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PUBLICATIONS A1 11 00989733

Barth, James R/Evaluatlng the Impact of QC100 .U556 V166;1980 C.1 NBS-PUB-C 1980

NBS MONOGRAPH 166

U.S. DEPARTMENT OF COMMERCE / National Bureau of Standards

Evaluating the Impact

of Securities Regulation

on Venture Capital Markets
NATIONAL BUREAU OF STANDARDS

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LIBKAKT

Evaluating the Impact sep • 9 ^0

of Securities Regulation

"*

^

on Venture Capital Markets

James R. Barth, Joseph J. Cordes

Department of Economics

George Washington University

Washington, D.C. 20052

and

Gregory Tassey

Planning Office

National Bureau of Standards

Washington, D.C. 20234

U.S. DEPARTMENT OF COMMERCE, Philip M. Klutznick, Secretary

Luther H. Hodges, Jr., Deputy Secretary

Jordan J. Baruch, Assistant Secretary for Productivity, Technology and Innovation

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director

Issued June 1980
Library of Congress Catalog Card Number: 80-600036

National Bureau of Standards Monograph 166

Nat. Bur. Stand. (U.S.), Monogr. 166, 43 pages (June 1980)

CODEN: NBSMA6

U.S. GOVERNMENT PRINTING OFFICE

WASHINGTON: 1980

For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402

Price $2.25

(Add 25 percent for other than U.S. mailing).
Foreword

Center for Field Methods

The Experimental Technology Incentives Program

The Experimental Technology Incentives Program (ETIP) of the National Bureau of

Standards pursues an understanding of the relationships between government policies and

technology-based economic growth. The pursuit of this objective is based on three premises:

• Technological change is a significant contributor to social and economic development in the United States.

• Federal, State, and local government policies can influence the rate and direction

of technological change.

• Current understanding of this influence and its impact on social and economic

factors is incomplete.

ETIP seeks to improve public policy and the policy research process to facilitate technological change in the private sector. The program does not pursue technological change per se. Rather, it examines and experiments with government policies and practices to identify and

help remove government-related barriers and to correct inherent market imperfections that

impede innovation.

ETIP helps other government agencies to design and conduct policy experiments. Key

agency decisionmakers are intimately involved in these experiments to ensure that the results are incorporated in the policymaking process. ETIP provides its agency partners with both

analytical assistance and funding for the experiments while it oversees the evaluation function.

Because all government activities can influence the rate and direction of technological

change, ETIP works with a wide variety of agencies, including those that have regulatory,

procurement, R&D, and subsidy responsibilities. Programs are currently underway with the

General Services Administration, Food and Drug Administration, Veterans Administration,

Securities and Exchange Commission, Department of Energy, Environmental Protection

Agency, Occupational Safety and Health Administration, other Federal agencies, and State

and local agencies.

The accompanying report was prepared by NBS staff. This document represents the

views of the originating organization and does not necessarily reflect those of the National

Bureau of Standards.

Director

Center for Field Methods

National Engineering Laboratory

National Bureau of Standards

U.S. Department of Commerce

iii
Contents

Page

Foreword iii Abstract 1

1. Introduction 1 1.1 Motivation for the ETIP/SEC study 1 1.2 The general scope of the ETIP/SEC study 4

1.3 Summary.... 4

2. SEC regulations of particular relevance to small technology-based firms and venture

capitalists 5

2.1 Introduction 5

2.2 Regulation A 5

2.3 SEC rules 144 and 146 6

2.4 Impacts of rule 144 and 146 7

3. The efficient markets model and the capital asset pricing model 9

3.1 Introduction 9

3.2 The efficient markets model 9

3.2.1 Submartingale and Martingale models 10

3.2.2 The random walk model 11

3.2.3 The information set and tests of the efficient markets model 12

3.3 The capital asset pricing model 13

3.3.1 The efficient frontier 13

3.3.2 Determinants of the return of a single security 15

3.3.3 Capital asset pricing model and the efficient markets model 16

3.3.4 Test of the capital asset pricing model 17

3.3.5 Capital asset pricing model and the role of information 17

3.4 Testing the impact of SEC regulations on security prices 18

3.5 Empirical studies of SEC regulation 19

3.5.1 Stigler and Friend/Herman studies 19

3.5.2 Benston study 21

3.6 Evidence on impact of specific SEC regulation 24

3.7 Conclusions 25

4. Some limitations of the efficient markets hypothesis 26

4.1 Introduction 26

4.2 Major limitations of the efficient markets hypothesis 26

5. Benefits and costs of securities regulation 28

5.1 Introduction 28

5.2 Cost-benefit analysis: A general overview 28

5.2.1 Introduction 28

5.2.2 The welfare economic basis of cost-benefit analysis 29

5.2.3 Definition of social benefits and costs 30

5.3 Costs and benefits of SEC regulation 32

5.3.1 Costs of SEC regulation 33

5.3.2 Benefits of SEC regulation 34

5.3.3 Models of securities markets and cost-benefit evaluations 35

6. Summary and conclusions 36

6.1 Output of the ETIP/SEC project and policy making 36

7. References 37

iv
Evaluating the Impact of Securities Regulation on Venture Capital Markets

James R. Barth, Joseph T. Cordes, and Gregory Tassey

A detailed and analytical assessment is provided of the economic techniques used by

researchers to evaluate the efficiency of capital markets. The application of these techniques

to that portion of the capital market which supplies venture funds to small, technologybased firms is emphasized. The primary elements of such analysis are the "efficient market

hypothesis" and the "capital asset pricing model." The empirical analogue of the latter

is commonly referred to as the "market model."

There have been several previous reviews of capital market theory but this is the first one to apply these techniques to a particular segment of the capital market and the existing

policy structure affecting its operation. This policy structure is the set of regulations imposed

on venture capital flows by the Securities and Exchange Commission. The important SEC

regulations and the analytical approaches to assessing their impacts on capital market

efficiency are discussed. Because such analysis cannot be effectively utilized by policymakers

such as the SEC if it is conducted on an ad hoc basis or in isolation of the decision-making

process, a monitoring system is described, which is based on the market model and which

is designed to provide timely and decision-relevant information to the SEC.

Key words: Capital asset; capital market; market model; pricing model; SEC regulations;

venture capital.

1. Introduction

In recent years, numerous criticisms have been voiced concerning the lack of adequate

assessment of the benefits and costs of regulation. Most regulatory functions were defined in

terms of protecting some element of society. This focus on protection (the "benefits" of regulation) tend to bias the activity of the institution charged with carrying out the regulatory function away from considerations of the "costs" of regulation. In the case of security markets,

the Securities and Exchange Commission (SEC) was established to carry out the function of

protecting investors from fraud and other potential abuses arising from the issue and subsequent trading of securities. The act of protecting investors imposed certain costs on various

participants in securities markets, including investors in some cases. Without an accurate

measure of the relative magnitudes of the benefits and costs resulting from the regulation of

securities market, it has not been possible for the SEC to systematically determine when and

under what conditions it was facilitating or inhibiting the primary function of a capital market

which is to finance economic activity. To answer this fundamental policy question, economists

have developed a number of analytical tools derived from capital market theory. Until

recently, however, these tools have not been systematically applied to securities regulations for

the direct purpose of developing policy.

In February 1977 the Experimental Technology Incentives Program (ETIP) within the

National Bureau of Standards agreed to collaborate with the Securities and Exchange Commission (SEC) in examining the impacts of financial market regulations on small, technologybased firms. This study has made significant headway. In this report, we endeavor to provide a

description and evaluation of analytical elements of a system for monitoring the capital market

impacts of securities regulations. The ETIP-SEC project is a prototype and therefore is used

as a case study of such a system.

1.1 Motivation for the ETIP/SEC Study

Ultimately, the motivation for the ETIP/SEC effort is the same as that which led to the

establishment of ETIP itself; namely, concern about the extent and quality of technological

innovation in the U.S. economy. It is, of course, difficult to quantitatively measure either levels or trends in innovation. Innovation is best viewed as a process, rather than a single output. 1

1 For an excellent discussion of innovation as a process, see National Research Council (1978), pp. 11-17.

1
It is, however, possible to measure levels and trends of some inputs into innovation. Examples

of such measures are: (a) statistical series of research and development (R&D) outlays; (b)

data on the employment of R&D personnel; and (c) data on the formation of small, technologybased firms. With little exception, each of these measures points to a reduced flow of sources

to technological innovation. R&D spending as a share of GNP has declined fairly steadily since

the middle 1960s. 2 The number of technical R&D personnel per 10,000 population declined

between 1969 and 1972, and has remained virtually constant in recent years.3 Finally, the

formation rate of small, high-technology firms has declined significantly since 1969.4 The ETIP/SEC study is an analytic response to the decline in the formation-rate of small,

high technology firms. Government policymakers have been concerned with this phenomenon

for at least two reasons. First, there is a political ethic which has been supportive of smallbusiness generally, regardless of whether small firms are technologically or nontechnologically

oriented. Second, it is widely believed that small, technology-based firms (STBFs) play a

particularly important role in the innovation process.

Some observers have gone so far as to argue that STBFs, in particular industries, have a much higher propensity to innovate than do their larger, more established counterparts. Others

have been more cautious, citing cases in which an initial invention was developed by an STBF,

but then subsequently developed by a larger firm, or vice versa. Under either view, however,

it is clear that small technology-oriented enterprises plan an important role in the overall

development of technological innovations.5 Those concerned with U.S. technological innovation

should, therefore, be legitimately concerned about the recent difficulties experienced by en- trepeneurs who wish to form STBFs.

Those wishing to form an STBF must typically do so within the institutional framework

of domestic capital markets. In obtaining financing for the activities of an STBF, choices must

be made between alternative financial instruments. Broadly defined, funds may be obtained

through the sale of ownership rights in the firm, "equity financing"; or through borrowing,

"debt finance." In addition, choices must be made between alternative sources of funds. That is,

equity shares may be sold to individual investors, institutional investors, banks, and so forth;

debt may be issued to a similar variety of institutions.

A recent study undertaken by Charles River Associates (CRA) for ETIP found that

STBFs depend rather heavily on particular financial instruments and a particular group of

investors. These findings, which in large part motivated the ETIP/SEC effort, are summarized

below.6 An important finding of the CRA study was that equity has recently become the most

important source of external finance for STBFs. As shown in table 1, this source accounted for

48 percent of all funds raised in 1970-74 versus 34 percent in 1960-63. Moreover, as shown in

table 2, small technology-based firms are much more dependent on equity as a source of finance

than are small nontechnological firms. The relevant figures are 48 percent for technology-based

firms and 14 percent for non-tecnnology-based firms.

Moreover, as shown in table 3, equity is especially important as a means of finance for

technology-based firms in their start-up years. During the first 4 years, approximately 60

percent of all external funds received come from equity.

Finally, the findings of the CRA study suggest that venture capitalists are an important

source of equity finance for STBFs in their early years. The term venture capitalist refers to the

group of investors specializing in the financing of newer and/or riskier enterprises. The CRA

study found that this type of investor contributed between 15 and 93 percent of total equity

raised by STBFs in their early years.

2 National Science Board (1977), p. 2 and p. 95.

» Ibid., p. 2.

4 U.S. Department of Commerce (1976), p. 15.

s See Charles River Associates (1976), Jewkes et. al., (1969), and National Research Council (1978).

6 See, generally, Charles Eiver Associates (1976).

2
Table 1. Corn-position of external financing of small technologybased firms.

1960-63

(percent)

(2)

1964-67

(percent)

(X)

1968-71

(percent)

C4.1

1970-74

(percent)

Equity 34 22 54 48

Lone-Term Debt 31 28 16 15

Short-Term Debt 23 30 22 25

Trade Debt 12 20 8 12

Source: Columns (l)-(3): Charles River Associates, op. cit., p. 13. Column (4) : Ibid., p. 18.

Table 2. Composition of sources of all financing of a sample

of small firms making initial public offerings 1970-1974-

TechnologyBased

(percent)

Nontechnological

(percent)

Equity 48 14

Long-Term Debt 15 26

Short-Term Debt 25 18

Trade-Debt 12 16

Internal Funds less than 1 25

Source: Charles River Associates, op. cit., p. 18.

Table 3. Composition of net external funds received by small technology-based firms

[Percentage distribution by source of funds and by stage]

Source of Funds 1 2 3 4

Equity 75. 4 34. 5 67. 2 5. 2

Short-Term Debt 1. 1 17. 0 2. 9 3. 5 Long-Term Debt 11. 0 26. 6 6. 6

Trade-Debt 9. 2 12. 2 11. 5 5. 8

Other Current Liabilities 3. 3 9. 7 11. 8 20. 0

Total external 100. 0 100. 0 100. 0 34. 5 Internally Generated 65. 5

Total 100. 0 100. 0 100. 0 100. 0

where stage 1 = first year; stage 2=second and third years; stage 3=fourth through

seventh years; stage 4=eighth year and after.

Source: Charles River Associates, op. cit., p. 299.

3
The SEC is the major government agency involved in the regulation of capital markets.

In principle, the regulatory mandate of the SEC extends to all financial instruments and to all investors. It is, however, widely believed that the incidence of SEC regulation falls more heavily

on some segments of the U.S. capital market than on others. Two such segments are the equity

capital market generally, and the venture capital market in particular.

It is noteworthy that these two segments of the capital market, believed to be particularly

sensitive to SEC regulation, were identified in the CRA study as important sources of funds for

STBFs. It is therefore plausible to expect SEC regulation to affect the ability of STBFs to raise

needed capital. Unfortunately, little is known quantitatively about the relationship between

SEC regulation and the flow of funds to STBFs, and/or the functioning of venture capital

markets. The ETIP/SEC effort is designed to provide more precise information about these

relationships.

1.2 The General Scope of the ETIP/SEC Study

There has been considerable conjecture in financial circles about the impact of SEC regulations on the ability of STBFs to raise capital. For example, William Casey, former chairman o

f

the SEC, has argued that certain SEC regulations (to be discussed in detail) have discouraged

venture capitalists from investing in new, unproven enterprises.7 This conclusion is shared by

Charles Smith who argues that certain SEC regulations represent a "threat to technological

innovation." 8

Prior to the ETIP/SEC project, little evidence was available to either substantiate or to

refute such claims. The available evidence was of two main types: anecdotal case studies and

econometric analyses conducted at a fairly high level of aggregation. Both sources of evidence

provided some information about the impact of SEC regulation on financial markets. However,

there was neither an adequate data set, nor a satisfactory analytic approach for determining the

impact of specific SEC regulation on venture capital markets, or for determining the impact of

specific changes in SEC regulations on the flow of funds to STBFs.

The absence of a framework for rigorously analyzing SEC regulatory policies is striking

when compared with other segments of the federal government. For example, the use of econo- metric models has permitted the Executive Branch, Congress, and the Federal Reserve System

to assess the impacts of alternative monetary and fiscal policies. Considerable progress has been

made in developing models that permit the impacts of alternative income-maintenance (welfare)

policies to be simulated. Finally, the economic methodology of cost-benefit analysis has been

usefully applied to evaluate government regulation of product quality, occupational health and

safety, and environmental quality.

One objective of the ETIP/SEC study is to develop a sound framework for determining the

impact of SEC regulations on venture capital markets. More ambitiously, the ultimate aim of

the ETIP/SEC project is to develop a system for monitoring SEC regulations that: (a) is based

on sound economic theory; (b) is easily used by SEC decisionmakers; and (c) is capable of re- sponding quickly to shifts in SEC policies and/or the general economic environment. 9

1.3 Summary

In sum, the ETIP/SEC project should help determine whether SEC regulatory policies

have impeded the flow of funds to STBFs, thereby contributing to the "innovation recession"

observed in recent years. More generally, the project is an attempt to develop an analytic

system that may be effectively used to monitor the economic impact of SEC regulations and

to describe the results of the analyses in a form that can be readily used by decisionmakers.

The critical elements of such a system are: (1) identification of particular SEC regulations that

are most relevant for the economic sector under evaluation; (2) identification of certain "critical"

i Casey (1977).

» Smith (1973).

! For a thorough discussion of these issues, see Tassey, (1978, 1979) and Experimental Technology Incentives Program (1977).

4
questions that must be answered in order to evaluate the performance of SEC regulations;

(3) development and estimation of appropriate models of financial markets; and (4) development of a framework for policy analysis that utilizes information generated by the models.

The following section describes the regulations that have been analyzed by the SEC monitoring

system. Next, the basic economic tools upon which the system is based are described, followed

by an analysis of their limitations. Finally, the process of aggregating the resulting impact

information into a decision-oriented framework based on benefit-cost analysis is described.

2. SEC Regulations of Particular Relevance to Small Technology-Based Firms and

Venture Capitalists

2.1 Introduction

The Securities and Exchange Commission (SEC) draws its regulatory mandate from the

Securities Act of 1933 and the Securities Exchange Act of 1934. The 1933 Act regulates the

amount and nature of information to be disclosed in connection with initial offerings and sales

of securities. The 1934 Act established the SEC as the agency responsible for regulating disclosure of financial information and also designed a system for continuous disclosure of financial

information for publicly traded securities.

As a result of SEC regulation, financial statements filed by firms must, unless exempted,

meet certain standards as to specific form and content. Such compliance with SEC guidelines

is costly. Since firms that wish to raise new capital through sale of equity must satisfy SEC

disclosure standards, SEC regulation increases the cost to the firm of obtaining equity capital.

If SEC compliance costs are not uniformly distributed among firms, SEC regulation may

unintentionally discriminate in favor of some investments and against others. In particular, concern has been expressed that the extra burden of SEC regulation falls relatively more

heavily on small firms in general, and small technology-based firms in particular. This concern has focused around proposals for change in SEC regulations of particular importance to small

firms: Regulation A, Rule 144, and Rule 146.10

2.2 Regulation A

The costs of complying with SEC disclosure regulations are largely "fixed" costs. That is, the costs of gathering the information required by the SEC are relatively insensitive to the

amount of capital to be raised. Consequently, SEC disclosure costs per dollar decline with

increasing size of the offering. Failure to recognize this fact would introduce a regulatory bias

in favor of larger issues—presumably issued by "larger" firms—and against smaller issues

presumably issued by smaller firms.

In recognition of the fact that costs of SEC registration may constitute a significant part

of the proceeds of small securities offerings, Rule 254 under Regulation A specifies that offerings

below a certain ceiling amount are exempt from full SEC registration requirements. Small

companies issuing securities under Regulation A must still provide information in the form of

an offering circular provided to the prospective purchaser at least 48 hours prior to the con- firmation of any sale. However, the offering circular need not be filed or used in connection

with an offering of securities.

The exemption ceiling imposed by the SEC on Regulation A offerings has been changed

several times since 1933. At the start of the ETIP/SEC project, it was $500,000. This was

widely criticized as being so low as to effectively preclude most small offerings from obtaining

relief from SEC disclosure requirements. It has since been raised to $1.5 million, and it has

further been proposed that it be further raised to $3 or $3.5 million.

10 For a more detailed discussion of these SEC regulations, see Barth and Cordes (1978).

5
Clearly, raising the Regulation A exemption ceiling would exempt more small issues from

costly full SEC disclosure requirements. This would presumably benefit small firms in general,

especially new small firms. Increasing the Regulation A exemption ceiling might, therefore,

stimulate the formation of all small enterprises, including small technology-based firms. However, relaxing the Regulation A ceiling would also permit additional new issues to be offered

without full SEC disclosure. It is, therefore, possible that some investors would be induced to

purchase small new issues of poor quality because of inadequate financial disclosure.

The trade-off involved in relaxing the Regulation A ceiling may be stated more precisely.

Exemption from SEC disclosure standards reduces the costs of raising equity capital for both

"good" and "bad" issues. Relaxing the Regulation A ceiling would make it easier for both

"good" and "bad" ideas to obtain financing through sale of small, new issues. Relaxing the

Regulation A ceiling would, therefore, be desirable or undesirable depending on whether the

net social benefits financed by "unusually good" issues exceeded the net social costs incurred

by allowing unproductive ventures to be financed.

At present, there is very little evidence on the nature of this trade-off. For example, it is not known whether relaxing the Regulation A would significantly facilitate the sale of new

issues by STB's as opposed to small firms generally. One purpose of the ETIP/SEC project is to obtain better information on the precise impact of relaxing the ceiling on Regulation A

issues.

2.3 SEC Rules 144 and 146

Regulation A is one means of obtaining exemption from SEC disclosure requirements.

Securities transactions that are legally defined as "non-public," or "private" offerings are also

exempt from SEC registration requirements. Rules 144 and 146, which became effective in

1972 and 1974, respectively, both deal with non-public offerings (restricted securities) within

the meaning of the Securities Act of 1933.

Although promulgated after Rule 144, it is conceptually appropriate to first discuss Rule

146. Rule 146 provides some standards for determining when a new security issue is exempt

because it is a "private" offering. These standards define the manner of offering, the nature of

the offerees, the information to be furnished about the issue, and number of purchasers, and the

subsequent disposition of securities acquired pursuant to the Rule.

With respect to the manner of offering, the rule prohibits the issuer from offering or selling

the securities through any form of general advertising or general solicitation. Moreover, any

meetings or written communication can only involve offerees and their representatives.

The rule also requires that the issuer must have reasonable grounds to believe that the

offeree is capable of evaluating the merits and risks of the prospective investment, and/or that

the offeree can bear the economic risk of the investment. Important considerations are whether

the offeree could afford to hold unregistered securities for an indefinite period, and whether,

at the same time of the investment, he could afford a complete loss.

The rule also stipulates that the offeree must have access to information of the type

registration would otherwise disclose. This would occur if the offeree: were an employee of the

issuer; were related to the issuer; or had the economic bargaining power to obtain the information required to evaluate the risks and merits of the investment. Presumably, "outsiders"

could be offerees.

The rule requires that there be no more than 35 purchasers in any offering. For purposes

only of counting the number of purchasers, those who purchase or agree in writing to purchase

securities for $150,000 or more would be excluded.

Finally, the rule requires that the issuer set standards of minimum care to see that purchasers are not really involved in a distribution of the securities. This would then be a public

offering and the exemption would be lost.

6
Rule 144 regulates the distribution (or re-sale) of restricted securities after initial sale.

Hence, Rule 144 complements Rule 146 in insuring that "purchasers are not involved in a

distribution of the securities." Rule 144 was ostensibly designed to reduce these uncertainties

by providing objective criteria for maintaining exemption from registration when a previously

unregistered security is resold. These criteria pertain to: (a) the length of holding period, (b)

limitations on the amount of securities sold, and (c) the manner of sale and the availability

of information.

Until recently there was one uniform holding period requirement, namely, that the seller

must have owned the security for at least 2 years prior to sale. The acquisition during the

2-year period of other securities of the same issues, whether restricted or non-restricted, would

not restart the holding period.

There is also a limitation on the amount of securities that may be sold in any 3 month period.

If the securities are traded on a registered national securities exchange, the amount sold may

be the greater of : (a) the average weekly volume of sales of that security on all exchanges for

the 4 weeks prior to the sell order, or (b) 1 percent of the outstanding shares or units of the

class of stock as most recently published. If the securities are not traded on an exchange, the

amount sold must not exceed 1 percent of the outstanding units in its class.

However, a recent change in this Rule makes it possible to sell restricted securities without

any limitations on the rate of sale if the stock has been held at least 4 or 5 years and if the

holder is not "affiliated" with the issuer. The category of "non-affiliation" determines which

holding period applies.

Rule 144 also requires that adequate public information about the issuer be available.

Furthermore, the securities must be sold in brokers' transactions and the person selling the

securities must not solicit or arrange for the solicitation of buy orders.

2.4 Impacts of Rule 144 and Rule 146

The practical impact of Rules 144 and 146 depend in part on the amount of capital raised

through non-public offerings. This is difficult to calculate because of its private nature. However,

one source estimates that of the $349 billion in corporate bonds issued from 1946-1973, some

$132.6 billion, or 38 percent, were private placements.11 Another source estimates that in 1972

gross proceeds from corporate private offerings were $10.8 billion. Only a small portion of this

amount represented equity stocks since roughly 96 percent of the gross proceeds were from

privately placed corporate bonds. Institutions purchased the largest share of privately offered

securities, with individual investors purchasing less than 10 percent.12

Rules 144 and 146 themselves were ostensibly designed to make it easier for firms to seek

and obtain private placement exemptions from SEC regulations by reducing uncertainties

associated with issuing unregistered securities. However, the costs of meeting the requirements

of Rules 144 and 146 may be high enough to deter small firms from seeking private placement exemptions.

This point is elaborated upon in table 4. Under SEC regulation, it is not possible to simply

issue a security, as would be the case in the absence of regulation. Rather, one must either

issue restricted securities or unrestricted securities. In order to transform a security into either

of these two forms, certain criteria must be satisfied. Meeting such criteria imposes costs on the

issuer. If one chooses to issue a restricted security, the costs of formal SEC registration need not

be incurred. However, as pointed out by William Casey, in exchange for this exemption, the

issuer must bear the costs of meeting the requirements of Rule 144 and Rule 146.13 These costs

include the transactions costs of complying with the rules, and the costs of higher interest

premiums paid to compensate purchasers for the reduced liquidity of restricted securities.

11 Arthur (1974).

1J U.S. Securities and Exchange Commission (1969).

'» Casey (1977).

7
Table 4. Options facing issuer of securities under SEC regulations.

Qualify for Private Placement and Sell "Restricted Securities" Qualify for Public Offering and Sell "Unrestricted Securities"

Criteria to be Met if Chosen Criteria to be Met if Chosen

(1) Regarding Initial Sale: Compliance with Rule 146. Compliance with registration procedures

(a) Manner of Offering—no advertising or solicitation; re- set forth by the SEC.

strictions on general meetings.

(b) Nature of Offerees—sell only to those "able to bear risk."

(c) Provision of Information—sell only to those with access to the sort of information disclosed by registration.

(d) Number of Investors—sell to no more than 35 purchasers.

(2) Regarding Re-sale: Compliance with Rule 144.

(a) Hold security for at least 2 years

;

(b) sell security in restricted amounts, unless not affiliated in which case one can hold securities four to five years

before being able to sell in unrestricted amounts

;

(c) sell at "arms length" through a broker, without solicita- tion by the seller; (d) existence of public information about issuer.

Costs of Choosing Option Costs of Choosing Option

(a) costs of meeting the criteria above; Costs imposed by SEC registration pro-

(b) costs of higher premium demanded by purchasers to cedures.

compensate for reduced liquidity due to resale restrictions

;

(c) expected costs of legal actions arising from unregistered

nature of restricted securities.

Benefits of Choosing Option

(a) Avoidance of costs imposed by full registration with the

SEC.

Benefits of Choosing Option

(a) Benefits of ability to sell unrestricted

securities.

(b) Legal benefits of registration.

(c) Image or "goodwill" benefits of being a public company.

Moreover, if unregistered securities decline in value after initial purchase, it may be possible

for the buyer to sue for recovery of the initial investments simply because the securities are unregistered, even though such securities were issued in compliance with Rule 146. Hence, in

exchange for the exemption from registration, the issuer must also bear the expected costs of

such legal actions.

The liquidity effects of Rule 144's restrictions on the re-sale of unregistered securities have

been viewed as particularly vexing by venture capitalists. Indeed, it has been claimed that any

cost savings due to exemption from SEC registration requirements may be largely offset by

the cost of higher interest premiums that must be paid to compensate purchasers for the reduced

liquidity of restricted securities.

Smith has also argued that the incidence of Rules 144 ani 146 has not been neutral across

all firms. 14 With respect to Rule 146, he notes that the most useful information about technical

innovation is not contained in financial statements. Rather, it may be necessary for the potential

investor to understand the problems and potentials of the innovation considered. As a result,

STBFs may find it difficult to satisfy the "equivalent information" requirement for exemption

under Rule 146. With respect to Rule 144, he notes that: (a) the requirement that new securities

be sold at arms length, and (b) the restrictions on re-sale of restricted securities make it difficult

« Smith (1973).

8
for STBFs to qualify for exemption from registration requirements. The arm's length requirement makes it difficult for STBFs to provide important qualitative information about the

innovation being financed through new issues. Application on the volume restriction on resales

would require that each partner in a venture keep track of the sales of every other partner.

When the number of partners is large, this may impose large enough transactions costs to

offset the cost savings gained through registration. Furthermore, there are impacts on competition as a result of mergers which may be induced by the demand for liquidity.15

Thus, although Rules 144 and 146 may make it easier for some new issues to qualify for

exemption, they may make it harder for others to qualify. Specifically, these rules may make it harder for STBFs to obtain exemption from SEC registration requirements when they attempt

to raise equity capital through the issue of new securities in private placements to venture

capitalists.

At present, statements concerning the impact of Rules 144 and 146 on the flow of funds to

STBFs and on venture capital markets are largely conjectural. A major part of the ETIP/SEC

project is to develop and estimate an economic model that may be used to systematically determine the impact of Rules 144 and 146 on STBFs and on venture capitalists.

3. The Efficient Markets Model and the Capital Asset Pricing Model

3.1 Introduction

In order to evaluate the impact of SEC regulations on particular markets, one must understand how these markets function. More specifically, it is necessary to understand how prices

and quantities are determined in the market to be regulated. Two economic models that have

been quite useful in analyzing the behavior of securities markets have been the efficient markets

model and the capital asset 'pricing model}*

These models form the analytical basis for the monitoring system that is to be developed

by the ETIP/SEC project. This section describes these models in some detail, and discusses

how they may be applied to the empirical analysis of securities markets.

3.2 The Efficient Markets Model

The efficient markets model describes the determinants of the absolute price of a security

under the assumption that security prices "fully reflect" all available information. A market

is described as efficient when security prices embody all available information. This definition

of efficiency is clearly so general as to be untestable. However, the concept of market efficiency

implies that security prices will display certain properties in equilibrium if they embody

"full" information. The presence or absence of these properties therefore provides some evidence about the efficiency of securities markets.

Individuals who purchase securities do not generally know the future price of those securities with complete certainty. Purchasers therefore base their decisions on the expected price of

securities. Any model of security prices must take this into account. If the market is efficient,

all available information will be used in determining the equilibrium expected price.

Suppose it is assumed that the expected price of a security is determined by its expected

percentage return. Then the relationship between information and the equilibrium expected

price can be formally expressed as

:

E(Pt+1 \It ) = [l +E(rt+1 \It )]Pt , (1)

15 See Tassey (1977) for a discusion of this point.

16 For a survey of these models, see Fama (1970), Barth and Cordes, forthcoming, Jensen (1972) and Vasicek and McQuown, (1972).

9
where E is the expected value operator, Pt is the price of a security at time t, Pt+l is its price

at t+1, rt+\ is the one period percentage return and /, represents all the information available

at time t. The time subscript attached to the information set implies that additional information is eventually reflected in the expected price and return. Expected prices and returns therefore

respond to new information, at least all relevant new information. Presumably, this response

occurs over a short period of time. It should be noted that P and r are random variables. That

is, forecasts of future security prices and returns will err, even when all available information

is used. One does, however, make the best possible predictions on the basis of the information

set

The major implication of the efficient markets model is that trading systems (that is, rules

for buying and selling securities) which rely only on information in It will not earn expected

profits that exceed equilibrium expected profits. 17 In short, if the market price already embodies

all available information, it is impossible to systematically "beat the market."

To express this mathematically, let

Xt+l =Pt+1 -E(Pt+1 \It ), (2)

where Xt+ \ is the excess market value of a security at time £+1. This amount is the difference

between the observed price at time 2+1 and the expected price at time f+1 that was projected

based upon information available at time t. Since the expected price would not always equal

the actual or observed price, Xt+\ is itself a random variable.

The efficient markets model implies that the expected value of X\s zero, given the information set It

. That is, E(Xt+l \It)=0. (3)

When this equality holds, the sequence {Xt } is said to be a "fair game" with respect to the

information sequence {/,}. This means that one cannot use currently available information

to outperform the market. In other words, the market is efficient.

Eq. (1) implies that eq. (2) may be equivalently expressed in terms of returns as:

z t+1 =r(+1 -E(rt+1 \It ) (4)

where z is the excess percentage return at time f+1. If the sequence {z t } is a fair game with

respect to the information set 7„ then,

E{zt+l \I,) =0. (5)

When this condition is satisfied one cannot expect to earn excess returns by using trading rules

that are based solely on the information set

3.2.1 Submartingale and Martingale Models

Thus far, P has simply been defined as a random variable. The "fair game" efficient markets

model makes no specific assumption about the path which the price sequence {P t } follows. In

the submartingale and martingale models, the price of a security is assumed to follow a particular

path.18 These models are therefore viewed as special cases of the efficient markets model.

The price sequence for a security follows a submartingale when the expected value of next

period's price, projected on the basis of all currently available information, is equal to or

greater than the current price. If the two are strictly equal, the sequence is said to follow a

martingale. A martingale is clearly a special case of a submartingale. Formally, a price sequence

{P t } follows a submartingale with respect to the information sequence {/,} when

17 This, of course, requires that all relevant information be readily and freely available to everyone.

18 This is also true of the random walk model.

10
E(Pt+1 \It )>Pt , (6)

for all t and It

. 19 When the strict equality holds, the price sequence {P t } follows a martingale.

We now show that both of these models imply a fair game. To do so, it is convenient to

rewrite eq. (6) as: tf(Pl+1 |Jl ) = (l+a)Pl , (7)

where a is greater than or equal to zero. When a is greater than zero, P follows a submartingale.

When a is equal to zero, P follows a martingale. Eq. (2) may therefore be rewritten as: Xi+1 =Pt+l-(l+a)Pt . (8)

Assuming the information set It consists of Pt , the expected value of X(+1 , conditional upon P t becomes

:

E(X(+1 1 P,)=E(Pt+1 1 Pt ) - (1 +a)E{Pt | Pt ) (9) or E(Xt+i | Pt ) =(1 +«)P,- (1 +a)Pt =0. (10)

Since this last expression holds when a is greater than zero as well as equal to zero, both the

submartingale and martingale models are "fair game" efficient markets models.

When a is less than zero the price sequence [P,] does not follow either a submartingale or a martingale. However, eq. (10) still holds. This demonstrates that the submartingale and martingale models are indeed special cases of the "fair game" efficient markets model.

The submartingale model has an important implication as to the choice of trading rules.

In this model one may expect next period's price to exceed the current period's price. If the market price embodied all available information, one would always expect the price to rise. Hence,

the "best" trading system, based only on the information set /„ would be a simple "buy and

hold" strategy. Comparing various trading systems with a buy-and-hold strategy therefore

provides evidence on the "fair game" efficient markets model. If a trading system could be

found that was superior to a policy of always buying-and-holding a security, and the price

sequence of that security followed a submartingale, the market would be inefficient. Of course,

one may find such a system ex post, but not ex ante.

3.2.2 The Random Walk Model

The random walk model is a version of the efficient market model that has received considerable attention. In this model, successive price changes or one-period returns are assumed to

be independent and identically distributed. It is further assumed that the distribution of price

changes or returns is independent of the information set In spite of this assumption, information does play a role in this model. Indeed, since the mean and other moments of the distribution are not conditional upon the information set, past prices and returns provide an excellent source of information.

The random walk model is more restrictive than the "fair game" efficient markets model. In

the latter, the distribution of price changes is not necessarily independent of the information set. Moreover, price changes need not be independent to constitute a fair game. For these reasons,

the random walk model is also a special case of the efficient markets model. This implies that

evidence inconsistent with the random walk model does not automatically refute the efficient

markets model.

We now show that the random walk model implies a fair game. When the price of a security

follows a random walk, it may be written as Pt+l =Pt +6t+1> (11)

" This is equivalently expressed in terms of returns as -E(n+i| 7i)>0.

11
where the expected value of e, is equal to zero and the pair e t , e,_s (for s^O) are independent.

The fact that the pair e t , e«-s (for s^O) are independent implies that they are also uncorrected.

However, if the e t are uncorrelated, they are not independent, unless the pair e t , e ? _s (for s^O)

come from a bivariate normal distribution. This means that the random walk model imposes

rather stringent restrictions upon the e t. The expected value of Pt+i conditional upon Pt is given by

E(Pt +1 \Pt)=E(Pt \Pt )+E(et+1 \

P

t ) (12)

or E(Pt+1 \Pt )=Pt . (13)

Assuming that the information set consists of Pt , substitution of eq. (13) into eq. (2) yields:

Xt+1 =Pt+1 -Pt . (14)

The expected value of Xt+i conditional on Pt is therefore:

E(X,+i\ Pt ) =E(Pt+1 1 Pt ) -E(Pt \ Pt ) ; (15)

or E(Xt+1 \Pt )=P-Pt =0. (16)

The random walk model, therefore, is a "fair game" efficient markets model. If the price of a

security follows a random walk, this would be evidence consistent with the efficient markets

model.

The random walk model is also a special case of the martingale model. A security price

sequence follows a martingale when

:

E(Pt+1 \Pt )=Pt . (17)

This is exactly eq. (13). Thus, when Pt follows a random walk it also follows a martingale.

The converse is not true, however. This can be seen by examining eq. (12). So long as E(e t+i \P t ) is equal to zero, Pt will follow a martingale. All that one need assume for this condition to hold

is that the pair e ( , e ( _ s (for S9^0) be uncorrelated.20 Since the pair e t , e t_s (for s^O) may be

uncorrelated without being independent (though the reverse does not hold) , the price sequence

{P t \ may follow a martingale but not a random walk. The random walk model requires that

the pair ft , e!_s (for s^O) be independent. Since it is extremely difficult to test for independence,

some researchers have mistakenly examined serial correlation coefficients to test the random

walk model.

3.2.3 The Information Set and Tests of the Efficient Markets Model

The information set It has not yet been discussed in any detail. In empirical tests, specification of the information set is extremely important. It is therefore necessary to discuss the

assumptions made in empirical work about the information set. Three basic sets of information are used to test the efficient markets model. These sets are

:

(1) all available information, including that possessed by individuals or groups who have monopolistic access to information, (2) all publicly available information, and (3) information only

available in current and past security prices. Empirical tests of the efficient markets model using

information sets (1), (2), and (3) are classified as strong form, semistrong form, and weak form

tests, respectively. The more information used in testing the model the "stronger" the test of

20 Assume, for example, that ei=pei-i+ai, where the ui are independently and identically distributed with zero mean and constant variance.

If p is unequal to zero, then and Pt are correlated. See Theil (1971), p. 413.

12
that model. Clearly, tests of the random walk model are weak-form tests of the efficient markets

model since only historical prices are used. The number of weakform tests of the efficient

markets model is quite large, whereas there are substantially fewer semistrong form tests and

only a few strong form tests. Evidence from weak and semi-strong form tests are generally consistent with the efficient markets hypothesis. There is, however, limited evidence against the

hypothesis in the strong form tests.

3.3 The Capital Asset Pricing Model

Thus far, the analysis has focused on the determinants of the absolute price of a security.

The determinants of relative security prices are also important. An investor, for instance, might

be concerned about the performance of a single security relative to the performance of the market portfolio (defined as a portfolio consisting of all securities in the market) . In this particular

case, the random walk model does not apply (nor, for that matter, do the submartingale and

martingale models). Instead, the appropriate framework would be the capital asset pricing

model.

The capital asset pricing model is based on a number of assumptions. Earlier versions of the

model assume that: (1) investors are price-takers who may borrow and lend any amount at an

exogenously determined risk-free rate of interest; (2) investors are risk-averse; (3) investors

form the same estimates of expected return and risk; (4) all assets are perfectly divisible and

liquid, though in fixed supply ; and (5) there are no transactions costs and taxes.

Many of these assumptions have recently been relaxed. As a result, there are many versions

of the capital asset pricing model. The one described here is the more widely known version,

based on the above assumptions.

3.3.1 The Efficient Frontier

One important element of the model is the efficient frontier, depicted by the curved line

AB in figure 1. In figure 1, E(r) represents the expected return and <r(r) the standard deviation

of that return or risk. All points on or below AB are obtainable portfolios of assets given an

individual's wealth. That is, an individual can obtain portfolio A, B or C, but not D, with his

available wealth. Each portfolio is completely characterized by its expected return and standard deviation or riskiness. The efficient frontier AB thus contains portfolios with maximum

expected return for any given level of risk, or alternatively with minimum risk for any given

expected return.

Figure 1.

13
Points on the frontier are preferable to points, such as C, which are inside the frontier.

By reallocating his wealth among securities, an individual could move from point C to point M,

and increase the expected return without adding to risk. Alternatively, risk could be reduced

without reducing the expected return by reallocating wealth so as to move from point C to

point E.

Since utility for a risk-averse investor is positively related to expected return and negatively related to risk, the rational investor would always choose a portfolio of risky assets on

the efficient frontier. However, there are an infinite number of obtainable portfolios on the

efficient frontier. Hence, the investor must also choose among the various efficient portfolios.

Once the set of efficient portfolios is identified, one must trade off greater expected return

for greater risk in selecting a specific portfolio. The optimal combination of E(r) and <r(r) is that which maximizes expected utility. In figure 1 this combination is attained at F, where the

individual enjoys the greatest level of utility, I0 , given his wealth. The three indifference curves I0 , Iu and I2 represent successively higher levels of utility and are positively sloped because

individuals are assumed to be risk averse.21 To hold utility constant, an increase in risk must be

matched by an increase in expected return.

This analysis is extended by allowing an investor to purchase a risk-free security as well

as risky securities. This case is shown in figure 2. If the risk-free return were rf) portfolio return

would be rs when total wealth was allocated to the risk-free asset while the standard deviation

or risk would equal zero.

E(r)

G

B

r

f

'a

Mr)

Figure 2.

An individual could also allocate total wealth to any portfolio of risky securities on the

efficient frontier. For example, at J the individual would allocate total wealth to portfolio J

and expect a return of rf, but with a positive amount of risk. However, the rational investor

would never choose portfolio J (or any portfolio on the efficient frontier below J), since rf may

be earned without any risk by allocating total wealth to the risk-free asset.

Similarly, total wealth could be allocated to portfolio K so as to earn an expected return

greater than rf , in exchange for greater risk. Alternatively one could obtain any combination

of E{r) and <r(r) along the line rfK. This process would be repeated until point M was attained.

At this point one could combine the risk-free security and portfolio M so as to obtain any mix

of E(r) and a(r) along the line rfG. Of all the lines generated in this way, rfG contains the most

preferable combinations of E(r) and <r(r) because for any line below rfG the expected return is always lower for any given level of risk.

" The indifference curve would be horizontal for individuals whoe are risk neutral and negatively sloped for individuals who are risk-seekers.

14
The line rfG is tangent to the efficient frontier, and is called the market line. Every individual will allocate his wealth between the risk-free security and portfolio M, producing a combination of E(r) and o-(r) somewhere along the line rfG. Since everyone will hold only the

risky securities in portfolio M, this portfolio must consist of all of the securities supplied.

Portfolio Mis therefore referred to as the market portfolio.

The optimal combination of E(r) and a(r) along the market line is located at point D

where the individual maximizes expected utility. In this case expected utility is maximized by

allocating some fraction of wealth to the market portfolio and the remaining fraction to the

risk-free security.

3.3.2 Determinants of the Return of a Single Security

We may now determine the return paid to a single security.22 The expected return of a

portfolio on the market line is given by:

E(r)=Xr,+(l-X)E(rm), (18)

where E(r) is the expected return on the entire portfolio (consisting of both risk-free and risky

securities) , rf is the return on the risk-free security, E(rm) is the expected return on the market

portfolio, and X is the fraction of wealth allocated to the risk-free security. The standard

deviation of r or the risk of the total portfolio is

:

a(r) =(l-X)a(rm), (19)

since the standard deviation of the risk-free asset is zero. Solving eq. (19), one obtains:

Substituting this expression into eq. (18) yields the equation for the market line,

m=rf +

E{[f~r' *(r). (21)

0" V ml

The slope of this line is

dE(r)_ E(rm)-r,

Mr)' *<Tm) (22)

which indicates the trade-off between expected return and risk.

Assume now that shares of security,; are added to or subtracted from the market portfolio

M. If a is the proportion of the market portfolio diverted to security j, then (1 —a) is the proportion of the market portfolio remaining after the transaction. When a is equal to zero, there is no alteration in the composition of the market portfolio.

The expected return on this portfolio is

E(r)=aE(r,) +(l- a)E(rm) (23)

and the risk or standard deviation is

(r (r)= [a2

(r2 (ri) +(1 _a)2(7

2 (rm)+2a(1 _a) cov (rj>rm) ]W (24)

Given these two equations, we can solve for the trade-off between expected return and risk.

This trade-off may be expressed as

:

This derivation is found in Jean (1970), pp. 165-169, and Francis and Archer [2, pp. 134-143].

15
dE(r)_dE(r) da

d<r{r) da da(r)

From eqs. (23) and (24), it follows that

^-=E(rj)-E(rm) (26)

and

^= {l/2[2a<72 (r,)-2(l- a)a2 (rnt ) + 2(l-2a) cov (r} ,rm)]

[aV(r,) + (l-a)V(rm)+2a(l-a) cov (rj,rm)]-^}-l (27)

Since we wish to determine the return of security j in relation to the market portfolio, M, we

evaluate (27) at a=0, obtaining:

da _ <r(rm) (2g) da (r) cov (rj, rm) —

a

2 (rm)

Substituting (26) and (28) into (25) therefore yields

dEir)

d<x(r)

JM-JW >w (29)

t=0 cov (rh rm)—

a

2 (rm)

Since (29) is evaluated at point M (where a=0), it must also equal the slope of the market

line. Hence, we have

a&ta_ y-'M „ (rj (so

<r(rm) cov (r.,,rm)-o-2 (rm)

Solving this equation for E(r}), one finds that

AW'r,+

«r(rm) «r(rm) (31)

This equation is extremely important because it states that the expected return on security j is equal to the rate of return on a riskless asset plus a "risk premium" that is proportional to

cov(7-; ,rm)/«r(/'m)-

23 The premium, E(rm—rf)/a-(rm) is typically referred to as the market price

of risk. Eq. (31) shows that the expected return of a security depends not upon its own risk

(standard deviation), but only its contribution to the overall risk of the market portfolio.

The former type of risk is referred to as specific and the latter as systematic. An individual is not compensated for specific risk, only for systematic risk. 24

3.3.3 Capital Asset Pricing Model and the Efficient Markets Model

It is easily shown that the capital asset pricing model is a special case of the "fair game"

efficient markets model. From eq. (31), the expected return on security j at time t+1 is:

23 The c ovariance can also be expressed in terms of standard deviations. For example, the covariance between x and y may be written as where tzv is the simple correlation between x and y, <r* is the standard deviation of x and <r„ is the standard deviation of y.

!* It has been shown by Whitmore (1974) that V—C <r*(r)=C+^

where Vis the average variance and Cis the average covariance for the Nsecurities in the market portfolio. As AT goes to infinity, then <r'(r) = C, which demonstrates that the individual standard deviations in variances (V) contribute nothing to the overall riskiness of the market portfolio

[a'(r)]. Such risk has been diversified away as iV becomes infinitely large.

16
*

its - ,E{1 m, t+1 ^(^. <+ l if) =»'/.i+H —71 frx -7Z Tr^j (32) a \rm, t+l \J-tJ ff \rm.t+l\J-t)

where It is the information set at time t. This may be rewritten as

E(rj. t+1 1 It ) =[a+bE(rm, t+1) \ lt ) , (33)

where

Substituting eq. (33) into eq. (4) yields

«/. 1+1=^.1+1—I<H-&-S<»«.i+i !£)] (34)

If the information set consists of rf, E(rm), cov(r}, rm), and <r(rm), the expected value of Zj, t+i,

conditional upon this information set, is

:

E[Zj, t+1 \a + bE(rm, f+I)] =E{r}, t+1 \ a + bE(rm. t+1)] - E[a + bE(rm,

| a + 6#(rm, <+1)] (35) or

.EJ [zu t+1 1 a + 6#(rm, (+I)]=o + 6^(rm. - [a + &#(rm. «+i)]=0 (36)

It therefore follows that the capital asset pricing model is a "fair game" efficient markets model.

Thus, if the price of a security conforms to the capital asset pricing model, this would be evidence

consistent with the efficient markets model.

3.3.4 Tests of the Capital Asset Pricing Model

To test the capital asset pricing model, many researchers have regressed the average re- turns for a cross-section of securities against each security's covariance with a representative

index of the market portfolio. That is, one can rewrite eq. (31) as

E(r})=aj + [E{rm)]0]} (37)

where _ cov (rh rm) . f

cov (r„ rm) ~\

The term B is usually referred to as the "beta" of a security. Since the beta of the market portfolio is unity, beta indicates the relative riskiness of a security. Securities whose betas are less

(greater) than unity have less (more) systematic risk than the market as a whole.

A model often encountered in the literature is the "diagonal" or "market" model. This

model is written as

:

rj.t=aj+BJ rm, t +Ui. t (38)

where the parameters can vary across securities. The expected value of the random error term

Uj, t is assumed to be zero. Eq. (37) is not equivalent to eq. (38) . The latter equation is stated

in terms of expectations, while the former is stated in terms of realized observations. However,

if eq. (38^ satisfies the usual assumptions of regression analysis, the market model is the empirical analog of the capital asset pricing model.

3.3.5 Capital Asset Pricing Model and the Role of Information

In the capital asset pricing model, the expected return on a security depends upon specific

variables and also presumably all information at a point in time. Thus, if available information

is not fully reflected in expected returns, predicted returns should systematically deviate from

actual returns. Unless this information is immediately embodied in the expected return, as the

information set changes over time, there should be noticeable and systematic movements in the

17
Z7's from eq. (38). For example, stock splits, mergers and announcements of financial reports

by firms should have only a temporary impact on the U's in the market model. If there is a

persistent and systematic movement in the U's (or the residuals in a regression using the market

model), then this is evidence against the efficient markets model. Tests of whether this is so

are semi-strong form tests of the efficient markets model, because information other than just

the historical return sequence is being included in the information set. These types of tests,

though limited in number, appear to lend support to the notion that the stock market is efficient.

3.4 Testing the Impact of SEC Regulations on Security Prices

In this section, we discuss how models of security pricing may be used to assess the impact

of various SEC regulations. Typically, empirical studies of SEC regulation have relied on the

"market model" of eq. (38).

Two key elements of this model are the slope coefficient, Bh and the disturbance term,

Uj f-Bj is the systematic risk or relative nondiversifiable risk of security j. The term UjA

measures security j's "extraordinary" or "abnormal" return. If the SEC adopts regulations

which make new information available to investors about firm j, such information should be

reflected in j's price if it alters expectations about j's future performance. This has the

important implication that any event specific to firm j should be reflected in UJit , unless it has

no impact on the future performance of the firm.

Before discussing the tests which are performed using the market model it is important to

recall what is meant by market efficiency. If a capital market is efficient, security prices should

fully and instantaneously reflect all available information. Accoiding to the efficient markets

hypothesis, at each point in time prices should fully adjust to all available information. This

means that if an event (such as the adoption of an SEC regulation) occurs which is specific

to firm j, its impact should be reflected immediately and fully in the price of security j.

Consequently, favorable or unfavorable impacts of regulation should be reflected in

"abnormal" movements in security prices during the period in which the regulation is adopted.

These price movements are captured in the estimated residuals of the market model regression:

tf*.i=»V.i-ty-AfVi (39)

where a} and Bj are the ordinary least squares estimates of a} and B}

. Now assume that the SEC has adopted a regulation which may affect the price of security

j. Further assume that this regulation is adopted in time period zero (t=0). If capital markets

are efficient, this regulation should be reflected in the price of security,/. This means that if the

A

regulation is firm specific, U}, , should be affected during the period when the information about

A

the regulation becomes available. If the regulation is interpreted as being favorable, Uj_ ( should

be unusually large and positive. If the regulation is interpreted as being unfavorable, Ujit should be unusually large and negative.

To assess the impact of a particular SEC regulation on the price of security j, one would

estimate eq. (38) and calculate the residuals U}, , for a selected time period. In estimating this

equation, the period in which the residuals are to be calculated is eliminated. That is, the period

in which the residuals might behave abnormally is eliminated from the estimation period.

Otherwise, there might be a bias in the estimated parameters. The exact time period chosen for

the calculation of U}t t is somewhat arbitrary. One would attempt to pick a period which ex- cludes the earliest possible date (t= —m) the regulation could be reflected in the price of securityj and the latest possible date (t=+N). Since Ujtt may be abnormal in periods other than

the one affected by the SEC regulation, the empirical results may be sensitive to the interval

of time chosen.

18
One would then examine the residuals for the chosen time period. If the SEC regulation

had no systematic impact, the residuals would follow a random pattern with roughly alternating positive and negative values. One could also cumulate the residuals over successive time

periods. If the SEC regulation had little impact, these cumulative residuals

should also vary little from zero during the relevant sub-period. If this is so, one could infer that

the SEC regulation did not cause investors to significantly alter their expectations about the

future performance of firms affected by regulation.

Often, a number of security prices are examined simultaneously. One then obtains estimates of many U

a

t 's, one for each security.

...

If this procedure is adopted, one may wish to calculate an average residual for each time period (t=—m, . . . , 0,

.

. . , +N) for all of the included securities. The pattern of these average residuals would then be examined. If the SEC

regulation had no impact, the residuals would be distributed randomly around a mean of zero. Similarly, if the average residuals are cumulated starting at period t=—m, this cumulated

series should differ little from zero over the interval t= —m to t= -\-N.

In sum, to assess the impact of an SEC regulation, one examines the residuals obtained from

the market model.26 If these estimated residuals vary randomly around zero and/or cummulate

to an amount little different from zero over the appropriate time interval, one infers that the

regulation in question did not alter investors' expectations enough to produce abnormal or unexpected price movements.

We now briefly review the empirical literature on the impact of securities regulation. With

few exceptions, this literature deals with impact of SEC regulation in general, rather than the

impact of specific provisions su'ch as Regulation A, Rule 144, or Rule 146. Nevertheless, a

review of this literature is useful for at least two reasons. First, evidence on the overall impact

financial disclosure requirements may be useful for assessing the impact of specific disclosure

provisions. Second, the methodology of such general studies may be applicable to the analysis of

Regulation A, Rule 144, and Rule 146.

Presumably, investors must have certain necessary information if capital markets are to

function well. An implicit assumption of the 1933 and 1934 Acts was that sufficient information

would not be provided to investors in the absence of explicit regulation. Hence, a major purpose

of mandatory registration and disclosure requirements was to improve the functioning of capital

markets by improving the flow of information to investors.

In order to assess whether SEC regulation has achieved this goal, one must develop an

operational measure of market performance. In early studies of SEC regulation, the price

behavior of new securities before and after regulation was used for this purpose. If, in the absence

of regulation, sellers of new securities were able to misinform potential buyers, investors would

overestimate the value of new issues. However, investors' estimates would be revised downward

over time as more accurate information about such securities became available, and their price

would fall below the purchase price over time. If registration and disclosure requirements made

25 Schwert (1977) examines the impact of SEC regulations on the price of seats on both the New York and American Stock Exchanges. Schwert makes use of a procedure referred to as "intervention analysis." Briefly, Schwert uses Box and Jenkins techniques to transform his data to unexpected or abnormal components. He then relates these components of different variables to each other using ordinary least squares techniques.

However, a dummy variable is inserted into the regression equation to capture the impact of an SEC regulatory change. This variable takes on

a value of one at the time of the change. As Schwert points out, his approach is analogous to the analysis of outliers from the residuals from an

equation like eq. (38) in the text.

3.5 Empirical Studies of SEC Regulation

3.5.1 Stigler and Friend/Herman Studies
it more difficult to misinform investors, investors would, ceteris paribus, be less likely to over- estimate the value of new issues than they would in the absence of SEC legislation. Thus, if SEC regulation were successful, one would expect the prices of new securities to "hold-up"

better over time, than they would without such regulation.

This proposition is examined by Stigler (1964) and Friend/Herman (1964), who compare the

5-year price performance of new issues sold during a period prior to SEC regulation (1923-1928)

with the performance of new issues sold during a period following SEC regulation (1949-1955).

In order to control for the effect of overall market conditions, the ratio of the market price of a new issue in year t (p t) to its offering price (pQ) is divided by the ratio of the market average in

year t (Mt ) to the market average prevailing in year o (M0). The basic results of the Stigler and

Friend/Herman studies for new issues of common stock are summarized in table 5.

Table 5. New Stock Prices Relative to Market Averages — -r— ; Common Stocks

Po -LV1 0 (Issue Year =100)

Year After Issue

1 2 3 4 5

Stigler

Pre-SEC:

Average. _ _ _ 81. 9 65. 1 56. 2 52. 8 58. 5

Standard Deviation 43. 7 46. 7 43. 7 48. 5 65. 1 No. of Issues 84 87 88 85 84

Post-SEC:

Average- 81. 6 73. 3

i 72. 61 71. 9 69. 6

Standard Deviation 23. 9 27. 7 31. 0 30. 9 38. 9

No. of Issues 47 47 47 47 47

Friend-Herman

Pre-SEC:

Average 79. 2 64. 8 56. 3 50. 7 53. 8

Standard Deviation 40. 2 46. 7 44. 5 47. 9 64. 6

No. of Issues . _ 78 81 81 82 81

Post-SEC:

Average. 83. 8 « 76. 1

1 76. 4 i 75. 1

i 73. 2

Standard Deviation. _ 20. 9 26. 1 29. 1 28. 4 37. 2

No. of Issues 45 45 45 45 45

1 Difference between relative price performance in post-SEC and pre-SEC periods is statis- tically significant.

Source: Stigler (1964), Table 1, Friend and Herman (1964), Appendix 1.

In both studies, the computed price relatives perform better in the post SEC period. Moreover, the standard deviations of the new issue price relatives are lower in the post SEC period.

However, these findings are given rather different interpretations by the two authors. Since the

difference in price relatives is statistically insignificant for one, two, and five years after initial

sale, Stigler argues that SEC regulations were of little relevance to investors in new issues.

Moreover, he interprets the reduction in price dispersion as resulting from the removal of both

unusually good and unusually bad issues from the market. In contrast, Friend/Herman interpret

their findings as basically favorable to SEC regulation. Since their price relatives perform

significantly better in the post-SEC period for 2, 3, 4, and 5 years after initial sale, they conclude

that SEC regulations significantly improved the flow of information to investors.

20
3.5.2 Benston Study

In an important paper, Benston (1973) uses the analytical framework of the efficient

market model and the capital asset pricing model to assess the impact of SEC disclosure on

stock prices. Two issues addressed by Benston seem praticularly relevant. The first is whether

the information contained in SEC-mandated disclosure statements is useful to investors. The

second is whether the overall flow of market information is improved by the presence of SEC

disclosure requirements.

In order to address the first issue, Benston draws on the market model, in which the

return to security j in month t is determined by

rn=return to security j in month t I'Mt— return on a market index in month t constant

TJn —random disturbance

Since rMt controls for changes in general market conditions, Ujt reflects changes in rn caused

by events other than general market fluctuations at time t. Such events might include : changes

in expected dividend payments made by firm j that are announced at time t(Ajt ), changes

specific to the industry of which firm j is a part that occur at time t(IKt), and receipt of unexpected, new information about firmj at time t(F%). SEC disclosure requirements are assumed

to affect the last variable (F*t ), in one of four ways:

(i) Financial statements contain information about actual sales of firm j that may differ

from expected sales. At the time of disclosure, t, F*t would thus represent the difference between

actual and expected sales revealed by SEC disclosure;

(ii) Financial statements contain information about actual cash flow net income of firm j

that may differ from expected cash flow net income. Hence F*t could be defined as the difference

between actual and expected cash flow net income revealed by SEC disclosure;

(hi) Financial statements contain information about actual operating income of firm j that

may differ from expected operating income. Thus, F*t could be defined as the difference between

actual and expected operating income revealed by SEC disclosure at time t;

(iv) Finally, financial statements provide information about net income after extraordinary

gains and losses. Thus, F*t could be defined as the difference between actual and expected net

income after extraordinary gains and losses revealed by SEC disclosure.

The quantitative impact of financial statement information is estimated through a twostep procedure. First, estimates of Ujt are obtained. This is done by fitting eq. (40) to data

that excludes months when the financial statements were made public, using the regression

estimates to generate predicted values of rjt for the months when such statements were made

public; and then using the difference between predicted and actual values to compute estimated

residuals for the months when financial statements were made public. That is, Un is used as a proxy for Ujt where

Having thus estimated Ujt , Benston proceeds to estimate a regression of the general form:

Tit =a} +B} rMt +U}t (40)

where

aj—Bjrmt and <=time of disclosure. (41)

Ujt =f(Fft ,Ajt ,IKt , Qjt , Ujt ) (42)

21
where Q} , represents the impact of third quarter financial data required by the NYSE, and

UJt is a random variable.

A number of different regressions are estimated, corresponding to different definitions of

income (that is, to alternative measures of F*t ) , and to different specifications of the manner in

which expectations of income and sales are formed. In all the regressions, the coefficient of F*t is statistically significant. However, with the exception of sales data, the elasticity of stock

prices with respect to changes in financial information is small. A 100-percent change in annual

sales revealed by SEC disclosure results in a 10.4-percent change in stock prices during the

month of disclosure. In contrast, a 100-percent change in income results, on average, in a

2-percent change in stock prices during the month of disclosure. Based on these results,

Benston concludes that the evidence

... is not consistent with the underlying assumption that financial data made

public are timely or relevant, on average.26

These results suggest that much of the data made public in SEC financial statements may

be anticipated by investors on the basis of preliminary information contained in preliminary

reports, press releases, and so forth. This suggests that changes in SEC disclosure requirements

may not significantly affect the flow of useful information to investors. However, such information may be reliable only because of the discipline imposed by SEC disclosure requirements.

Impacts of this sort would not be captured by the procedure just discussed. Instead, a comparison of the behavior of stock prices/returns before and after the introduction of SEC is necessary.

The procedures used by Benston for making such a comparison may be outlined as

follows. First, corporations traded on the NYSE prior to the 1934 Securities Act are grouped

into two categories: disclosure corporations and nondisclosure corporations. The group of

disclosure corporations consists of those firms who reported all of the information required by

the 1934 Act, including sales, while the group of nondisclosure corporations consists of those

firms who reported all of the information required by the 1934 Act, except sales. Thus, the

major difference between "disclosure corporations" and "nondisclosure corporations" is that

the former disclosed sales while the latter did not. Second, Benston proposes five testable

hypotheses about the behavior of nondisclosure corporations in the absence of SEC regulation

relative to disclosure corporations.

Hypothesis 1 : Managers of nondisclosure corporations deliberately withheld information

to hide poor performance;

Hypothesis 2: Managers of nondisclosure corporations failed to provide information

because they did not realize its value to investors

;

Hypothesis 3 : Required disclosure imposed a cost on nondisclosure corporations without

providing compensating benefits to stockholders

;

Hypothesis 4 : Required disclosure produces benefits to the market as a whole because

investors prefer registered to unregistered stock. However, some costs are imposed on nondisclosure firms in order to achieve this "certification."

Hypothesis 5: Required disclosure did not impose sufficient costs or benefits to be

measured.

Each of these hypotheses may be tested through the market model. More specifically,

each hypothesis has certain implications for the behavior of Uj(, Bh and <j2 (JJ^ during the

initial period of disclosure. These implications, and their interpretations are summarized

in table 6.

» Benston (1973), p. 139.

22
Table 6. Behavior of market model statistics of disclosw, e and non-disclosure corporations during initial period of SEC disclosure.

Ui

[residual returns

to security j]

Bi

[systematic

portfolio risk of

Recnrit.v ll

[specific risk

of security j]

Disclosure Firms

:

Hypothesis 1, 2, 3, and 5 no impact no impact no impact

Hypothesis 4 AUjt >0 no impact no impact

Non-Disclosure Firms: Hypothesis:

1oid3

4

5

AUit <0

U \J j t ' AUit <0

AUjt <0

no impact

ABj>0

ABjgO

no impact no impact no impact

A(r*>0

i-XV \J no impact no impact

no impact

Benston first examines the behavior of the B/s in the pre- and post-disclosure periods.

His data indicate that the B/s of all firms in the pre-disclosure period differ from those of the

post-disclosure period. However, the differences in behavior of the B's of the disclosure and

nondisclosure firms are consistent with neither hypothesis 1 nor 2. Moreover, Benston views

the measured differences as small enough to justify treating the B/s as constant for the entire

pre- and post-disclosure sample period.

In examining the behavior of the residuals, Benston employs a methodology that closely

parallels that discussed earlier. That is, Benston first runs the market model regression for

each security j excluding the period from February 1934 through June 1935. This estimated

regression is then used to compute estimated residuals Uu for the period from February 1934

through June 1935. That is, Un is given by:

A A A

Ujt^rj—aj+Bj)^, (43)

where

:

r^=return to security,;' actually observed during period t (February 1934-June 1935) a A

a,, B} =estimated coefficients of market model regression for security obtained for time

period excluding (February 1934-June 1935).

Benston then computes two different summary statistics of residuals for both the disclosure

and nondisclosure groups. One such statistic is the algebraic mean residual for each month of

the j securities. That is for each month of the disclosure period, he calculates the statistics:

Ut =±Ujt/n (44)

i where

t=month

7i=number of securities.

The second statistic represents the average of the absolute values of the residuals. That is, Benston calculates:

23
\Ut \ =±\Ujt \/n (45)

These summary statistics are used to examine the impact of required disclosure. The

behavior of the average residuals of the non-disclosure firms is not statistically significantly

different from the average residuals of the disclosure firms. Moreover, the average algebraic

residuals of both disclosure and nondisclosure firms are not significantly different from zero. This suggests: (a) that contrary to Hypotheses 1, 2, 3, and 4, SEC disclosure did not significantly affect the behavior of firms not disclosing all information prior to SEC ; and (b) that the

advent of SEC disclosure did not result in abnormal returns for either disclosure or nondisclosure firms.

Benston also cumulates residuals over time for each of thej securities. That is, he computes

U} = T,Un (46)

<=i for each security j.

Again, the behavior of U} for the nondisclosure firms does not differ significantly from

that of the disclosure firms. Moreover, neither the cumulative residuals of the disclosure firms nor of the nondisclosure firms differs significantly from zero. Finally, Benston examines the behavior of the residual variances. Though the residual

variances of the post-SEC period are less than those computed for the pre-SEC period, the

differences are not statistically significant. Moreover, the pre- and post-SEC behavior of the

residual variances for disclosure firms are not significantly different from the behavior of

variances of nondisclosure firms. Hence, Benston concludes that the 1934 Act did not significantly reduce the specific risk of securities traded on the NYSE.

Thus, usin£; the framework of the market model, Benston draws the following conclusions

about the desirability of SEC disclosure provisions.27

(1) The "additional" information revealed by SEC-mandated financials is neither timely nor useful in the sense that stock prices change relatively little in response to such information.

(2) The introduction of SEC disclosure requirements during 1934-1935 did little to improve

the flow of information to investors in the sense that market prices/returns were reevaluated

in any systematic fashion during the initial period of disclosure. This was so both for firms

whose disclosure was unaffected by SEC and for firms who were required to disclose "more"

information as a result of SEC.

(3) The introduction of SEC disclosure did not reduce the specific risk of securities traded

on the NYSE.

(4) The introduction of SEC disclosure did not make markets more fair and/or efficient in

the sense that prices/returns "better" reflected all available information after SEC regulation

than before.

Benston's analysis suggests a useful framework for evaluating the impact of SEC regulation. However, the results do not pertain to the impacts of specific SEC regulations. In the next

section, we briefly discuss the results of empirical studies of one such provision, Regulation A.

3.6 Evidence on Impact of Specific SEC Regulation

Of the SEC regulations identified as most relevant to small technology-based firms, Regulation A has received the most attention. In their general analysis of SEC regulation, Friend and

Herman (1964) present evidence on the impact of Regulation A on the price performance of

small issues. We reproduce these results in table 7. These results are consistent with those

presented in table 5.

" For a criticism of Benston's analysis, see Friend and Westerfleld (1975).

24
Table 7. New stock prices in fifth year after issue relative to averages in 1968 {issue

year = 100)

Size of Issue

Ps Mt

Po M0

EXEMPT

< 300,000

Number of Issues

82. 6

7 NON-EXEMPT

300,000-5,000,000

Number of Issues

90. 2 15

Source: Friend and Herman (1964),

table 2.

That is, the relative price performance of new small issues not exempt from registration and

disclosure was superior to that of small issues exempted from such requirements under Regulation A.

More recent work by Stoll and Curley (1976) provides further indirect evidence on the

impact of Regulation A. In testing for the presence or absence of a small business equity gap,

Stoll and Curley compare the rates of return on new small issues floated under Regulation A

with the return on a portfolio of large firm stocks represented by Standard & Poor's Industrial

Average. While the short-run price appreciation of Regulation A issues was considerably greater

than the appreciation in the Standard & Poor's index, small issues floated under Regulation A

provided lower long-run rates of return than the Standard & Poor's portfolio. Stoll and Curley

interpret these findings as consistent with those of Friend and Herman. That is, investors in

small issues may initially overvalue new small issues relative to established securities. However,

with the passage of time, this overvaluation is corrected. It should, however, be noted that Stoll

and Curley do not compare the relative performance of small issues which were and which

were not subject to SEC regulation.

Some conclusions may be drawn about the relevance of security pricing models for assessing

the impact of specific SEC regulations such as Rules 144 and 146 and Regulation A. First, the

general framework of the market model, applied by Benston to evaluating general impacts of

SEC regulation, is also applicable to assessing the impact of specific SEC regulations. Second,

the studies suggest testable hypotheses that merit further examination.

For example, suppose one assumes that increasing the Regulation A ceiling and/or liberalizing the sales restrictions of Rule 144 makes it easier to obtain exemptions from full disclosure.

An hypothesis consistent with the Stigler and Friend/Herman studies would be that the dispersion of subsequent market prices about the offering price would be greater for issues qualifying for exemption.

Alternatively, it has been conjectured that Rules 144 and 146 make it harder for small

technology-based firms to obtain exemption from SEC registration. Suppose one could distinguish between the securities of "technology-based" and "non-technology based" firms in a sample of firms all of whom were affected by Rule 144. If so, the hypothesis of differential impacts on technology-based firms could be tested by using a framework similar to that used

by Benston for assessing the impact of SEC on disclosure and non-disclosure firms.

Thus, security pricing models based on the efficient markets and capital asset pricing

models provide a useful empirical framework for evaluating SEC regulations. However, these

models also have significant conceptual and empirical limitations which should be acknowledged. These are discussed in the next section.

3.7 Conclusions

25
4. Some Limitations of the Efficient Markets Hypothesis

4.1 Introduction

The efficient markets model or hypothesis is an extremely important proposition in econo- mics which has been supported by numerous scholarly studies. As has already been noted, this

model may prove useful in assessing certain impacts of various SEC regulations on small,

technology based firms. If so, this methodology can contribute to a determination of which

disclosure requirements are least valuable to prospective buyers of securities issued by STBFs.

In particular, it can contribute to an analysis of the costs and benefits of securities regulations.

To the extent that the costs of meeting any disclosure requirement are greater than the benefits

one is in a position to argue for the removal of this particular regulation.

Of course, the use of the efficient markets hypothesis is not without its limitations and to

better understand the usefulness of this hypothesis it is worthwhile discussing some of the

important ones. At present, it is the best objective model for providing the necessary information

for assessing the impact of SEC disclosure requirements on SBTFs. But by being aware of the

limitations of the efficient markets model, one is better able to intrepret the usefulness of the

results obtained by using this model.

4.2 Major Limitations of the Efficient Markets Hypothesis

The efficient market hypothesis essentially states that a market is efficient with respect to

the information set 7( if it is impossible to use that information to make economic profits. Under

perfect competition in a static and certain world, firms are also assumed not to make any eco- nomic profits. The efficient markets hypothesis extends this result to speculators in a dynamic

and uncertain world. In this particular setting, the markets for securities become fair games for

those participating in these markets.

One limitation of the efficient markets hypothesis involves testing for market efficiency.

Any test of market efficiency necessarily involves the use of a particular model. If one uses the

two-parameter market model, then the test of market efficiency is really a test of a joint

hypothesis. In other words, one is testing both a particular model and the efficient markets

hypothesis. But, if this is the case, it becomes difficult for one to know what to conclude on the

basis of the test results. Does one conclude that the market is inefficient based upon the use of a

particular model, or that the particular model was inappropriately used to test for market

efficiency?

Another limitation concerns the information set, used to test for market efficiency. As

earlier noted, there are three forms in which the efficient market hypothesis is expressed, each

depending upon the amount of information which is presumed to be available. These three forms

are: (1) weak form, (2) semistrong form, and (3) strong form. The information set is taken to

be only the information contained in past prices for the weak form, all publicly available information in the semistrong form, and all information known to anyone in the strong form. If

one tests for market efficiency, one must base the test upon one of these three information sets. Clearly, unless one performs a strong form test, one cannot claim that there is no evidence of

market inefficiency. If one performs a weak form test, for example, and finds that the results are consistent with market efficiency, it is still possible that a stronger form test would produce

results which are inconsistent with market efficiency. This type of concern is quite important

because it is extremely difficult to conduct strong form tests of the efficient markets hypothesis.

It is also possible that SEC regulations have altered the boundaries which separate these

forms of the efficient markets hypothesis. That is to say, the SEC may have increased the

amount of information which is publicly available, while simultaneously reducing the amount

of information which is only known by insiders. One may then conclude that the market is

efficient based upon a semistrong form test, but not fully realize that this result applies to a

26
larger information set due to the SEC. This would mean that if the market is inefficient when

performing a strong form test, it should be "less" inefficient due to the smaller information set. In short, the SEC may have reduced the magnitude of the departures from a fair-game, even

though most strong form tests provide evidence which is inconsistent with the efficient markets

hypothesis. In any event, more attention should be given to the exact position of the boundaries

so that one fully realizes the applicability of any empirical results.

Another limitation is the particular variable which is chosen for the return on the market

portfolio (see eq. (38) in sec. 3.3.4.). Indeed, Roll (1977) states that ". . . the critical issue of

contention will always be the identity of the true market portfolio" (p. 147). Researchers have

typically used the NYSE index, the Dow Jones index, the OTC index or the Standard and

Poor's index to represent the market portfolio. But clearly the market portfolio would also consist of marketable land and durable goods. In any event, any results obtained from the use of one proxy for the market portfolio might be quite different when one employs a different

proxy. According to Roll (1977) ". . . even a small mis-specification of the proxy's composition

can lead to the wrong conclusion" (p. 158).

A further limitation is the extent to which the results from any given empirical test can be

used to generalize to other market situations. One may find, for instance, that an SEC regulation

does not appear to be useful because it has no impact on the price or return of a given security or group of securities. If the required disclosure of information were useful, presumably, this

would be reflected in the value of securities. However, this type of finding would be specific

to the security or group of securities analyzed, the particular time period chosen, the SEC

regulation examined, and the estimation technique used. Perhaps a different statistical or econometric technique would produce varying results. Or perhaps the results would be sensitive

to the group of securities or time period chosen. Of course, this point can be made about any

empirical study. Nevertheless, it does mean that the extent to which any results can be ex

trapolated to these other situations is somewhat limited.

Furthermore, all of the empirical results generated are based upon historical data. Such

results are therefore most relevant for evaluating the repeal of existing rules. If a new SEC

regulation is implemented, there is always the question as to how long it will take to be able to

assess its impact. It is not clear that past studies provide a reliable guide as to the length of time

it will take to conduct an "impact analysis" which will generate more than highly tentative

and perhaps reversible results. In any event, a critical question is whether any meaningful

results can be generated in time to be of use in regulatory proceedings.

A related point is that if a regulation is implemented at one point in time and at a later

point, when analysis is desired, relevant data has "perished." Subsequent changes in the regulation may be relatively small as compared to the initial intervention. This means that efficient

market tests of these changes may lead to no discernable impacts on prices or returns, thus

leading to erroneous conclusions about the entire regulation.

Still another possible limitation of the tests of the efficient markets hypothesis concerns

inflation. Since these tests are conducted over a number of time periods, it is not clear what

impact inflation has on the results when, say, the inflation rate is very high in one-half of the

time span and very low in the other half. In short, does one obtain the same results whether one

uses real rates of return or nominal rates of return in tests for market efficiency?

There is also the limitation, as Mendelson (1978) notes, that even though SEC disclosure

regulations may be shown to have no impact on market efficiency, these regulations may re- distribute wealth. If one considers equity as well as efficiency criteria, one might view SEC

regulations quite differently. In other words, tests of market efficiency provide no evidence

bearing on the issue of equity. If this issue is deemed to be important, one must therefore go

beyond the results obtained solely from tests of the efficient markets hypothesis.

One may also be interested in the extent to which SEC regulations affect aggregate investment, output employment and/or R&D expenditures. It is not clear that tests of the efficient

markets hypothesis tell us anything about these broader effects. SEC regulations may generate

27
aggregate effects on economic activity not captured in tests of market efficiency. If so, one might

wish to consider these effects before deciding upon the merit of any particular SEC regulation.

Finally, any decision to remove or add a particular regulation should involve a cost-benefit

analysis. Whether done implicitly or explicitly, one weighs the benefits against the costs to

arrive at a decision. Unfortunately, the efficient markets hypothesis is unable to provide nice,

neat measurements of these costs and benefits. Tests of the efficient market hypothesis should

therefore be viewed as providing important, but not the only, evidence bearing on decisions

about the desirability of SEC disclosure regulations.

5. Benefits and Costs of Securities Regulation

5.1 Introduction

The ultimate aim of the SEC-ETIP project is to develop a model that can be used to

evaluate SEC regulatory policies. Models such as the efficient markets model and the capital

asset pricing model permit one to ascertain whether certain SEC regulations have had an impact

on financial markets and what the impact has been. However, these models do not, by themselves, permit one to determine whether .the impact of SEC regulation is beneficial.

Benefit-cost analysis is a formal framework for policy analysis that is widely used to

evaluate whether public actions have a beneficial economic impact. This section discusses the

way in which benefit-cost analysis may be used to evaluate securities regulation. The next

section identifies both the uses and limitations of benefit-cost analysis. The third section dis- cusses a number of general conceptual and definitional issues which arise when attempting to use benefit-cost analysis. These concepts are then used in the fourth section to identify the

major social costs and benefits of (SEC) regulations. Finally, we examine whether existing

models of securities markets may be used to assess the costs and benefits of SEC regulation

5.2 Cost-Benefit Analysis: A General Overview

5.2.1 Introduction

As Peter Steiner (1974) has recently pointed out, the term "benefit-cost analysis" is susceptible to several interpretations, many of which are potentially misleading. At one extreme,

cost-benefit analysis may simply be viewed as a formal definition of rational behavior. That is, once all the "desirable and undesirable" consequences ("benefits" and "costs") of any given

public activities have been identified, an appropriate strategy would be to choose among the

possible activities so as to maximize the positive difference between benefits and costs. This

view of cost-benefit analysis is, of course, too general. At another extreme, the contribution of a public program to "social income" or GNP, measured in terms of the difference between

that program's economic benefits and its economic costs, may be viewed as the only "acceptable" indicator of performance.

The correct view of cost-benefit analysis undoubtedly lies somewhere between these

alternatives. Cost-benefit analysis is simply a social accounting framework for determining

whether or not a particular program expands the aggregate production and consumption

potential of an entire society. As such, it contains certain rules for determining which program

effects ought properly to be viewed as social benefits and social costs. Since "benefits" and

"costs" have a fairly precise meaning within this accounting framework, cost-benefit analysis can provide some fairly specific information to a decisionmaker about a program's attributes.

It is this very specificity, however, that limits the power of cost-benefit analysis as a decisionmaking tool. When properly used, cost-benefit analysis can provide information as to whether

or not a particular program increases the aggregate dollar value of production and/or consumption for society as a whole. Generally, this is but one attribute that is of policy interest. Decisionmakers may be equally interested in evaluating the distribution of a program's aggregate

28
benefits and costs among individuals. However, economists have tended to view this as a problem separate from that of determining whether total economic benefits exceed total economic

costs. As a result, the distributional issue is usually viewed as being outside the scope of the

formal accounting framework of cost-benefit analysis. Similarly, decisionmakers may be interested in the impact of a particular policy on psychological, cultural, or legal variables. Such

impacts will often be difficult to quantify in terms of the dollar values which are required for

rigorous application of formal cost-benefit calculations.

Cost-benefit analysis should therefore be viewed as a set of accounting rules that: (a)

defines which program impacts ought properly to be viewed as social benefits and/or costs; and

(b) sets forth procedures to be used in order to consistently aggregate different costs and benefits.

The value of using cost-benefit analysis is that it allows a variety of different programs to be

compared with each other in terms of a common performance indicator: the ratio of aggregate

economic benefits to aggregate economic costs. However, it must be emphasized that this

performance indicator measures program performance in only one dimension, namely, the

impact of the program on aggregate production and consumption possibilities.

5.2.2 The Welfare Economic Basis of Cost-Benefit Analysis

Normative economics has long been preoccupied with developing criteria by which to judge

the desirability of alternative and competing resource allocations. A well known social welfare

criterion is that proposed by Vilfredo Pareto which holds that any change in the social state is "desirable" if at least one person is made better off because of the change, while no one is made

worse off. This so-called Pareto criterion has gained wide acceptance as a basis for evaluating public

policies. However, in its strict form, it is applicable to few, if any, public policy decisions that

must be made in the real world. Change in trade policies, regulatory policies, and public facility

siting decisions, to mention only a few public actions, simultaneously impose costs on some

individuals and benefits on other individuals, while causing shifts in resource allocation. Since

such policies inevitably make some individuals worse off, while making others better off, the

Pareto criterion in its strict form cannot be used to evaluate the desirability of such policies.

Due to this limitation of the Pareto criterion, economists have attempted to develop welfare criteria that are still broadly consistent with the Pareto criterion, but which are of greater

applicability. One such criterion is that proposed by both Nicholas Kaldor (1939) and J. R.

Hicks (1939) which states that a change in resource allocation is desirable if the Pareto criterion is met or those who gain by the resource allocation could compensate those harmed by

it so as to leave the losers at least as well off as they would have been in the absence of the re- allocation. The former case would represent an actual Pareto improvement, the latter case would represent a potential Pareto improvement.

Benefit-cost analysis is a tool for determining whether particular public policies represent

potential Pareto improvements.28 This point, as well as the role played by compensation in

linking the concepts of potential vs. actual Pareto improvements, can be readily illustrated

by a simple example.

For simplicity only, consider the case of a public program that confers benefits (B) on one

group of individuals (i=l. . . m), while imposing losses (C) on a different group of persons

. . n). In this case, row 1 of table 8 would represent the impact of the program on the

"gainers" and the "losers."

According to the Kaldor-Hicks hypothetical compensation criterion, the program represented in table 8 would be desirable if the gainers could fully compensate the losers for their

losses, and still be better off. This would require that gainers be able to pay the losers an amount

at least equal to

28 For an excellent discussion of the welfare economic foundations of cost-benefit analysis, see Mishan (1976),p. 382

29
(§ c<>

and still show a net gain from the program after making such side payments, (25,—2O/>0).

Table 8. Comparison of benefits and costs.

Gainers Losers

m n Gains or Losses Before Compen

sation

n n Amount Paid or Received if Full

Compensation Made

SO,

Net Gain or Loss After Full Compensation

25,-2(7, 0

From table 8 it is easily seen that the Kaldor-Hicks criterion would be satisfied only if the total benefits of the program (2 P,) exceeded its total costs (2 (7,). Moreover, if compensation were paid, the program would represent an actual Pareto improvement in the sense

that, through compensation, no one would be made worse off, while some would be made

better off. Thus, determining that a public program has a benefit-cost ratio greater than one implies

that the program constitutes a potential Pareto improvement. Whether compensation is actually paid or not, most benefit-cost analysts view this potential improvement in welfare,

by itself, as desirable on economic efficiency grounds.

Given the welfare economic foundations of cost-benefit analysis, it is appropriate to

distinguish between those program impacts that expand or contract the total production and

consumption potential of society as a whole and those effects that do not expand the total

potential, but rather increase the income of some segments of society at the direct expense of

others. The former types of impacts are often referred to as the "real" benefits or costs of a program, while the latter are referred to as the pecuniary benefits or costs. 29 For. purposes of

computing benefit-cost ratios, it is the "real" impacts of a program that are treated as social

benefits or costs. The distinction between real and pecuniary effects of policy changes can be illustrated by

a simple regulatory example. It has recently been argued that federal regulation of the trucking

industry may have the effect of restricting competition in that industry, with the result that

trucking services are provided at a price that exceeds marginal cost. Assume that this proposition is correct. Further assume that deregulation would generate enough competitive pressure

so that trucking services were provided at a price equal to marginal cost. Using the simple

supply-demand framework of figure 3, the benefits and costs of this policy change would be

those depicted in table 9. From both the figure and the table, two distinct types of impact can be identified. First,

ignoring output changes, deregulation would enable consumers to purchase the pre-deregulation

output level (Qi), at a lower price, P2 , rather than the noncompetitive price of Pi. This cost

saving would produce a benefit to consumers of PiPJBD. However', this gain to consumers

would occur at the direct expense of producers since their profits would fall by an amount

exactly equal to the consumers' gain. This particular impact of deregulation would be an example

" For an excellent discussion of issues relating to the definition of social benefits and social costs, see Haveman and Weisbrod (1975).

5.2.3 Definition of Social Benefits and Costs

30
0 Q 2

Figure 3.

Table 9. Impacts of deregulation

Benefits Costs

Consumer gains due to fall in the price of Qi

units (amount produced prior the deregulation)

BPiP2 D

Loss in monopoly profits earned on units

sold at price above marginal cost prior to deregulation

BPiP2 D

Value of Additional Services Produced After

Deregulation

Opportunity Cost of Resources Used to Produce (Q2-Q1) Extra Units

DQxQt C

of a pecuniary impact. That is, it does not represent an expansion of total potential income,

but rather a redistribution of an existing amount of income. If both truckers and consumers

derived the same utility per dollar of income gained or lost, this particular impact would leave

potential welfare unchanged since the pecuniary gains (PxPJjD) would just be sufficient to

provide compensation for the pecuniary losses {Pl P2BD) . In addition to lowering the price of the good, deregulation would also result in increased

output of the good. This impact of deregulation would clearly affect the allocation of resources between trucking and other activities. Specifically, deregulation would withdraw resources from other activities worth DQiQ2 C to produce services valued by consumers at BQiQ2 C. These

impacts of deregulation would be examples of "real" effects. That is, such policy-induced output

changes would affect the production and consumption potential of the entire society, and hence

would be viewed as social costs (DQiQ2 G) and social benefits (BQiQ2 d) of deregulation.

To the extent that certain impacts of programs can be readily identified as being

"pecuniary" in nature, such impacts need not be included as either social benefits or as social

costs for purposes of making cost-benefit calculations. The rationale for this convention can be

explained in two different ways. First, since such impacts can be identified, a priori, as not

affecting potential welfare, there is no need to include them in the analysis. Alternatively, it is easily seen that "pecuniary effects," if correctly imputed to both the cost and benefit sides of

the account, would automatically cancel out in any comparisons made between aggregate

benefits and aggregate costs.

Once program impacts have been determined, and the real social benefits and costs identified, it is necessary to aggregate consistently individual costs and benefits. Though seemingly

31
straightforward, this aspect of cost-benefit analysis has provoked considerable controversy

among economists.

The reason for this controversy is that many public expenditures produce non-synchronous

time flows of costs and benefits. As a result, it is necessary that a consistent method be used

for aggregating and comparing costs and benefits that occur at different points in time. This is accomplished by first transforming all future benefits and costs into current dollars through the

technique of discounting. Under this procedure, dollars of future benefits and/or cost are adjusted or discounted to reflect society's appraisal of the relative values of dollars received

today versus dollars received at some future date. The adjustment, or discount factor applied,

is based on some "appropriate" interest rate, since interest rates are viewed as providing information about the effect on values of time and waiting. Once all present and future social benefits

and costs have been transformed into current dollars, total present values of benefits may be

compared with total present values of costs. The ratio of the present value of social benefits to the present value of social costs, however, can be quite sensitive to the value of the interest rate chosen. This is seen in table 10, which

shows the impact of using different interest rates on estimated present values of social benefits

and costs.

Table 10. The effect of discounting on the evaluation of an investment, under alternative discount rates. =

I =

Interest Rate (in percent)

0 3 5 10

Value touay of Total Benefits $15, 000 $10, 448 $8, 456 $5, 442

Value today of Total Costs $7, 500 $6, 741 $6, 409 $5, 906

Benefit- Cost Ratio 2. 0 1. 55 1. 32 0. 92

Excess of Benefits Over Costs $7, 500 $3, 707 $2, 047 $-464

Source: Haveman and Margolis (1977), p. 18.

Though the issue of what constitutes the appropriate social discount rate has not been fully

resolved at the theoretic level, most economists agree that the "appropriate" interest/discount

rate is the opportunity cost rate implicit in the private spending that is displaced or affected by

public expenditure.30 In the case of SEC regulation, this suggests that the appropriate interest

rate would be based on the return to private capital, since SEC regulations may have important

impacts on private investment flows.

5.3 Costs and Benefits of SEC Regulation

In this section we apply some of the concepts discussed in Section A to identify a number of

social costs and social benefits of SEC regulation. The distinction between real and pecuniary

effects seems particularly applicable for this type of regulation. The reason is that the imposition

of, or changes in, regulation of securities transactions will inevitably alter the distribution of

income among buyers and sellers of securities. While acknowledging that such distributional

effects may have important political and policy implications, it should also be recognized that

they need not represent social benefits or social costs of regulation. That is, the losses suffered by

some are redistributed dollar for dollar as gains to others. In general, SEC regulations will

generate social costs and/or social benefits only to the extent that investors are compelled to

behave in ways that lower or raise total production and consumption potential.

» See Baumol (1968).

32
5.3.1 Costs of SEC Regulation

There are three distinct ways in which SEC regulation may lower the total production and

consumption potential of society, and hence impose social costs. These potential social costs can

be attributed to: (1) the administrative requirements of complying with SEC regulations; (2)

any reduction in total private savings and capital formation attributable to SEC regulation;

and (3) any detrimental change in the composition of private investment due to differential

impacts of regulation.

SEC regulations have been widely criticized for imposing substantial administrative costs

on firms that must comply with a myriad of registration and reporting requirements. In a fully

employed economy, any resources used up in the process of complying with SEC disclosure

requirements would have to be withdrawn from alternative production and/or consumption

activities. Hence, the costs of compliance would be a social cost of SEC regulation.

However, the analyst should take care that only those additional compliance costs directly

attributable to SEC regulation are counted as social costs. To illustrate this point, let D represent

the total value of resources, public and private, devoted to enforcing and complying with SEC

disclosure requirements. Similarly, let F represent the value of resources that would be devoted

to voluntary disclosure of information in tbe absence of any SEC regulation. The correct estimate of the compliance costs resulting from SEC regulation would be given by (D—F), notD,

as is sometimes implied in criticisms of SEC disclosure rules. To argue that D represents the

social costs associated with enforcement and compliance is to assume that no resources would be

devoted to disclosure activities in the absence of SEC regulation (F=0). However, this assumption is inconsistent with empirical evidence and a variety of theoretical disclosure models.31 To the extent that increased compliance costs reduce the level of private capital formation,

SEC regulation would generate additional social costs. This point is illustrated in figure 4, where

the horizontal lines <S and S' represent the cost of obtaining capital with and without SEC

disclosure requirements, and D represents the demand for capital, where the price that the

firm is willing to pay for additional units of capital is assumed to equal the value of the marginal

product produced by those units. In the absence of SEC regulation, the cost of capital facing

the firm would equal S, the equilibrium capital stock would be K*, and the net social gain would

be given by ABC. With SEC regulation, the real cost of obtaining capital facing the firm would

K' K*

Figure 4.

31 See, for example, Benston (1973), and Barth and Cordes (1980).

33
rise to S' due to increased costs of complying with SEC disclosure rules, the equilibrium capital

stock would fall to K', and the net social gain to investment by the firm would fall to AEG.

In terms of figure 4, the social cost of regulation would be given by the area CGEB. Part

of this social cost, represented by area CGEF, occurs because additional resources must be devoted to satisfy the disclosure requirements for obtaining K' units of capital. The remaining

component of social costs, area BEF, would represent a reduction in net social income due to

reduced capital formation.

Finally, SEC regulation may lower the level of income, and hence impose social costs

if disclosure requirements unintentionally discriminate against some investments and in

favor of others. This could, for example, occur if economies scale permitted larger firms to

spread disclosure costs over larger amounts capital obtained. This point is illustrated in

table 11.

Table 11. SEC regulation and investment impacts

Investment Investment

Option A Option B

Return Per $100 of Capital $8 $7

SEC Disclosure Costs Per $100 of Capital

Obtained $2 $0. 50

Maximum That Firm Could Pay Potential Investors $6 $6. 50

In the absence of SEC disclosure requirements, investment option A, paying a return of 8

percent, would clearly be preferred to option B, paying a return of 7 percent. However, with

disclosure requirements alternative B would be preferred. Thus, if SEC disclosure requirements

produced an incentive for investors to purchase option B, rather than A, the social cost of such

regulation per $100 of capital would equal the additional costs of such disclosure ($0.50 per

$100) plus the increment in output foregone due to investment in B rather than A ($1 per $100).

5.3.2 Benefits of SEC Regulation

While it is relatively easy to identify some important social costs of SEC regulations, it is more difficult to identify potential benefits. Ostensibly, the major output of SEC regulations is "information." However, economists disagree as to whether the information provided by

disclosure leads to a superior allocation of investment resources as well as resources totally

(investment versus consumption) , and hence produces social benefits, or whether access to such

information merely redistributes income between buyers and sellers of securities, and hence

produces pecuniary effects.

Hirschleifer (1971), and Fama and Laffer (1971), argue that allocating resources to information-gathering will be "wasteful" if such information is used solely for trading purposes, because

the information obtained will not affect the level of income, but only its distribution among

traders. However, this view has been challenged by Barzel (1977), as well as by Gonedes (1975),

who considers the impact of information gathering in a general equilibrium model in which

information flows are used to make resource allocation decisions.

The debate concerning the nature of the "benefits" provided by information-gathering

is sufficiently complex so as to preclude its detailed discussion here. However, the theoretical

debate has important policy implications. If the Hirschleifer-Fama-Laffer view is accepted,

SEC regulation can be critized as inefficient on purely theoretical grounds. If SEC disclosure

only serves to redistribute income among traders in securities markets, but does so while

generating identifiable social costs, the ratio of social benefits to social costs would be close to

34
zero, since pecuniary effects would not be viewed as social benefits. However, if the GonedesBarzel view is accepted, information-gathering may produce social benefits in the form of improved resource allocation in capital markets. In this case, determining the efficiency of SEC

regulation would be an empirical issue, requiring comparison of estimated social benefits with

estimated social costs.

5.3.3 Models of Securities Markets and Cost-Benefit Evaluations We conclude by examining whether empirical tests of market efficiency and/or empirical

studies based on the market model can provide information about the benefits and costs of

SEC regulation. In general, it will be shown that empirical studies of SEC regulation based on

existing models of securities markets can at best provide evidence on the presence or absence

of certain benefits or costs. However, such studies cannot be expected to yield estimates of the

size of the benefits and/or costs resulting from SEC regulation.

Consider first tests of market efficiency. Suppose, for sake of illustration, it was found that

markets behave as a fair game in the presence but not the absence of SEC regulation. This

would permit one to conclude that SEC regulation prevented some traders from making abnormal profits. Whether this would imply that SEC regulation produced real benefits is, however, uncertain. If SEC regulation did not change the underlying "information set" of financial

markets, but only access to that information, the impact of regulation would be distributional

in nature. That is, the same investments would continue to be made, but the prices charged by

traders would differ. Some traders would gain and others would lose, while underlying investment flows would remain the same. In this case, SEC regulation would be "beneficial" only if the group of traders who gained as the market became more fair were regarded as "deserving."

On the other hand, if SEC regulation altered the underlying information set, investment flows

would change. In this case, SEC regulation would affect real output. However, without additional data, one could not determine whether this real impact was a cost or a benefit. It is conceivable that SEC regulation could improve access to information, while distorting the

information set. In this case, SEC regulation would make the securities market more fair, but

at a price.

In fact, the available evidence indicates that securities markets will behave as a fair game

in the absence of SEC regulation. This simply means that SEC regulation cannot be credited

with reducing abnormal trading profits. However, this would not necessarily imply that SEC

regulation was of no social benefit. For example, if SEC regulation improved the underlying

quality of the information, social benefits would result from improved portfolio choices made by

investors. However, if regulation did not change the underlying information set, or distorted it,

SEC regulations would generate social costs in addition to any administrative costs of compliance.32 We have discussed how the pattern of estimated residuals from market model regression

may provide some information about the impact of SEC regulations. Presumably the signs of

such residuals indicate whether particular SEC regulations have had a favorable (positive

residuals) or unfavorable (negative residuals) effect on the price of particular securities. It is tempting to interpret the signs of such residuals as indicators of whether changes in SEC

regulations impose benefits or costs. However, caution should be exercised in making such

inferences. For example, if the introduction of some SEC regulation caused unfavorable

information to be disclosed that would otherwise have been withheld, securities prices would

fall, and the estimated residuals would be negative. However, in this case, SEC regulation would

have discouraged investors from making an inferior investment. While SEC regulation would

impose some costs on those selling the affected securities, society would benefit from an improved

allocation of investment resources.

" This conclusion has recently been drawn by Benston (1977).

35
However, it is also possible that the prices of some securities might fall because SEC regulation unintentionally made some securities less attractive as investments relative to others.

(An example of this might be the liquidity restrictions associated with Rule 144.) In this sase, the presence of negative residuals would indicate that SEC regulations imposed a cost. Thus, in

order to determine whether SEC regulation was beneficial or harmful, it would not be sufficient

to examine the pattern of residuals. Rather, analysis of residuals would have to be combined

with additional information about the impact of the regulation in question.

6. Summary and Conclusions

This report has described both the objectives and the methodology of the ETIP/SEC

study of SEC regulation. The ETIP/SEC study endeavors to determine whether SEC regulations have a significant effect on the ability of small, technology-based firms to raise capital.

This emphasis is particularly appropriate at a time when policy makers are concerned by manifestations of an "innovation recession." Small technology-based firms have historically depended more on equity as a source of finance than other firms. SEC regulations affect the terms

under which small, technology-based firms can raise equity capital. These firms may therefore

be particularly sensitive to SEC regulations.

Although there has been considerable conjecture in financial circles about the impact of

SEC regulations on the ability of small, technology-based firms to raise capital, there is, at

present, no empirical model for systematically assessing the impact of securities regulations

on "venture" capital markets. The ETIP/SEC project hopes to develop such a model. More

specifically, the project aims to develop an easily used system for monitoring the impact of

SEC regulations on small, technology-based firms.

Three specific SEC regulations which observers believe to be particularly important to

small, technology-based firms are Regulation A, Rule 144 and Rule 146. Regulation A ex- empts "small" issues from SEC registration requirements. In principle, it is designed to reduce

the cost of regulation imposed on small firms. In practice, the ceiling amount that defines a "small" issue maybe too restrictive to permit many firms to qualify for the Regulation A ex- emption. Rules 144 and 146 govern the terms under which capital may be raised through a "private" offering. Private offerings require less SEC disclosure than do public offerings. However, Rules 144 and 146 together impose some restrictions on the resale of private securities

that reduce their liquidity. Some important issues which were discussed are: the severity of

these restrictions, and the impact on venture capital markets of relaxing them.

Assessing the impact of these and other SEC regulations on small, technology-based firms

is a difficult task. The ETIP/SEC approach relies on some formal models of security-pricing.

The models most widely used are the efficients markets model, the capital asset pricing

model and the market model. These models have been widely used to determine the quantitative

impact of "changes in the environment" on securities prices. Changes in SEC regulations are one way in which the environment may change. Hence, these models are clearly useful for

determining the quantitative impacts of SEC regulations on securities prices. These models are, naturally, subject to several important limitations which have been identified and discussed.

6.1 Output of the ETIP/SEC Project and Policymaking

As a result of the ETIP/SEC project, an extensive data base has been developed. This

data base makes it possible to estimate empirical models that, in turn, can be used to determine

how SEC regulation affects various securities prices. Using the output of the ETIP/SEC

monitoring system to assess policy requires an additional step. This step involves determining

whether estimated impacts are desirable or undesirable.

Benefit-cost analysis is a widely used methodology for determining whether government

expenditure programs and regulations are desirable. Empirical studies of SEC regulation based

on existing models of securities markets are of some value in performing benefit-cost analyses.

36
Specifically, these models can be used to determine whether certain regulations have positive or negative effects. However, these models do not lend themselves readily to estimating the dollar

magnitudes of benefits and costs. Such models should, however, be quite useful in performing regulatory analyses of the sort

recently required by Executive Order 12044: Improving Government Regulations. Essentially, this

order requires that agencies first determine whether proposed regulations are likely to have a

substantial economic impact. If they have such an impact, the agencies must conduct regulatory

analyses of the proposed regulations.

The models developed by the ETIP/SEC project should be quite useful in complying with

the first phase described in the Executive Order. For example, the pattern of estimated residuals

from market model regressions could be used to determine whether particular SEC regulations

had a significant impact on securities prices at the time they were imposed. This evidence could

then be used to assess whether proposed regulations similar to those analyzed would have a

significant impact.

The ETIP/SEC monitoring system should also facilitate compliance with the requirement

that "significant" regulations be subjected to a regulatory analysis. As part of the regulatory

analysis, agencies must attempt to: (a) justify the need for the proposed regulation; and (b)

choose the least burdensome means of attaining the regulatory objective. Determining the

presence of market inefficiency would presumably offer a plausible rationale for certain SEC

regulations. Estimates of the market model could, in principle, be used to compare empirically

the impact of alternative regulations on security prices and returns. The results of such simulations would be of considerable value in identifying regulatory options that were less burdensome

than others.

Thus, like all empirical approaches to policy analysis, the ETIP/SEC monitoring system

is subject to important limitations. However, once fully developed, this system can be

expected to contribute significantly to the systematic evaluation of government policies toward

securities markets.

The authors would like to acknowledge the constructive comments of Robert Atkins,

Robert Goldfarb and Richard Penn.

7. References

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37
Fama, Eugene F. Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance,

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38
NBS-114A (REV. 9-78)

lie. OFPT. OF COMM.

BIBLIOGRAPHIC DATA

1. PUBLICATION OR REPORT NO 3. R^iiliii^i@I: i^^^^^^^^^^S

SHEET

4. TITLE AND SUBTITLE

NBS Monograph 166

5. Publication Date

Evaluating the Impact of Securities Regulation on

Venture Capital Markets

June 1980

6. Performing Organization Code

i 7. AUTHOR(S) 8. Performing Organ. Report No.

James R. Barth, Joseph T. Cordes, and Gregory Tassey

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, DC 20234

11. Contract/Grant No.

12. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street, city, state. ZIP) 13. Type of Report & Period Covered

M tonnwHnt Agwnrv Pnrfu "

15. SUPPLEMENTARY NOTES

Library of Congress Catalog Card Number: 80-600036

I | Document describes a computer program; SF-185, Fl PS Software Summary, is attached.

16. ABSTRACT (A 200-word or leaa factual summary of moat significant Information. If document includes a significant bibliography or

literature survey, mention it here.)

A detailed and analytical assessment is provided of the economic techniques used

by researchers to evaluate the efficiency of capital markets. The application of

these techniques to that portion of the capital market which supplies venture funds

to small, technology-based firms is emphasized. The primary elements of such analysis

are the "efficient market hypothesis" and the "capital asset pricing model." The

empirical analogue of the latter is commonly referred to as the "market model."

There have been several previous reviews of capital market theory but this is the

first one to apply these techniques to a particular segment of the capital market and

the existing policy structure affecting its operation. This policy structure is the

set of regulations imposed on venture capital flows by the Securities and Exchange

Commission. The important SEC regulations and the analytical approaches to assessing

their impacts on capital market efficiency are discussed. Because such analysis cannot

be effectively utilized by policymakers such as the SEC if it is conducted on an ad

hoc basis or in isolation of the decision-making process, a monitoring system is described, which is based on the market model and which is designed to provide timely

and decision-relevant information to the SEC

17. KEY WORDS (six to twelve entries; alphabetical order; capitalize only the first letter of the first key word unless a proper name;

separated by semicolons)

Capital asset; capital market; market model; pricing model; SEC regulations;

venture capital.

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