boom
stock turnover -0.256 stock turnover 0.0996 stock turnover -0.285
(4.57)“ (0.50) (4.50)”
t -0.051 t -0.096 t -0.048
(-5.50) (4.65) (-5.12)
intercept 6.256 intercept 9.298 intercept 6.029
(10.29) (7.10) (10.15)
Obs 39 Obs 9 Obs 30
R_sq rd 86% R_sq rd 92% R_sq rd 85%

49

Table 4: The relationship between liquidity and investment at ﬁrm level

Q is deﬁned as the market value of equity plus assets minus book value of equity over assets, that is, C RSP market
value of equity plus COMPUSTAT data 6 minus the sum of data 60 and data 74 over data 6. where data 60 is common
equity and data 74 is deferred taxes on balance sheet. Firm’s cash ﬂow (CF) equals the sum of earnings before
extraordinary items (data 18) and depreciation (data 14) over year beginning assets (lagged data6). There are two
measures of liquidity: DLIQ and T0. Variable DLIQ is obtained by taking average of ratio of daily volume (dollar
volume) to daily absolute return for each year and each ﬁrm. DLIQ is scaled by 1,000,000. T0 is stock turnover. There
are three measures of corporate investment. Variable IN V] is calculated by dividing capital expenditure
(COMPUSTAT data 128) by year beginning total assets (lagged data 6). INV2 is calculated by dividing sum of capital
expenditure and ﬁrm’s research and development expense (data 46) by year beginning total assets (lagged data 6). RD
is research and development (data 46) divided by total assets (data 6). SLACK] (ﬁnancial slack) is the ratio of cash and
short-term investments (data 1) and sales (data 12); CASH (cash balance) is the ratio ofcash and short-term investments

(data 1) and assets (data 6). Following investment speciﬁcation is estimated.

3
INVESTME.V7',-, = c, +1; + bOLlQUIDITYi,_1 + z b,,Q,-,_,, +

n=l

3
z OHICFII“m +b4SlaCkit_1+I+8it

mzl

Where INVESTMENT can be [NV]. RI) or INV2. LIQUIDITY can be DILQ or T0. C, is a time-varying intercept, f,
is an individual ﬁxed effect. Because Q and CF may be persistent, we also include Q”_2, Qm3 , and CFu-z ,

CF”_3. Slack can be either SLACK] or CASH. We also test above investment speciﬁcation for under different

economic situations. Business cycle data are from NBER. We use two procedures to identify recession years. We treat
one speciﬁc year as “recession” if at least two quarters are classiﬁed as “trough” by NBER. Otherwise, we treat the
year as “normal or boom”. Alternatively, we identify recession years by using BAA-AAA bond default spread. If the
spread in one year is higher than the average spread in past 40 years. then this year is classiﬁed as a recession year.
This procedure generates very similar recession years as the other approach does. Recession years are 1960, 1970,
1974, 1975, I980, 1981, 1982, 1990, 1991, and 2001. Boom years are years between 1962 and 2001 but except
recession years. Standard error robust T-statistic is in the parenthesis. *. **, and *" represent signiﬁcance at the 1

percent, 5 percent and 10 percent levels respectively.

 

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54

Table 6: Relationship between liquidity and investment (Relative Liquidity)

We split sample into two subsamples, one has all ﬁrms with top 50 percent relative liquidity; the other one has all ﬁmis
with bottom 50 percent relative liquidity. Relative liquidity is calculated by dividing each ﬁnn's liquidity by market
liquidity. The market liquidity is formed by taking value-weighted average of all ﬁrms in the sample for each year.
Variable DLIQ is obtained by taking average of ratio of daily volume (dollar) to daily absolute return for each year and
each ﬁrm. DLIQ is scaled by 1,000,000. Variable T0 is stock turnover. There are three measures of corporate
investment. Variable INVI is calculated by dividing capital expenditure (COMPUSTAT data 128) by year beginning
total assets (lagged data 6). INV2 is calculated by dividing sum of capital expenditure and ﬁrm’s research and
development expense (data 46) by year beginning total assets (lagged data 6). RD is research and development (data
46) divided by total assets (data 6). Other independent variables are Q, CF . SLACK (Coefﬁcients and t-statistics are not
reported for simplicity). Standard error robust T-statistic is in the parenthesis. *, ", and ”"' represent signiﬁcance at
the 1 percent, 5 percent and 10 percent levels respectively.

Panel A: Dollar Liquidity

 

 

 

 

Bottom 50% Relative Liquidity TOP 50% Relative Liquidity
inv1 rd inv2 inv1 rd inv2
dliqlag 0.0001 -0.0002 -0.0002 dliqlag -1.6e-6 -1.4e-7 -1.9e-6
(0.82) (-2.55)** (-1 .19) (-5.00)*** (-0.67) (4.79)“
Obs 7896 371 1 3655 Obs 10123 5496 5401
R_sq rd 6% 8% 8% R_sq rd 1 1% 1 % 4%

Panel B: Stock Turnover

 

 

 

 

Bottom 50% Relative Liquidity TOP 50% Relative Liquidity
inv1 rd inv2 inv1 rd inv2
tolag -0.004 0.0008 -0.002 tolag -0.002 -0.00003 -0.002
(-2.1 3)“ (1 .28) (-0.86) (-3.24)*** (-0.07) (-3.12)***
Obs 8333 4125 4046 Obs 9588 5088 5018
R_sqrd 9% 14% 17% R_sqrd 8% 4% 2%

55

Table 7: Relationship between liquidity and investment (Financial Constraint: KZ
index)

We construct an index of ﬁrm ﬁnancial constraints based on Kaplan and Zingales (1997) and sort ﬁrms according to
this index (KZ index). [(2 index is a measure of ﬁnancial constraints based on ﬁve variables: Tobin’s Q. leverage, cash
flow, cash balance and dividends. Then we compare the effect of liquidity on investment. Variable DLIQ is obtained by
taking average of ratio of daily volume (dollar) to daily absolute return for each year and each ﬁrm. DLIQ is scaled by
1,000,000. Variable T0 is stock tumover. There are three measures of corporate investment. Variable INVI is
calculated by dividing capital expenditure (COMPUSTAT data 128) by year beginning total assets (lagged data 6).
IN V2 is calculated by dividing sum of capital expenditure and ﬁrm's research and development expense (data 46) by
year beginning total assets (lagged data 6). RD is research and development (data 46) divided by total assets (data 6).
Other independent variables are Q, CF, SLACK (Coefﬁcients and t-statistics are not reported for simplicity). Standard
error robust T-statistic is in the parenthesis. ‘, *"', and ""' represent signiﬁcance at the 1 percent, 5 percent and 10
percent levels respectively.

 

Panel A: Dollar Liquidity

 

 

 

 

Bottom 50% K2 Top 50% KZ
inv1 rd inv2 inv1 rd inv2
dliqlag -1.1e-6 -2.4e-7 -1.3e-6 dliqlag -3.0e-6 -9.7e-7 -4.1e-6
(-3.80)*** (-1 .36) (-3.34)*** (-3.68)*** (-1 .51 ) (-3.67)**‘*
Obs 8649 3971 3894 Obs 9259 5194 5120
R_sq rd 1 2% 1 8% 1 0% R_sq rd 1 6% 9% 1 %

 

Panel B: Stock Turnover

 

 

 

 

Bottom 50% K2 Top 50% KZ
inv1 rd inv2 inv1 rd inv2
tolag -0.002 -0.001 -0.003 tolag -0.001 0.002 -0.00002
(-3.95)*** (-5.06)*** (-5.98)*** (-2.12)** (3.13)“ (-0.02)
Obs 9079 4165 4088 Obs 8737 5008 4936
R_sq rd 1 3% 1 2% 5% R_sq rd 1 5% 2% 2%

 

56

Table 8: Relationship between liquidity and investment (Leverage)

The leverage is deﬁned as the sum of long-term debt (data 9) and debt in current liabilities (data 34) divided by the sum
of long-term debt (data 9), debt in current liabilities (data 34) and total stockholders’ equity (data 216). Variable DLIQ
is obtained by taking average of ratio of daily volume (dollar) to daily absolute retum for each year and each ﬁrm.
DLIQ is scaled by 1.000.000. Variable T0 is stock turnover. There are three measures of corporate investment.
Variable IN V] is calculated by dividing capital expenditure (COMPUSTAT data 128) by year beginning total assets
(lagged data 6). INV2 is calculated by dividing sum of capital expenditure and ﬁrm’s research and development
expense (data 46) by year beginning total assets (lagged data 6). RD is research and development (data 46) divided by
total assets (data 6). Other independent variables are Q, CF, SLACK (Coefﬁcients and t-statistics are not reported for
simplicity). Standard error robust T-statistic is in the parenthesis. *, ”, and *" represent signiﬁcance at the 1 percent.
5 percent and 10 percent levels respectively.

Panel A: Dollar Liquidity

 

 

 

 

Bottom 50% leverage Top 50% leveragLe
inv1 rd inv2 inv1 rd inv2
dliqlag -1.60-6 2.1e—7 -1.1e-6 dliqlag -3.0e-6 -1.4e-6 -5.0e-6
(43-90)“ (0.78) (-2-32)** (-5.45)*** (-3.44)*** (-6.25)***
Obs 9280 5213 5142 Obs 8635 3955 3875
R_sqrd 8% 2% 2% R_sqrd 12% 5% 7%

 

Panel B: Stock Turnover

 

 

 

 

Bottom 50% leverage Top 50% leverage
inv1 rd inv2 inv1 rd inv2
tolag -0.001 -0.0001 -0.002 tolag -0.0009 0.0003 -0.0008
(-2.95)*** (-0.30) (-3.24)*** (-1 .45) (0.68) (-1 .04)
Obs 8957 51 38 5068 Obs 8865 4037 3958
R_sq rd 9% 3% 1 % R_sq rd 1 0% 4% 6%

 

57

Table 9: Relationship between liquidity and investment (Income Performance)

Income Performance (INCPF) is a ﬁrm’s income performance, which is deﬁned as net income (data l3) divided by
year beginning assets (data 6). Variable DLIQ is obtained by taking average of ratio of daily volume (dollar) to daily
absolute return for each year and each ﬁrm. DLIQ is scaled by l,OO0.000. Variable T0 is stock turnover. There are
three measures of corporate investment. Variable [NV I is calculated by dividing capital expenditure (COMPUSTAT
data 128) by year beginning total assets (lagged data 6). IN V2 is calculated by dividing sum of capital expenditure and
ﬁrm’s research and development expense (data 46) by year beginning total assets (lagged data 6). RD is research and
development (data 46) divided by total assets (data 6). Other independent variables are Q, CF, SLACK (Coefﬁcients
and t-statistics are not reported for simplicity). Standard error robust T-statistic is in the parenthesis. *, ", and "*
represent signiﬁcance at the 1 percent, 5 percent and 10 percent levels respectively.

Panel A: Dollar Liquidity

 

 

 

 

Bottom 50% income performance Top 50% income performance
inv1 rd inv2 inv1 rd inv2
dliqlag -1.9e-6 -1.9e-6 -4.6e-6 dliqlag -1.99-6 3.5e-8 -1.7e-6
(-3.16)*** (-2.94)*** (4.76)“ (4.45)“ (0.20) (-3.92)***
Obs 8601 41 73 4088 Obs 941 6 5032 4966
R_sqrd 4% 0.3% 0.3% R_sqrd 4% 9% 6%

 

Panel B: Stock Turnover

 

 

 

 

Bottom 50% income performance Top 50% income performance
inv1 rd inv2 inv1 rd inv2
tolag -0.0004 0.0005 -0.001 tolag -0.001 0.0004 -0.001
(-0.79) (1.10) (-1.41) (-2.27)** (1.44) (-1.79)*
Obs 8887 4321 4237 Obs 9031 4889 4824
R_sq rd 5% 3% 1 % R_sq rd 3% 1 2% 6%

 

58

Table 10: Relationship between liquidity and investment (Firm Size)

Firm size is measured by a ﬁrm's market equity. Variable DLIQ is obtained by taking average of ratio of daily volume
(dollar) to daily absolute return for each year and each ﬁrm. DLIQ is scaled by 1.000.000. Variable T0 is stock
turnover. There are three measures of corporate investment. Variable [NV] is calculated by dividing capital expenditure
(COMPUSTAT data l28) by year beginning total assets (lagged data 6). INV2 is calculated by dividing sum of capital
expenditure and ﬁrm’s research and development expense (data 46) by year beginning total assets (lagged data 6). RD
is research and development (data 46) divided by total assets (data 6). Other independent variables are Q. CF, SLACK
(Coefﬁcients and t-statistics are not reported for simplicity). Standard error robust T-statistic is in the parenthesis. ‘, ”_,
and "" represent signiﬁcance at the l percent, 5 percent and 10 percent levels respectively.

Panel A: Dollar Liquidity

 

 

 

 

Bottom 50% firm size Top 50% firm size
inv1 rd inv2 inv1 rd inv2
dliqlag -0.00002 -0.00001 -0.00003 dliqlag -1.Te-6 5.2e-8 -1.9e-6
(-2.55)** (-3.25)*** (-3.22)*** (-5.59)*** (0.17) (--4.54)*"”“r
Obs 10477 5047 4972 Obs 7542 4160 4084
R_sq rd 6% 1 8% 9% R_sq rd 14% 6% 5%

 

Panel B: Stock Turnover

 

 

 

 

Bottom 50% firm size Top 50% firm size
inv1 rd inv2 inv1 rd inv2
tolag -0.0007 -0.0008 -0.0016 tolag -0.0015 0.002 0.0007
(-1 .30) (-2.28)** (-2.44)** (-2.55)** (5.81 )*** (1 .02)
Obs 10975 5264 5185 Obs 6946 3949 3879
R_sqrd 6% 18% 9% R_sq rd 13% 1% 5%

 

59

Table 11: Relationship between liquidity and investment (BE/ME)

Book-to-market ratio (BE/ME) is common equity (data 60) plus deferred taxes (data 74) and then divided by market
value of equity. Variable DLIQ is obtained by taking average of ratio of daily volume (dollar) to daily absolute return
for each year and each ﬁrm. DLIQ is scaled by 1,000,000. Variable T0 is stock turnover. There are three measures of
corporate investment. Variable INVI is calculated by dividing capital expenditure (COMPUSTAT data 128) by year
beginning total assets (lagged data 6). IN V2 is calculated by dividing sum of capital expenditure and ﬁrm’s research
and development expense (data 46) by year beginning total assets (lagged data 6). RD is research and development
(data 46) divided by total assets (data 6). Other independent variables are Q, CF, SLACK (Coefﬁcients and t-statistics
are not reported for simplicity). Standard error robust T-statistic is in the parenthesis. ‘, ", and "" represent
signiﬁcance at the 1 percent. 5 percent and 10 percent levels respectively.

ELLE] A: Dollar Liquidity

 

 

 

 

Bottom 50% BEIME Top 50% BEIME
inv1 rd inv2 inv1 rd inv2
dliqlag -2.2e-6 -3.0e-7 -2.4e-6 dliqlag -1.Be-6 -4.69-7 -1.4e-6
(-5.40)*** (-0.97) (4.67)” (-1 .34) (-1 .70)‘ (-1 .55)
Obs 8785 4798 4741 Obs 9234 4409 4315
R_sqrd 6% 4% 2% R_sq rd 14% 6% 13%

 

Panel B: Stock Turnover

 

 

 

 

Bottom 50% BEIME Top 50% BEIME
inv1 rd inv2 inv1 rd inv2
tolag -0.0022 0.0006 -0.003 tolag -0.0004 -0.0001 -0.0008
(-3.98)*** (1 .30) (-3.39)*** (-0.71) (0.48) (-1.10)
Obs 8537 4719 4658 Obs 9384 4494 4406
R_sqrd 6% 10% 1 % R_sqrd 1 2% 6% 1 1%

 

6O

Table 12: Joint effect of relative liquidity and leverage

Relative liquidity is calculated by dividing each ﬁrm‘s liquidity by market liquidity. The market liquidity is formed by
taking value-weighted average of all ﬁrms in the sample for each year. The leverage is deﬁned as the sum of long—term
debt (data 9) and debt in current liabilities (data 34) divided by the sum of long-term debt (data 9), debt in current
liabilities (data 34) and total stockholders' equity (data 216). There are four subsamples based on relative liquidity and
leverage. Variable DLIQ is obtained by taking average of ratio of daily volume (dollar) to daily absolute return for each
year and each ﬁrm. DLIQ is scaled by 1,000,000. Variable T 0 is stock turnover. There are three measures of corporate
investment. Variable IN V I is calculated by dividing capital expenditure (COMPUSTAT data 128) by year beginning
total assets (lagged data 6). INV2 is calculated by dividing sum of capital expenditure and ﬁrm’s research and
development expense (data 46) by year beginning total assets (lagged data 6). RD is research and development (data
46) divided by total assets (data 6). Other independent variables are Q, CF, SLACK (Coefﬁcients and t-statistics are not
reported for simplicity). Standard error robust T-statistic is in the parenthesis. *, “2 and "‘" represent signiﬁcance at
the l percent, 5 percent and 10 percent levels respectively.

Panel A: Dollar Liquidity

Bottom 50% relative liquidity and Bottom 50%

 

 

 

 

 

Bottom 50% relative liquidity and Top 50%

 

 

 

 

 

leverage leverage
inv1 rd inv2 inv1 rd inv2
dliqlag 0.0001 -0.0002 -0.0001 dliqlag -0.0002 -0.0003 -0.0006
(0.79) (-1.73)' (-0.65) (-0.95) 02.35)“ (4.19)”
Obs 4033 2053 2022 Obs 3797 1635 1610
R_sq rd 3% 16% 11% R_sqrd 7% 3% 5%
Top 50% relative liquidity and Bottom 50% Top 50% relative liquidity and Top 50%
leveraqe leverage
inv1 rd inv2 inv1 rd inv2
dliqlag -1.5e-6 6.4e-7 -7.6e-7 dliqlag -2.9e-6 -1.2e-6 4.4e-6
(-3.75)*** (2.15)” (-1.56) (-5.39)*** (-5.07)*** (-6.39)***
Obs 5247 3160 3120 Obs 4838 2320 2265
R_sq rd 9% 3% 0.4% R_sq rd 15% 1 1 % 9%

 

Panel B: Stock Turnover

Bottom 50% relative liquidity and Bottom 50%

 

 

 

 

 

Bottom 50% relative liquidity and Top 50%

 

 

 

 

 

levegqe leveraqL
inv1 rd inv2 inv1 rd inv2
tolag -0.0016 0.0025 -0.0008 tolag -0.0094 0.0002 -0.0026
(-0.71) (2.95)“ (-0.29) (-2.92)*** (0.17) (-0.71)
Obs 4524 2421 2379 Obs 3772 1693 1656
R_sqrd 1 1 % 16% 16% R_sq rd 7% 8% 1 1%
Top 50% relative liquidity and Bottom 50% Top 50% relative liquidity and Top 50%
leveraqe leverage
inv1 rd inv2 inv1 rd inv2
tolag -0.0021 -0.0017 -0.0037 tolag -0.001 0.0003 -0.0007
(-3.39)*** («1.41)W (4.78)“ (-1 .23) (0.42) (-0.67)
Obs 4433 2717 2689 Obs 5093 2344 2302
R_sq rd 8% 1 3% 0.4% R_sq rd 1 1% 5% 7%

 

61

Table 13: Joint effect of relative liquidity and BE/ME

Relative liquidity is calculated by dividing each ﬁrm‘s liquidity by market liquidity. The market liquidity is formed by
taking value-weighted average of all ﬁrms in the sample for each year. Book-to-market ratio (BE/ME) is common
equity (data 60) plus deferred taxes (data 74) and then divided by market value of equity. There are four subsamples
based on relative liquidity and BE/ME. Variable DLIQ is obtained by taking average of ratio of daily volume (dollar) to
daily absolute return for each year and each ﬁrm. DLIQ is scaled by l.000,000. Variable T0 is stock turnover. There
are three measures of corporate investment. Variable [NV] is calculated by dividing capital expenditure (data 128) by
year beginning total assets (lagged data 6). [NV2 is calculated by dividing sum of capital expenditure and firm’s
research and development expense (data 46) by year beginning total assets (lagged data 6). RD is research and
development (data 46) divided by total assets (data 6). Other independent variables are Q, CF, SLACK (Coefﬁcients
and t-statistics are not reported for simplicity). Standard error robust T-statistic is in the parenthesis. *, ”, and “"
represent signiﬁcance at the 1 percent, 5 percent and 10 percent levels respectively.

Page! A: Dojllar Liquidity

Bottom 50% relative liquidity and Bottom

 

 

 

 

 

Bottom 50% relative liquidity and Top 50%

 

 

 

 

 

50% BEIME BEIME
inv1 rd inv2 inv1 rd inv2
dliqlag 0.0002 -0.0006 -0.0008 dliqlag -0.0001 -0.00001 -0.0001
(1 .02) (-2.54)** (-2.02)** (-1 .00) (-0.21) (-0.46)
Obs 2790 1421 1410 Obs 5106 2290 2245
R_sq rd 2% 3% 2% R_sq rd 9% 6% 7%
Top 50% relative liquidity and Bottom 50% Top 50% relative liquidity and Top 50%
BEIME BEIME
inv1 rd inv2 inv1 rd inv2
dliqlag -2.0e~6 3.8e—8 -2.0e-6 dliqlag -1.5e-6 4.4e-7 -2.0e-6
(-5.42)*** (0.1 3) (4.28)” (-1 .82) (-2.16)** (-2.84)***
Obs 5995 3337 3331 Obs 4128 21 19 2070
R_sq rd 9% 0.1 % 1 % R_sq rd 19% 5% 21%

 

Panel B: Stock Turnover

Bottom 50% relative liquidity and Bottom

 

 

 

 

 

Bottom 50% relative liquidity and Top 50%

 

 

 

 

 

50% BEIME BEIME
inv1 rd inv2 inv1 rd inv2
tolag -0.008 0.002 -0.0025 Tolag 0.0006 -0.0005 -0.00001
(-2.78)*** (2.70)“ (-0.78) (0.20) (-0.47) (-0.00)
Obs 3822 2001 1965 Obs 451 1 2124 2081
R_sq rd 7% 1 5% 12% R_sq rd 1 0% 2% 9%
Top 50% relative liquidity and Bottom 50% Top 50% relative liquidity and Top 50%
BEIME BEIME
inv1 rd inv2 inv1 rd inv2
tolag -0.002 0.0002 -0.002 tolag -0.0008 0.0002 -0.0006
(-3.06)*** (0.29) (-2.49)** (-0.96) (0.88) (-0.71)
Obs 4715 2718 2693 Obs 4873 2370 2325
R_sq rd 6% 4% 0.6% R_sq rd 12% 8% 1 3%

 

62

Table 14: Joint effect of relative liquidity and income performance

Income Performance ([NCPF) is a ﬁnn‘s income performance, which is deﬁned as net income (data 13) divided by
year beginning assets (data 6). There are four subsamples based on relative liquidity and income performance. Variable
DLIQ is obtained by taking average of ratio of daily volume (dollar) to daily absolute return for each year and each
ﬁrm. DLIQ is scaled by l,000.000. Variable T0 is stock tumover. There are three measures of corporate investment.
Variable [NV] is calculated by dividing capital expenditure (COMPUSTAT data l28) by year beginning total assets
(lagged data 6). [NV2 is calculated by dividing sum of capital expenditure and ﬁrm’s research and development
expense (data 46) by year beginning total assets (lagged data 6). RD is research and development (data 46) divided by
total assets (data 6). Other independent variables are Q, CF, SLACK (Coefﬁcients and t-statistics are not reported for
simplicity). Standard error robust T-statistic is in the parenthesis. *, ”, and ‘”” represent signiﬁcance at the 1 percent,

5 percent and l0 percent levels respectively.

Mel A: Dollar Liquidity

Bottom 50% relative liquidity and Bottom
50% income performance

 

 

 

 

 

Bottom 50% relative liquidity and Top 50%
income performance

 

 

 

 

inv1 rd inv2 inv1 rd inv2
dliqlag -5.3e-6 -0.0003 -0.0006 dliqlag 0.0001 -0.0002 0.00002
(-0.04) (4.19)“ (-2.60)*** (0.26) (4.54)“ (0.07)
Obs 4483 21 1 1 2068 Obs 341 1 1598 1585
R_sqrd 4% 1 1 % 1 1 % R_sq rd 0.1 % 3% 0.4%
Top 50% relative liquidity and Bottom 50% Top 50% relative liquidity and Top 50%
income performance income performance
inv1 rd inv2 inv1 rd inv2
dliqlag -1.8e-6 -1 .2e-6 369-6 dliqlag -1.8e-6 7.6e-8 -1.4e-6
(-3.02)*** (-1 .58) (-3.55)*** (-5.09)*** (0.54) (-3.95)***
Obs 41 18 2062 2020 Obs 6005 3434 3381
R_sq rd 4% 1 7% 7% R_sq rd 7% 1 1 % 9%

 

[gel B: Stock Turnover

Bottom 50% relative liquidity and Bottom
50% income performance

 

 

inv1 rd inv2
tolag 0.001 0.0004 -0.0001
(0.56) (0.44) (-0.04)
Obs 3965 1846 1812
R_sq rd 5% 2% 2%

 

Top 50% relative liquidity and Bottom 50%
income performance

 

 

Bottom 50% relative liquidity and Top 50%
income performance

 

 

inv1 rd inv2
tolag -0.007 0.001 -0.003
(4.29)” (1.18) (-1.06)
Obs 4366 2277 2232
R_sq rd 2% 1 5% 9%

 

Top 50% relative liquidity and Top 50%
income performance

 

inv1 rd inv2 inv1 rd inv2
tolag -0.0006 0.0005 -0.001 tolag -0.002 0.001 -0.0006
(-1.15) (0.66) (-1.07) (4.38)" (1.45) (-0.64)
Obs 4922 2475 2425 Obs 4665 2612 2592
R_sq rd 4% 1 % 1% R_sq rd 3% 1 1 % 6%

 

63

Table 15: Joint effect of relative liquidity and ﬁnancial constraint

Relative liquidity is calculated by dividing each ﬁrm's liquidity by market liquidity. The market liquidity is formed by
taking value-weighted average of all ﬁrms in the sample for each year. KZ index is a measure of ﬁnancial constraints
based on ﬁve variables: Tobin‘s Q. leverage, cash ﬂow, cash balance and dividends. There are four subsamples based
on relative liquidity and ﬁnancial constraint. Variable DLIQ is obtained by taking average of ratio of daily volume
(dollar) to daily absolute return for each year and each ﬁrm. DLIQ is scaled by 1,000,000. Variable T0 is stock
turnover. There are three measures of corporate investment. Variable [NV] is calculated by dividing capital expenditure
(COMPUSTAT data 128) by year beginning total assets (lagged data 6). [NV2 is calculated by dividing sum of capital
expenditure and ﬁrm’s research and development expense (data 46) by year beginning total assets (lagged data 6). RD
is research and development (data 46) divided by total assets (data 6). Other independent variables are Q, CF, SLACK
(Coefﬁcients and t-statistics are not reported for simplicity). Standard error robust T-statistic is in the parenthesis. ‘, ",
and *""" represent signiﬁcance at the 1 percent, 5 percent and 10 percent levels respectively.

 

Panel A: Dollar Liquidity

Bottom 50% relative liquidity and Bottom

 

Bottom 50% relative liquidity and Top 50%

 

 

 

50% KZ KZ
inv1 rd inv2 inv1 rd inv2
dliqlag -0.0003 -0.00006 -0.0002 dliqlag -0.00002 -0.0006 -0.0005
(-1.92)* (-1.27) (-0.99) (-0.08) (-3.04)*** (-1.42)
Obs 4695 2045 2009 Obs 3130 1640 1620
R_sq rd 8% 9% 6% R_sq rd 1 % 4% 3%

 

Top 50% relative liquidity and Bottom 50%
K2

 

 

Top 50% relative liquidity and Top 50% K2

 

 

 

inv1 rd inv2 inv1 rd inv2
dliqlag -2.1e-6 -9.4e-7 -3.7e-6 dliqlag -2.0e-6 4.5e-8 -1 .8e-6
(-3.01)*** (-5.26)*** (~6.08)*** (-5.45)*** (0.16) (-3.79)*”
Obs 3954 1926 1 885 Obs 6129 3554 3500
R_sq rd 18% 3% 1 5% R_sq rd 7% 0.6% 0.8%

 

Panel B: Stock Turnover

Bottom 50% relative liquidity and Bottom

 

Bottom 50% relative liquidity and Top 50%

 

 

 

 

 

 

 

 

 

50% K2 KZ
inv1 rd inv2 inv1 rd inv2
tolag -0.001 6 -0.0001 0.0008 tolag -0.0055 0.0024 -0.0028
(-0.53) (-0.17) (0.25) (-1 .89)‘ (2.79)*** (-0.96)
Obs 4187 1909 1877 Obs 4106 2204 2157
R_sqrd 8% 5% 12% R_sqrd 5% 12% 9%
Top 50% relative liquidity and Bottom 50% Top 50% relative liquidity and Top 50% KZ
KZ
inv1 rd inv2 inv1 rd inv2
tolag -0.0024 0.0002 -0.001 5 tolag -0.0027 -0.0024 -0.0051
(-3.00)*** (0.58) (-1.88)* (-3.97)*** (-4.81)*** (-6.05)***
Obs 4892 2256 221 1 Obs 4631 2804 2779

R_sq rd 1 1% 4% 10%

R_sq rd 5% 8% 0.1 %

 

Table 16: Joint effect of BEIME and leverage

The leverage is deﬁned as the sum of long-term debt (data 9) and debt in current liabilities (data 34) divided by the sum
of long-term debt (data 9), debt in current liabilities (data 34) and total stockholders’ equity (data 216). Book-to—market
ratio (BE/ME) is common equity (data 60) plus deferred taxes (data 74) and then divided by market value of equity.
There are four subsamples based on BE/ME and leverage. Variable DLIQ is obtained by taking average of ratio of daily
volume (dollar) to daily absolute return for each year and each ﬁrm. DLIQ is scaled by 1,000,000. Variable T0 is stock
turnover. There are three measures of corporate investment. Variable IN V ] is calculated by dividing capital expenditure
(COMPUSTAT data 128) by year beginning total assets (lagged data 6). [N V2 is calculated by dividing sum of capital
expenditure and ﬁrm’s research and deveIOpment expense (data 46) by year beginning total assets (lagged data 6). RD
is research and development (data 46) divided by total assets (data 6). Other independent variables are Q, CF, SLACK
(Coefﬁcients and t-statistics are not reported for simplicity). Standard error robust T-statistic is in the parenthesis. *, ",
and *" represent signiﬁcance at the 1 percent, 5 percent and l0 percent levels respectively.

 

Panel A: Dollar Liquidity

 

 

 

 

 

 

 

 

 

 

Bottom 50% BEIME and Bottom 50% Bottom 50% BEIME and Top 50% leverage
levergqe
inv1 rd inv2 - inv1 rd inv2
dliqlag -1 .8e-6 4.0e-7 -1.2e-6 dliqlag -3.1 e-6 -2.1e-6 -6.5e-6
(-3.72)*** (1 .08) (-2.12)** (-4.34)"'M (-2.56)** (-4.78)"'W
Obs 5017 2952 2923 Obs 3755 1844 1816
R_sq rd 5% 1 % 0.1 % R_sqrd 9% 1% 4%
Top 50% BEIME and Bottom 50% Top 50% BEIME and Top 50% leverage
leverage
inv1 rd inv2 inv1 rd inv2
dliqlag -1 .1 e-6 -1 .8e-7 -1 .8e-6 dliqlag -2.4e-6 -7.2e-7 -2.8e-6
(-0.79) (-0.34) (-1 .22) (-1 .69)* (-2.86)*** (-2.59)***
Obs 4263 2261 2219 Obs 4880 21 1 1 2059
R_sqrd 9% 8% 10% R_sqrd 17% 2% 16%

 

Panel B: Stock Turnover

 

 

 

 

 

 

 

 

 

 

Bottom 50% BEIME and Bottom 50% Bottom 50% BEIME and Top 50% leverage
levme
inv1 rd inv2 inv1 rd inv2
tolag -0.003 -0.0003 -0.004 tolag -0.001 0.0016 -0.0004
(4.35)” (-0.39) (4.06)” (-0.99) (1 .85)* (-0.30)
Obs 4734 2842 2814 Obs 3792 1876 1843
R_sq rd 5% 1 % 3% R_sq rd 6% 2% 5%
Top 50% BEIME and Bottom 50% leverage Top 50% BEIME and Top 50% leverage
inv1 rd inv2 inv1 rd inv2
tolag 7.0e-6 0.0001 0.00005 tolag -0.0008 -0.0002 -0.0003
(0.01) (0.55) (0.06) (-0.85) (-1.16) (-0.28)
Obs 4223 2296 2254 Obs 5073 2161 21 15
R_sq rd 1 1 % 9% 12% R_sq rd 1 3% 1 % 13%

 

65

Table 17: Joint effect of ﬁrm size and income performance

income Performance ([NCPF) is a ﬁrm’s income performance, which is deﬁned as net income (data 13) divided by
year beginning assets (data 6). Firm size is measured by a ﬁrm’s market equity. There are four subsamples based on
ﬁrm size and income performance. Variable DLIQ is obtained by taking average of ratio of daily volume (dollar) to
daily absolute return for each year and each ﬁrm. DLIQ is scaled by 1,000,000. Variable T0 is stock turnover. There
are three measures of corporate investment. Variable IN V] is calculated by dividing capital expenditure
(COMPUSTAT data 128) by year beginning total assets (lagged data 6). [N V2 is calculated by dividing sum of capital
expenditure and ﬁnn’s research and development expense (data 46) by year beginning total assets (lagged data 6). RD
is research and development (data 46) divided by total assets (data 6). Other independent variables are Q. CF, SLACK
(Coefﬁcients and t-statistics are not reported for simplicity). Standard error robust T-statistic is in the parenthesis. ‘, *",

and *” represent signiﬁcance at the 1 percent, 5 percent and 10 percent levels respectively.

 

Panel A: Dollar Liquidity

Bottom 50% size and Bottom 50%
income performance

 

 

 

 

inv1 rd inv2
dliqlag -9.3e~6 -4.7e-7 -6.2e-6
(-0.66) (-0.06) (-0.38)
Obs 5828 2765 2707
R_sq rd 4% 23% 17%
Top 50% size and Bottom 50% income
performance
inv1 rd inv2
dliqlag -1.6e-6 5.1e-8 -2.5e-6
(-3.03)*** (0.04) (-2.11)**
Obs 2773 1408 1381
R_sqrd 9% 14% 14%

Bottom 50% size and Top 50% income

 

 

 

 

 

performance
inv1 rd inv2
dliqlag -0.0001 -0.00004 -0.0001
(-1 .93)* (-2.05)** (-2.54)**
Obs 4647 2280 2263
R_sqrd 0.3% 5% 3%
Top 50% size and Top 50% income
performance
inv1 rd inv2
dliqlag -1 .9e-6 8.4e-8 -1.4e-6
(4.96)” (0.62) (-3.83)***
Obs 4769 2752 2703
R_sq rd 9% 1 2% 1 2%

 

Panel B: Stock Turnover

Bottom 50% income performance and
Bottom 50% size

 

 

 

 

inv1 rd inv2
tolag -0.00002 -0.001 -0.002
(0.04) (-2.43)** (-2.30)**
Obs 6393 3031 2970
R_sqrd 4% 25% 1 7%
Top 50% size and Bottom 50% income
performance
inv1 rd inv2
tolag -0.001 0.011 0.007
(-1.33) (8.01)“ (4.81 )***
Obs 2494 1290 1267
R_sq rd 9% 5% 9%

Bottom 50% size and Top 50% income

 

 

 

 

 

performance
inv1 rd inv2
tolag -0.001 0.001 -0.0002
(-1.25) (1 .71)* (-0.13)
Obs 4579 2230 2212
R_sqrd 0.1% 10% 3%
Top 50% size and Top 50% income
performance
inv1 rd inv2
tolag -0.002 -0.0002 -0.0017
(-2.41)** (-0.93) (-2.23)**
Obs 4452 2659 2612
R_sq rd 9% 10% 12%

 

66

 

Table 18: A portfolio approach

Four portfolios (S/L, S/M, S/H, B/L, B/M. B/[[) are formed yearly (1963 to 2001) from a simple sort of ﬁrms into two
group on market equity value (ME) and another sort into two groups on BEME. Two BEME groups are based on the
breakpoints for the bottom 50 percent (low), and top 50 percent (high). The data are collected mainly from

COMPUSTAT and C RSP. We only use four portfolios (S/L. S/H, B/L, B/H) in our tests.

Q is deﬁned as the market value of equity plus assets minus book value of equity over assets, that is, CRSP market
value of equity plus COMPUSTAT data 6 minus the sum of data 60 and data 74 over data 6, where data 60 is common
equity and data 74 is deferred taxes on balance sheet. Firm’s cash ﬂow (CF) equals the sum of earnings before
extraordinary items (data 18) and depreciation (data 14) over year beginning assets (lagged data6). There are two
measures of liquidity: DLIQ and T0. Variable DLIQ is obtained by taking average of ratio of daily volume (dollar
volume) to daily absolute return for each year and each ﬁrm. T0 is stock turnover. There are three measures of
corporate investment. Variable [NV] is calculated by dividing capital expenditure (COMPUSTAT data 128) by year
beginning total assets (lagged data 6). [N V2 is calculated by dividing sum of capital expenditure and ﬁrm’s research
and development expense (data 46) by year beginning total assets (lagged data 6). RD is research and development
(data 46) divided by total assets (data 6). SLACK] (ﬁnancial slack) is the ratio of cash and short-term investments (data
1) and sales (data 12); CASH (cash balance) is the ratio of cash and short-term investments (data 1) and assets (data 6).
Following investment speciﬁcation is estimated for each portfolio.
3 3
INVESTMENT, = c + bOLIQUIDITY,_1 + z b,,Q,_,, + z amCF,_m + b4Slack,_1 + 5,
n=1 m=l
Where [NVESTMEN T can be [NV], RD or [N V2. LIQUID] TY can be DILQ or T0. c is an intercept. Because Q and CF

may be persistent, we also include Q,_2 , Q,_3 , and CF;_2 , C F,_;. Slack can be either SLACK] or CASH. Because

there is no clear time trend in investment variables, we do not include a time variable in the regressions using
portfolios. Standard error robust T-statistic is in the parenthesis. *, **, and *’” represent signiﬁcance at the 1 percent, 5

percent and 10 percent levels respectively.

 

67

 

 

 

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Figure l: The difference between dollar liquidity and stock turnover for four
portfolios

Six portfolios are formed yearly from a simple sort of firms into two group on market equity value (ME) and another
sort into three groups on BE/ME. Three BE/ME groups are based on the breakpoints for the bottom 30 percent (low),
middle 40 percent (medium) and top 30 percent (high). We focus on four of these portfolios: S/L, S/H. B/L and B/H.
For example, the S/L portfolios contains the stocks in small ME group that are also in the low BE/ME group. For each
portfolio formation year (from 1966 to 1997), the dollar liquidity and stock turnover are calculated for year Hi. i=-5, -
4. ..., 4, 5. The liquidity measures for year (+i are then averaged across portfolio formation years, respectively.

Dollar Liquidity (different portfolios)

 

 

 

 

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71

Figure 2: The relationship between real investment growth and R&D growth

“rdg” is R&D growth rate throughout time. “invi g" is actual investment growth rate throughout time.

Real Investment Growth vs. R&D Investment Growth

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72

Figure 3: Liquidity growth and investment growth

Market level investment growth rates are calculated by ﬁnding average of all firms’ investment for each
year. Firms’ investment data are collected from COMPUSTAT. Market liquidity is obtained by taking
value-weighted average of all firms’ dollar liquidity for each year. MLLG is the growth rate of firm dollar
liquidity, which is deﬁned by daily trading volume divided by daily absolute return.

Liquidity Growth vs. Investment i
Growth W

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73

APPENDIX 13 The Difference between Dollar Liquidity and Stock Turnover

The difference between dollar liquidity and stock turnover may be from the fact that they
represent different aspects of liquidity. Dollar liquidity is more related to transaction cost
feature of liquidity. Stock turnover represents more about level of trading activity. The
difference between dollar liquidity and turnover can be illustrated in the following
manner. We follow the portfolio technique used in Lakonishok, Shleifer and Vishny
(1994), and Fama and French (1995). Six portfolios (S/L, S/M, S/H, B/L, B/M, B/H) are
formed yearly from a simple sort of firms into two groups on market equity value (ME)
and another sort into three groups on BE/ME. Three BE/ME groups are based on the
breakpoints for the bottom 30 percent (low), middle 40 percent (medium) and top 30
percent (high). We focus on four of these portfolios: S/L, S/H, B/L and B/H. For example,
the S/L portfolios contains the stocks in small ME group that are also in the low BE/ME
group. For each portfolio formation year (from 1966 to 1997), the dollar liquidity and
stock turnover are calculated for year t+i, i=—5, -4, ..., 4, 5. The liquidity measures for

year r+i are then averaged across portfolio formation years, respectively.

Figure 1 illustrates the different patterns between dollar liquidity and stock turnover. In
Figure 1 Panel A, B/L portfolio has the highest dollar liquidity, while S/H portfolio has
the lowest. The dollar liquidity is increasing for all four portfolios. It appears that
investors are able to identify, through dollar liquidity, the change in fundamentals (e.g.
profitability, earnings growth, and etc.) around the portfolio formation, although the
evidence is not very strong. Figure 1 Panel B shows the pattern of stock turnover, as a

function of size and BE/ME, for a long period around portfolio formation. Different from

74

the pattern in Panel A, the 571. portfolio has the highest stock turnover (liquidity), while
S/H portfolio has the lowest. The stock turnover is increasing for B/H and B/L portfolios
as shown in Panel B. However, stock turnover changes direction around portfolio
formation for S/L and S/H portfolios. Again, it seems investors are able to identify,
through stock turnover, the change in fundamentals around the portfolio formation. The
evidence is stronger for stocks contained in smaller ME portfolios. While we do not want
to dispute whether this approach has a valid theoretical basis,'6 our liquidity analysis
shows that, for some portfolios, the investor sentiment may be developing aﬁer portfolio
formation when earnings actually start declining, because liquidity continues to increase
after portfolio formation for some portfolios. The ﬁgures show that two liquidity
measures behave differently even we control for ﬁrms size and BE/ME. This suggests
they may capture different things about liquidity, although they also share some aspects
of liquidity. (According to the deﬁnitions of these liquidity variables, we think dollar
liquidity is more related to transaction cost feature of liquidity but stock turnover
represents more about level of trading activity. This is because that dollar liquidity
implies how many dollars are needed if stock return is driven up or down by 1 percent,
while turnover captures how many shares are being traded compared to the total

outstanding.

 

'6 For exam le, some authors ar e there is no theo indicatin ﬁrms have similar
P
economic shocks every eleven years.

75

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76

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77

Jensen, MC. and W. Meckling, 1976, “Theory of the ﬁrm: Managerial behavior, agency
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78

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79

CHAPTER 2

WHY DO SEASONED OFFERING FIRMS UNDERPERFORM
IN INVESTMENT GROWTH?

2.1 Introduction

The long-term performance of the ﬁrms conducting seasoned issues (seasoned equity
offerings and seasoned debt offerings) has been a hot topic for past two decades. Despite
the well-docurnented evidence indicating that seasoned issuers usually underperform
their stylized matches in equity returns, it is still highly controversial on how to explain

the long-term underperformance in the literature.

In this article, we are going to revisit this issue by focusing on the growth of corporate
investment. We use this approach for two reasons. First, to the extent that ﬁnancial
market reﬂects a ﬁrm’s investment opportunities like a mirror, equity returns and
investment growth are two sides of the same coin (e.g. Gibbons and Fama (1982), and

Cochrane (1991, 1998)).l Thus, it is interesting to investigate how investment growth

 

' Practically, if stock price is the present value of future cash ﬂows, which could be from
existing investment and/or new investment, then, the change in stock prices, or stock
returns, should be closely related to the change in the cash ﬂows of existing investment
and/or the change in the cash ﬂows of new investment. The change in the cash ﬂow of
existing investments is usually predicted based on historical performance of the existing
investment (operating income, sales, etc). However, the change in the cash ﬂows of new
investment is very difﬁcult to predict due to lack of historical data about future new
investment. The current investment growth provides an alternative for “current
expectation about future growth” because, after all, the future new cash ﬂows will be
generated from new investment. Since the cash ﬂows from the existing investment are
predictable and should be already priced, both stock returns and investment growth rates
will only reﬂect expectations about future new cash ﬂows from new investment. This

80

performs. Second, Loughran and Ritter (1997) show that the issuing ﬁrms continue to
have higher investment levels than their stylized matches do after offering. This seems to
suggest that the issuing ﬁrms continue to have better investment opportunities and should
have no fundamental difﬁculties doing new investments. However, they do not explain
why this is relevant for the underperformance of the issuing ﬁrms. We are going to
provide more details on how issuing ﬁrms invest around offerings. More importantly, we
carefully consider several hypotheses and examine which theory is the most consistent

with the evidence.

Using 2247 ﬁrms conducting seasoned equity offering (SEO ﬁrms) and 802 ﬁrms
conducting seasoned debt offering (SDO ﬁrms) between 1985 and 1996, we ﬁnd that the
seasoned issuing ﬁrms signiﬁcantly increase their investment grth before offering,
however, they signiﬁcantly reduce investment growth aﬁerward. On average, the
seasoned equity offering (SEO) ﬁrms increase investment growth rate from 33 percent to
56 percent during four years before offering, then investment growth rate drops to 17
percent during three years after offering. This pattern is also true for seasoned debt
offerings ﬁrms. The SDO ﬁrms, on average, increase investment growth rate from 18
percent to 34 percent during four years before offering but reduce it to only 13 percent
during three years after offering. More interestingly, we ﬁnd the issuing ﬁrms’
investment growth rates outperform their stylized (matched by issuing years, industry,
ﬁrm size and book-to-market (BE/ME» matches’ investment growth rates before

offering, but signiﬁcantly underperform their stylized matches after offering. For the

 

thus leads to the conclusion of identical movements in stock returns and investment
growth.

81

SEO ﬁrms, their stylized matches‘ investment growth rates outperform by an average of
333 basis points during a three-year window one year after offering. The SDO ﬁrms are
not better. Their stylized matches’ investment growth rates outperform by an average of
700 basis points during a three-year window one year after offering. Moreover, if we split
SEOs into primary SEOs and secondary SEOs,2 we ﬁnd the primary SEOs signiﬁcantly
underperform in investment growth but secondary SEOs do not. Using different matching
benchmarks (matched by issuing years, industry, and ﬁrm size, or matched by issuing
years, industry, ﬁrm size, and operating income performance) does not change the above
conclusions. Our evidence shows that there exists a signiﬁcant underperformance in

investment growth after offering for seasoned issuing ﬁrms.

Our results are not surprising because, as we mentioned above, theoretically stock returns
and corporate investment growth rates can behave similarly. Nevertheless, our new
evidence on investment growth raises a natural question: why do seasoned offering ﬁrms
underperform in investment growth rates? For example, if the underperformance in stock
returns can be explained by the lowered risk after the seasoned offering (e.g. Eckbo,
Masulis and Norli (2000)), then, can lowered risk explain the underperformance in
investment growth? Interestingly, the “lowered risk” explanation is very doubtful because
there is no particular theory indicating lowered risk would necessarily lead to a more
conservative corporate investment. Actually, if lowered risk suggests cheap cost of

ﬁnancing, then it would rather lead to higher investment. In addition, given newly raised

 

2 The difference between primary SEOs and secondary SEOs is that primary SEOs will
bring in new funds to the issuing ﬁrms, while secondary SEOs are sold by large
shareholders and will not generate new cash for the ﬁrms.

82

fund from capital market and lowered risk, managers might be more likely to conduct

more aggressive expansion, as suggested by Ritter (2002).

Our extensive robustness checks conﬁrm that the underperformance in investment growth
can be explained by the market sentiment hypothesis, ﬁrst documented by Li (2003). This
explanation suggests that, if high liquidity followed by low stock returns signals investor
sentiment as argued in Baker and Stein (2003), then for the same reasons, managers also
interpret low stock returns as a consequence of investor sentiment. Thus, both individual
investors and managers tend to reduce their investment due to worse-than-expected
business conditions and lack of knowledge about the true value of the risky assets.3 This

explanation is supported by both primary SEOs data and SDOs data in this article.

Further, our evidence on the underperformance in investment growth casts new light on
the explanations of “New Issues Puzzle”. From the existing evidence and the new
ﬁndings documented in this article, we suggest that the market sentiment is the dominant
reason that causes both types of the underperformance following seasoned offerings, i.e.,
the underperformance in stock returns and the underperformance in investment growth.
The lowered risk hypothesis (e.g. Eckbo, Masulis, and Norli (2000)) may be able to
provide a reason for the underperformance in equity returns but fails to explain the
underperformance in investment growth. Because there is so far no particular theory that
can explain why lowered risk can result in lowered investment. The earning management

hypothesis (e.g. Teoh, Welch, and Wong (2000)) suggests that the issuing managers tend

 

3 It is helpful to think that both managers and individual investors are investors, but
individual investors are investors in ﬁnancial market while managers are investors in real
economy. When market sentiment is very high, both tend to hold cash and reduce their
investments.

83

to borrow future earnings to dress the offering window prior to offering. Thus, this will
lead to some bad earning numbers and bad performance in equity returns afterward.
However, if managers really cannot deliver sound earning numbers, then why do they not
increase corporate investment to fulﬁll investors’ high expectations, especially, given the
fact that they have enough cash? It is hard to believe that, on the one hand, managers
manipulate earnings to decorate issuing window prior to offering, but on the other hand,
the same managers do nothing to reduce the effect of the bad earnings after offering.4 In
this sense, the earning management hypothesis is inconsistent. Finally, the agency
hypothesis in Jung, Kim, and Stulz (1996) suggests that managers tend to squander
corporate resources when given the opportunities, although this may not be intentional.
Clearly, our evidence of the underperformance in investment growth does not support

this hypothesis, because managers actually tend to hold cash after offering.

The rest of the article is organized as follows. Section 2 brieﬂy summarizes the
hypotheses. Section 3 describes the data and main sample characteristics. Section 4
discusses the methodologies. The empirical results are reported in Section 5. We

conclude in section 6.

2.2 Hypotheses

The evidence shows that the issuing ﬁrms tend to have a lower investment growth after
offering, compared to their stylized matches. We propose ﬁve hypotheses that potentially

explain this underperformance.

 

4 Burning cash (doing more investment) is even more credible.

84

“Mean reversion hypothesis”: Because we observe an outperforrnance prior to offering
but an underperformance afterward, this hypothesis simply suggests a pure mean-
reverting process in the issuing ﬁrms’ investment growth. The underperformance in
investment growth after offering is simply due to the issuing ﬁrms’ lower investment
growth in the ﬁrst place prior to offering. We consider this hypothesis as our
“benchmark” hypothesis. This hypothesis predicts investment growth increases before

offering but regresses gradually to the mean after offering.

“Cash shortage hypothesis”: This hypothesis suggests that seasoned offering ﬁrms
aggressively and quickly use up most of their cash during the ﬁrst year after offering.
Thus, managers will not have enough cash to do more investment in the following years.
Nevertheless, their matches do not have this situation. This hypothesis predicts that,

compared to their matches, the offering ﬁrms will have lower cash growth aﬁer offering.

“Substitution hypothesis”: To make investors believe issuing ﬁrms are doing well and
having plenty of investment opportunities, managers tend to delay some expenses and
save most of their cash for investment before offering. In other words, managers tend to
substitute some expenses with investment through “cash tunneling”. This hypothesis
implies that this is the reason why we usually observe high investment growth before
offering, because investors may believe issuing ﬁrms face more investment opportunities.
However, after offering, the issuing ﬁrms will have to reduce their investment and use
some newly raised cash to pay off the delayed expenses since the operating income
usually deteriorates after offering. This hypothesis predicts a lower investment growth

after offering because issuing ﬁrms do not have enough cash for new investment if they

85

will have to use some cash to pay off more expenses. According to this hypothesis, one
would observe the underperformance in both cash growth and investment growth after

offering.

“Mismatched investment opportunities hypothesis”: This hypothesis implies that
seasoned offering ﬁrms actually have fewer investment opportunities than the selected
matches do after offering, due to mismatched investment opportunities. As a result, they
underperform in investment growth. The reason is that one of the matching criteria, book-
to-market ratio, is no longer a good proxy for equal investment opportunities when
investor sentiment provides a false market perception for the issuing ﬁrms. This
hypothesis predicts no underperformance once the matches are selected based on the
same market perception. In this article, we will use different matching benchmarks to test

this hypothesis.

“Market sentiment hypothesis”: According to Baker and Stein (2003), a big increase in
liquidity followed by low stock returns signals the high market sentiment. After offering,
managers realize that the business conditions are actually not as good as they were
thought to be, and they are not sure about the true value of the risky assets after high level
of market sentiment. They thus reduce investment and prefer to hold cash. This then
results in an underperformance in investment growth. This hypothesis predicts that
issuing ﬁrms will experience higher level of investor sentiment prior to offering, thus

they reduce investment growth afterward.

2.3 Data

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2.3.1 Sample Selection

Our data are selected from three different sources. The data about stock prices, stock
returns, trading volume, capitalization, value-weighted market returns, equal-weighted
market returns, and shares outstanding are drawn from CRSP.5 The ﬁnancial accounting
data are collected from COMPUSTAT. The seasoned equity offering ﬁrms, seasoned
debt offering ﬁrms, and the information about the use of proceeds are all selected from

SDC Platinum database.

We choose all ﬁrms that have valid ﬁnancial and accounting numbers. We ignore those
ﬁrms with negative accounting numbers for book assets, capital, or investment. We also
drop ﬁrms with assets less than 5 million, and other extreme observations. Because assets
in utilities, ﬁnancial institutions, investment funds, and REITs have different trading
characteristics from ordinary equities, we exclude all of them from the sample by deleting
observations with SIC code between 4911 and 4941 (utilities), between 6000 and 6081

(ﬁnancial institutions), and 6722, 6726, 6792 (investment funds and REITs).

It is also possible that some ﬁrms have multiple offerings in ﬁve years, so we may have
the problem of overlapping returns (this is usually called the problem of cross-sectional
dependence). To deal with this problem, we strict our analysis to the ﬁrms that do not

repeat offerings in a ﬁve-year post-issue window. Above procedures yield 2066 primary

 

5 The value-weighted market returns and the equal-weighted market returns are used to
replicate the evidence of the underperformance in equity returns to make sure our data are
comparable to other studies.

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seasoned equity offering ﬁrms, 181 secondary seasoned offering ﬁrms. and 802 seasoned

debt offering ﬁrms between 1985 and 1996.

2.3.2 Sample Characteristics

Table 1 shows the number of primary SEOs, secondary SEOs, and SDOs for each issuing
year. For the primary SEOs (Panel A), 71.4 percent of the sample is after 1991,
corresponding to the heavy issuing activities associated with the hot market that
commenced in 1992. For the secondary SEOs (Panel B), the heavy issuing activities
occurred around 1986 and 1992, which are both years that market was becoming hot.
Similar to the SEOs, the SDOs (Panel C) also experienced heavy issuing activities around
1986 and 1992. The distribution of the issuing activities conﬁrms that ﬁrms usually wait
for an issuing window when market situations become favorable. Interestingly, the
investor sentiment is also most likely to be developed in hot market rather than in cold

market.

In addition, Table 1 reports the industry classiﬁcation using two-digit Standard Industrial
Classiﬁcation codes for the sample. The evidence suggests that most of the seasoned
offering ﬁrms are from the manufacturing industry (ﬁrms with SIC between 20 and 39)
and the services industry (ﬁrms with SIC between 70 and 89). For the primary SEOs
(Panel A), manufacturing industry and services industry are the two major industries that
have relatively more primary SEOs (41.38 percent and 17.47 percent, respectively). The

secondary SEOs (Panel B) usually cluster in the manufacturing industry (38.12 percent).

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For the SDOs (Panel C), we ﬁnd 41.65 percent of SDOs is in the manufacturing industry,

and 8.60 percent is in the services industry.

2.4 Methodologies

2.4.1 Matching Techniques

We mainly use four criteria to select matches for the issuers in our analysis: issuing years,
industry, ﬁrm size, and book-to-market ratio. Fama and French (1992, 1996) suggest that
ﬁrm size and book-to-market ratio factors can better explain cross-sectional stock returns.
According to Gibbons and Fama (1982) and Cochrane (1991, 1998), if ﬁnancial market
reﬂects investment opportunities like a mirror, then investment growth should be linked
to stock returns, and thus should be related to ﬁrm size and BE/ME ratio. In addition,
BE/ME ratio itself is sometimes used as a proxy for investment opportunities, like Q
variables. Only those non-issuing ﬁrms that are listed on the NYSE/AMEX/Nasdaq
available on both CRSP and COMPUSTAT databases are used as a pool of possible
matching ﬁrms. We ﬁrst sort stocks by their equity market-caps into quintiles. Within
each size quintile, book-to-market (BM) ratio quintile cut-off points are deﬁned. The
cutoff points for size quintiles are based on the market capitalization at the end of each
month. The cutoff points for BM quintiles are based on the book value of equity divided
by the market value of equity at the end of each month, using all NYSE/AMEX or
Nasdaq ﬁrms available on both CRSP and COMPUSTAT databases. We obtain 25
portfolios from above procedures. To identify matching ﬁrms for a given issuing ﬁrm, we

go into the same size quintile ﬁrst, and then choose at least one but at most ﬁve ﬁrms

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with the closest BM ratio. Because ﬁrms in different industries will have quite different
investment patterns, we also match issuing ﬁrms according to industries. The two-digit
SIC codes are used to select matching ﬁrms. Finally, we only select our matches in a

four-year window (two years before and two years after) of the offering dates.

To ensure our results are robust, we also try other matching techniques. One of them is to
ﬁnd matching ﬁrms by using issuing years, industry, ﬁrm size, BE/ME, and operating
income performance. The operating income performance is deﬁned as operating income
divided by previous year’s assets. We form 10 portfolios based on the operating income
performance. To select a match, we ﬁrst repeat the above selection procedure and then
choose a ﬁrm in the related income performance portfolio. In addition, the matching
ﬁrms found based on only issuing years, industry, and ﬁrm size are also considered to

make sure our results are independent of matching techniques.

2.4.2 Fiscal Year-End Month and Calendar Year-End Month

Since we need to investigate the investment growth before and after offerings, and many
ﬁrms do not issue new equity or debt in the same month that ﬁscal year ends, it is usually
difﬁcult to determine the correct amount of investment around offering dates. For
example, if the ﬁscal year-end month is February and the issuing month is February, then
the data about investment (capital expenditure) from COMPUSTAT will not have any
problem. We can easily treat the year before February (this year) as “-1” year --- the year
before offering, and the next ﬁscal year as “+1” year --- the year after offering. However,

if the issuing month is February and the ﬁscal year-end month is December of the same

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year, then we should not treat next ﬁscal year as “+1” year because most of the

investment has been made in this year. Instead, we should treat “this year” as “+1” year --

- the year after offering, and the last year as “-1” year --- the year before offering.

Clearly, distinguishing between the ﬁscal year-end month and the issuing month is

crucial for measuring correct amount of investment.

To deal with the above problem, we use the following procedures.

11.

III.

IV.

If the ﬁscal year-end month is after the issuing month, and if there are less than six
months between ﬁscal year-end month and issuing month, then we treat this year as
“the year before offering” and next year as “the year after offering”.

1f the ﬁscal year-end month is after the issuing month, and if there are more than six
months between ﬁscal year-end month and issuing month, then we treat last year as
“the year before offering” and this year as “the year after offering”.

If the ﬁscal year-end month is before the issuing month, and if there are less than
six months between ﬁscal year-end month and issuing month, then we treat this
year as “the year before offering” and next year as “the year after offering”.

If the ﬁscal year-end month is before the issuing month, and if there are more than
six months between ﬁscal year-end month and issuing month, then we treat next
year as “the year before offering” and the year after next year as “the year after

offering”.

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Table 0: Year Selection Procedures

 

Fiscal vs. Issuing Difference Year Selection

 

-1 YR: last year

 

2 6 months +1 YR: this year

 

Fiscal > Issuing

< 6 months '1 YR: thlsl’eaf

 

+1 YR: next year

 

-1 YR: next year

 

2 6 months +1 YR: the year after next
Fiscal < Issuing year

 

< 6 months ’1 YR: ““3 year

 

+1 YR: next year

 

 

-1 YR: this year

 

Fiscal = Issuing ---

 

 

+1 YR: next year

 

Notes: 1. “Fiscal” is the ﬁscal year-end month, and “Issuing” is the issuing month.
2. “Fiscal > Issuing” means the ﬁscal year-end month is before the issuing month. “Fiscal <
Issuing” means the ﬁscal year-end month is after the issuing month.
3. “-1 YR” means the year before of fering, and “+1 YR” means the year after offering.
4. “last year” means last ﬁscal year, and “this year” means this ﬁscal year.

The above table illustrates how we distinguish between the ﬁscal year-end month and the

issuing month.

2.4.3 The Calculation of Buy-and-Hold Returns (BHRs)

The three-year and ﬁve-year buy-and-hold returns (BHRs) are calculated to replicate
long-term underperformance in equity returns. Barber and Lyon (1997a), and Kothari and
Warner (1997) both indicate BHRs are attractive in comparison to cumulative abnormal
returns (CARS), which implicitly assumes frequent rebalancing and thus ignore the
potentially high transaction costs. Blume and Stambaugh (1983), Roll (1983), and Conard
and Kaul (1993) offer empirical evidence that frequent rebalancing can lead to upward

bias due to bid-ask bounce.

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We calculate the BHRS by compounding daily returns over either 1250 trading days (5
years) or the number of trading days from the offering date until the delisting date,

whichever is smaller. The following formula is used to calculate BHRs.

T
BHRi=H(1+Gt)-1
t=l
The same holding periods are used to calculate the BHRs of matching ﬁrms. If a
matching ﬁrm is delisted before the end of the three-year/ﬁve-year anniversary or the
issuing ﬁrm’s delisting day, whichever is earlier, either CRSP value-weighted returns or

CRSP equal-weighted returns are inserted into the calculation of the BHRs from the

removal date.

2.5 Results
2.5.1 Replicating BHRs

We ﬁrst replicate the well-known long-term underperformance in equity returns found by
other authors. Panel A of Table 2 shows our results. Using 2247 SEO ﬁrms, we ﬁnd
equally weighted average 5-year buy-and-hold return is 31.3 percent, compared to their
stylized matches’ 49.9 percent. The annualized difference is around —3.2 percent, which
is similar to —3.9 percent found by Brav, Geczy and Gompers (2000). Cai and Loughran
(1998) use Japanese data and ﬁnd an annualized difference of —3.5 percent. Notice that

the annualized difference in Eckbo, Masulis and Norli (2000) is higher (—4.8 percent).

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This is due to the different sample period and different number of years used to calculate

annualized returns.

We also divide whole sample into two subsamples: primary issues and secondary issues.
The difference is that primary issues generate new cash to the issuing ﬁrms while
secondary issues generate cash to the large shareholders except issuing ﬁrms. Different
from the evidence documented in primary issues, the existing evidence on secondary
issues indicates an outperformance of issuing ﬁrms in equity returns. In our sample, the
primary issues have an average of —3.5 percent annualized return difference for 5 years
after offering, and the secondary issues have an average of 4.5 percent annualized

difference for 5 years after offering.

We also ﬁnd long-term underperformance of equity returns for SDO ﬁrms. See Panel B
of Table 2. On average, the annualized return difference is —2.6 percent for 5 years (-2.4
percent for 3 years) after the debt offerings in this article. Eckbo, Masulis and Norli
(2000)’s paper is the other one considering debt issues. They ﬁnd ﬁrms issuing
convertible debt have an annualized difference of —3.3 percent, while ﬁrms issuing
straight debt have an annualized difference of —2.3 percent. Our result is a little different
because we do not distinguish between convertible debt and straight debt. Overall, our
results on the long-term underperformance in equity returns are very similar to other

authors’ ﬁndings. This indicates a very comparable sample in this article.

2.5.2 Main Results

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As we brieﬂy mentioned before, there exists difference between the performance of the
primary equity issuers and the performance of the secondary equity issuers. We divide
our SEO sample into primary SEOs and secondary SEOs, and then report our results
accordingly. Keep in mind that the difference is that primary issues generate new cash to
the issuing ﬁrms while secondary issues generate cash to the large shareholders except

issuing ﬁrms.

2.5.2.1 Primary Seasoned Equity Issues

2.5.2.1.] The Underperformance in Investment Growth

There are 2066 primary SEO issues between 1985 and 1996 in our sample. Three or four
years before offering. the issuing ﬁrms appear to have similar or lower investment growth
rates than their stylized matches. In Panel A of Table 3, four years before offering, the
issuing ﬁrms have an average of 32 percent investment growth, compared to an average
of 37 percent investment growth of their matches. This situation changes as the offering
announcement date approaches. During a three-year window before offering, the issuing
ﬁrms have signiﬁcantly higher investment grth rates than their matches’ investment
growth rates. The issuing ﬁrms’ investment growth rates increase from 33 percent to 56
percent, while their matches’ investment growth rates increase from 35 percent to 39
percent. The average difference (issuers’ growth minus their matches’ growth) in
investment growth between the issuing ﬁrms and their matches is 13.5 percentage points,

and the difference is statistically signiﬁcant at 99.9 percent level. Right before offering,

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the difference of investment growth peaks at 17 percentage points. The evidence suggests

a greater investment growth rates run-up for the issuing ﬁrms before offering.

We observe that issuing ﬁrms continue to outperform their matches in investment growth
during the ﬁrst year after offering. There are two reasons why issuing ﬁrms continue to
outperform their stylized matches in investment growth during the ﬁrst year after
offering. First, it may be due to momentum effect: the issuing ﬁrms continue to perform
well and managers want to keep investing more. Second, it may be because that issuing
managers want to fulﬁll investors’ expectation right after offering. However, the high
investment growth quickly disappears after the ﬁrst year. We ﬁnd that both issuers and
their matches experience decreasing investment growth after the ﬁrst year of offering.-
Shown in Panel A of Table 3, the issuers’ investment growth rates decrease from 53
percent to 17 percent, while their matches’ investment growth rates decrease from 41
percent to 23 percent during four years after offering. Surprisingly, the evidence shows
that the issuers actually keep underperforming their matches in investment growth
beginning in the second year until the ﬁfth year aﬁer offering. The three-year average
difference (issuers’ growth minus their matches’ growth) in investment growth after the
ﬁrst year is —4.67 percent and is statistically signiﬁcant at 95 percent. This tells us that
there exists a signiﬁcant underperformance in investment growth for the issuing ﬁrms.
Graph 1 illustrates the underperformance in investment growth and compares it to the

graph of investment level.6

 

6 The investment level is deﬁned as a ﬁrm’s capital expenditure divided by its year
beginning total assets. We provide the evidence of investment level, cash level, sales
level, and expenses level only for the reason of comparison. Actually, ﬁrms’ total assets
are affected by many things, the above “level measures” may thus be biased.

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As we discussed above, since we are dealing with how issuing ﬁrms invest around the
offerings, the amount of investments is crucial in this article. To make sure our results
about investment growth are correct, we perform following two robustness checks. First,
instead of raising new funds for more future investments after offering, issuing ﬁrms may
raise new funds for other reasons. For example, they may issue new equity to collect cash
for retiring debt. Thus, lower investment growth may be pre-detennined even before
offering. To investigate this issue, we check the issuing ﬁrms’ use of proceeds. We ﬁnd
more than 50 percent ﬁrms do not provide explicit information about how they are going
to use the new funds. Their uses of proceeds are either “others” or “general corporate
uses”. This indeed leads to a very ﬂexible use of new funds. Further, one of our matching
benchmark is book-to-market ratio, which is closely related to the measure of investment
opportunities (Q variables). Thus, even for those issuing ﬁrms that may not raise funds
for new investment, they should have similar investment growth, especially they have
more funds after offering. However, on the other hand, book-to-market ratio also reﬂects
market perception, and what if the market perception is different for issuers and their
matches in the ﬁrst place? After all, issuers usually have better performance before
offering. This may potentially produce a “mismatched investment opportunities”
problem. To ensure this is not the case, we add one more matching criterion --- similar
income performance --- to make sure the market perception is similar for both issuers and
their matches. We ﬁnd the identical results: a signiﬁcant outperformance in investment
growth before offering, but a signiﬁcant underperformance in investment growth after

offering. The results are shown in Panel B of Table 3.

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Second, issuers may not want to invest more in capital expenditures, instead they do more
mergers and acquisitions. This thus makes those issuers look like they reduce investment
(capital expenditure) growth. To deal with this concern, we ﬁrst look at the use of
proceeds and ﬁnd the issuers that will use new funds to do “merger and acquisition” only
count for 4.55 percent in the primary SEOs sample. Excluding those ﬁrms that explicitly
indicate they will do “merger and acquisition” after offering yields almost identical
results. Therefore, we think this will not cause any troubles in our results. The results are

shown in Panel C of Table 3.

Overall, for primary SEO ﬁrms, our results indicate a signiﬁcant outperformance in
investment growth before offering, but a signiﬁcant underperformance in investment
growth after offering. Also noteworthy is that the underperformance should not be due to
mismatched investment opportunities since even after controlling for similar income
performance, their stylized matches still show strong investments after offering. Although
issuers do have high investment before offering, if the assumption that ﬁrms’ current
investment decisions are not correlated with their past investment decisions holds, then
this tells us that the “mismatched investment opportunities hypothesis” is not supported

by the data.

Finally, shown in Graph 1 (Panel B), notice that issuers appear to have higher investment
level (investment divided by assets) than their stylized matches. However, the calculation
of investment level can be biased by changes in many variables such as cash, inventories,

investments, and etc.

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2.5.2.1.2 Cash, Sales, and Expenses

It is natural to think that the above underperformance may be due to the fact that the
issuing ﬁrms have already had higher investment level before offering, thus it becomes
more difﬁcult for them to always keep the investment growth rates at a higher level. To
answer this question, we look at other variables to investigate whether issuing ﬁrms’ high
post-issue starting level can cause any difﬁculties in sustaining growth. We mainly
consider issuing ﬁrms’ cash growth rates, sales growth rates and expenses growth rates.
The idea is, if issuers can sustain growth in cash, sales, and expenses, then they should
also be able to sustain investment growth. Especially, they just raise a lot of money in the
capital market and have a higher investors’ expectation to fulﬁll. Otherwise, there must
exist other reasons that produce the underperformance in investment growth. Further, the
evidence of cash growth, sales growth, and expense growth will help us determine
whether the underperformance in investment growth is caused by cash shortage or

managers’ manipulation of cash.

Table 4 (Panel A) shows our evidence on cash growth. We ﬁnd both issuers and their
stylized matches increase cash growth rates before offering, and issuers have more
signiﬁcant increase than their matches. During three years before offering, issuers
generally have higher cash growth than their matches. Especially, during the year before
offering, the issuers have a cash growth rate of 167 percent, compared to 54 percent of
their matches’ cash growth, although part of the reason is the new cash raised in the
ﬁnancial market. After offering, although both issuers and their matches have lower cash

growth rates, issuers continue to have signiﬁcantly better cash growth than their matches

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in next four or ﬁve years except the second year after offering. From Graph 2 (Panel A),
it is clear that issuers’ cash-to-assets ratios decrease after they issue new equity in the
capital market, but continue to outperform their matches’ cash-to-assets ratios. According
to our matching techniques, because matches have similar size, this evidence also

indicates a better cash position in the issuing ﬁrms after offering.

This suggests that the issuing ﬁrms are able to maintain a better cash growth after
offering. In other words, many issuers will not experience cash shortage after offering.
Thus, the underperformance in investment growth should not result from the lack of cash.
The cash shortage hypothesis is not supported by our data. However, right now we still
cannot rule out the possibility that issuers have fewer investment opportunities, and this
may also cause the lower investment growth and higher cash growth. We will address

this point later in this article.

We further examine the sales growth and expenses growth of the issuing ﬁrms. Tables 4
(Panel B) shows the results of sales growth. Consistent with the existing evidence, the
issuing ﬁrms usually have very good sales numbers before offering. Their stylized
matches however do not have such good sales growth. During the three-year window
before offering, the issuing ﬁrms outperform their matches by an average of 16
percentage points in sales growth rates. This situation does not change after offering.
During the three-year window after offering, the issuers continue to outperform their
matches by an average of 13 percentage points, although both issuers and matches have
lower sales growth. The evidence implies that, after offering, the issuing ﬁrms still

manage to maintain better performance in terms of sales growth.

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Tables 4 (Panel C) shows the evidence on expenses growth. We ﬁnd issuers outperform
their matches in expenses growth prior to offering, and continue to have higher expenses
growth afterward. The evidence is statistically signiﬁcant. Our results show that, as
issuers’ sales growth continues to be higher than their matches’ sales growth after
offering, their expenses growth is also higher than their matches’ expenses growth. This
however results in a lower proﬁt margin in the issuing ﬁrms. Combined with the fact that
issuers have enough cash after offering, our ﬁndings raise an interesting question: if
issuers can have higher cash growth, sales growth, and expenses growth, then what

causes a lower investment growth?

From the available evidence, we can conclude that the mean reversion hypothesis is not
supported by our data because (1). we actually ﬁnd a prompt drop in investment growth
only one year after offering instead of a slow and smooth mean-reverting process. (2).
The investment growth will converge to the mean, but we actually ﬁnd the investment
growth drops to the level that is signiﬁcantly lower than the starting point four years
before the offering. (3). It seems that the issuing ﬁrms have no difﬁculties sustaining the
better growth in sales, cash, and expenses. Intuitively, given ample cash, there should
have had a better investment growth as well. However, why do we observe a bigger
decline in investment growth? (4). We do ﬁnd a mean-reverting process in investment
growth for those matching ﬁrms. However, the gap in the investment growth process
between the issuing ﬁrms and their matches is signiﬁcantly bigger than the gap that a
simple mean-reverting process may suggest. That is, the mean reversion hypothesis may

be able to explain a decrease in investment growth, but fails to explain why investment

10]

growth suddenly drops to the level that is lower than their matches’ investment growth,

i.e. an underperformance. This phenomenon thus requires a different theory to explain.

In summary, our evidence on growth in investment, cash, sales, and expenses suggests

the following conclusions about the issuing ﬁrms’ post-issue investment decisions:

I. There should not be any cash problem regarding future investments.
11. A simple mean revision or a diminishing growth cannot convincingly explain the
underperformance in investment growth.

111. Since the issuing ﬁrms have no problems maintaining growth in cash, sales, and
expenses, they should not have any fundamental problems sustaining the growth in
investment, given that they do have similar investment opportunities after issuing.

The underperformance in investment growth should be due to other reasons, especially

non-fundamental reasons.

2.5.2.1.3 Liquidity and Market Sentiment

In the corporate investment literature, corporate investment decisions are usually affected
by investment opportunities, ﬁnancial constraints, and non-ﬁmdamental factors. If the
assumption that ﬁrms’ current investment decisions are not correlated with their past
investment decisions holds, and since the issuing ﬁrms’ stylized matches have higher
investment growth, the issuing ﬁrms should at least have similar investment growth,
especially given ample amount of cash. Therefore, the underperformance in investment

growth should not come from either lack of investment opportunities or lack of cash. We

102

then need to investigate whether the underperformance in investment growth is due to a

non-fundamental reason.

Li (2003) documents a signiﬁcant and negative relationship between corporate
investment and equity liquidity. Because equity liquidity can be treated as a mirror of
investor sentiment (e.g. Baker and Stein (2003)), Li suggests that the negative
relationship can be explained by the market sentiment hypothesis. That is, if high
liquidity followed by low stock returns signals investor sentiment as argued in Baker and
Stein (2003), then for the same reasons, managers also interpret low stock returns as a
consequence of investor sentiment. Thus, both individual investors and managers tend to
hold cash and reduce their investment due to worse-than-expected business conditions
and lack of knowledge about the true value of the risky assets. According to this
argument, we want to study whether issuing ﬁrms experience big liquidity increase
before offering. If we do observe a big increase of liquidity in issuing ﬁrms before
offering, then issuers’ underperformance in investment growth may be due to high
market sentiment. Eckbo, Masulis and Norli (2000) compare the liquidity level of the
issuing ﬁrms to the liquidity level of their matches. They ﬁnd that the issuing ﬁrms
usually have higher liquidity level before offering. However, they do not report any
evidence about change of liquidity. As we mentioned before, because investor sentiment
is most likely to emerge when liquidity signiﬁcantly increases, investor sentiment should

be more related to the change of liquidity instead of liquidity level.

Thus, according to the market sentiment hypothesis, our goal is to ﬁnd whether there is a

signiﬁcant liquidity run-up prior to offering. The evidence shown in Panel A of Table 5

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indicates such a liquidity run-up before offering. For 2066 primary SEO ﬁrms, we ﬁnd
SEO ﬁrms’ stock turnover on average increases by 75.2 percent from three years before
offering to one year before offering. While their matches’ stock turnover on average
increases by 50.7 percent. The difference between above two groups (issuing ﬁrms’
liquidity change minus their matches’ liquidity change) is statistically signiﬁcantly
greater than zero as t-statistic is 5.88. We also test stock turnover run-up during two years
before offering. The primary SEO ﬁrrns’ stock turnover on average increases by 40.3
percent but their matches’ stock turnover increases by 20.6 percent. The difference is
statistically signiﬁcantly greater than zero as t-statistic is 6.18. The evidence suggests a
signiﬁcantly bigger liquidity run-up for primary SEO ﬁrms before offering, and it implies

a stronger investor sentiment in those issuing ﬁrms in a hot market.

During a three-year post-issue window, primary SEO issuers have a signiﬁcant decrease
(average of -l9.4 percent decrease) in stock turnover, compared to their matches’ slight
increase (average of 3.07 percent increase) in stock turnover. The difference between
their changes of liquidity is statistically signiﬁcant (issuing ﬁrms’ liquidity change minus
matches’ liquidity change, t=-24.61). This can be thought as another indicator of the
investor sentiment in the issuing ﬁrms before offering, because their liquidity

signiﬁcantly reverses while their stylized matches’ liquidity continues to increase.

To make sure the market has the same perception for both issuers and their matches, we
again add income performance as one more criterion to select matches. Panel B of Table
5 shows even stronger evidence of investor sentiment in issuing ﬁrms. It is clear that

matches with similar prior-issue income performance actually have a much lower

104

increase of liquidity (only 20.2 percent, compared to 75.2 percent of issuers’ liquidity
change). The difference between them is statistically signiﬁcant (t=9.l6). After offering,
the issuers’ turnover drops by 19.4 percent, while their matches’ turnover continues to
increase by 5.6 percent. The statistically signiﬁcant difference, on the other hand, implies

that issuers do have abnormally big increase in stock turnover.

Overall, our results indicate that both investors and managers generally reduce their
investing interest in the issuing ﬁrms after offering, because they all realize that the
business conditions of these ﬁrms are not as rosy as they were thought to be, and they are
not sure about the true value of the risky assets. As a result, we observe simultaneous less
trading activities (liquidity) in the capital market and reduced investments in the real

economy. This conclusion is, therefore, consistent with the market sentiment hypothesis.

2.5.2.2 Secondary Seasoned Equity Issues

We now turn to study those ﬁrms conducting secondary seasoned equity issues. Different
from the underperformance in stock returns for primary SEOs, the existing evidence
suggests an outperformance in stock returns for secondary SEOs. However, so far there is
no consensus in explaining why secondary SEOs exhibit such an outperformance. In
addition, there is no evidence about how secondary SEO ﬁrms invest around offering

date.

We again start with replicating the existing evidence to make certain our data are
comparable. For 181 secondary SEO ﬁrms, Table 1 shows that there is an

outperformance in stock returns. During a ﬁve-year post-issue window, the issuing ﬁrms’

105

buy-and-hold return (BHR) is 100.39 percent, compared to their stylized matches’ 67.75
percent. The annualized difference of above BHRs is 4.52 percent. Using three-year
window reduces the BHRs, but still indicates a weak outperformance. This is consistent
with Clarke, Dunbar, and Kahle (2003), in which they also ﬁnd outperformance in stock

returns after secondary SEOs, although the results are statistically insigniﬁcant.

Looking at the investment growth, shown in Table 6 (Panel A), we ﬁnd the secondary
SEO ﬁrms outperform their matches in investment growth around the offering dates.
However, unlike the strong underperformance in investment growth for primary SEOs,
they only exhibit very weak underperformance of investment growth during a three-year
window after the ﬁrst year of the offering. The difference of the investment growth
between the issuing ﬁrms and their matches is 1.67 percent, and the difference is not
statistically signiﬁcant. This suggests that there is almost no underperformance in
investment growth for the secondary SEO ﬁrms. Now it is engrossing to see if there is

investor sentiment before offering for those secondary SEO ﬁrms.

An examination of the liquidity changes leads to no evidence of the investor sentiment.
We actually ﬁnd that the issuers have lower liquidity increase than their matches prior to
offering. Table 7 shows that the issuers have an average of 8.8 percent liquidity change
during a three-year window before offering but their matches’ stock turnover on average
increases by 28.7 percent. The difference between above two groups (issuing ﬁrms’
liquidity change minus their matches’ liquidity change) is statistically signiﬁcantly
greater than zero as t-statistic is —2.50. We also test stock turnover run-up during two

years before offering. The secondary SEO ﬁrms’ stock turnover on average increases by

106

 

6.7 percent but their matches’ stock turnover increases by 25.6 percent. Once again, the
difference is statistically signiﬁcantly (t-statistic is —3.52). The evidence suggests a
signiﬁcantly lower liquidity run—up for secondary SEO ﬁrms before offering, and it
implies no evidence of investor sentiment for those secondary equity issuing ﬁrms. After
offering, the issuers’ stock turnover drops while their matches’ turnover continues to
increase, although the increase is small. We ﬁnd that issuers have a 16.9 percent decrease
in liquidity, compared to their matches’ 3.1 percent increase in liquidity after offering.
The difference is statistically signiﬁcant (t=-6.52). Using a more detailed matching

benchmark does not change our results (Shown in Panel B of Table 7).

Nevertheless, the evidence offers a new question, even if there is no investment sentiment
for secondary SEOs, why do they have a decrease in stock liquidity? What causes this
decrease? This can be answered by the nature of the secondary SEOs. We know that the
secondary SEOs are sold by large shareholders instead of ﬁrms. This could send a
negative signal about a ﬁrm’ quality to the market place. Other investors thus will reduce
their investments in the issuing ﬁrms. However, for primary SEOs, the signal is rather
very mixed, because primary SEO ﬁrms may want to raise new funds for future growth.
Combined with the evidence of their matches, it is thus believed that primary SEOs’ post-

issue liquidity decrease is from the investor sentiment.

Looking at income performance, sales, cash, and expenses, we ﬁnd similar but weaker
outperformance as in the primary SEO ﬁrms. The results are reported in Table 6, Panel B,
C, and D. Overall, it seems that the only signiﬁcant difference is from the evidence of

liquidity change. This implies that the different pattern in the investment growth is most

107

likely from the different pattern in the liquidity change, or the investor sentiment. A
closer investigation of the data tells us that, different from the primary SEO ﬁrms, the
secondary SEO ﬁrms are usually larger ﬁrms with lower growth. Although the secondary
SEO ﬁrms experience a little better performance and more active trading prior to
offering, the weak outperformance will not make investors put much weight on the
expectations. This explains why there is no huge liquidity increase for the secondary
issuers prior to offering. Therefore, the investor sentiment for those issuers should not be
high, and the investors and the managers will not be affected. Hence, we do not ﬁnd
strong evidence of the underperformance for either secondary SEO issuing ﬁrms or their

matches.

2.5.2.3 Seasoned Debt Issues

As we demonstrated before, our debt data are comparable to the data in other researches.
Our goal is to explore whether there is an underperformance in investment growth for the
seasoned debt offering ﬁrms (SDOs). The results are shown in Table 8. During the year
before offering, the SDC issuers usually have very high investment growth, but then they
signiﬁcantly underperform their stylized matches in investment growth after offering.
The average difference of investment growth is —7.0 percent and statistically signiﬁcant
(p=0.001) for a three-year window. Similar to the underperformance in investment
growth for primary SEO ﬁrms. the evidence suggests that the SDO managers tend to

invest less after offering.

108

To ensure our results about investment growth are correct for SDO ﬁrms, we also
perform following two robustness checks. First, we check the issuing ﬁrms’ use of
proceeds. We ﬁnd 38.26 percent ﬁrms do not explicitly disclose information about how
they are going to use the new funds. Their uses of proceeds are either “others” or
“general corporate uses”. Like those primary SEOs, this indeed leads to a very ﬂexible
use of proceeds. Further, to consider “mismatched investment opportunities” problem, we
add one more matching benchmark --- similar income performance --- to make sure the
market perception is similar to both issuers and matches. We ﬁnd the identical results: a
signiﬁcant outperformance in investment growth before offering, but a signiﬁcant
underperformance in investment growth after offering. Second, issuers may do more
mergers and acquisitions instead of other investments in ﬁxed assets, it thus seems that
issuers reduce investment growth. To deal with this concern, we ﬁrst look at the use of
proceeds. We ﬁnd the SDO issuers that will use new funds to do “merger and
acquisition” only count for 4.81% in the sample. If we exclude those ﬁrms that explicitly
indicate they will do “merger and acquisition” after offering from the sample, the new
sample yields almost identical results. The evidence conﬁrms the existence of the
underperformance in investment growth for SDOs. For the reason of simplicity, we do

not report these results in this article.

Next, we investigate the growth in cash, sales, and expenses for the SDO ﬁrms. Overall,
the results shown in Table 8 indicate that the issuers usually underperform in the income
performance, but they are able to have better or at least same cash and sales growth than
their matches. The evidence implies that (1) similar to the evidence for primary SEOs,

there should not be any cash problem regarding future investment for SDO ﬁrms, and (2)

109

because the issuing ﬁrms have no problems maintaining growth in cash, sales, and
expenses, they should not have any fundamental problems sustaining the growth in
investment. Therefore, like the case in primary SEO ﬁrms, the underperformance in

investment growth should be also related to the non—fundamental reason.

Now we want to study whether or not the underperformance is due to investor sentiment.
To investigate this issue, we again look at the liquidity change prior to offering. Shown in
Table 9, during three years before offering, the SDO ﬁrms on average have 14 percent
increase in stock turnover. Meanwhile, their matches only have an average increase of 9
percent. The difference is statistically signiﬁcant (t=2.64). To reduce the possibility that
the market has different perception for issuers and their matches, we also add income
performance as one more criterion to select matches. Panel B of Table 9 shows even
slightly stronger evidence. Once again, the results conﬁrm the existence of investor

sentiment.

The evidence of the SDO ﬁrms further conﬁrms our market sentiment hypothesis. It is
well known that ﬁrms conducting seasoned offerings are usually small and fast-growing
ﬁrms. When they decide to raise new funds in the capital market, they wait for a window
in which they have superior performance. However, at the same time, their companies are
inducing high investor sentiment. Both investors and managers think the issuing ﬁrms
would have very good growing opportunities and would continue to have superior
performance. This is then reﬂected in their investing activities. Thus, we usually observe
very signiﬁcant increase in liquidity in the stock market, and very high investment

growth in the real economy. Nevertheless, after managers issue new securities in the

110

capital market, the worse-than-expected performance reveals the existence of investor
sentiment and worse-than-expected business conditions in the issuing ﬁrms, then
investors and managers both realized that things are not as rosy as they were thought to
be, and they do not have the certain knowledge about the true value of the risky assets.
Consequently, we observe signiﬁcant decrease in trading activities in the stock market,
and an underperformance in investment growth in the real economy. Our results also
conﬁrm that the negative adjustments in investment growth are not due to lack of cash, or

lack of investment opportunities.

2.5.3 Robustness Checks

2.5.3.1 Different Matching Benchmarks

We perform some robustness checks to make sure our results are robust. Because book-
to-market ratios reﬂect market perception, and market perception may be different for the
issuing ﬁrms and matching ﬁrms in the ﬁrst place, this may lead to a mismatching
problem. One of the solutions is to add the income performance as one more criterion to
provide similar market perception. In the previous discussion, we have already shown
identical results after we consider similar income performance. To further guarantee our
results are not sensitive to the matching benchmark selection, we exclude the book-to-
market ratio as one of our matches selection criteria. We will match by issuing year,
industry, ﬁrm size and income performance. Later, we also try to match by issuing year,

industry, and ﬁrm size only.

111

To match by issuing year, industry, ﬁrm size and income performance, we ﬁrst limit our
matches to the two years around the issuing date, and ﬁrms with same two-digit SIC
code. Then, we form ten size portfolios and ﬁve income performance portfolios for all
available matching candidates. We select at least one but at most ﬁve ﬁrms for each
issuer. Table 10 shows our results. Clearly, the underperformance in investment growth
after offering shows up in our new sample. As we expect, the issuers also have
signiﬁcantly higher increase in stock turnovers than their size-and-income performance
matched ﬁrms. To rule out the cash shortage hypothesis, we further study the growth of
cash, sales, and expenses. Again, we ﬁnd that the primary SEO issuers can continue to
sustain growth in sales, cash, and expenses. The SDO ﬁrms have a little different pattern.
We ﬁnd the SDO ﬁrms usually do not have better performance in sales, cash, and
expenses. However, no difference between the issuers and their matches is statistically
signiﬁcant. This implies that the SDO ﬁrms should at least have similar performance.
Using a new matching benchmark conﬁrms that the underperformance in investment

growth is due to high investor sentiment, instead of different matching techniques.

Only matching by ﬁrm size does not change the underperformance pattern in the
investment growth. We form 10 portfolios for all available matching candidates, and then
select at least one and at most ﬁve matching ﬁrms for each issuer. The underperformance
in investment growth is shown in Table 11. This underperformance pattern is also true for
the SDO ﬁrms. Further, we investigate the liquidity change and ﬁnd that the issuers’
liquidity increase is signiﬁcantly bigger than their size-matched ﬁrms. Other patterns of
sales, cash, and expenses also remain the same. For the reason of simplicity, we do not

report the results in this article.

112

2.5.3.2 Regression Analysis

Next, we want to use regression analysis to conﬁrm the market sentiment hypothesis. The
advantage of the regression analysis is that the signiﬁcance level can be calculated. If
there is a signiﬁcant negative adjustment in investment growth after high investor
sentiment (liquidity change), one would expect to see this adjustment captured by the
regression analysis. We control for year dummies because we wan to see whether this

relationship shows up for only a few hot issuing years.
The regression model is the following,

INVGi = a + bLIQCI- + cSEODummyi +
1996

2 dj YRDummyj + e,-
j=1985
where [N V G is the investment growth rate for a window of three-year after the ﬁrst year
of issuing. LIQC is the change of the stock turnover measured in percentage during three
years before offering. We also include year dummies between 1985 and 1996. Variable
SEODummy is another durmny variable that takes value of one if the observation is a

seasoned security issuer, and zero if it is a match. In our regression sample, we limit one

match for each issuer.

Table 11 shows our regression results. As we expect, the coefﬁcient of the stock turnover
change is —0.02, and it is statistically signiﬁcant (t=-2.24). The negative sign conﬁrms

that the relationship between liquidity change (investor sentiment) and the investment

113

growth rate is negative. We then split the sample into two subsamples: one has only
negative or zero liquidity change, and the other one has positive liquidity change. If
investor sentiment emerges only when stock turnover increases, then only the sample
with positive stock turnover changes will exhibit signiﬁcant negative relationship. The
results from subsample regressions conﬁrm this is the case. In addition, for those issuing
ﬁrms with positive liquidity changes, the coefﬁcient not only remains statistically
signiﬁcant, but also becomes greater (coefﬁcient=-0.02, t=-2.53). However, for the
primary SEOs that have negative or zero turnover changes during three years before
offering, the coefﬁcient of the liquidity change is statistically insigniﬁcant. Our results
imply that the bigger the investor sentiment, the stronger the negative adjustment in

investment growth.

Finally, following Amihud (2000) and Li (2003), we also try a different measure of
liquidity: dollar liquidity. The dollar liquidity is obtained by applying following
procedures: we ﬁrst ﬁnd ratio of daily volume (dollar volume) to daily absolute return,

and then average all ratios for each year and each ﬁrm.

 

DLIQi = Z abs(r)

T [ Dollar Volume ]
t=l

According to Amihud (2000), if ratio of absolute return to volume is a measure of
illiquidity, then the inverse should be a measure of liquidity. The use of another liquidity
measure does not qualitatively change our results, we thus do not report the results in this

article.

114

2.6 Conclusions

We investigate how seasoned offering ﬁrms invest in this article. We ﬁnd a signiﬁcant
long-term underperformance in investment growth for primary seasoned equity offering
ﬁrms and seasoned debt offering ﬁrms. This underperformance can be explained by our
market sentiment hypothesis, i.e., high liquidity followed by low stock returns signals
investor sentiment as argued in Baker and Stein (2003), then for the same reason,
managers also interpret low stock returns as a consequence of investor sentiment. Thus,
both individual investors and managers tend to reduce their investment due to worse-
than-expected business conditions and lack of knowledge about the fundamental value of
the risky assets. As a result, both investors and managers tend to be conservative in
investment and prefer to hold cash. This thus leads to an underperformance in investment
growth, compared to their stylized matches. In addition, we ﬁnd this post-issue negative
adjustment in investment growth is signiﬁcantly and negatively related to the investor
sentiment being developed prior to offering. Our extensive robustness checks conﬁrm our

conclusions.

Our conclusions are important in the following ways. First, the underperformance in
investment grth conﬁrms the well-known argument that ﬁrms’ stock returns and their
investment growth rates should behave similarly. We prove this is true for the seasoned
offering ﬁrms. Second, the negative relationship between investor sentiment and
corporate investment found in Li (2003) is supported by our seasoned offering data. This
shows that the corporate investment can also be signiﬁcantly affected by the non-

fundamental components in the ﬁnancial market. Third, our results provide a new

115

research direction in explaining well debated “new issues puzzle”. In the existing
literature, the “new issues puzzle” usually refers to the long-term underperformance in
the equity returns. However, our evidence indicates a new long-term underperformance
in investment growth for the seasoned offering ﬁrms. Therefore, we suggest that the two
types of the underperformance should be explained together to provide a reasonable

explanation to the “new issues puzzle”.

In this sense, the results help us to reconsider some possible explanations. In the
literature, four well-known hypotheses about the long-term underperformance in stock
returns are: lowered risk hypothesis, market sentiment hypothesis, earning management
hypothesis, and agency hypothesis. Combined with the results found in this article, the
market sentiment hypothesis is clearly the one that can explain two types of
underperformance together. The lowered risk hypothesis is capable to offer a reason for
the underperformance in equity returns but fails to explain the underperformance in
investment growth. Because there is so far no particular theory that can explain why
lowered risk can result in lowered investment. Actually, if lowered risk suggests cheap

cost of ﬁnancing, then it would rather lead to higher investment.

The earning management hypothesis cannot simultaneously explain the two types of
underperformance either. The earning management hypothesis suggests that the issuing
managers tend to borrow future earnings to dress the offering window prior to offering.
Thus, this will lead to some bad earning numbers and bad performance in stock returns
afterward. However, the earning management is inconsistent in the following sense. To

fulﬁll investors’ high expectations, if managers really cannot deliver sound earning

116

numbers, they need to do other things. Making more investments is obviously one of the
most efﬁcient ways. Nevertheless, the evidence shows that those issuing ﬁrms
underperform their matches in investment growth after offering, given that they have
enough cash. Finally, the agency hypothesis in Jung, Kim, and Stulz (1996) suggests that
managers tend to squander corporate resources when given the opportunities, although
this may not be intentional. Clearly, our evidence of the underperformance in investment
growth does not support this hypothesis. Another noteworthy fact is that it seems
investors never learn a lesson in the market. This suggests that investors have their own
problems identifying or processing the correct signals sent by the issuing ﬁrms. Hence,

they are more likely to develop investor sentiment when market becomes hot.

Our results conclude that the market sentiment hypothesis is able to explain both types of
underperformance together, or at least, market sentiment is the dominant factor in this

context.

117

APPENDIX 2A Tables and Figures

118

Table 1: Number of seasoned offerings by year and industry

The sample includes all available ﬁrms that conduct seasoned offerings between 1985 and 1996. We exclude ﬁrms that
issue twice in ﬁve years. We choose all ﬁrms that have valid ﬁnancial and accounting numbers. The ﬁrms with
negative accounting numbers for book assets. capital, or investments are ignored. We also exclude ﬁrms with assets
less than 5 million, and extreme observations. We delete observations with SIC code between 491 1 and 4941 (utilities),
between 6000 and 6081 (ﬁnancial institutions), and 6722, 6726, 6792 (investment funds and REITs). The two-digit
Standard Industry Classiﬁcation codes (SIC code) are used.

 

Panel A: Number of primary SEOs by year and industry

 

 

Number of Primary SE03 bLCaIendar Year

 

 

Year Number of Primary SEOs Percentage of Sample
1985 95 4.60%
1986 153 7.41%
1987 124 6.00%
1988 55 2.66%
1989 91 4.40%
1990 73 3.53%
1991 220 10.65%
1992 186 9.00%
1993 251 12.15%
1994 179 8.66%
1995 286 13.84%
1996 353 17.09%
Total 2066 1 00.00%

 

 

Number of Primary SEOs by Industrial Classification

¥

 

\ Industry SIC code Number of Primary SEOs Percentagg
Chemicals, pharmaceuticals, and biotech 28 242 11.71%
Office and computer equipment 35 194 9.39%
Communication and electronic equipment 36 235 11.37%
Transportation equipment 37 40 1.94%
Measuring, analyzing, and controlling instruments 38 144 6.97%
Wholesale trade durable goods 50 99 4.79%
Eating and drinking places 58 58 2.81%
Miscellaneous retail 59 66 3.19%
Computer and data processing services 73 216 10.45%
Health services 80 101 4.89%
Engineering, accounting, research, and others 87 44 2.13%
Other - 627 30.35%
Total - 2066 100.00%

 

119

Table 1: Number of seasoned offerings by year and industry (Continued)

 

Panel B: Number of Secondary SEOs by year and industry

 

 

Number of Secondary SEOs bﬂalendar Year

 

 

Year Number of SecondarLSEOs Percentage of Sample
1985 18 9.94%
1986 23 12.71%
1987 12 6.63%
1988 7 3.87%
1989 6 3.31 %
1990 7 3.87%
1991 1 5 8.29%
1992 25 13.81%
1993 27 14.92%
1994 18 9.94%
1995 12 6.63%
1996 1 1 6.08%
Total 1 81 100.00%

 

 

Number of Secondary 8503 by Industrial Classiﬁcation

 

 

Industry SIC code Number of Secondary SEOs Percentagg

Food and kindred products 20 6 3.31%
Apparel and others 23 7 3.87%
Printing, publishing, and allied industries 27 5 2.76%
Chemicals. pharmaceuticals, and biotech 28 11 6.08%
Ofﬁce and computer equipment 35 10 5.52%
Communication and electronic equipment 36 17 9.39%
Measuring, analyzing, and controlling instruments 38 5 2.76%
Miscellaneous manufacturing industries 39 8 4.42%
Communications 48 8 4.42%
Wholesale trade durable goods 50 5 2.76%
Wholesale trade non-durable goods 51 9 4.97%
Apparel and accessory stores 56 6 3.31%
Miscellaneous retail 59 8 4.42%
Computer and data processing services 73 14 7.73%
Other - 62 34.25%

Total - 181 100.00%

 

120

Table 1: Number of seasoned offerings by year and industry (Continued)

 

Panel C: Number of SDOs by year and industry

 

 

Number of SD03 by Calendar Year

 

 

Year Number of Secondary SDOs Percentage of Sample
1985 97 12.09%
1986 136 16.96%
1987 75 9.35%
1988 30 3.74%
1989 50 6.23%
1990 34 4.24%
1991 47 5.86%
1992 78 9.73%
1993 81 10.10%
1994 37 4.61%
1995 54 6.73%
1996 83 10.35%
Total 802 100.00%

 

 

Number of SDOs by Industrial Classiﬁcation

 

 

Industry SIC code Number of 8003 Percentage
Oil and gas 13 55 6.86%
Food and kindred products 20 38 4.74%
Paper and allied products 26 36 4.49%
Chemicals, pharmaceuticals, and biotech 28 74 9.23%
Office and computer equipment 35 66 8.23%
Communication and electronic equipment 36 42 5.24%
Transportation equipment 37 39 4.86%
Measuring, analyzing, and controlling instruments 38 39 4.86%
Computer and data processing services 73 35 4.36%
Health services 80 34 4.24%
Other - 344 42.89%

 

Total - 802 100.00%

121

 

 

 

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124

Table 5: The indicator of investor sentiment: liquidity run-up (Primary SEOs)

The proxy for liquidity is stock turnover, which is average ratio of daily trading volume to total shares outstanding.
Variable Tob3_l is the stock turnover growth rate for a time window of three years before offering. Variable To3_l is
the stock turnover growth rate for a time window of three years after offering. T-statistics are reported in the table. In
Panel A. matches are selected based on year. industry, size, and BEIME. ln Panel B, matches are selected based on

year, industry. size. BE/ME, and income performance.

Panel A: The evidence on liquidity

 

 

 

 

 

 

Before Mean 95% Conﬁdence Interval t-statistic (diff.>0)
tob3_1 0.752 [0.697, 0.808]
tob3_1_m 0.507 [0.442, 0.572]
tob3_1-tob3l1_m 0.245 [0.163, 0.327] 5.88
After Mean 95% Conﬁdence Interval t-statistic (diff.>0)
to3_1 -0.194 {-0.206, -0.183]
t03_1_m 0.031 [0.016, 0.046]
t03_j-to3_1_m 0225 [0163,0327] -24.61

 

 

Panel B: The evidence on liquidity (different matches)

 

95% Conﬁdence Interval t-statistic [diff.>0)

 

 

[0.333, 0.627] 9.16

 

 

 

 

95% Conﬁdence Interval t-statistic (diff.>0)

 

 

Before Mean
tob3_1 0.751 [0.697, 0.807]
tob3_1_m 0.202 [0.177, 0.230]
tob3!1 -tob3_1_m 0.541
After Mean
to3_1 -0.195 {-0.206, -0.186]
t03_1_m 0.056 [0.036, 0.076]
t03_1-t03_1_m -0.251

{-0.293, -O.207] -26.67

 

 

125

 

 

 

 

 

 

 

 

 

 

 

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126

Table 7: The indicator of investor sentiment: liquidity run-up (Secondary SEOS)

The proxy for liquidity is stock turnover. which is average ratio of daily trading volume to total shares outstanding.
Variable 'l'ob3_l is the stock turnover growth rate for a time window of three years before offering. Variable To3_l is
the stock turnover growth rate for a time window of three years after offering. T-statistics are reported in the table. In
Panel A. matches are selected based on year. industry. size, and BE/ME. ln Panel B, matches are selected based on
year. industry, size, BE/ME, and income performance.

Panel A: The evidence on liquidity

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Before Mean 95% Conﬁdence Interval t-statistic (diff.>0]

tob3_1 0.088 {-0.026, 0.203]

tob3_1_m 0.287 [0.172, 0.403]

tob3L1-tob3_1_m -0.200 {-0.356, -0.042] -2.50

After Mean 95% Conﬁdence Interval t-statistic (diff.>0)

t03_1 -0. 169 {-0.208, -O.130]

to3_1_m 0.031 {-0.017, 0.079]

to3_1-t03_1_m -O.200 {-0.260, -O.140] -6. 52
Panel B: The evidence on liquidity (different matches)

Before Mean 95% Conﬁdence Interval t-statistic (diff.>0)

tob3_1 0.098 [0.035, 0.175]

tob3_1_m 0.226 [0.160, 0.292]

tob3_1-tob3_1_m -O.127 {-0.206, -0.036] -2.81

After Mean 95% Conﬁdence Interval t-statistic (diff.>0)

t03__1 -0.160 {-0.171, -0.145]

t03_1_m 0.023 [0.011, 0.037]

to3_1-to3_1_m -0.184 {-0.201, -0.168] -21.98

 

 

127

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128

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Table 9: The indicator of investor sentiment: liquidity run-up (SDOs)

The proxy for liquidity is stock turnover. which is average ratio of daily trading volume to total shares outstanding.
Variable Tob3_l is the stock turnover growth rate for a time window of three years before offering. Variable To3_l is
the stock turnover growth rate for a time window of three years after offering. T-statistics are reported in the table. In
Panel A. matches are selected based on year. industry, size, and Bli/ME. ln Panel B, matches are selected based on
year, industry, size, BE/ME. and income performance.

Panel A: The evidence on liquidity

 

 

 

 

 

Mean 95% Conﬁdence Interval t-statistic (diff.>0)
tob3_1 0.14 [011, 0.16]
tob3_1_m 0.09 [0.07, 0.12]
tob3_1-tob3_1_m 0.05 [0.01 , 0.08] 2.64

Mean 95% Conﬁdence Interval t-statistic (diff.>0)
t03_1 0.01 {-0.01, 0.03]
to3_1_m 0.003 {-0.02, 0.02]
to3_1-to3_1_m 0.007 {-0.02, 0.03] 0.485

 

 

Panel B: The evidence on liquidity (different matches)

 

 

 

 

 

Mean 95% Conﬁdence Interval t-statistic (diff.>0)
tob3_1 0.14 [011, 0.16]
tob3_1_m 0.09 [0.06, 0.13]
tob3_1-tob3_1_m 0.05 10.01, 0.08] 2.89

Mean 95% Conﬁdence Interval t-statistic (diff.>0)
t03_1 0.01 {-0.01, 0.03]
t03_1_m 0.04 [0.01, 0.08]
t03_1-t03_1_m -0.03 {-0.08, 0.03] -2.99

 

 

129

 

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131

Table 11: Regression analysis (Primary SEOs)

The sample contains 2206 primary SEOs between I985 and I996. Van'able 1N VG is the investment growth in a period
of three years after the first year of offering. LIQC is the stock turnover change during a period of three years before
offering. SEODUMMY is a dummy variable. It takes a value of 1 if the ﬁrm is a primary SEO. YRDummy is year
dummy variable. Each primary SEO has only one match in the sample. Panel A reports the regression results using all
primary 8503. In Panel B, we only use the primary SEOs that have positive turnover changes during three years before
offering. Panel C shows the regression results of the primary $503 that only have negative or zero turnover changes
during three years before offering. The regression speciﬁcation is the following

1996
INVGi = a + bLIQCi + cSEODummyi + Z d j YRDummy j + e,-
j=l985

Robust t-statistics are reported in parentheses.

Panel A: Regression results using whole sample

 

Parameter Estimates

 

 

Intercept LIQC SEODummy R _§qured Observations
0.07 -0.02 -0.17
(4.76) (-2.24) (-2.85) 2.70% 1916

 

 

Panel B: Regression results only using the primary SEOs that have positive turnover
changes before offering

 

Parameter Estimates

 

 

Intercept LIQC SEODummy R_squred Observations
0.07 -0.02 -0.28
(3.53) (-2.53) (-3.38) 3.20% 1030

 

 

Panel C: Regression results only using the primary SEOs that have negative or zero
turnover changes before offering

 

Parameter Estimates

 

 

Intercept LIQC SEODummy R_squred Observations
0.63 0.17 -0.04
(2.45) (0.75) (—0.51) 3.60% 886

 

 

132

Figure l: The underperformance in investment growth (Primary SEOs)

The investment growth rate is deﬁned as the difference between this year’s capital expenditure and last year’s capital
expenditure, divided by last year‘s capital expenditure. The investment level is calculated as a ﬁnn‘s capital
expenditure to last year's assets ratio. The matches are selected based on year, industry, size and BE/ME ratios.

Panel A: The underperformance in investment growth

 

The underperformance in investment growth I

 

 

 

I 0.60 I
I 0.50 :
0.40 ~ I
I , -- -I
I 0.20 Illssuers W

- 0.10 LI M70000;
I 0.00 I
I
I

Panel B: The evidence on investment level

' _ 7'” i T" T ' ’ T ’ ’ TI
Eveldence on Investment level ‘
I
I
. 0--- , .I
‘ ““~~ Ilssuers II
‘ ‘IMstcheiI

 

 

133

Figure 2: The evidence on cash, sales, and expenses (Primary SEOs)

The cash growth is deﬁned as a ﬁrm’s this year’s cash or cash equivalent minus last year’s cash or cash equivalent, and
then divided by last year's cash or cash equivalent. The sales or expenses growth rates are calculated by using similar
approaches. The cash level is deﬁned as a ﬁrm’s cash or cash equivalent to last year‘s assets ratio. The sales or
expenses levels are calculated by using similar approaches.

 

Panel A: Cash growth vs. Cash level

 

Cash Growth

Maw”..- . -~ -

 

@ Issuers ‘
watches. I

 

 

 

 

I
I
I
I
I

itiillssers I
iIMatchesf{
"' I

 

 

 

 

 

 

134

Figure 2: The evidence on cash, sales, and expenses (Primary SEOs) (continued)

Panel B: Sales growth vs. Sales level

 

 

 

 

 

 

 

   

Sales Growth
0.50 ‘
0.40
0.30 7
0.20 TalssﬁuerST
010 I'MatchesI
0.00
Sales Level
2 - _- 2.---.. a
I
issue-s .
[IMatchesI
Issuers

 

 

 

 

135

Figure 2: The evidence on cash, sales, and expenses (Primary SEOs) (continued)

 

Panel C: Expenses growth vs. Expenses level

I Expenses Growth

I

gags-.3: i I
@2222» -

I
I
I

II Issuersir‘ ‘
I !_M§tcht§ I I

 

 

136

Figure 3: The underperformance in investment growth (Secondary SEOS)

The investment growth rate is deﬁned as the difference between this year’s capital expenditure and last year’s capital
expenditure, divided by last year’s capital expenditure. The investment level is calculated as a ﬁrm’s capital
expenditure to last year’s assets ratio. The matches are selected based on year, industry, size and BE/ME ratios.

Panel A: The underperformance in investment growth

Investment growth

I
I
I
I
I
I

 

 

 

0.50
I 0.40
‘ 0.30
I 0.20 I 583547 I I
‘ 0.10 IEMichéri II
0.00 I
I
Panel B: Evidence on investment level
Investment Level I
I
I 0- 0. :-
I IIIssuers I
I!,M'L‘°I‘2§I

 

 

I37

Figure 4: The underperformance in investment growth (SDOs)

The investment growth rate is deﬁned as the difference between this year’s capital expenditure and last year's capital
expenditure, divided by last year’s capital expenditure. The investment level is calculated as a ﬁnn’s capital
expenditure to last year's assets ratio. The matches are selected based on year, industry, size and BE/ME ratios.

Panel A: The underperformance in investment growth

I The underperformance in investment growth

  

  
 
  

 

 

 

 

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Panel B: Evidence on investment level
Evidence on investment level
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I38

REFERENCE Reference for Chapter 2

Amihud, Y., 2002, “llliquidity and stock returns: Cross-section and time-series effects”,
Journal of Financial Markets 5: 31-56

Baker, M., Stein, J. and J. Wurgler, 2003, “When does the market matter? Stock prices
and the investment of equity-dependence ﬁrms”, Quarterly Journal of Economics,
Forthcoming

Barber, Brad M., and John D. Lyon (1997) "Detecting Long-Run Abnormal Stock
Returns: The Empirical Power and Speciﬁcation of Test-Statistics," Journal of Financial
Economics 43, 341-372.

Barberis, Nicholas, and Richard Thaler (2002) “A Survey of Behavioral Finance,”
Handbook of Financial Economics, 2003

Bayless, Mark, and Susan Chaplinsky (1996) “Is There a ‘Window of Opportunity’ for
Seasoned Equity Issuance?” Journal of Finance 51, 253-278

Billett, Matthew, Mark J. Flannery, and Jon A. Garﬁnkel (2001) “The Long-Run
Performance of Firms Following Loan Announcements,” unpublished University of Iowa
and University of Florida working paper.

Brav, Alon (2000) “Inference in Long-Horizon Event Studies: A Bayesian Approach with
Applications to Initial Public Offerings,” Journal of Finance 55, 1979-2016.

Brav, Alon, and Paul Gompers (1997) “Myth or Reality? The Long-Run
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