THE 1') '5 AN STAT l SiITY LIBRARIE lllllllllllll will mm; H 3 1293 01583 4660 ill L l 5"" *1 ’. “"3? Ln ”a" M ‘49- -— A I m 7-... '3 o , F4 -4 i ”fluA-WIKI‘. "3;: ‘..-—.> I 0 . _ “A- I -[.';: .‘1 “0' a" --- i V‘- '1- -' This is to certify that the dissertation entitled The Effect of The Motor Carrier Act of 1980 on The Risk and Return Characteristics of Common Equity Issues presented by Duncan J. Kretovich has been accepted towards fulfillment of the requirements for Ph. D. degree in Business Administration /Zv 14/ 44vij George W. Kutner Major professor Date 18 November 1985 MSU is an Affirmative Action/Equal Opportunity Institution 0-12771 MSU LIBRARIES “ RETURNING MATERIALS: Place in book drop to remove this checkout from your record. FINES will be charged if book is returned after the date stamped below. ~20a ”Q a THE EFFECT OF THE MOTOR CARRIER ACT OF 1980 ON THE RISK AND RETURN CHARACTERISTICS OF COMMON EQUITY ISSUES By Duncan J. Kretovich A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Finance and Insurance 1986 Copyright by DUNCAN J. KRETOVICH 1986 ABSTRACT THE EFFECT OF THE MOTOR CARRIER ACT OF 1980 ON THE RISK AND RETURN CHARACTERISTICS OF COMMON EQUITY ISSUES By Duncan J. Kretovich The objective of this research was to measure the effect of the passage of the Motor Carrier Act of 1980 (MCA) on the common stocks of motor carriers. Capital market data were analyzed to determine how the risk and return characteristics changed for a sample of motor carriers for a time period before and after the passage of the Motor Carrier Act. Congress passed the Motor Carrier Act based on arguments that the existing economic regulation no longer protected the consumers of motor carrier services. The motor carrier industry, represented by the management of the carriers and the Teamsters union, opposed deregula- tion. Industry opposition was based on the argument that deregulation would lead to "cut-throat" competition and result in the loss of ser- vice to some communities. Additionally, management argued that deregu- lation would cause an adverse reaction in the capital markets to the securities of the motor carriers. Management maintained that the adverse reaction would take the form of an increased level of risk and higher required rates of return by the market. Estimates of systematic risk and abnormal return performance were obtained from the market model on a sample of publicly traded motor carriers. T—tests were done to determine how risk estimates changed before and after passage of the MCA and if abnormal returns existed around the passage of the MCA. The uniqueness of this study is that tests for changes in risk were performed. The results of the analysis were that the total risk of trucking securities did not change after deregulation. However, the systematic risk estimates changed significantly downward. The results of the cumulative average residual analysis indi- cated that significant positive abnormal returns were available begin- ning in August of 1980. Since the systematic risk of the portfolio declined after deregulation, the abnormal returns found after August 1980 were biased downward. Thus, investors earned higher abnormal return-1 than were indicated by the cumulative average residual analys DEDICATION This dissertation is dedicated to Susan Moeller. ii ACKNOWLEDGMENTS I would like to thank my committee members, Drs. George Kutner, Kenneth D. Boyer, and Paul Rubin for their assistance with this research. I am grateful for all the valuable advice and guidance provided by my committee members throughout all phases of the study. iii Chapter TABLE OF CONTENTS I INTRODUCTION I O O O O O O O 0 O 0 Research Objective . . . . . . Institutional Setting. . . . . Theoretical Framework. . . . . II LITERATURE REVIEW. . . . . . . . . Economic Regulation. . . . . . Research Methodology for Event III RESEARCH DESIGN AND METHODOLOGY. . Objectives . . . . . . . . . . The Market Model . . . . . Risk Measurements. . . . . . . Abnormal Performance . . . . Statistical Testing Procedures . IV RESEARCH RESULTS Changes in Risk Measures . . Studies Cumulative Average Residual Analysis . V SUMMARY AND CONCLUSIONS. . . . . Summary. . . . . . . . . . Conclusions. . . . . . . . . . Implications of the Results. . Limitation of the Study. . . . Suggestions for Future Research. . . . LIST OF REFERENCES . . . . . . . . . . . APPENDIX A . APPENDIX B . APPENDIX C . iv Page \ObN 17 17 22 26 26 27 29 31 32 37 45 52 52 54 56 57 58 61 66 67 68 Page APPENDIX D . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 APPENDIX E . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Table LIST OF TABLES Summary of Changes in Systematic Risk . . . . . Summary of Changes in Total Risk. . . . . . . . Summary of Changes in Unsystematic Risk . . . . Summary of Changes in the Coefficient of Variation. Annual Changes in Systematic Risk . . . . . . . Annual Changes in Total Risk. . . . . . . . . Annual Changes in Unsystematic Risk . . . . . . Annual Changes in the Coefficient of Variation. vi Page 39 40 40 41 42 43 43 44 Figure LIST OF FIGURES Page Cumulative Residuals June 1979 to June 1980 O O O O O O O O O I O O O O O O O 49 Cumulative Residuals June 1980 to June 1981 . . . . . . . . . . . . . . . . . 50 Cumulative Residuals June 1981 to June 1982 . . . . . . . . . . . . . . . . . 51 vii LIST OF FIGURES Figure Page 1 Cumulative Residuals June 1979 to June 1980 . . . . . . . . . . . . . . . . . 49 2 Cumulative Residuals June 1980 to June 1981 . . . . . . . . . . . . . . . . . 50 3 Cumulative Residuals June 1981 to June 1982 . . . . . . . . . . . . . . . . . 51 vii CHAPTER I INTRODUCTION In June 1980, the Motor Carrier Act of 1980 (MCA) was signed into law. The MCA ended most of the economic regulation of the trucking industry. It eliminated the rigid pricing structure for services, the privately owned rights to carry freight between cities, and the bar- riers that prevented new firms from entering the interstate freight business. The motor carrier industry has been regulated by the Interstate Commerce Commission (ICC) since 1935. The ICC has regulated rates, routes, entry, exit, and quality of service. The actual administration of the ICC Act of 1935 was delegated to regional rate bureaus con- trolled by the trucking companies in each region. The close associa- tion of the regulated firm with its regulators might have caused inves- tors to perceive that an implicit level of income would be assured to the motor carriers. The expectation that trucking companies would earn a certain amount of income might have influenced how investors per- ceived the level of risk and returns on the common stocks of motor carriers. Beginning in the 1960's academics questioned the value of the existent regulatory process and policies. The issues raised in the academic studies gave rise to policy studies which ultimately led to the Motor Carrier Act of 1980. re and 2 Investors evaluated many variables when assessing the common stock of a motor carrier as a potential investment. A partial list of the variables included: 1. amount of debt relative to equity, 2. stability of management, 3. long term strength of consumer demand, 4. predictability of operating cash flows. Due to the many years of close collaboration between motor carriers and the rate bureaus, and the strict entry controls and constraints on competition, the uncertainty surrounding operating cash flows was minimized. Deregulation might have caused the stock market to revalue the common stocks of motor carriers. There were several reasons for this: 1. Because there was more latitude in the pricing of services, the cash flow from revenues was not as predictable. 2. Without the barriers to entry that existed before deregulation, motor carriers were confronted by new carriers competing for a share of the market. 3. Exemption from antitrust legislation allowed rate bureaus to foster price collaboration. 4. The balance sheets of motor carriers previously listed as assets the route certificates purchased from other companies. With deregulation these intangible assets became worthless, thereby increasing the debt ratios of the firms. This research examined the effect of deregulation on the risk and returns to the common stockholders of motor carriers. Research Objective The primary objective of the study was to determine how the risk and return characteristics of the common stocks of motor carriers 3 changed at the time the motor carrier industry was deregulated. The following empirical questions were addressed using capital market data: Were abnormal returns1 earned by investors who purchased the common stocks of trucking firms after deregulation? Or in other words, did the market react to the news of deregulation efficiently and incor- porate the news into its pricing mechanism for the trucking firm. The effects of the MCA were also evaluated by determining whether or not the systematic and total risk of the motor carriers shifted signifi- cantly around the time the MCA was passed into law. The risk-return measures were estimated using the market model and capital market data. The cumulative average residual technique was then applied to the data to determine if the stocks of motor carrier firms earned abnormal returns after deregulation, and if the market incorporated the news of deregulation efficiently into the prices of these stocks. The formal research hypotheses were stated as follows: 1. H : The purchase of the common stocks of motor carrier firms did not result in abnormal returns for investors. H : The purchase of the common stocks of motor carrier firms did result in abnormal returns for investors. 2. H : The risk of motor carrier firms did not change after deregulation. H . The risk of motor carrier firms did change after deregulation. These questions and the results of the tests of the hypotheses are important to economic policy makers. Additionally, investors might 1In the context of event studies, abnormal return means the actual return is different from the expected return when the expected was calculated without the information about the event (Spiller, 1983). 4 be better able to form expectations about the effects of major economic regulatory events. Institutional Setting The decade of the 1970's may be marked in the annals of economics as the beginning of a period. The period was characterized by a re-examination of traditional economic regulation and the subsequent reduction or elimination of the regulation. The actions taken were best described as regulatory reform. The regulatory reform movement of unprecedented proportions has touched all areas of regulation from health and safety to banking practices to transportation. Transportation is closely tied to social and economic forces.2 It is a very visible industry whose actions have far reaching conse- quences to the economy. The very existence and survival of many com- munities were tied to the availability of transportation. The first federal regulatory agency to have jurisdiction over a transportation mode was the Interstate Commerce Commission (ICC), formed in 1887 to regulate railroads. The regulatory system for railroads was originally implemented to assure the availability of transportation at a reason- able cost to farmers. Since the inception of the ICC, airlines and motor freight have developed and been included under the Civil Aero- nautics Board (CAB) and ICC umbrellas. Accompanying the proliferation of transportation modes has been a number of regulatory bodies for the respective modes. Motor freight came under the jurisdiction of the ICC with the Motor Carrier Act of 1935. The airlines were removed from ICC jurisdiction and provided an exclusive regulatory agency in 1938 with 2Department of Transportation, 1974 Annual Report, p. vii. 5 the establishment of the Civil Aeronautics Board (CAB). Water-borne commerce (inland, Great Lakes, and ocean) also came under the authority of various regulatory agencies. The regulatory bodies have promulgated extensive regulations and rulings since their respective inceptions. These regulations have developed into a mosaic of conflicting and contradictory rules that have been extensively criticized. The ICC came under attack from consumer groups, congressional quarters, and from every U.S. President beginning with John F. Kennedy and continuing through Ronald Reagan. The central theme of all the criticism was that increased consumer costs for services resulted from the regulations. The magnitude and critical nature of the impact of the business costs attributable to the regulatory constraint has been estimated to range between nine and sixteen billion dollars annually.3 Critics of regula- tion contend that the regulatory constraint misallocates resources.4 Additionally, investors believe that the regulatory environment in transportation has constrained the ability of companies to earn higher or less risky returns.S By the late 1970's many American economists had come to the con- clusion that ICC regulation was protective of the carriers and that, far from reducing costs to users, regulation almost certainly increased 3Davis, Grant M. and Leon J. Rosenberg. "Physical Distribution Management and Independent Regulatory Commissions: An Empirical Assessment," Research Issues in Logistics, ed. James F. Robeson, The Ohio State University, 1975, pp. 95. 4Weiss, Leonard W. "The Regulatory Reform Movement," in Case Studies in Regulation, Revolution and Reform, Weiss, Leonard W. and Klass, Michael, eds., Little, Brown, and Company, Inc., 1981, pp. 3. 5Department of Transportation, Internal Memorandum by J. L. Hazard, January 26, 1972. costs by perhaps billions of dollars a year.6 These concerns and criticisms led to the increased interest in regulatory reform of the transportation industry. The regulatory reform movement met its first legislative success in 1975 with the deregulation of stock brokerage fees. The Securities and Exchange Commission (SEC) ordered the stock exchanges to end the practice of fixing brokerage fees. The result was the deve10pment of discount brokers and negotiated fees. In spite of the dire predictions of disaster, the brokerage industry continued to operate without major problems. Transportation was the next arena for the regulatory reform movement. One of the most visible actions of the Administration of President Jimmy Carter was the reduction in airline regulation by the Civil Aeronautics Board (CAB). This deregulation movement possibly had its beginning during the Administration of President John F. Kennedy with the introduction of several bills in Congress calling for changes in the regulatory systems.7 The bills failed to win legislative approval and the regulatory systems remained unchanged. During the Nixon Administration and continued by President Gerald Ford, discus- sions of regulatory reform were again raised. The discussions did not lead to the passage of legislation. It was not until the Carter Administration that the debate and academic research were successful in challenging the regulatory status-qua. In 1975 the U.S. Senate held hearings on the CAB where the tradi— tional concerns and criticisms of regulation were aired. Before the 6Op. Cit., Weiss, pp. 2. 7Op. Cit., Davis and Rosenberg, pp. 95. 7 Senate passed legislation (Cannon-Kennedy Bill, 1978) substantially reducing the level of economic regulation, the airlines were deregu- lated through administration dictate by the new chairman of the CAB, Alfred E. Kahn. The industry initially prospered as a result of its ability to reduce fares in those markets where there was potential for high volume tourist traffic. The airlines discovered that the demand for their services was more price elastic than previously believed. Prompted by this success, the Carter Administration concentrated on the motor carrier industry. Regulatory reform of transportation was gain- ing momentum despite some old-line resistance from the industry and organized labor. Deregulation meant a change in the ability of firms to control rates and pass along all costs (including union won labor increases) to the users. The rates that motor carriers charged were set collectively through regional rate bureaus. Entry to the industry was tightly con- trolled, thereby protecting firms from competition. Routes were rigidly prescribed, thereby limiting carriers from soliciting new cus- tomers. In fact, the certificate to use specific routes was a major asset on the balance sheet of motor carriers. The Motor Carrier Act of 1980 changed these practices. The MCA removed the barriers to entry, eliminated the tightly prescribed routes and intermediate points, allowed for a zone of price competition, and eliminated the antitrust protection enjoyed by the rate bureaus in setting certain rates. Motor carriers cited the likelihood of many small cities being abandoned by carriers as an argument against deregulation.8 They also argued that 8Burek, Charles G. "Truckers Roll Toward Deregulation," Fortune (December 18, 1978), p. 70. 8 deregulation would result in chaos and job losses. The uncertainty surrounding the effects of deregulation on the motor carrier industry was noted by security analysts. Many industry analysts felt that deregulation would have an adverse effect on the market for transportation securities. The primary reason for this opinion was the uncertainty surrounding the ability of the firms in the motor carrier industry to make the transi- tion into the competitive market environment: "In the uncertain atmosphere of a free market, lending institutions or investors would be reluctant to advance new caBital pending some sort of Shakedown of the sys- tem."1 "Certainly with the passage of the Motor Carrier Act of 1980 both industry members and their lenders antici- pated significant changes in the months and years to come. No one expected this evolution from a 'regu- 1ated' to a 'free market' industry to be painless, but few could foresee the depth of the problems and the issues to be encountered."11 From the viewpoint of investors, the question was: What effect will deregulation have on the risk and returns to investors? From the view- point of the firms in the industry, a change in the risk and return of the securities would have an effect on the industry's financing abilities. If the deregulation were to increase the risk of the securities of the industry, motor carriers would face increasing difficulties 9Liddell, Florence. 1981 Financial Analysis of the Motor Carrier Industry, American Trucking Association, Inc., 1982. 10Standard and Poor's Industry Surveys, Vol. I, No. l, A-L, (July 1978). PP. 465. 11Lisciandro, Patricia. 1982 Financial Analysis of the Motor Carrier Industgy, American Trucking Association, Inc., 1983, pp. 6. 9 in securing the necessary capital to replace equipment and meet future demands for services. The hypothesis was that equityholders might demand higher returns to compensate for the increased risk, thereby reducing the market price of the equity. The lower price would increase the number of shares that had to be sold to raise a given amount of capital. Similarly, bondholders might have viewed deregula- tion as leading to increased uncertainty about the future cash flows to the firm. The uncertainty could lead to lower bond ratings and the debt holders would demand higher returns commensurate with the lower ratings. The net effect would be an increase in the cost of capital. In summary the above discussions have dealt with the effects of deregulation on shippers and carriers from a political and marketing perspective. The economics literature has been concerned with mea- suring the cost of the regulatory-induced inefficiencies and with the effects of deregulation on the costs of transportation firms. The next section discusses the relationship of the capital markets to the deregulation of the transportation industry within a risk-return theoretical framework. Theoretical Framework The previous section suggested that there was a relationship between market measures of risk and economic deregulation. One purpose of this study was to determine if economic deregulation of motor carriers affected the risk of their common stocks. The measures of risk used are those suggested by capital market theory. The total risk of a firm's common stock was measured by the variance of its return (Var R ; where Rj is the return on security j). The systematic risk .1 (the risk of a company relative to the market) was measured by the 10 covariance of a firm's returns with the market portfolio's return, standardized by the variance of the market returns (Cov (R , Rm)/Var Rm; 1 where Rm is the return on the market), commonly referred to as a firm's beta (8). These are the measures of risk used by the market when making investment decisions. This section reviews why economic deregu- lation might effect the risk of common stocks. The value of a share of common stock is the present value of future cash flows. Since the cash flows from operations for a given period (e.g. one fiscal year) are not reported (or perhaps even known) it is necessary to use a proxy variable to represent cash flows. A reasonable proxy for cash flows over the long run is net income. Therefore, any factors which alter the variability of net income are also altering the variability of a firm's cash flow. It is necessary to demonstrate why economic deregulation may have an effect on earnings variability and market measures of risk. Effects on Earnings Variability For motor carriers the main thrust of deregulation was a signifi- cant reduction in entry and rate controls. Prior to deregulation, a given market was serviced by certain carriers. New carriers were not able to enter the market without going through a long application and approval process. Existing carriers were able to object to the new applicant. As a result, new entrants were very uncommon and those admitted began the process many months before entering. As a practical matter in the case of motor carriers, the only way a carrier could enter a new market was by purchasing an existing carrier in the market. Of course, that approach did not lead to new competition because the number of carriers declined. Motor carriers purchased carriers in a 11 market they wished to service or for routes they wanted. Often the purchased carrier disappeared and the acquiring firm recorded as an asset the newly purchased "operating authority." However, those actions by motor carriers did not increase the number of competitors. The merger/acquisition activity was perhaps most like a game of musical chairs: after each round there was one less carrier. It is reasonable, therefore, to assume that the resulting level of competition after the passage of the MCA would cause revenue and income levels to be more uncertain for several reasons. First of all, the MCA specified the maximum amount of time the respective agencies have to deny entrance to new applicants. The time limit is now only a few months. Thus, the primary reason for expecting greater uncertainty of revenues was that the market may be shared by more carriers. Then the existing companies would sell fewer units or would have to lower their per unit price in order to keep their customers. Both scenarios or any combination of lower output and lower selling price would lead to lower revenues and incomes. A second reason for increased uncer- tainty was the willingness of management to undertake riskier business decisions because of reduced regulatory barriers to innovative ser- vices. Certainly both of these possibilities have been realized in the airline industry since deregulation. There are more new airlines com- peting for business, there have been fare "wars" to attract or keep customers, and some firms have expanded too rapidly and suffered as a result (e.g. Braniff Airlines and Air Florida went bankrupt). Several large motor carriers have filed for bankruptcy. 12 One effect of these changes in the industry was that the income of each carrier became more uncertain. The revenues earned by a firm were sensitive to the changes in the environment, while the costs associated with motor carrier operations were not as sensitive. A motor carrier may not abandon warehouses or reduce inter-city delivery because of lower revenues. Levels of service in the industry were maintained for reasons of safety and marketing. In economic terms, deregulation has forced carriers to become pricetakers whereas pre- viously, carriers set prices based on recovering all of their costs and earning a reasonable profit. The result has been the increased uncer- tainty of revenues resulting in increased uncertainty of net income. This relationship can be demonstrated more formally as follows: (1) R - E = I where R = Revenues E = Expenses I = Net Income. The uncertainty can be represented by (2) Var (R - E) = Var (I) where Var (R - E) = Variance of the difference between revenues and expenses and Var (I) = Variance of the net income. It follows that (3) Var (R) + Var (E) - 2Cov (R,E) = Var (I) where 13 Var (R) Variance of a firm's revenues Var (E) Variance of a firm's expense and Cov (R,E) = Covariance of a firm's revenues to its expenses. Thus if Var(R) increased with deregulation due to an increase in the uncertainty of revenues, then it is possible that the variability of net income also increased. It is reasonable to assume some increase in the Var(E) since some costs are highly correlated with the amount of activity. Var(E) would have increased less than Var(R) because of the high fixed cost component of total costs. Thus some increase in the variability of net income would be expected whether or not Var(E) increased. It can also be argued that the covariance between revenues and expenses will decline because of the change in how prices for services were established. Before deregulation, prices were set by the regu- latory agencies so as to completely recover all costs. The covariance between prices and costs was high as a result of this price setting procedure. After passage of the MCA, prices were more sensitive to competitive markets for services and not simply a function of costs. It is not unreasonable to assume that the covariance between revenue and expenses has declined. Since the covariance term is subtracted from the Var(R) and Var(E) in equation (3), a lower value of the covariance also results in an increase in the variance of the net income. 14 Effects of Market Measures of Risk The relationship between deregulation and earnings variability for motor carriers has been argued previously in this section. The link between earnings variability and market measures of risk is also cited in the literature. Using multiple regression, Rosenberg and McKibbon (1973), Lev and Kunitsky (1974), and Beaver, Kettler, and Scholes (1970) all found earnings variability or cash flow variability to be positively related to systematic risk. It is possible to demonstrate that a reason for the existence of the positive relationship between earnings variability and systematic risk found by researchers rests in the components of systematic risk. The Sharpe (1964) - Lintner (1965) two parameter model defined sys- tematic risk for an asset (j) as: COV (st Rm) (4) 8j = Var (Rm) where Bj = the systematic risk of j Rm = the return on the market portfolio Rj = the return on asset j Cov (R ,R ) = the covariance of the return on the 3 market portfolio with the return on asset j. Var (R ) = the variance of the return on the market portfolio. The volatility of a given asset relative to the volatility of the mar- ket is described by B , and it is through the covariance term that J deregulation affects the systematic risk. 15 Thompson (1974) developed a model which related systematic risk to the variability of a firm's earnings. He showed that Bj can be directly related to three covariant risk measures such that e. = ““232: ;' 3°::::;ei’f.::‘;:“;"‘m’ m m m where dj = the expected dividend yield on firm j dm = the expected dividend yield on the market ej = the forecasted earnings of firm j em = the forecasted earnings of the market kj = the forecasted earnings multiple of firm j and k = the forecasted earnings multiple of the market. m Of interest in this study is the covariance of a firm's earnings with the earnings of the market, Cov(ej,em). The Cov(e ,em) is equal 3 r. o o where r is the correlation coefficient between the je j m je earnings of security j and the market. Thus if a firm's earnings to become more variable, then a firm's systematic risk will increase, assuming the constancy of the other relationships.12 In summary, the first part of this section gave detailed reasons for why the earnings of motor carrier firms have become more variable. The second part of this section describes a model which relates earn- ings variability for firms to their systematic risk measures. Addi- tionally, three studies are cited that give empirical evidence which 12It is reasonable to assume that the earnings of the market were not significantly affected by the deregulation of the motor carrier industry, nor was there a significant change in the correlation coefficient. 16 link earnings variability to systematic risk. The reasons presented in the first section plus the empirical evidence and theoretical model in the second part demonstrate that deregulation affects the risk and returns of motor carrier common stocks. CHAPTER II LITERATURE REVIEW Economic Regulation Recent literature on the economic regulation of transportation has shown a growing disenchantment with regulation. In a pioneering article, George Stigler (1971) articulated a position that was perhaps harbored by many observers of the regulatory process. He helped to crystallize the disenchantment with the usefulness of regulation as a mechanism to allocate the consequences of market failure. Certainly, the early motivation for the regulation of the transportation industry was to protect small shippers from the whims of the marketplace. In addition, the role of the regulatory agencies was to protect consumers from rapacious transportation companies. Stigler brought the issue into focus for the decade of the seventies and beyond. Stigler gave his support to the creeping recognition that regula- tion seemed seldom to correct the resource misallocation that it was supposed to correct. In fact, regulation may have contributed to increasing the misallocation of resources. He formulated a theoretical foundation for his "producer protection" view of the regulatory process. He did not address the role of the capital markets or the effects of regulation on the common stocks of regulated companies. The importance of his work was that it allowed others to begin questioning the tradi- tional role and methods of regulation. Other authors began to examine the microeconomics of the regulatory process. A principle theoretical 17 18 work by Sam Peltzman (1976) draws on Stigler and presented a general theory of regulation that can be integrated into the theory of finance. Peltzman made an important addition to Stigler's early work when he developed a research design model which could be used in the study of regulated industries. He maintained that regulation should reduce the conventional measures of risk experienced by the common stock- holders of regulated firms. He believed that such a risk reduction would occur because regulation shelters a firm from changes in the demand and cost structures for its products and services. As a con- sequence, the variability of a firm's profits and its stock price would be reduced. He stated that "to the extent that cost and demand changes are economy—wide, regulation should reduce systematic as well as l Peltzman cited two empirical studies he had done diversifiable risk." with respect to the effect of new regulation on the risk of a firm as evidence with which to support his position. In the case of new regulations applied to the drug industry resulting from the thalidomide scare in Europe, Peltzman (1973) found that both the total and systematic risks of the stocks of drug com- panies decreased substantially after regulation. In a second, unpub- lished, study he correlated annual changes in the Standard and Poor's indexes of railroad and utility stock prices with those of the indus- trial index for equal periods spanning the onset of regulation. He found that the risk decreased but not significantly. The first major work that addressed the impact of deregulation on a firm‘s stock return was by Pablo Spiller (1983). In his work Spiller lPeltzman, S. "Towards a More General Theory of Regulation," Journal of Law and Economics, Vol. XVIX (August 1976) pp. 211-240, The University of Chicago. l9 addressed many of the issues associated with the deregulation of air- lines, such as pricing policy, load characteristics, and the demand function. He then tested to see how changes in these variables due to deregulation effected the stock returns of airline firms. It is interesting to note that Spiller took the position in an earlier paper (Spiller, 1982) that it was necessary to differentiate changes in the value of a firm that are the result of profitability changes from those that result from risk changes. In his airline study Spiller assumed constant risk. Holding risk constant he found, using residual analy- sis, that the common stocks of some airlines showed abnormal returns. His evidence would seem to indicate that the effects of deregulation are not constant across all firms. Morash and Enis (1982) examined stock price data to detect the stock market perspective of the effects of motor carrier deregulation. They were interested in determining whether the MCA resulted in a net positive benefit for shareholders. Their measure of performance was average monthly change in stock price. They calculated the average monthly security performance for a portfolio of motor carriers for the twelve months before and after June 1980. They found a statistically significant increase in performance after deregulation in both absolute terms and in comparison with the NYSE Composite Index. They also examined systematic risk using the Value Line Investment Survey finan- cial betas. The betas of a sub-sample of firms were compared over time and they concluded that there was a decrease in systematic risk as measured by Value Line beta. The methodology they used was deficient for the following reasons: 20 1. They did not measure for abnormal return perfor- mance. They used stock price changes rather than stock returns. 2. They did not calculate betas from return data. Value Line estimates were used without regard for the time periods used by Value Line in calculating those betas. 3. The conclusion that beta decreased after the passage of the MCA was not based on a statistical test, but rather on a visual inspection. 4. The sub-sample of firms used in the calculation of betas was not randomly selected or otherwise identified. 5. Their conclusion that a net benefit accrued to stockholders was based only on price changes with total disregard for risk changes. 6. Using only beta as a measure of risk for a non diversified portfolio has been shown by Levy (1978) to be a poor measure of risk. Levy sug- gested that the variance of total return was a better measure. In another study, Edward Bruning and Edward Morash (1983) attempted to estimate the cost effects of deregulation on the cost of capital of publicly held motor carriers. They were primarily inte- rested in testing for a differential effect between general freight carriers and specialized carriers. They used the Capital Asset Pricing Model to estimate the required rate of return on the equity capital of the firms. They found that the cost of equity capital increased for most carriers. In their study they did not report or test for changes in risk around the time of passage of the MCA. Edward Morash and Charles Enis (1983) examined the return on equity (ROE) and the earnings per share (EPS) for the period 1971 through 1982. They divided the industry by carrier type. They con- cluded that there was an industry wide decrease in ROE associated with the MCA. They do not present statistical tests of the changes in ROE. 21 It was not clear from examining the ROE data that there was a statisti- cally significant change in ROE. For their examination of the EPS of the carriers, they focused on whether the write-down of earnings to reflect the loss in book value of the operating rights had an effect on monthly share prices. They concluded that the write-down of earnings due to loss of operating rights had no effect on the changes in market price. This conclusion is consistent with the body of accounting research on anticipated accounting changes. Morash and Enis also examined share price performance by com- paring monthly motor carrier stock performance with the New York Stock Exchange (NYSE) Composite Indicator, they found that for the l-year and 2—year periods before the MCA, the carriers performed significantly worse than the market. However, for the l-year and 2-year periods following the MCA, they found that the carriers' stock performance was not significantly different from the NYSE Composite Indicator. Their conclusion was that the financial performance of the carriers after the MCA was a function of economic conditions and not the MCA. Their study, like the others cited, explicitly assumed no change in risk throughout the deregulation period. Thomas Gale Moore (1983) offered further evidence to the effects of deregulation on railroads and motor carriers. He presented finan- cial information indicating that motor carriers were less profitable, had lower returns on investment, and lost market share as a result of their deregulation. A recent study on the impact of deregulation on motor carrier firms was done by Howard Van Auken and Michael R. Crum (1984). They studied the impact of the Motor Carrier Act of 1980 on the common stock 22 returns of motor carriers. Using monthly return data and the market model they calculated cumulative average residuals around the time of deregulation. Their analysis indicated that the effect of the MCA on the residuals was short-lived during the twelve weeks before and after the passage of the act. When they looked at a longer time period, the twenty-six weeks before and after the passage of the act, they found no long term effect on the residuals. The weakness of their study was that they assumed no change in the risk measures throughout the deregu- lation period. The latest known study on the impact of deregulation at the time of this research project was done by Nancy Rose (1985). Her paper attempted to answer two questions. The first was--did monopoly rents exist in the motor carrier industry? The second question, predicated on the first, attempted to determine who benefited from these rents.. Using share price data and a multiple factor Capital Asset Pricing Model she determined that there were monopoly rents. The study found that there were declines in profits of 8 to 19 percent of a firm's value. She concluded that the monopoly profits had been accuring to the owners and the unionized labor represented by the Teamsters. Throughout her study she assumed constant measures of risk and did not test for abnormal returns after deregulation. Research Methodology for Event Studies The most common research methodology used in event studies is cumulative average residual analysis. Fama, Fisher, Jensen, and Roll (1969) wrote the seminal article on the topic. In their article they used the existence of non-zero cumulative average residuals as a test for the presence of abnormal returns for investors who purchased the 23 common stocks of firms which had just split. After forming portfolios of randomly chosen stocks they averaged their measure of abnormal returns across firms for specific time periods. The average abnormal return for a given time period t was specified by the following equation: AR 13': {c422 - J (6) ARt — N where N was the number of firms in a specific portfolio, ARt was the average residual for all the firms in a specific portfolio at time t and Zfijt was the average residual of firm j at time t estimated using the market model. In addition to examining the behavior of the average abnormal returns for each period, the cumulative effects of abnormal behavior in the periods surrounding a stock split were examined. The cumulative average residual or abnormal return was defined by l-i AR (7) CAR = 0 t T "M t where CART= the cumulative deviation from time 0 to time T, where 0 is the beginning of the test period prior to the passage of the MCA. and AR = the average abnormal return in each time period t relative to the event of the stock splits. The cumulative average residual at time t shows the cumulative effects of the movements of the actual returns of the common stocks from their expected returns as measured by the market model. Since the time of 24 this article, many researchers have used cumulative average residual analysis to determine how certain events affect the stock returns of firms and if investors purchasing these stocks after the events had occurred could earn abnormal returns. These authors were testing to see if the market incorporated the news of certain events into their assessments of the prices of the affected stocks before and after the events occurred. Since one of the objectives of this research project was to determine if the market for the common stocks of motor carrier firms behaved efficiently around the time of the passage of the Motor Carrier Act of 1980, cumulative average residual analysis was also used in this study with some modifications. Since 1969, many researchers have questioned the use of cumula- tive average residual analysis as a way of determining if abnormal returns can be earned by investors. Sunder (1973) believed that the technique falsely indicated that abnormal returns had occurred when in fact what really happened was that the risk of the firm changed due to the occurrence of a specific event. He stated that in the presence of changes in risk the estimated abnormal returns on stocks are dependent on the time series data used for the estimation of relative risk. Brenner (1977) also found evidence which supported Sunder's argument. He claimed that where a change in risk is associated with an event in time, an observed pattern in the time series of residuals may be due to a risk change and is not evidence for or against efficient markets. Brown and Warner (1980) studied various methodologies for mea- suring abnormal performance. Using simulation on monthly data, they generated abnormal performance of different levels and tested to see 25 which methodology correctly specified that significant abnormal return performance occurred in the data. “With regard to cumulative average residual analysis, they found that if the exact month of the event could be pinpointed then the technique correctly indicated the pre- sence of abnormal returns, but that the level of those abnormal returns was not correctly specified. Binder (1985) argued that the standard event study using market data.was more difficult for research on regulatory changes. He charged" that when the event date could not be correctly specified. He also argued that when a regulatory change affects all firms in the same in- dustry, it is not possible to seperate the effects of the regulatory change from some other industry specific shock. This research has the benefit of a specific month for the announcement of regulatory change. Also, there were no other industry specific shocks around the time of the passage of the MCA of I980. Binder (1985) argued that the standard event study using stock market data was more difficult to use for research on regulatory changes. He charged that when the event date could not be correctly specified, then it is not possible to know when expectations change. He also argued that when a regulatory change affects all firms in the same industy, it is not possible to separate the effects of the regulatory change from some other industry specific shock. It could be argued for this research that it is not possible to accurately determine when expectations about the effect of the MCA were known. This criticism applies to most event studies and is most valid when no effect has been found. When a significant effect is found and there are no other industry specific shocks around the event, Binder's arguments are less convincing. In summary, although the study used the cumulative average residual technique to determine if abnormal performance was evident around the time of the MCA of 1980, tests for changes in the market measures of risk were also done around the time of deregulation. In addition, a method used by Asquith and Kim (1982) which analyzed the significance of cumulative abnormal performance measures using t-tests was employed in the study. CHAPTER III RESEARCH DESIGN AND METHODOLOGY Objectives The research was designed to test for the effects, if any, that the MCA of 1980 had on investors' assessments of the risk and return of the equities of the trucking industry. It was concerned with deter- mining if the announcement of the passage of the MCA into law was efficiently incorporated into the market prices of such securities. This type of research has become known in the finance literature as an "event study." The event of this study was the passage of the deregu- lation act which occurred in May 1980. The study addressed two empirical questions using capital market data. First, the market measures of risk were analyzed by comparing the risk characteristics before and after deregulation to determine if a change occurred. The risk measures used were those suggested by the market model. Second, the data was analyzed to determine if there were any abnormal returns earned by investors holding the common stock of motor carriers. The risk-return measures derived from capital market data provided a theoretical basis for analyzing risk in that they reflected investors' expectations with regard to the future of the motor carrier industry. 26 27 The Market Model The most common technique used in event studies for examining common stock price behavior is the market model method. The most famous study in the finance literature using this technique is the pre- viously mentioned study of stock splits by Fama, Fisher, Jensen, and Roll (1969). They examined residuals from the market model for a period of time before and after stock splits. Much of the discussion which follows on the use of the market model in financial research was taken from Fama (1976). The use of this model required an assumption of a multivariate normal distribution for the returns on all securities. This, in turn, implied a bivariate normal distribution for the returns of any single security, R. jt’ and the return on a market portfolio of all securities, Rmt. The assumption of bivariate normality, in turn, implied that the regression function of R. on R can be written as: Jt mt (8) E(RthRmt) = ojt + Bijt where Cov(Rjt,Rmt) Bj = ~ Var(Rmt) and ajt = E(Rjt) — BjE(Rmt) B is a measure of the systematic risk of a firm j when it is added to J a portfolio, while a is a measure of the abnormal performance jt exhibited by the stocks of firm j at time t. The return on security j will not be equal to its conditional expectation in every period t. 28 In any given period, the return on security j will be expressed by the expost form of the market model equation such that R + e (9) Rjt = O‘jt: + Bjt mt j 2 where the s's are iid normal with mean zero and variance, oed' The market model as described above was assumed to be the process by which the security price returns were generated for the motor car- rier industry. It was the first observed by King (1966) that all securities were influenced to different extents by certain common fac- tors. The return of the market portfolio, Rmt, reflected all informa- tion available at time t that affected the returns of all securities. The market model coefficient Bjt in equation (9) was used to measure the sensitivity of the return on security j to the return on the mar- ket. The residual term Ejt in equation (9) was used to reflect all information available at time t that was specific to security j. Since the study was concerned with how industry-specific information affected security prices, the analysis focused on the behavior of estimates of Ejt' To use the theoretical market model with real world data, it was necessary to assume that the distributions of the following variables were stationary over time: ajt = aj Bjt = Bj E(eJt) = E(ej) where t = 1, ...., T. If stationarity holds, then the necessary parameter estimates can be 29 obtained from an ordinary least squares (OLS) regression which can be written as: (10) fijt = 93, + ’s‘jfimt + ’e‘jt where R. ,R = the actual returns observed over time for Jt mt firm j and the market portfolio at time t. and A A A o B e = OLS estimates. J’J’j If the market is efficient and equilibrium returns are generated according to a stationary market model equation, then the following condition must hold: A — (ll) E(ejt - 0 IIt-l’Rmt) where It-l = information available at period t-l. Thus, in an efficient market with stationary return distributions, there is no way to use information available at t-l to form correct non—zero assessments of the expected value of th. However, non-zero expected values of ejt can be assessed correctly on the basis of infor- mation available at time t. When information having implications for the future prospects of a firm arrives in the market, prices should adjust rapidly to reflect the valuation consequences of such informa- tion. Risk Measurements The risk of an investment in common stock can be described as the variability of its rates of return. The variance of the rate of return on a share of common stock is the measure of total risk perceived by 30 the shareholder. The market model in equation (9) incorporates the "systematic" risk of security j, 8 The measure of the relative j' risk of the security is Bj. Systematic risk depicts the sensitivity of the security's return to returns on the market for all investments. The other component of total risk is the unsystematic or residual risk of holding a security. It is the risk that is specific to security j and is not explained by market movements. The unsystematic risk is measured by the variance of the residuals from the expost form of the market model. Fama (1976) described the total risk of a security to be equal to: Total Risk = Systematic Risk + Unsystematic Risk. He demonstrated that the total risk can be given by the following equation: (12) Var(Rj) 8§Var(Rm) + Var(Ej) where Var(R.) = the total variance of the returns of J security j. Var(Rm) = the total variance of the returns of market portfolio. and Var(E.) = the variance of the residuals or measure of unsystematic risk. To examine the question of changes in the risk of the motor carrier stocks it was necessary to determine whether any possible shift in total risk was the result of firm-specific effects or was the result of changes in market-wide factors. Chapter II suggested that the mar- ket's evaluation of the risk of motor carrier stocks may change due to 31 deregulation since the environment may be more competitive after deregulation then it was beforehand. Abnormal Performance Abnormal performance of securities implies that an investor may earn returns in excess of those suggested by the equilibrium return generating process. The market model provides a return generating pro- cess that incorporates risk. Abnormal return are returns in excess of those suggested by the risk of the security. In this study the measure of abnormal performance was generated by the market model and was the residual term (gjt) from equation (10)- The purpose of examining market model residuals was to abstract from the effects of market-wide factors which influence the returns on all securities. This technique prevented the confounding of security returns which were the result of economic deregulation with those caused by other market-wide factors. Equation (10) can be rewritten to focus on the residual returns such that A A A = = — a (13) EJt ARjt Rjt ( j + Bijt) where AR. = the abnormal return in period t. 3t Equation (13) defined the ex-post "abnormal" return for firm j in period t. The return is called abnormal if its value is significantly different than zero. When firm-specific information such as deregula- tion of an industry arrives at the market in time t, and is not pro— perly incorporated into the common stock pricing structure of the affected firms, then a non-zero abnormal return is expected. 32 Since this study was concerned with abnormal returns associated with economic deregulation, the cumulative average residual technique as described in Chapter II was used to determine if abnormal returns occurred after deregulation. Statistical Testing Procedures Data Selection The data set for the study consisted of publicly traded motor carrier firms for which daily return data was available on the tapes from the Center for Research in Security Prices (CRSP) at the Univer- sity of Chicago. Additionally, data for two major carriers for which CRSP data was not available were drawn from the Standard and Poor's Daily OTC Stock Price Record. All companies included in the data sample had return data available for the five year period 1978 through 1982. Daily return data from the Standard and Poor's 500 Index was used as a surrogate for the returns on the market portfolio. One of the difficulties in this study was to determine when the market anticipated the beginning and understood the extent of the deregulation. May 1980 was used as the "event" point because that was the month that the MCA was approved by a House panel. The approval meant that the bill would not go to a conference committee. The time period of interest was chosen so that it straddled the event year by two years before and after deregulation. It was believed that the anticipation and effect of deregulation would have been incorporated into stock prices beginning two years before and by two years after deregulation. The companies included in the study are listed below: 33 Arkansas Best Corporation Branch Industries Carolina Freight Carriers Corporation Consolidated Freightways, Inc. Leaseway Transportation Corporation Overnite Transportation Corporation Roadway Express, Inc. RLC Corporation Telecom Corporation Transway International Corporation Tristate Motor Transportation Company Xtra Corporation Yellow Freight, Inc. For testing purposes, an equally-weighted portfolio was formed of these companies' returns for each calendar date between 1978 and 1982. An equally-weighted portfolio was formed by adding together the per dollar invested returns of each firm in the sample and dividing the total by the number of firms. This was done to determine the effect that deregulation had on the industry itself, and also because it has been shown in the literature (Fama, 1976) that portfolio risk measure- ments are more stable than those of individual firms. Tests for Changes in Risk Since a conflict exists in the literature as to whether or not deregulation resulted in an increase in the risk of motor carrier firms, four risk measures were estimated for the portfolio of trucking firms. The risk measures were: . systematic risk . unsystematic risk . variance of returns . coefficient of variation Equation (NI)was used to estimate the portfolio's systematic risk, Bp, 34 and unsystematic risk, Var(Ep). Each of the four measures was cal- culated over the following time periods: . Before deregulation occurred on May 30, 1980 and after deregulation occurred through December 31, 1982. . On an annual basis for the years around and includ- ing deregulation--1978, 1979, 1980, 1981, and 1982. To test for differences between the systematic risk measures over time the following t-statistic was calculated and tested for signifi- cance at the .05 level: 14 t = ( ) .05 _ 2 2 ‘1) C O 81 82 + n1 1“2 L- .— where n1 and n2 = the degrees of freedom for each period and 81,82 are for two periods. Changes in the variance terms were tested for using the following F-test statistic at a significance level of .05: I—‘N (15) F = Y1 ’Y2 0 MN where Y1 ’Y2 = the degrees of freedom for each period. The unsyste- matic risk was calculated using equation (12). The change in unsyste- matic risk between years was tested for using the F-test from equation (15). In summary, these risk estimates were analyzed to detect a statistically significant change in the component measures of total risk, as well as the measure of total risk, that occurred from before to after deregulation. 35 Testing for Abnormal Return Performance Cumulative average residual analysis was used to determine if abnormal returns accrued to holders of the common stocks of the truck— ing industry around the time of its deregulation. The procedure began by estimating the systematic risk, Bp’ with the market model given by equation (9) using daily returns from 1978. Thus, the year 1978 was used as the estimation period. The testing period for the abnormal returns began with January 1, 1979. From this date forward through December 31, 1982, the residual term was calculated by subtracting the expected daily return, calculated using equation (9), from the actual daily return for a specific date. These residuals were then accumu- lated over time and plotted against time. The resulting plots were then examined to see if a pattern of abnormal performance existed around the time of deregulation. The use of the residual analysis technique required the assumption that the parameters of the market model were stationary over time. The technique was used even though it was determined that the systematic risk as measured by 8p changed. The technique allowed for a visual inspection of the CAR pattern sur- rounding May 1980. The change 8p affects the level of pattern and not the pattern itself. Accordingly the interpretation of the results was more difficult. In addition the procedure described by Asquith and Kim (1982) was used to test the accumulated residuals (CAR's) for significance. T-tests were done on the CAR's to determine if they were significantly different from zero. This would indicate that abnormal return perfor- mance occurred over a certain time interval. The CAR's were tested to SWETM'v-m 36 see if they were significantly different from zero at the .05 level using the following t-statistic: CARt - 0 (16) t = —-— .05 OCARt ”711' where the degrees of freedom associated with time t n = and OCARt = the standard deviation of the CAR's up until time t. :5, i s I '.‘:'-.—._i CHAPTER IV RESEARCH RESULTS Changes in Risk Measures The research results indicated that definite changes in all forms of risk occurred for the sample portfolio around the time the Motor Carrier Act of 1980 was passed into law. Using the market model, the average systematic risk was estimated for the portfolio for the time periods before and after May 30, 1980, and on an annual basis for the years 1978 - 1982. Daily return data for each period was regressed against the corresonding daily market returns. The following discus- sion of the results of this analysis are divided into two sections. The first section discusses the results from a global perspective-- before and after deregulation. The second section attempts to deter- mine at what point in time the actual risk changes occurred. Changes in Risk Before and After Deregulation Table 1 shows that before deregulation the portfolio's systematic risk estimate was .761, while after deregulation the estimate declined to .601. The before deregulation regression parameter, Bbp’ had a t-statistic of 25.12 which implied that the estimate was significantly different than zero at the .05 level. The market model also proved to be a statistically good fit for the relationship between the portfolio returns and the market returns before deregulation. With a coefficient 37 38 of determination (R2) of 50.9% and a F-ratio of 637.98,1 the market returns explained a large portion of the variance in the portfolio returns. In addition, the Durbin-Watson statistic of 1.99 indicated that no autocorrelation existed among the regression's residuals at the .05 significance level. Therefore, the parameter, Bbp’ was an efficient and unbiased estimator of the portfolio's systematic risk. The after deregulation regression equation also fit the data well. The systematic risk estimate was significantly different from zero at the .05 level given that it had a t-statistic of 11.58. The R2 of the equation was lower at 18.1%, but it still had a strongly significant F-ratio at 133.59 which was significant at the .05 level. Finally, the Durbin-Watson statistic was 1.94. It can thus be con- cluded that both the before and after regression models fit the data well and further analysis taken from the models should provide unbiased results. The results of the analysis also show that the systematic risk changed significantly after deregulation. Table 1 illustrates that a change in BP occurred after deregulation. The t-statistic at 65.68 indicates that at a significance level of .05 the systematic risk of the portfolio declined after deregulation. 1Tinic and West (1979) indicated that a high explanatory power is in evidence for the market model regressions if they explain more than 30% of the variance in a return. Also, if the F-ratios in this study are greater than 3.84 at the .05 significance level, then the regres- sion equations provided a good fit to the data. 39 TABLE 1 SUMMARY OF CHANGES IN SYSTEMATIC RISK t-statistica Degrees of Time Period 8p Freedom Before .761 625 65.68 After .601 625 8The 8 were significantly different than zero at the .05 level. For a complete summary of the regression equation statistics see Appendix B. If the t-statistic is greater than 1.645, then the null hypothesis of no change in BP can be rejected at the .05 level for degrees of freedom greater than 29. Source: From M. Merrington, "Table of Percentage Points of the t—Distribution," Biometrika, 1941, 32.30. In addition, the variance of the portfolio returns increased significantly after deregulation. Table 2 shows this result along with a F-ratio of 1.50 which was significant at the .05 level. This ratio indicates that the variance increased significantly after deregulation. 40 TABLE 2 SUMMARY OF CHANGES IN TOTAL RISK F-statisticb Degrees of Time Period Var(RP) . Freedom Before .00008 625 1.50 After .00012 625 be the F-statistic is greater than 1.35, then the null hypothesis of no change in the variances can be rejected at the .05 level for degrees of freedom greater than 120. Source: From M. Merrington and C. M. Thompson, "Tables of Percentage Points of the Inverted Beta (F)-Distribution," Biometrika, 1943, 33, 73-88. Using equation (12) from Chapter III and the variance of the mar- ket returns during these time periods it was determined that the unsystematic risk increased after deregulation significantly. The F—statistic which supported this conclusion can be seen on Table 3. TABLE 3 SUMMARY OF CHANGES IN UNSYSTEMATIC RISK F-statisticc Degrees of Time Period Var(ep) . Freedom Before .000038 625 2.44 * After .000094 625 c The unsystematic risk was measured using the mean squared residuals from the regressions. *Significant at the .05 level. 41 The results of using the coefficient of variation as a risk mea- sure conflict with the previously shown evidence that the risk of the portfolio increased after deregulation. Table 4 shows that although the total risk as measured by the variance of returns increased after deregulation, the actual returns also increased. Thus the coefficient of variation declined for the portfolio after deregulation. This result indicated that the portfolio had become a less risky investment 3 for investors in terms of risk per unit of return. : TABLE 4 SUMMARY OF CHANGES IN THE COEFFICIENT OF VARIATION Time Period Rp Var(Rp) Coefficient of Variation Before .00047 .00008 19.03 After .00083 .00012 13.19 In summary, using a global approach to the study of risk changes from before to after regulation showed that the systematic risk for the trucking firm portfolio declined after deregulation. However, as expected by many trucking industry executives the variance of the returns increased as did the firm-specific risk. For investors, higher returns from investing in these firms were available for a lower per unit measure of risk after, as compared to before deregulation. Annual Changes in Risk In an attempt to determine at what point in time the change in risk occurred for the firms in the portfolio, annual estimates of the risk measures were calculated. The study assumed that the entire year 42 of 1980 was an event year for these firms. An analysis done in a simi- lar fashion to the one done on the portfolio risk measures, was per— formed on the returns on an annual basis for the years 1978 - 1982. The analysis of how well the market model fit the yearly data can be seen in Appendix B and will not be discussed here except to comment that as with the before and after regressions, the market model fit the data well. High R2's and F-statistics were in evidence. Tables 5 - 8 illustrate how the different risk measures changed each year. Included in these tables are the t and F-statistics which r “m“ "' “W verify the significance of the changes at the .05 level. TABLE 5 ANNUAL CHANGES IN SYSTEMATIC RISK t-statisticd Year 8 P 1978 .800 17.77 1979 .880 58.98 1980 .606 18.08 1981 .688 6.02 1982 .640 dSee Appendix B and Table l for an explanation of how the significance of the t-tests was determined and of evidence that the beta coefficients were significantly different than zero at the .05 level. 43 TABLE 6 ANNUAL CHANGES IN TOTAL RISK F-statistice Year Var(R ) P 1978 .00008 1.00 1979 .00008 1.00 1980 .00008 1.14 1981 .00007 * 3.71 1982 .00026 eThe F—statistic used here is explained on Table 2. * Significant at the .05 level. TABLE 7 ANNUAL CHANGES IN UNSYSTEMATIC RISK F-statisticf Degrees of Year Var(ep) Freedom 1978 .000034 250 1.18 1979 .000040 250 1.15 1980 .000046 250 1.07 1981 .000043 * 250 5.37 1982 .000231 250 fSee Appendix C for calculations of unsystematic risk. * Significant at the .05 level. -.‘. a -—-.— 44 TABLE 8 ANNUAL CHANGES IN THE COEFFICIENT OF VARIATION Year RP Var(Rp) Coefficient of Variation 1978 .00033 .00008 27.10 1979 .00115 .00008 7.78 1980 .00041 .00008 21.82 1981 .00010 .00007 83.67 1982 .00155 .00026 10.50 During 1980, when the deregulation bill passed in Congress, the systematic risk level declined significantly from .880 in 1979 to .606 in 1980. In fact it was significantly lower for all years after 1979. This result is consistent with the results obtained from the analysis of the data before and after passage of the MCA of 1980. A result that is not consistent with the results obtained from the before and after data was that the variance of the portfolio returns did not signficantly change around the time of deregulation. It was not until 1982 that the variance increased significantly. This increase in variance of return in 1982 would have caused the variance in the after data to be higher than the before data even though the variance did not increase significantly in 1980. Thus, it appears that the variance of the portfolio returns did not increase as a result of the deregulation act. A look at Table 7 also shows that the unsystematic risk did not increase significantly in 1980. This result indicates that the Motor Carrier Act of 1980 itself did not increase the exposure to industry 45 spanific risk of the trucking firms in the sample. In conclusion, there was no statistically significant decline in the overall risk of the security returns of the firms after the passage of the MCA. How- ever, there was a small change in variance in absolute value between 1980 and 1981. Using the coefficient of variation as risk measure shows a dif- ferent story for investors than the one described above. The coeffi- cients of variation change every year. The risk per unit return declined from 1978 to 1979, but increased from 1979 to 1980. In 1981, the risk increased again but in 1982 it declined. This result, how- ever, does not consider what was happening in the market for all stocks. Thus, it was determined that the coefficient of variation was not a good indicator of overall risk. In summary, when comparing the before and after deregulation risk measures to those calculated on an annual basis, it can be noted that the changes in return and variance of return from 1982 weighed heavily in the overall after deregulation measures. Thus, the annual measures gave a better indication of the deregulation effect. Finally, it can be concluded that the market measure of systematic risk (8p) declined significantly after deregulation, although the total risk did not change for investors after deregulation. Cumulative Average Residual Analysis Using the market model, the average systematic risk was estimated for the portfolio 24 months prior to deregulation. Daily return data for 1978 was regressed against corresponding daily market returns for the same calendar dates. The procedure resulted in the following regression equation: 46 (17) E(Rpt) = -.OOO358 + '80(Rmt) where E(R t) = the expected return of the portfolio p at time t. and R = the return on the market portfolio at mt time t. The systematic risk estimate was equal to .8 and had a t-statistic of 18.4 which implied that the systematic risk estimate was significantly different from zero at the .05 level. The market model also proved to provide a statistically good fit for the relationship between the portfolio and the market returns. With a coefficient of determination of 57.4% and a F-ratio of 340.50, the market returns explained a large portion of the variance in the portfolio's returns. The Durbin—Watson statistic was 2.01 and was insignificant at the .05 level. This result indicated that no autocorrelation existed among the regression's residuals, and thus that the parameter, 8p, was an efficient and unbiased estimator of the portfolio's systematic risk. Where the risk of the portfolio to remain unchanged through time, equation (17) was used to derive a time series of expected daily returns for the years 1979 through 1982. By subtracting the expected from the actual returns, abnormal return performance was measured for the portfolio around the time of deregulation such that: (18) Rpt - E(RPtIRmt) = Xfipt where R t = the actual return of the portfolio at p time t. and 47 AR = the average abnormal return for the port- pt folio at time t. These average excess returns or residuals were then cumulated across time (CARpt) to see how efficiently the market reacted to the passage of the MCA in 1980. Figures 1-3 display graphically the pattern of the cumulative average residuals for the following time periods around deregulation: June 1, 1979 - June 1, 1980 June 1, 1980 - June 1, 1981 June 1, 1981 - June 1, 1982 Theoretically, if the market correctly priced the stocks in the portfolio then the expected value of the CARPt's should be zero. Figure 1 shows that beginning in June 1979 the residuals display a trend that is above zero and increasing. The trend levels off at the beginning of 1980, although it is still above zero. In March 1980 through June 1980 there is a dramatic decline in value in the cumula- tive residuals time series. Evidence of abnormal returns performance shows up in June 1980 and continues until July 1980. In Figure 2 the series returns to an equilibrium position by August 1980. At this point the residuals time series returns to zero and is relatively stable until 1982 where again abnormal returns begin an upward trend and are greater than zero as seen on Figure 3. When using the cumulative average residual technique to demon- strate the market's reaction to an event, it must be recognized that the underlying assumption of the technique is that the systematic risk of the portfolio does not change. In this study that was not the case, since the systematic risk declined for the portfolio during the event 48 year. Thus it is possible that the negative abnormal return perfor- mance which occurred in June of 1980 was falsely interpreted. The decline in systematic risk would indicate that the portfolio could earn lower returns and still compensate investors for their risk. All that can be said from viewing the CAR graphs is that a change occurred at the time of deregulation and that the process returned to an equilib- rium position within 2 months of the passage of the MCA. The technique used by Asquith and Kim (1982) for testing for the significance of the CAR values was done on all the data points from 1979 through 1982. The results showed significant negative t-statistic values around the time of deregulation. This result is consistent with the observed pattern of the residuals around the time of deregulation. However, it was expected that the t-statistics would be insignificant after August 1980. This was not the case. In fact, the t-statistic indicated that significant positive abnormal returns were present for the entire period after deregulation. The t-tests and their component parts can be seen in Appendix D. In summary, the results of the analysis on the risk and return characteristics of firms in the trucking industry around the time of deregulation indicated that the overall risk, as well as its unsystematic risk , decreased after deregulation, and that investors were able to earn abnormal returns if they purchased the stocks of these firms after deregulation. 49 CUMULATIVE RESIDUALS LN PERCENTAGE POINTS MHOCwm d OGZCW>EUHzo U>ww ammowm w>mw>om Om Hmm 30> om demo 50 CUMULATIVE RESIDUALS IN PERCENTAGE POINTS ‘0. undo _ wHchm N Odzdrbi/Nm wmmHvdbrm udzm $50 so udZm Hg: do How ewe _ Noe Hw>oazo c>mm >mamw m>mm>om ow Hmm 20> om demo mmm_Uc>rm 51 CUMULATIVE RE SIDUALS J PERCENTAGE POINTS \ Ir wHchm u casewsfié 115655 Ezm 83 so EZm 5mm U0 I mmm_Uc>rm NO I do-.. 01% ll._o _,_:m._:_:.;:__:.._:_;._.. :__.__;.. :___ 2:32.. . :_ .2: __ . ._::__ _ :.......I ._ .2 : .: :_ . _ . :. _: wOH me bOH bMH mow HW>UHZG U>Km >mamw m>mm>om ow HIE 30> om demo CHAPTER V SUMMARY AND CONCLUSIONS Summary The objective of this study was to test for the effects that the Motor Carrier Act of 1980 had on investors' assessments of the risk and return characteristics of the common stocks of trucking firms. The Motor Carrier Act was signed into law in May of 1980. This act ended most of the economic regulation of the trucking industry. It elimi- nated the rigid pricing structure for services, the privately owned rights to carry freight between cities, and the barriers that prevented new firms from entering the interstate freight business. Most research in this area up until the time of this study focused on the macroeconomic effects of deregulation on society. The latest known empirical work on the impact of deregulation on the security holders' risk and return was done by Van Auken and Crum (1984). Their results indicated that the effect of the Motor Carrier Act was short-lived during the twelve weeks before and after the passage of the act. The weakness of their study was, however, that they assumed no change in the risk measures throughout the deregula- tion period. The uniqueness of the current study is that not only were tests for abnormal return performance done on the returns of the trucking firms around the time of deregulation, but also tests were done to 52 53 ascertain whether the market measures of risk changed during the time of deregulation. Changes in the market measures of risk were analyzed by comparing the risk characteristics before and after deregulation on a global basis and also on an annual basis. The risk measures used were those suggested by the market model as are listed below: . The systematic risk as measured by 8p. . The unsystematic risk as measured by Var(ep). . The variance of returns as measured by Var(Rp). One additional risk measure which was calculated was the coefficient of variation of returns. The data was also analyzed to determine if there were any abnor- mal returns earned by investors holding the common stock of motor car- riers after deregulation and also if the market reacted efficiently to the news of deregulation. The cumulative average residual technique as suggested by Fama, Fisher, Jensen and Roll (1969) was used to analyze the effect the MCA of 1980 had on the equity returns of the trucking industry. Daily return data was collected for all trucking firms listed on the CRSP tapes and from daily OTC data for two firms. After forming a portfolio of these firms, portfolio returns were used to calculate the four previously listed risk measures. These measures were derived over the following two time intervals: 1. Before and after May 1980 2. On an annual basis for the years 1978-1982. T-tests and F-tests were used on the .05 significance level to deter- mine if the risk measures changed significantly around the time of deregulation. It was hypothesized that the risk of the portfolio would 54 change due to deregulation, and that the direction this change would take was an increase in the risk measures. To test for market efficiency and abnormal return performance, the systematic risk was estimated with the market model using daily portfolio returns from 1978. Thus, the year 1978 was used as the esti- mation period for the cumulative average residual analysis. The test- ing period for the abnormal returns began in 1979. After subtracting the expected daily returns, calculated using the market model, from the actual daily return for a specific date, the resulting residuals were cumulated over time and presented graphically. These plots were visually examined to see if a pattern of abnormal return performance existed around the time of deregulation. In addition, t-tests were used to determine if the CAR values were significantly different from zero at the .05 level. Conclusions The results of the analysis were that the total risk of the trucking portfolio .udecreased after deregulation. However, the systematic risk estimates changed significantly, as tested by the t-test at the .05 significance level, from .880 in 1979 to .606 in 1980. In 1981, the systematic risk estimate had increased from the 1980 level to .688 but did not return to the level before deregulation. On the other hand, the variance of the portfolio returns did not change significantly from 1979 to 1980. The F-test indicated that no change occurred at the .05 significance level. In absolute terms the variance was .00008 in both 1979 and 1980. When incorporating the variance of the market returns, it was discovered that the level of the 55 portfolio's unsystematic risk did not change significantly from 1979 to 1980. The F-test was again used to test for any significant changes. The value of the unsystematic risk in 1979 was .000040 and .000046 in 1980. This difference was not significant. Thus, the firm-specific risk of the trucking industry did not increase significantly after deregulation as many critics of deregulation claimed would happen. The results of the cumulative average residual analysis indi- cated that the market reacted efficiently to within two months before the passage of the Motor Carrier Act in 1980. Significant positive abnormal returns were available beginning in August 1980. Since the systematic risk of the portfolio declined after deregulation, the abnormal returns found after August 1980 were biased downward. Thus, investors earned higher abnormal returns then were indicated by the cumulative average residual analysis. One possible explanation for this behavior was that the market overreacted to the news of deregula- tion. One of the concerns of the opponents of deregulation was that the MCA would have an adverse impact on the ability of firms to provide services and that the MCA would increase the risk for these firms. The results of the current study indicated that the expected change in risk was not substantiated. The risk of the firms did not increase. In fact, the portfolio systematic risk estimate declined after deregu- lation. In conclusion it also appears that the market did not react efficiently to the actual impact of deregulation on these firms. The market tended to undervalue the stocks resulting in negative abnormal 56 returns before deregulation and positive abnormal returns after deregu— lation. Implications of the Results The results as presented in Chapter IV were surprising as it was indicated earlier that the author thought the market measures of risk to the portfolio returns would increase with deregulation. One economic justification for the conclusion of no change in total risk is that prior to deregulation the industry was subject to some form of regulatory risk that disappeared in 1980. In the case of the motor carrier industry, regulation seems to have protected the least efficient carriers from the vagaries of the market place. Since the industry is composed of hundreds of small privately held carriers, it may be that the regulation was detrimental to the large publicly held carriers. In effect, the regulatory process may have been a risk to the carriers included in this study in that they were constrained. Once the large carriers were free to operate competitively, the market reacted correctly by re-evaluating the poten— tial cash flows to these carriers. Another explanation of the results deals with the provisions of the MCA itself. The MCA of 1980 as finally passed by Congress was a compromise package that had the support of the American Trucking Asso- ciation. The opponents of deregulation were alarmed at the aggres- siveness of the deregulation movement and the unilateral deregulation actions of the ICC itself. Therefore, to some extent, this act halted the passage of more sweeping reforms. The market may have been anti- cipating different legislation and accordingly, the decrease in CAR's 57 bottomed out after the market determined that the act was not as sweeping as anticipated. Limitation of the Study The effect of the MCA of 1980 on investors' perceptions of risk was evaluated at the portfolio level because past research indicated that firm level risk analysis lacks statistical power. The author attempted to examine the market model parameters at the firm level and confirmed the past research. A limitation of the portfolio level analysis was that it was not capable of providing information which may be disaggregated to the individual firm level. As a result it was possible that the total risk of some firms in the sample increased or decreased as a result of the passage of the MCA of 1980 although there was no change at the portfolio level. The firms used in this study were drawn from the CRSP tapes which meant that the sample was limited (except for two firms) to firms listed on the New York and American Stock Exchanges. In addition the data on two large firms was hand pulled from over the counter daily price data. Accordingly, the results of this analysis cannot be generalized beyond the firms in the sample. To the extent that there are firms similar to those in the sample that are not included, logical but not statistical inferences can be made about the effect of the MCA of 1980. The results might be different if small firms with limited distribution of common stocks were included. 58 Suggestions for Future Research Further research may overcome some of the limitations of this current study. Several extensions of this research may expand the understanding of the deregulatory process by focusing on different other variables, and other methods of looking at returns. The author has begun developing a data base on all publicly traded motor carriers and railroads. The express purpose of this data base is to develop criteria for portfolio formation and identifying those variables that best explain market reactions to regulatory events. The variables to be examined include accounting measures, market values, and firm characteristics. The accounting variables include the following: The dollar value of investment in property, plant, and equipment for motor carrier operations. Estimated cash flow per share of common stock. Accounting return on investment. Debt to equity ratio. . Ratio of motor carrier revenues to total revenue. . Net working capital to total assets. The market values include the following: Return per share of common stock. . The systematic risk as measured by beta. The return on the market as measured by the Standard and Poor's 500 Index. Firm characteristics include: . Type of carrier. 59 . Size of carrier. . Number of years the carrier has been in service. The initial portfolios will be formed by dividing the firms by class of motor carrier. Then the returns will be regressed on the mar— ket returns for the periods before and after the passage of the MCA. The goal of this regression will be to determine if the risk measures change uniformally for all classes and sizes of carriers. Based on the results of the initial regression, subsequent regressions will be formulated to determine which of the variables best explains the level of systematic risk. The goal of this work will be to determine why there are differential market reactions to deregu— lation. The past decade has witnessed the deregulation of airlines, rail- roads, banks, stockbrokers, as well as trucking. It would be useful to know if the capital markets reacted in a similar fashion in these industries. From the perspective of public policy any information on likely market reactions to major changes in economic regulation would allow elected officials to make more informed decisions. Another extension of the study would include a detailed study of all the firms in the industry. Such a study might uncover other vari- ables such as asset base, leaseholds, management profile, or return on assets that may allow for the development of a model that better explains risk or return. The author has begun an extension of this research with new portfolios. The firms used in the present study have been portioned for one set of portfolios by high and low debt/equity ratios. This will provide information about the relationship of capital structure 60 and deregulation. A second set of portfolios has been formed by par— tioning the firms on level of sales. This will provide information on whether there is a "size effect" to deregulation. These extensions will provide additional information to policy makers for use in the legislative process. The investment community would be interested in further research that examined holding-period returns. Several different holding periods would be examined: - purchase securities at deregulation and hold for some number of months - purchase securities short before deregulation and clear the account near the formal passage of the MCA. LIST OF REFERENCES LIST OF REFERENCES Aharony, Joseph, Charles P. Jones, and Itzak Swary. "An Analysis of Risk And Return Characteristics of Corporate Bankruptcy Using Capital Market Data " The Journal of Finance 8 . . t pp. 1001-1016. . ( eP ember 1980), Asquith, P. and E. Han Kim. "The Impact of Merger Bids on the Partici- pating Firms' Security Holders." The Journal of Finance. (December 1982), pp. 1209—1228. 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APPENDIX A APPENDIX A CHANGES IN UNSYSTEMATIC RISK Before Deregulation .00008 = (.761)2 (.000072) + Var (5p) where Var(tp) = .000038 After Deregulation .00012 = (.601)2 (.000073) + Var (ep) where Var (ep) = .000094 66 APPENDIX B APPENDIX B SUMMARY OF REGRESSION EQUATION STATISTICSa Annual Regressions Year 8p t-ratio R2 Durbin-Watson F-ratio 1978 .800 18.4 57.4% 2.01 340.50 1979 .880 15.59 49.3% 2.02 241.43 1980 .606 12.83 39.7% 2.03 164.74 1981 .688 12.70 39.2% 1.88 161.42 1982 .640 5.62 11.2% 2.07 31.54 Before and After MCA Regressions Time 8p t—ratio R2 Durbin-Watson F-ratio Before .761 25.12 50.9% 1.99 637.98 After .601 11.58 18.1% 1.94 133.59 aAll statistics were tested for significance at the .05 level. 67 APPENDIX C APPENDIX C CHANGES IN ANNUAL UNSYSTEMATIC RISK MEASURESa 1978 .00008 = (.8)2 (.0000721) + Var(ep) where Var(tp) = .000034 1979 .00008 = (.880)2 (.0000522) + Var(ep) where Var(ep) = .000040 1980 .00008 = (.606)2 (.0000916) + Var(tp) where Var(ep) = .000046 1981 .00007 = (.688)2 (.0000580) + Var(tp) where Var(Ep) = .000043 1982 .00026 = (.640)2 (.0000704) + Var(tp) where Var(tp) = .0002312 aThe general form of the equation used to calculate the unsystematic risk estimates is: 2 _ 2 2 2 OR - 8 OR + 06 p m 1:————9 confounding 68 APPENDIX D c.0carr r p -. '\ '.' a A - - I.‘ cl - I 0‘ —~ 6. _ a .w—\ -‘C ‘ 2'15 .7 ’ '-K' -. “. . -, ._ ".\-\k 4.7 .( .TICI.1:‘ - C “0.0000: (c O i. Q -0 ID. h as IOVIv / .00834 .01093 .003c6 .008:3 -0.0025? 0 0 . 0 '2‘ .0136? .02010 .02494 - . _ vvvvv 'IO‘TC' I I" ‘ll' (.0. ' (.OTCTI ( C-“ 4 C 0201? I A nfir x.vI‘7t C.01°8° (.01°FS 0.019?1 0.01964 0.01957 0.01950 0.01944 0.01937 0.01933 0.01928 0.01922 0.01920 0.01921 0.01922 0101019 0.0292: 0001936 '0.01951 0.01957 0.01980 0.02007 0.02073 0.02148 0.02211 0.02279 0.02340 0.02415 0.02501 0.07575 0.02649 0.02712 0002771 0.02835 0.02883 0.02923 0.02956 0.03002 0.03037 0.03069 0.03110 0.0333: 0.0325? 0.03167 0.02184 0.03rzv 0.’7:11 0.03:15 0.03211 0.03:10 0.07207 0.07201 0.031Ce APPENDIX D June 1979 - January 1980 135.0092“ 136.0003? 137.0000} 136.0000C 139.00000 140.000Cfi 141.0000? 142.0000' 143.00000 144.0000? 145.00030 146.00000 147.00000 148.00000 149.00000 150.00090 151.00000 15?.00000 153-0000C 154.00000 155. 0300 156.00020 157.00000 15€.OOOOC 159.00000 160.00000 161.00000 162.00000 163.00000 164.00000 165.00000 160.0(000 167.00000 168.00000 169.00000 170.00000 171-0000C 172.00000 173.00000 174.00000 175.03000 176.(0000 177.00(QG 176.00000 175' , (:OCCH‘] 18(.00300 191.0“?00 IFC.OOOOT 1513 .(‘(~.I 0 IR..COCCC IE‘.(?CO( 18c.nn' 19*.000n' t-score CAR ‘r ?c\ o.r7vr- . 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I‘._ ."t4~ 15 .053. 1 15.51 10. 21. 3.0 NH 61‘ ‘JxlN-JO “(mnabr H t— h :. \me-j ‘JCAOL'IO\Juxxxlumt'lmxl H'.) ant—65.5.” 11 74v_4._\_v101..."n ;,H I ¢.r A7J.s -4'.(~ 1' 5 ‘. ._n ‘. r}! 1,; f b '_1 (4 (.1 ‘J t}! I» b .4 C I J .71 \10P no Hanoou-a 000. 0’: o 0‘ .“ \J UN) 1. p .. o. 'J<_r OVJL-lb‘" ‘10.}, 0'7 ... " ‘h ficgo \J’anjwc‘ _. m y‘- a)... P o . O . "4.45:0'7~' I .‘. ‘fl ‘\ D“ ' IC' |—I f“ p' In '_" '31:. . 04L" 0' ...._‘, ,-_ ,N'A',n -,.r_n*i D . U '11 '.~ I‘. .‘, 1 (LAR 0.018: C {11‘ 5.01' ' f-OZ-13 0.0314: Q.OI"ST (.Ci7fif {.0382} £.G40¢ 0.34343 (.6333: 0.03993 6.03318 0.04214 0.0543O 0.05573 0.05129 0.05754 0.05396 .0549? .05391 00 5b ~O\l 4") (no 0 0 5r y..- ULfl a J .- OOOOOOO 0.05114 0.05993 0.06125 0.06907 0.0808: 0.08867 0.08633 0.083P9 0.07683 0.07317 0.0 57: 0.08002 0.07485 0.05927 0.05541 0.05570 0.06676 0.07658 0.06837 0.06580 '.07017 .09257 .09753 .n=740 C>O¢fif f) C. [I 0‘ ~wtflh -n (:7 f. 1:. O \ o J C». 5.. H ~41 ;\ J! C O .4c.ognc. D'DOKI' .I_n .. 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I.. l.o .-. --. ... . . t-score 33.734; .- 33.534:‘ 35- . 5305's 40.50040 36.?‘Qxé 37.73”}? 4’_ w..- ‘l- .J I 5 .‘IL- 48 84838 44.0rgna 4:.7€¢=: 43.44568 43.40976 39.09:c: 43.23186 40.91835 37. 5348 40.76470 ..221H3 .2135: Nb 1) 36.99525 3 7 . 53’.“ "‘ :- 34.71614 3? 77145 :lk‘or-kl.{ V" 35.193ZS nr‘ 19.er 18.03.14’ 19.74 T ?('I1(".‘1;J Pf.56€7’ 25IIIS"'I"( ‘ ?70:"-'::¢' 33.25573 )7. 1""1 -- . I ‘ APPENDIX E 1975 1975 1978 1979 1980 1980 1980 APPENDIX E SUMMARY OF MAJOR EVENTS PRECEEDING MCA SEC ordered stock exchanges to end the practice of fixing brokerage fees. U.S. Senate passes the Cannon-Kennedy Bill substantially reducing the level of economic regulation of the airline industry. ICC rulings begin the deregulation of motor carriers. Motor carrier regulatory reform bill jurisdiction is assumed by Senator Cannon which is seen as step away from regulatory reform. In March Senate Commerce Committee reports bill intact. In May House panel approves bill thereby ending the ability of senators and congressmen to weaken the bill. In June the MCA clears both houses of Congress. 73 HICHIGQN STRTE UNIV LI BRRRIES I IIIIIIIIIIIIIIIIIIIIIIIIIIIIII IIIIIIIIIIIIIII