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1978 Investors' Reaction to the Replacement Cost Information Provided as a Result of Asr #190: Some Empirical Results. William Carlton Fleenor Louisiana State University and Agricultural & Mechanical College
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Recommended Citation Fleenor, William Carlton, "Investors' Reaction to the Replacement Cost Information Provided as a Result of Asr #190: Some Empirical Results." (1978). LSU Historical Dissertations and Theses. 3280. https://digitalcommons.lsu.edu/gradschool_disstheses/3280
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University Microfilms International 300 N. ZEEB ROAD, ANN ARBOR, Ml 48106 18 BEDFORD ROW, LONDON WC1R 4EJ, ENGLAND 7911568
FLEENOR, WILLIAM CARLTON INVESTORS* REACTION TO THE REPLACEMENT COST INFORMATION PROVIDED AS A RESULT OF ASR #190: SOME EMPIRICAL RESULTS.
THE LOUISIANA STATE UNIVERSITY AND AGRICULTURAL AND MECHANICAL COL., PH.D., 1978
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University Microfilms International
300 N ZEEB RD.. ANN ARBOR. Ml 48106 '3131 761-4700 INVESTORS' REACTION TO THE REPLACEMENT COST INFORMATION
PROVIDED AS A RESULT OF ASR #190i
SOME EMPIRICAL RESULTS
A Dissertation
Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy
in
The Department of Accounting
William Carlton Fleenor B.B.A., Loyola University, 1972 M.S., University of New Orleans, 1975 December, 1978 ACKNOWLEDGEMENTS
The author wishes to express his appreciation to his committee members — Dr. J. David Spiceland
(Committee Chairman), Dr. C. Willard Elliot, Dr. Bart
P. Hartman, Dr. Charles G. Martin, and Dr. Jerry E.
Trapnell — for their time and assistance throughout this study0
In addition special thanks are also owed to
Mr. Bruce L. McManis for his suggestions and computer programming assistance.
Finally, I wish to express my sincere appre ciation to my wife, Sandy. Without her patience, assistance, encouragement, and typing, the successful completion of this study would not have been possible. TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS...... ii
LIST OF TABLES ...... vi
LIST OF FIGURES ...... vii
ABSTRACT ..oooo...... x
Chapter
1. INTRODUCTION ...... 1
Purpose of the S t u d y ...... 1
Nature of the Problem ...... 1
The Theoretical Framework ...... k
Preview of Research Methodology ...... 7
The Orgainzational Design ...... 10
2. RESEARCH METHODOLOGY ...... 11
Review of Related Literature ...... 11
Introduction...... 11
The Evans and Archer. S t u d y ...... 12
The Fama et al. S t u d y ...... 1^
The Archibald S t u d y ...... 20
The Sharpe and Walker S t u d y ...... 2k
The Harrison S t u d y ...... 27
P r o c e d u r e ...... 31
Sample Selection ...... 31
Market Index Selection ...... 32
Research Design ...... 36
iii Chapter Page
A s s u m p t i o n s ...... 40
Subsample Groupings ...... 45
The Level of Systematic Risk Grouping . , , 46
The Unsystematic Risk Grouping ...... 48
The Asset Ratio Grouping...... 49
The Industry Grouping ...... 50
The Current Impact of Inflation G r o u p i n g ...... , ...... 51
The Cumulative Impact of Inflation G r o u p i n g ...... 55
S u m m a r y ...... 5 8
3. PRESENTATION AND ANALYSIS OF FI N D I N G S ...... 59
Introduction ...... 59
The Primary Sample ...... 59
Tests to Determine the Stability of the Model Parameters ...... 64
The Asset Ratio Grouping ...... 68
The Systematic Risk Level Grouping ...... 71
The Unsystematic Risk Level Grouping ...... 72
The Industry Grouping ...... 76
Groupings Based on the Replacement Cost Figures ...... 83
Introduction ...... 83
The Current Impact of Inflation Groupings ...... 84
The Cumulative Impact of Inflation Groupings . . . , ...... 86
S u m m a r y ...... 90
iv Chapter Page
k. SUMMARY, CONCLUSIONS, LIMITATIONS, AND RECOMMENDATIONS ...... 91
Summary and Conclusions ...... 91
Implications of the Findings . 95
Limitations of the Study ...... 96
Recommendations for Further Research ...... 97
BIBLIOGRAPHY...... 99
APPENDICIES
A„ LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE ...... 106
B. LIST OF ALL FIRMS IN THE MARKET INDEX ...... 122
C. PLOTS OF THE CUMULATIVE AVERAGE RESIDUALS OF THE INDUSTRY SUBSAMPLES NOT INCLUDED IN THE T E X T ...... 127
D. MONTHLY RETURNS FOR THE MARKET INDEX ...... 1^7
VITA ...... 151
v LIST OF TABLES
Table Page
1. Summary of the Results cf the Test to Determine the Appropriate Market Index ...... 3^
2. Sets of Subsample Groupings ...... ^6
3. List of Industries Analyzed ...... 52
Statistics Relating to the Primary Sample and Twelve Subsamples ...... 75
5. Statistics Relating to Twelve of the Industry S u b s a m p l e s ...... 82
6. Statistics Relating to the Subsamples Based on the Replacement Cost Figures ...... 89
1 ! ,1
vi LIST OF FIGURES
Figure Page
1. The Impact of Naive Diversification on the Level of Unsystematic Risk in Portfolios ...... 13
2. Cumulative Average Residuals- All S p l i t s ...... 19
3. Cumulative Average Residuals- All Revaluations...... 26
Plot of the Average Residuals for the Primary Sample ...... 60
5. Cumulative Average Residuals for the Primary Sample ...... 62
6. Cumulative Average Residuals for the Original Model and Two Test M o d e l s ...... 67
7. Cumulative Average Residuals for the Asset Ratio Subsamples ...... 70
8. Cumulative Average Residuals for the Systematic Risk Level Subsamples ...... 71
9. Cumulative Average Residuals for the Unsystematic Risk Level G r o u p i n g ...... 73
10. Cumulative Average Residuals for Four Industries That Reacted Negatively to the Replacement Cost Information...... 78
11. Cumulative Average Residuals for Three Industries That Reacted Positively to the Replacement Cost Information ...... 79
vii Figures Page
12 . Cumulative Average Residuals for Three Industries Exhibiting a Modest Reaction to Replacement Cost Information . 80
13. Cumulative Average Residuals for the Current Impact of Inflation Relative to Return Grouping ...... 8^
1^. Cumulative Average Residuals for the Current Impact of Inflation as a Percentage Change in Profit Grouping ...... 85
13. Cumulative Average Residuals for the Cumulative Impact of Inflation as a Percentage of Stockholders' Equity Grouping ...... 87
16. Cumulative Average Residuals for the Cumulative Impact of Inflation as a Percentage of Historical Cost Accumulated Depreciation Grouping..... 88
17. Plot of the Cumulative Average Residuals for the Oil-Crude Producers Industry .... 128
18. Plot of the Cumulative Average Residuals for the Heavy Construction-Ex Hwy & St Industry ...... 129
19. Plot of the Cumulative Average Residuals for the Forest Products Industry ...... 130
20. Plot of the Cumulative Average Residuals for the Drugs-Ethical Industry...... 131
21. Plot of the Cumulative Average Residuals for the Oil-Integrated Domestic I n d u s t r y ...... 132
22. Plot of the Cumulative Average Residuals for the Oil-Integrated International I n d u s t r y ...... 133
23. Plot of the Cumulative Average Residuals for the Blast Furnaces & Steel Works Industry ...... 13^
viii Figures Page
2k. Plot of the Cumulative Average Residuals for the Machinery-Industrial I n d u s t r y ...... 135
25. Plot of the Cumulative Average Residuals for the Electric Household Appliances I n d u s t r y ...... 136
260 Plot of the Cumulative Average Residuals for the Auto Parts & Accessories I n d u s t r y ...... 137
27» Plot of the Cumulative Average Residuals for the Manufacturing Industries I n d u s t r y ...... 138
28. Plot of the Cumulative Average Residuals for the Railroads Industry ...... 139
29. Plot of the Cumulative Average Residuals for the Air Transport Industry ...... lkO
30. Plot of the Cumulative Average Residuals for the Electric Utilities-Flow Through Industry ...... lkl
31. Plot of the Cumulative Average Residuals for the Electric Utilities-Normalized Industry . . . 0 ...... 1^2
32. Plot of the Cumulative Average Residuals for the Natural Gas Transmission I n d u s t r y ...... 1^3
33. Plot of the Cumulative Average Residuals for the Natural Gas Companies I n d u s t r y ...... lkk
3k. Plot of the Cumulative Average Residuals for the Retail Food Chains Industry .... lk$
35. Plot of the Cumulative Average Residuals for the Conglomerates Industry ...... 1^6 ABSTRACT
The intent of this study was to determine whether
investors reacted to the replacement cost information pro
vided by firms in compliance with Accounting Series Release
No. 190. The purpose of the study was to conclude whether
the replacement cost accounting figures provide investors
with information which is useful in their evaluation of
the impact of inflation on particular firms. To determine
whether investors perceived the replacement cost figures
to be new and useful information, the returns of the common
•stocks of companies required to provide the replacement cost
figures were examined to determine whether the returns of
those companies were altered by the release of the replace
ment cost figures.
The research procedure employed the market residual
analysis technique to determine the impact of the initial
release of the replacement cost information on the return
of the common stocks of 735 companies. The firms examined
were the firms initially required to provide the replace
ment cost information.
The market model was used to construct parameters
to predict the monthly returns of the 735 firms for the
21 month period surrounding the announcement of the replace
ment cost information. The predicted returns were subtracted from the actual returns to produce the residuals. The residuals were cumulated month by month to form cum ulative average residuals. These cumulative average residuals and the related statistics were analyzed to determine if evidence existed indicating that investors reacted to the release of the replacement cost infor mation.
Because investors may have reacted to the replace ment cost information differently across firms, the primary sample of 735 firms was subdivided eight times into sub sample portfolios. Each of 50 different subsample port folios were evaluated using the cumulative average residuals analysis technique. The subsample groupings were formed by ranking firms on the basis of (l) the percentage of assets revalued in compliance with Accounting Series Release No.
190, (2 ) the relative levels of systematic risk of the firms, (3) "the relative levels of unsystematic risk of the firms, (4) industry lines, and (5) the actual diff erences between the replacement cost figures and the his torical cost figures.
The results of the cumulative average residuals analysis of the primary sample indicate that investors generally reacted negatively to the release of the replace ment cost information. This negative reaction began about three months before the detailed replacement cost figures were released publicly. Tests of the subsamples indicate that while there was a general negative reaction to the replacement cost
information which began about three months prior to the
release of the actual detailed replacement cost figures
to the public, the reactions of investors to the release
of the detailed replacement cost figures were quite diff
erent for different groups of firms. For some groups,
the reactions indicated that investors had generally
underestimated the impact of inflation. For other groups,
the indication was that investors had generally over
estimated the impact of inflation on the firms of those
subsample groupings. The results of the tests of the
subsample groups were consistent with the results of the
tests of the primary sample and helped to clarify the
extent and nature of investors' reactions to the release
of the replacement cost information.
The findings of this study indicate the investors
were generally unable to obtain accurate information
about the impact of inflation on particular firms either
through the conventional accounting model or through other
sources. The replacement cost figures provided as a result
of Accounting Series Release No. 190 were apparently new
and useful information which helped investors in their
evaluation of the impact of inflation on particular firms.
These results have implications for both suppliers and
users of financial accounting information. Chapter 1
INTRODUCTION
Purpose of the Study
The intent of this study is to determine whether
investors react to the replacement cost information pro
vided by firms in compliance with Accounting Series
Release No. 190 (ASR #190 ).^ The purpose is to con
clude whether the replacement cost accounting figures
provide investors with information which is useful in
their evaluation of the impact of inflation on partic ular firms.
Nature of the Problem
In recent years there has been a great deal of
concern in the business community about the ability of
the conventional accounting information model to convey
information needed by investors to assess the impact of
inflation on individual firms. The problem centers around the fact that the conventional accounting model uses historical cost valuation for balance sheet
i Securities and Exchange Commission, Rule 3-17 of Regulation S-X. Accounting Series Release, No. 190* March 23, 1976 (Washington: Government Printing Office, 1976).
1 presentation of fixed assets and for the related cal culation of depreciation. Historical cost valuation ignores the impact of inflation on assets until these assets are replaced. In March, 1976, the Securities and Exchange Commission (SEC) issued ASR #190, to help in dealing with this problem. The release requires approximately 1,000 of the largest nonfinancial companies in the United States to disclose replacement cost infor mation for; (l) inventories, (2 ) productive capacity
(i.e., plant, property and equipment), (3) cost of goods sold, and (4) depreciation for productive capacity based on replacement cost of productive capacity. Disclosure only of these items is required. The SEC makes no attempt to explain how the replacement cost calculations can be used in constructing a new measure of income.
ASR #190 poses a number of problems for the accounting community. Most prominent among the problems are; (l) determining the proper method(s) of calculating replacement cost, (2 ) fitting the replacement cost figures into an income model, (3) interpreting the results of income models using replacement cost figures, and (4) determining if the information is useful to investors. These problems are interrelated.
2 Ibid.
^Ibid. , pp. 1-2 . There is a considerable disagreement over which of the above problems is the most serious. The majority of the literature concerning ASR #190 is related to deter mining the proper computational method. Is one method the best measure of replacement cost? If several methods are justified, when should each be used? Who should decide which method should be used when several methods are accept able (management or auditor)? These are some of the relevant questions that require resolution.
Determining how the replacement cost figures are to be incorporated into the accounting model is one of the important issues to be resolved. The primary consideration in making this determination should be whether the infor mation is new and useful to investors. Investors get information from a variety of sources, of which published financial reports are only one. The possibility exists that replacement cost figures are useful information that investors already possess. Therefore, while one might establish, either through theoretical construction or empirical research, that replacement cost figures fit well into investors' decision models, the replacement cost valuations made in compliance with ASR #190 may not be helpful to investors in their evaluation of the impact of inflation on individual firms. The problem, therefore, is to determine if investors perceive the information provided by ASR #190 to be new and useful. The Theoretical Framework
The usefulness of replacement cost valuation in an accounting information model is not a new issue. As early as 1952, the Committee on Concepts and Standards of the American Accounting Association discussed replace- ment cost with the same conceptual interpretation of the subject as it is presently being given.^ Current inter pretations of replacement cost were given by Falkenstein and Weil,-5 Popoff, and Revsine. In their consideration of the role of replacement cost in the accounting model,
Falkenstein and Weil considered three measures of income;
(1 ) distributable or sustainable income, (2 ) realized
Q income, and (3) economic income.
Distributable or sustainable income is calculated by substituting replacement cost of goods sold and
L American Accounting Association, "Report of the Committee on Cost Concepts and Standards," The Accounting Review, XXVII (April, 1952), 1?6-178.
-5Angela Falkenstein and Roman L. Weil, "Replace ment Cost Accounting: What Will Income Statements Based on the SEC Disclosures Show? - Part I," Financial Analysts Journal, XXXIII (January-February, 1977)» 48-52. s Boris Popoff, "The Informational Value of Replacement Cost Accounting for External Company Reports," Accounting and Business Research. Winter, 1974, PP° 66- W.
^Lawrence Revsine, "Replacement Cost Accounting: A Theoretical Foundation," Objectives of Financial Statements. Vol. II (New York: American Institute of Certified Public Accountants, 1973)» PP« 178-189. g Falkenstein and Weil, pp. 48-50. replacement cost depreciation for their historical cost
counterparts. During periods of inflation, this sub
stitution would result in a smaller income figure than
the conventional historical cost calculation of income.
Since distributable income takes into account depreciation
based on current cost figures, this measure comes close to
representing the income that could be distributed to owners without impairing the future earnings potential of the firm0 The adequacy of distributable income as a measure
of the amount owners can withdraw without impairing future
earnings potential is determined in each case by the firm's
specific replacement policies. Replacement cost depreciation
expense is a computation of the past year's depreciation
expense based on replacement cost figures for productive
capacity. Replacement cost depreciation expense computed
annually does not take into consideration the effect of annual compounding. An inflation rate of 10 percent over
three years will increase the replacement cost of an asset
by 33*1 percent. Therefore, the sum of the annual replace ment cost depreciation calculations over the life of an
asset, will not equal replacement cost at the end of that
assets life. If the total cost of the fixed assets that
a particular firm replaces each year, approximately equals
depreciation expense calculated on a replacement cost basis,
then distributable income will serve adequately as a measure
of the amount that owners can withdraw without impairing future earnings potential. If, on the other hand, fixed assets are replaced in large groups at irregular inter vals, distributable income may not retain adequate amounts through depreciation for normal replacement,, This short coming is only minor and distributable income is a better measure of the amount that can be distributed to owners without impairing a firm’s capital than realized income.
While distributable income is generally less than realized income during inflation, economic income is generally greater. Economic income includes not only realized holding gains, which are not included in dis tributable income, but also takes into consideration unrealized holding gains which are not included in either of the other two models. Economic income measures how much better off a firm is at the end of the year than it was at the beginning of the year. Although this calcu lation may be useful in other disciplines, it is generally regarded as being of little value to the investor in assess ing a firm’s future earnings potential,,
The traditional explanation of stock prices is the present value of expected future cash flows to stock holders, discounted at an appropriate rate to reflect return requirements for the particular risk level. Distributable income is a yardstick with which investors can measure the impact of current distributions of earnings on future earn ings potential. If current distributions to- equity holders are smaller than distributable income, the expectation is 7 that the firm will grow as a result of the retention of distributable income and future earnings will increase.
Therefore, assuming a relationship between cash flow and earnings, distributable income can be a valuable tool to investors in estimating future flows.
Even if distributable income is a better tool than realized income in evaluating future flows to equity holders, this does not necessarily mean that replacement cost figures will be of value to investors. Published financial state ments are not the only source of information investors have in evaluating the expected future flows of firms. If inves tors are already able to obtain the same information through other channels, the replacement cost figures may be of no value to investors.
Preview of Research Methodology
If investors perceived the replacement cost infor mation provided by ASR #190 to be new and useful infor mation, then in an efficient capital market enviroment, they would react to the information.^ If the reported replacement cost figures were higher than investors had anticipated, distributable income would be lower than it
^For the purpose of this study, an efficient capital market enviroment is one in which current prices fully reflect all publicly available information about the underlying companies. For a complete discussion of the efficient market hypothesis see Mary T. Hamilton and James H. Lorie, The Stock Market-Theories and Evidence (Homewoods Richard D. Irwin, Inc., 1975)> PP° 70-110. had previously been assumed to be. The price of the stock
would fail to compensate for the revised expectations of
investors. If, on the other hand, investors overestimated
the impact of inflation and the replacement cost figures
were lower than expected, there should be a positive reaction
by investors to the release of the replacement cost figures.
The technique used in this study to determine the
reaction of investors to the release of the replacement
cost information is the market residual analysis technique, 10 pioneered by Fama et al. The market residual analysis
technique is particularly well suited to this research pro
ject for two reasons. First, the sample size is large and
the larger the sample size, the better the fit of the model.
Second, calculating and issuing the replacement cost figures
is a nondiscretionary change ordered by the SEC. There is
no justification for the conclusion that any observed
reaction by investors is a reaction to the information
conveyed by the fact that management decided to make the
• change.
The actual gathering and statistical analysis of
the data proceeded as follows:
1. The sample of firms to be analyzed was chosen.
The sample was the entire population of firms that were
required to provide the replacement cost information whose
1976 year was the calendar year. This included 753 firms.
10 Eugene F. Fama et al., "The Adjustment of Stock Prices to New Information," International Economic Review. X (February, 1969)» 1-21. 9
2 . The market index was chosen. Since over
1,000 firms were required to provide the information, commonly used market indexes were biased because these indexes include so many of the firms that were required to provide the replacement cost information. The market index used was constructed from all firms on the Conrpustat tapes. Firms with inventories and gross property, plant, and equipment of $100,000,000 or more were eliminated from the index because they were required to provide the replace ment cost information. Companies with less than $50,000,000 in inventories and gross property, plant, and equipment were also eliminated because small firms were found to have different risk characteristics than the firms that were required to provide the replacement cost information,, After these eliminations, there were 17^'firms remaining. These firms made up the market index.
3. The market residual analysis technique was used to determine if investors reacted to the release of the replacement cost figures. The residual analysis was was first applied to the entire sample and then to sub sample groupings which might more clearly reveal the full extent of investors' reactions to the release of the replace ment cost information.
11 Investors Management Services, Conrpustat (Denver: Investors Management Services, Inc., 1977). The Organizational Design
The next chapter discusses in detail meth odology used in analyzing the data and the related assumptions. A review and discussion of previous liter ature which formed the hasis for the methodology used in this research is included.
Chapter 3 presents the results of the analysis of the data and the related statistics.
In the final chapter, the study is summarized and conclusions ahout the findings and the usefulness of the replacement cost figures are given. Recommen dations for further research in this area are also made. Chapter 2
RESEARCH METHODOLOGY
Review of Related Literature
Introduction
In 1969» Fama et al. published their revolution ary article, "The Adjustment of Stock Prices to New 1 Information." The article was revolutionary because it gave the financial and accounting communities a new tool, better than others currently available, to use in determining the relationship between accounting changes and stock market prices. This tool, known as the market residual analysis technique, has been widely used since that time.
In developing their model, Fama et al. observed the well known phenomenon that rates of return are not independent across stocks. King had estimated that 30 to 60 percent of the average stock's variance in return 2 is explained by the market factor. In other words, 30
1 Eugene F. Fama et al., "The Adjustment of Stock Prices to New Information," International Economic Review, X (February, 1969), 1-21. 2 Benjamin F. King, "Market and Industry Factors in Stock Price Behavior," Journal of Business, XXIX (January, 1966), 139-190.
11 12 percent to 60 percent of the variance in the return of individual stocks results from systematic factors (such as changes in interest rates and changes in the rate of inflation) that are not peculiar to any particular stock.
The effect of these systematic factors on the market as a whole is called the market factor. Indexes such as the Standards and Poor's 400 Industrial Index and the
Dow Jones Industrial average are approximate measures of this market factor.
The Evans and Archer Study
Another phenomenon, documented by Evans and
Archer in 1968, was also part of the foundation of the residual analysis technique.-^ Evans and Archer con structed 60 different portfolios for each of 40 different sizes.^ In other words, 60 one-security portfolios,
60 two-security portfolios, and so on, up to 60 forty- security portfolios were constructed from randomly selected stocks. The average standard deviation of returns was calculated for the 60 portfolios of each size.-5 Figure 1 depicts the results of their investi gation. Increasing the size of the portfolios signif icantly decreases the level of unsystematic risk only
^John Evans and Stephen Archer, "Diversification and the Reduction of Dispersions An Empirical Analysis," Journal of Finance, XXIII (December, 1968), 761-767.
^Ibid., p. 764. -’ibid. 13
— ■ predicted Y .195- « * octuol Y ---° approximate 95% confidence limit Predicting equation 3 8(I/X) + A § .180' Values of parameters: A3.1191 ; B«.08625 1 Coefficient of determination 3 .9863 | .>65. I
^ .150-
*§ * .135-
.120- Estimated leva) of systematic variation 3 .1166 -I r r 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 Portfolio size
Figure 1
The Impact of Naive Diversification on the Level of Unsystematic Risk in Portfolios
Source:
John Evans and Stephen H. Archer, "Diversification and the Reduction of Dispersion: An Empirical Analysis,” Journal of Finance. XXIII (December, 1968), 7&5> 14- in the first few cases. On the average, the full benefits of naive diversification can be achieved by forming port folios of 10 to 15 stocks. Since Evans and Archer esti mated the level of systematic variation to be .1166, form ing portfolios of 15 or more securities eliminates on the average, 96 percent or more of the unsystematic variation 6 in portfolio returns. For this reason, indexes like the
Standard and Poor's 4-00 Industrial Index are considered measures of systematic variation, or in other words, measures of the market factor.
The Fama et al. Study
With the knowledge of these phenomena in mind,
Fama et al. developed the market residual analysis technique. The following is the model Fama et al. used to investigate the association between stock splits and investor reaction. Ordinary least squares regression was used to estimate the parameters.
R .. = A. + b .R . + V.. Jt 3 3 mt 3t where i
R., = the realized return on firms 3 over period t,
6Ibid., p. 765. 7 'Fama et al., pp. 1-21.
8Ibid. 15
R ^ = the realized return on wealth during
period t, commonly known as the market index,
A., B = estimated parameters for firm j, and J J = the residual for firm j over period t„
The beta coefficient (B..) represents the reaction J of an individual firm or portfolio of firms to the move ments of the market factor. Assuming the alpha coeffi- cent (A.) equals zero, stocks with a beta of 1.0 would J be expected to experience a 20 percent increase in real ized return attributable to the market factor, if the market factor (Rm-|.) increased by 20 percent. Firms with a beta of 2.0 would be expected to experience a 40 percent increase in realized return from an increase in R , of mt 20 percent. For individual stocks, the market factor explains only a portion (30 percent to 60 percent) of the total variance in return. The remaining 40 percent to 70 percent of the variance in the return of the indi vidual stock is attributable to unsystematic factors that relate specifically to individual firms. For this reason, the residual analysis technique cannot be used to examine firms on an individual basis. However, if firms are combined into portfolios, the effect of naive diver sification will be to eliminate most of the unsystematic portion of the variance in return. Combining the resid uals of different firms and computing the average resid ual has the same effect as naive diversification. In their study,' Fama et al. examined all stock splits of 25 percent or more on the New York Stock Ex change from January, 1927, through December, 1959*^
The market model was used to calculate the beta coef ficients for each of the 622 firms that met the require ments. The market was defined as the mean return on 10 all stocks listed on the New York Stock Exchange.
The estimated relationships were based on the ^20 months during the 1926-1960 period, with the exception of the
15 months before and the 15 months after the month of 11 the split. These months were excluded because unusual price behavior in months surrounding the split would obscure the long-term relationship.
Using the beta coefficients calculated in this manner, the expected return was calculated for each of the stocks in the study for each of the 29 months prior 12 to the split and the 30 months after the split. The residuals for each of the firms were averaged for each of the months prior to and following the stock splits.
The averaging of these residuals achieved the affect of naive diversification and eliminated unsystematic variation that could be attributed to events peculiar
g Fama et al., pp. 1-21.
10Ibid.
11Ibid.
12Ibid. to individual firms (changes in earnings, dividend
payout, ets.). Since the market model explains the
variance in return due to the market factor, the expected
value of the residuals is zero. Any deviation from zero
is explained as model error in the absence of some sys
tematic factor that exists in the firms in the sample
during the period tested that does not exist in the
market. In the Fama et al. research, this systematic
factor that existed in the sample was the fact that 11 all firms had significant stock splits. J
In analyzing the results, Fama et al. used the
technique of cumulative average residuals analysis.
Any time the residual analysis technique is used there
will he some residuals, unless the model is a perfect
predictor (i.e., has an R-square of 1.0 ). This creates
a problem in determining whether or not the residuals
result from random error or investor reaction. One
approach is to calculate standard deviations for the
residuals within the model to determine if they came 1 ^ from the same distribution. J However, the results
13Ibid. ^Ibid.
■^The residuals within the model are the diff erence between the actual and predicted values of the portfolio returns for the periods used to contrast the model. of this type of analysis could he misleading since investors may react to information over several time periods rather than all at once. If investors did react to the information over several time periods, the residuals would not necessarily have higher stan dard deviations than those in the model, since the calculation of the standard deviations would not take into account the fact that the residuals were all in one direction. To overcome this problem, Fama et al. 16 cumulated the average residuals over time. In other words, they took the average of the residuals for period t = -29 (the return for the month 29 months prior to the stock splits) and added it to the average of the residuals for period t = -28 to arrive at the cumulative average residuals for period t = -2 8. Then they added the cumulative average residuals for period t = -28 to the average of the residuals for period t = -27 to get the cumulative average residuals for period t = -27. This cumulating process was done for each month up through the thirtieth month after the stock split. The expected value of the residuals is zero, therefore, the expected value of the sum of the residuals is also.zero. ■ Since the cumulative average residuals at any particular time is just the sum of the average residuals up to that time, the expected
1 Fama et al., pp. 1-21. value of the cumulative average residuals is zero. Un like the calculation of the standard deviation of the residuals, the cumulative average residuals technique takes into account a pattern of residuals that all have the same sign.
The pattern of the cumulative average residuals found hy Fama et al. is illustrated in Figure 2. The
cumulative average residuals indicate that there was a positive reaction on the part of investors. For the
o.: • 1 1 1 j 1 r I I "" M I "I
7 o.x
u 0.11
o. 1 ‘ -29-25 -20 -15 -10 -5 0 5 iO 15 21) 25 Ml)
Montli relative to splitin
Figure 2
Cumulative Average Residuals-All Splits
Source s Eugene F. Fama et al., "The Adjustment of Stock Prices to New Information," International Economic Review X (February, 1969), 1-2 1 . sample as a whole, the cumulative average residuals
increased up to the date of the stock split. After
that time the rates of return of the firms, on the average, have the normal relationship to the rate of return on the market that was calculated in the model.
Therefore, the cumulative average residuals did not increase or decrease significantly for the remainder of the test period (through month t = +3 0) a17
Since the stock splits were not announced more than four months prior to the actual date of the split, the pattern of abnormal high returns that existed dur ing the 26 months prior to the split cannot be explained as investor reaction to the split itself„ The authors concluded that splits occur after periods of unusual prosperity for the company, and that this prosperity is reflected in the prices of the stocks prior to the 1 fl split. The authors found significant investor reaction during the 26 months that preceded the stock split, but found no significant residual associated with the stock split itself. 19
The Archibald Study
In 1972, Archibald concluded that for firms that switched back from an accelerated depreciation method to
17Ibid.
l8Ibid. 19Ibid. a straight line depreciation method the accounting change "... apparently had no immediate substantial 20 effect on stock market performance." Archibald studied a sample of 65 firms which had switched back from an accelerated depreciation method to a striaght line depre- 21 ciation method for financial reporting purposes only.
The sample of 65 firms represented substantially all the firms on the major stock exchanges which made this type of accounting change between January 1, 1955» and Decem- 22 ber 31, 1966. Archibald calculated the alpha and beta coefficients for each of the firms, excluding from the calculation the two years before and after the switch. 23
With these coefficients, he predicted the monthly returns for each of the firms during the two years before and after the switch. The average residuals for each of the months prior to and for the five months after the announce 2/l ment of the split were predominantly negative. In other words, the switch-back firms exhibited below normal stock market performance in the two-year period preceding the
90 T. Ross Archibald, "Stock Market Reaction to the Depreciation Switch-Back," The Accounting Review. XLVII (January, 1972), 22-30.
21 Ibid.
22Ibido
23Ibid. 2 ^Ibid. 22 change and for a few months after the change. This indicates that there is some relationship "between firms that change to accounting methods that artifically inflate earnings and firms that are experiencing below normal earnings. However, there was no evidence to conclude that the actual switch to a depreciation method that inflated earnings had any substantial effect on stock market performance.2^
The results of Archibald's study and similar studies that used the residual analysis technique to analyze the market's reaction to accounting changes are consistent with the semi-strong form of the efficient 2 6 market hypothesis,, " This hypothesis states that the market is efficient in the sense that: (l) market prices fully'reflect all publicly available information and, by implication, (2 ) market prices react instantaneously and unbiasedly to new information. 2 '7 All publicly available information includes a variety of sources, of which accounting is only one. Since, the change
2 ^Ibid., p. 30. 26 There are two other forms of the efficient market hypothesis, the weak form and the strong form. The weak form asserts that current prices fully reflect the information implied by historical price trends. The strong form asserts that the market fully reflects the content of all available information, even priv- leged information,,
^Eugene F. Fama, "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance. XXV (May, 1970), 383-^17. from one accounting technique to another normally does not alter the underlying economic information that the accounting figures are trying to represent, an efficient market would not he expected to react to the change. In most cases where the researchers found a reaction, that reaction was to underlying economic events which in them selves prompted management to make the change. This was
p O certainly the case in both the Fama et al. study and in the Archibald study.^
The fact that most of the market residual anal ysis research has revealed no market reaction to an accounting change does not mean than an accounting change cannot cause a market reaction. For an accounting change to affect the movement of stock market prices, the account ing change must either (l) alter the underlying economic situation which accounting numbers are trying to des cribe, or (2 ) provide new and useful information that investors have not previously been able to obtain. An example of the first situation would be switching from accelerated depreciation to straight line depreciation for both financial reporting purposes and for tax report ing purposes. Altering the method of recording depre ciation for tax purposes would alter the firms cash flow, which is one of the underlying economic events
'“Fama et al., pp. 1-21. 29 Archibald, pp. 22-30. accounting is trying to report. In an efficient market,
investors would be expected to react to this accounting
change.
The Sharpe and Walker Study
An example of the second situation, where new and useful information is provided by an accounting
change, is provided by Sharpe and Walker030 In their
research, Sharpe and Walker examined the reaction of
the investors in the Sydney Stock Exchange to the re vision of balance sheet figures based on a revaluation
of the assets.3^ Upward revaluation of fixed assets to their current selling prices for financial reporting, a procedure that is not allowed in the United States, is a common practice in Australia. The authors pointed out that although the revaluations were supposed to represent current selling prices, the basis of the revaluations was not always clearly stated. 32
In the sample selection, the authors eliminated firms that had only small revaluations (i.e., less than
$900,000) and firms where the revaluations constituted less than 10 percent of shareholders' equity. Also
3°I. G. Sharpe and R. G. Walker, "Asset Reval uations and Stock Market Prices," Journal of Accounting Research, XIII (Autumn, 1975)> 293-310.
31Ibid.
32Ibid., p. 297. eliminated from the sample were firms that were involved in take-over bids around the time of the revaluation.
The sample used by Sharpe and Walker consisted of 32 firms that met the above criteria. 33
Using 60 months of data, excluding the 12 months before and after the revaluation, Sharpe and Walker cal culated the beta coefficients for each of the stocks. In the same manner as Fama et al.,3^ they calculated the average residuals by date and the cumulative average residuals by date. 33 Figure 3 illustrates the cumu lative average residuals they obtained,, During the announcement month, there was an average positive resid ual of over 9s percent. There were also fairly large positive residuals the month before the announcement date and six months before the announcement date of
2 d 5 percent and 3*56 percent, respectively. The ex tremely large positive residual which occurred during the revaluation month is very strong evidence that inves tors perceived the revalued figures to be new and useful information in their evaluation of the firm’s future.3<^
Nicholas Gonedes, in an article which discussed the use of the residual analysis technique to evaluate external accounting information, stated that,
33Ibid., p. 296. 3/4. ^ Fama et al„, pp. 1-21. 33Sharpe and Walker, pp. 299-301.
36Ibid., p. 301. 26
Cumulative average residuals /»ci 22
20
Month Relative to Announcement Date
Figure 3
Cumulative Average Residuals-All Revaluations
Source; I. G. Sharpe and R. Walker, "Asset Revaluations and Stock Market Prices," Journal of Accounting Research, XIII (Autumn, 1975)» 300- 27
Since market transactors, in aggregate do not blindly accept and use accounting numbers only, the market's reaction to accounting numbers (e.g., . . 0) provide reliable indications of accounting numbers information content. If these reactions do exist, then the implication is that accounting numbers do reflect events that affect the values of firms (i.e., that they do have infor mational content).37
The fact that the cumulative average residuals leveled off after the announcement date, rather than working their way back to zero, indicates that the informational content of the asset revaluations had a lasting impact on investors' expectations.
The Harrison Study
In the Fama et al.,-^ Archibald, ^ and Sharpe kn and Walker articles, the accounting changes examined were made at the discretion of management. These types of changes are discretionary accounting changes. Non- discretionary accounting changes are those changes made by firms at the directive of some outside agency, like the Financial Accounting Standards Board or the Securities Lli and Exchange Commission. Harrison was aware of the
-^Nicholas J. Gonedes, "Efficient Capital Markets and External Accounting," The Accounting Review, XLVII (January, 1972), 16. qo Fama et al., pp. 1-21.
•^Archibald, pp. 22-30. l±() Sharpe and Walker, pp. 293-310. 4l . Tom Harrison, "Different Market Reactions to Discretionary and Nondiscretionary Accounting Changes," Journal of Accounting Research. LII (Spring, 1977)» 84-107. fact that in most cases, investor reaction around the time of an accounting change was a result of underlying economic factors that prompted management to make the change, and did not result from the change itself. In his research, Harrison classified firms making accounting changes according to whether or not the change was dis- |lO cretionary or nondiscretionary. He further subclass- ified the changes into groups, depending on whether the change had a positive or negative impact on income. These groups were classified further into high and low groups according to the beta coefficients of the firms. All of the classifications, with the exception of the dis cretionary and nondiscretionary classifications, were made to subdivide the firms into homogenous groups, based on preconceived ideas about how investors viewed certain types of accounting changes for certain stocks.
The primary classification of firms, according to whether they made discretionary or nondiscretionary accounting changes, tested the hypothesis that nondis cretionary accounting changes contain more information Zlt than discretionary changes. J The subdivision of firms into classes according to whether or not the change had a positive or negative effect on income is based on the assumption that investors perceive the motivations of
Ibid., p. 95- ^Ibido , p. 106. ZljLl management to "be different in each case. Subdividing the sample into groups according to the size of the beta coefficients is based on the assumption that investors might react differently to the same type of accounting change if it came from firms with different levels of systematic risk.^-5
By subdividing the firms in the manner described above, eight samples were formed. The cumulative average residuals analysis indicates that the firms in Harrison's study made discretionary accounting changes that had a positive effect on income generally performed poorer 46 than normal during periods that surrounded the change.
These results are consistent with those of Archibald, ^ and indicate that the accounting change, in this case, is not a casual factor, but was made in an attempt to make earnings look better during hard times.
On the other hand, the firms that made nondis cretionary accounting changes which had a positive effect on income, generally had better than average market performance during periods that followed the 48 changeo These results suggest that investors perceive
^Ibid., p. 85-90.
^5Ibid. 46 Ibid., p. 100c
^Archibald, pp0 22-30. 48- Harrison, p. 102 . the nondiscretionary accounting changes to have infor mational content that is similar to the impact of the kn change on income of the firm. 7
The conclusions of this study, as Harrison is quick to point out, are somewhat suspect for several reasons0-^° First, the sample sizes were small. Two
of the samples had only five firms. A second limitation
of the study was that 80 percent of the firms that exper
ienced a nondiscretionary accounting change experienced the same change; namely, a change to equity from cost
in reporting long-term investments.Despite these limitations, the results of this study are consistent with the hypothesis that investors perceive nondiscre tionary accounting changes to have a higher degree of informational content than discretionary accounting
changes.
The articles discussed in this section are
examples of how the market residual analysis tech nique has been used to examine the market impact of an accounting change. These articles are by no means the only examples of the use of this technique. The next section uses the methodologies of the articles
^Ibid. t pp. 84-107„ ■50Ibid., pp. 106-107.
-^Ibid., pp. 84-107.
52Ibid. discussed in this section to develop the procedure for the current research.
Procedure
Sample Selection
In order to increase the precision of the results and to allow for numerous subsample groupings, a very large sample was desired. The Compustat Price-Dividends-
Earnings tape-^ contains monthly price and dividend data on all companies that were required to provide replace ment cost figures under ASR #190.-^ However, because of the limited availability of post announcement date returns, only companies with fiscal years that ended between December 25» 1976, and January 5> 1977, were used. This reduced the sample from 1,072 (the entire population of firms that came under the requirements of ASR #190 in 1976) to 753 firms. Because of the limited availability of monthly returns, 18 more firms were eliminated.
The Compustat tapes do not contain replacement cost information. Furthermore, no list of firms that were required to provide the replacement cost figures was available from the Securities and Exchange Commission.
53 ^Investors Management Services, Compustat (Denver: Investors Management Services, Inc., 1978). 54 Securities and Exchange Commission, Rule 3-17 of Regulation S-X. Accounting Series Release, No. 190, March 23, 1976 (Washington: Government Printing Office, 1976). 32
Therefore, the determination of which firms were required to provide the replacement cost information was made by determining which firms met the requirements of ASH
#190. The requirements, as specified by ASH #190, were:
The new rule as adopted requires registrants who have inventories and gross property, plant and equipment which aggregate more than $100 million and which comprise more than 10% of total assets to disclose the estimated current replace ment cost of . 0 . .55
The Compustat Industrial File was used for the purpose of this calculation,, Inventories and gross property, plant, and equipment were combined for each firm on the
Com-pustat Industrial File for the year 1976. This total of inventories and gross property, plant, and equipment for each firm was then divided by total assets of that firm to determine if inventories and gross property, plant, and equipment comprised more than 10 percent of total assets. In many cases, inventories and gross pro perty, plant, and equipment aggregated to more than 100 percent of total assets.
Market Index Selection
The selection of a market index was more com plicated because of the composition of the primary sample. In previous research, where the residual anal ysis technique was used, the event being studied occurred at different times for different firms„ In those cases, general indexes such as the New York Stock Exchange
-^Ibid. , p. 1 . Composite Index and the Standard and Poor's ^00 Indus trial Average worked well. In the present research, all the firms in the primary sample disclosed the re placement cost information at the same time.-^ The use of one of the commonly used market indexes would produce biased results. Approximately 85 percent of the firms in the Standard and Poor's 400 Industrial Index were required to provide the replacement cost information.
Since the primary sample includes the majority of the largest firms in the United States, all of the commonly used market indexes were biased in this manner.
To overcome this problem, a special market index was needed. The index should be as much like the sample as is feasible. One major limitation in this respect was the size factor„ All firms with inventories and gross property, plant, and equipment of more than
$100,000,000 that met the 10 percent of total assets test were required to provide the replacement cost infor mation. Therefore, a market index of firms of comparable size cannot be constructed. Another related limitation was the problem of obtaining a market index with a level of systematic risk similar to that of the primary sample.
Since larger firms generally have lower levels of sys tematic risk (i.e., smaller beta coefficients), a market index of smaller firms would be expected to have a larger beta coefficient than the sample.
D See the following section on Research Design for a full discussion of the disclosure date. Three potential market indexes were examined to determine which would create a model that best fits the primary sample. None of the three indexes contained firms that failed to meet the 10 percent of total assets test, as specified in ASH #1900-^ The first index exam ined consisted of the stocks on the Compustat Industrial
File-^ which met the 10 percent requirement and had inventories and gross property, plant, and equipment of greater than or equal to $20,000,000 and less than
$100,000,000. Firms whose fiscal years did not end on or within five days of December 31* were deleted.
The first index contained 406 firms« The second index contained all firms with inventories and gross property, plant, and equipment of greater than or equal to
$50,000,000 and less than $100,000,000. Index two con tained 3^8 firms. Index three was the same as index two, except that all firms whose fiscal years did not end on or within five days of December 31» were deleted.
The three indexes were tested to determine which was most similar to the firms in the primary sample. For a 60-month period immediately prior to the announcement
Securities and Exchange Commission.,
-^Inventories Management Services.
7 Firms with fiscal years ending on or within five days of December 31 are defined as those firms that have fiscal years ending within the range of December 26, 1976, through January 5* 1977- 35 of ASH #190 (March, 1976) monthly returns were computed
for each of the potential market indexes and for the
primary sample. The returns were adjusted for the annual-
ized dividend rates of the stocks in the portfolios.
Ordinary least squares regression was used to determine
the goodness of fit of each of the potential market indexes.
Since the market index chosen was used to predict the
returns of the stocks in the primary sample, the market
index returns were considered the independent variable.
The results of this analysis are presented in
Table 1. All three indexes proved to be good fits, in
that they all produced very high F statistics. The third
Table 1 Summary of the Results of the Test to Determine The Appropriate Market Index
Model Statistics Index No.l Index No.2 Index No.3
Number of Firms 406 348 174
R-Square .859 .909 .917
Estimated Intercept .0032 . 0029 .00 25
Estimated Beta .65 22 .6940 .7272
F Ratio3 366.52 599.36 658.72
aIn all indexes significant at the .0001 level
^ 1 A more detailed description of how monthly return figures were calculated is given in the next section of this chapter. index was selected as the market index to be used for
two reasons. First, the R-square of the third group,
which is a measure of the goodness of fit, is highest.
Second, the beta calculations indicate that the level
of systematic risk of the third index is closest to
the levels of systematic risk of the primary sample.
Beta coefficients are estimates of the level of
systematic risk of stocks and portfolios. These estimates ^ O are relative to movements of the market factor. Indexes like the New York Stock Exchange Composite Index are used as surrogates for the market factor. Therefore, cal
culated betas are estimates of the level of systematic risk of stocks and portfolios relative to the level of
systematic risk of the index used. Because the beta
estimate calculated using the third index is closer to unity than the estimates of beta from the other two
indexes, the level of systematic risk of the third group
is nearer the level of risk of the primary sample.
Research Design
The intent of this study was to determine whether the disclosure of the replacement cost figures in compli ance with ASR #190 is associated with price adjustments in the market. The residual analysis technique, as it
^ O A more detailed discussion of the general mean ing of the beta- coefficients of stocks and portfolios is given on page 15. 37 was used by Fama et al.^3 was use(j -fc0 isolate the com ponent of price changes which resulted from the dis closure of the replacement cost figures. For the present research, the model is specified as followss
Hst - As + V e t + Vst where s
R ^ = the realized return on portfolio s
over period t,
R , = the realized return on the market ct index for period t,
A , B = the intercept and slope constants s s associated with the linear relation
ship, and
V t = that portion of the return of port
folio s which varies independently
of Rq for period t.
Unless there is some systematic element affect ing the return of the stocks in portfolios, not affect ing the stocks in the market index portfolios, the expected value of V x is zero, st In the primary sample and in each of the sub- sample groupings, the portfolio return (Rg^.) for each period was calculated ass
JFama et al., pp. 1-21. 38
n
Rs t ■ n C Rit i=l where:
n = the number of firms in the sample.
The represents the return for month t for the firm i. Fh-j. is calculated as:
ht - ht-i+
Pit = °l0SinS price for firm i for the month t, and
= the annualized dividend rate for firm i
for the month t as given on the Conrpustat 61l Price Dividend Earnings tape.
The monthly returns for the market index group were calculated in the same manner.
Using 61 months of monthly return data (Feb ruary, 1971» through February, 1976) the estimates of the alpha and beta coefficients for the primary sample and the subsample groupings were made. These estimates, and the monthly return figures for the market index group, were used to predict the monthly returns of the samples during the test period. The predicted
6k Investor Management Services. 39 monthly returns were subtracted from the actual monthly returns to compute the residuals□ These residuals were cumulated by month to form the cumulative average resid uals.
Of the 735 firms in the primary sample, 26 had some missing return observations. Out of the 6l periods used to construct the model, the number of missing monthly returns for the 26 stocks ranges from 3 to 56. The mean number of missing monthly returns is 23. The statistical procedure used to calculate the mean monthly return for the primary sample and each of the subsamples ignores missing values in the calculations. For example, if for month t = -29 the primary sample had missing observations for ^ of the 735 firms, the mean return for that period is based on the 731 return values that are present.
All statistical procedures used in this research treat missing values in this same manner.
The test period was March, 1975, through Nov
ember, 1977 (21 months). ASR # 190^ was announced
March 23, 1976. March, 1976, was, therefore, the first month in which investors could have reacted to the replace ment cost numbers or to the fact that the numbers were to be provided. November, 1978, eight months after the announcement date, was chosen as the end of the test period because eight months provided adequate time to determine whether any reaction was sustained.
6 ^ ■^Securities and Exchange Commission. Assumptions
The market residual analysis technique as specified above is based on the following assump tions :
1. That portion of variability in the return of individual stocks and portfolios that is attributable to unsystematic risk can be effectively eliminated through naive diversification.
20 That the systematic risk of the sample port folios relative to the market index portfolios is accur ately estimated and that it is a relatively stable para meter.
3« That the market is efficient in the sense that; (l) market prices fully reflect all publicly available information, and (2) market prices react immediately and unbiasedly to the release of new infor mation.
The first assumption concerning the elimination of unsystematic risk through naive diversification is 66 documented by Evans and Archer.
The accuracy of the second assumption is par tially dependent upon the first assumption.. If the assumption is made that unsystematic risk if effectively eliminated in both the sample and market index, then the accuracy of the estimated beta (B ) can be measured by s
66 Evans and Archer, pp. 761-767* how well the model fits (i.e., the R-square)0 Whether the estimated beta is a stable parameter is another question. The research to date is inconclusive on 67 68 this issue. However, studies by Blume ( and Levy indicate that the betas of portfolios are relatively stable over time and tend to regress to the mean of all betas, one.
The efficient market assumption is the basis for attributing the residuals (vs^) "to the accounting change being examined. Market efficiency as it is presently accepted (i»e., the semi-strong form)^ an(j as it is stated in assumption three, relates only to publicly available information. Market efficiency does not imply that investors will all react to infor mation in the same way. Market efficiency implies that when information becomes public, investors will not be able to earn superior returns by reacting to the infor mation.
The definition of R ^ as the realized return on portfolio "s" during period "t", differs some from how the model is generally specifiedo In most of the pre vious residual analysis research, the accounting changes
6 7 ^Marshall E. Blume, "Beta and Their Regression Tendencies," Journal of Finance. XXX (June, 1975 )> 785- 795. 68 Robert A. Levy, "Beta Coefficients as Pre dictors of Return," Financial Analysts Journal. XXX (January-February, 1974), 6I-6 9. 69 7Fama et al., pp0 1-21. ■being studied occurred at different times for different firms. As a result, the model was applied to each indi vidual firm to calculate the residuals around the period t = o (i.e., the announcement date)0 In the present research, the assumption is made that all firms disclosed the replacement cost information at essentially the same time, during the month of March, 1977* Because of this assumption, the estimates of the constants and the cal culation of the average residuals can he made for the entire sample population.
There are three possible avenues by which the replacement cost figures, generated in compliance with
ASR #190, could have become public information. The first possibility is that the information was made public through releases to the financial press, prior to the filing of the 10-K. In order to determine whether a significant number of firms released the information in this manner, a random sample of 5° firms was chosen from the primary sample. The Wall Street Journal Index list ings for each of the 50 companies for the periods March 1,
1976, through March 31» 1977 > were examined to determine if there were any early releases of the replacement cost 70 figures.‘ No early releases were found in either the individual company listings or in the subject heading, replacement cost. On the basis of these findings, the
"^Philip T. Wisner, ed., The Wall Street Journal Index 1976-1977 (Princeton: Dow Jones Books, 1978). conclusion was made that no significant early releases were made through the financial press.
Another possible avenue for the release of the replacement cost numbers was the firms’ annual reports.
Annual reports are not issued on any specific date and the date which annual reports are issued is not given in the annual reports. For the above mentioned reasons, no determination as to how many annual reports were re leased prior to March, 1977» could be made. However, an examination of the annual reports available in the
Louisiana State University Accounting Department Library revealed that only a few of the firms that were required to disclose the replacement cost information made detailed *71 disclosure in their annual reports.' Even if the replace ment cost figures were disclosed in the annual reports, the reports must have been issued before March 1, 1977» to invalidate the assumption that March is the appro priate t = o month. Considering the time required for the annual audit, for printing the annual report, the additional burden of cumputing replacement cost figures for the first time, and the fact that only a small per centage of the annual reports contained actual detailed replacement cost figures, the probability that a
^ ASR #190 gave firms the option of not dis closing the replacement cost figures in the annual report if the firm included a note in the annual report stating that the replacement cost figures were in the 10-K. 44 significant number of early (i.e., before March, 1977) public releases occurred through the issuing of annual reports is not great.
The third possible avenue by which the replace ment cost figures could have become public information was the filing of the 10-K. For the companies in the primary sample, the 10-Ks were due March 31* 1977.
Early releases could have occurred through the 10-Ks if they were filed more than a month early (i.e., before
March 1, 1977), The dates the 10-Ks were filed for the companies in the primary sample were not readily avail able and no investigation was made to determine if a significant number of 10-Ks were filed before March 1,
1977. Therefore, the possibility exists that some early releases of the replacement cost information were made in 10-K filings.
Three possible outlets (the financial press, annual reports, and the 10-K filings) through which the replacement cost figures could have become public information were examined. Investors in an efficient market are not limited to public information, partic ularly public accounting information. Therefore, assuming that March, 1977» is the appropriate month for t = 0 does not preclude the possibility that there were leaks of information that resulted in investor reaction before that date. Subsample Groupings
The possibility exists that even if investors reacted to the replacement cost information, they did not all react in the same manner for all firms. If, for example, investors underestimated the impact of inflation on some very capital intensive industry, like the steel industry, they would be expected to react negatively to the release of the replacement cost numbers.
On the other hand, if they had overestimated the impact of inflation on some other group of firms, they would be expected to react positively to the release of the replacement cost figures. A significant negative reaction in the primary sample would be expected only in investors had underestimated the impact of inflation on the majority of the firms in the primary sample.
For these reasons, the primary sample alone can not be relied upon to reveal the full extent of investor reaction to the replacement cost figures. Residual anal ysis of the primary sample will reveal whether the market as a whole underestimated or overestimated the impact of inflation. Residual analysis of the primary sample will not reveal the details of where, if at all, the market most poorly estimated the impact of inflation. Thus, this research has divided the primary sample into sub sample groupings which might more clearly reveal the extent of investors' reactions to the replacement cost figures. 46
Six different sets of subsample groupings were constructed. In each different set, the firms were divided into subsamples where investors might logic ally be expected to have similar expectations about the impact of inflation. Table 2 summarizes the diff erent groupings.
Table 2
Sets of Subsample Groupings
Logical Basis of the Groupings Number of Subsamples
A. The level of systematic risk 3
B. The level of unsystematic risk 3 C. The ratio of revalued assets to
total assets 3
D. By industry 29
Eo The current impact of inflation 6
F. The cumulative impact of inflation 6
These subsample groupings, the logic for their conception, and the details of the classifications are explained below.
The Level of Systematic Risk Grouping. Inflation is one of the basic elements of the market factor, and beta is a measure of a particular stock's reaction to the market factor. If investors are uncertain about the impact of inflation on a particular firm, this un certainty could result in the market assigning a higher ^7 level of systematic risk ("beta) to the stock of that
firm. Therefore, there may be some connection between
investors' ability to estimate the effect of inflation
on a particular firm and the size of the beta coefficient
for that particular firm. If this connection does exist,
investors' reactions to information which help reduce
the uncertainty about the impact of inflation on par
ticular firms may be correlated with the level of sys
tematic risk of particular firms. To test this hypothesis, the stocks of the pri
mary sample were grouped into classes according to the
size of the betas of the individual stocks. Using
ordinary least squares regression, the market index
returns, and 61 m o n t h s ^ of returns for each of the 735
firms in the primary sample, beta coefficients were cal
culated for each of the firms. The firms were ranked
according to the size of the beta coefficients and divided
into three groups according to the size of the betas.
If the reactions of investors were correlated
with the level of systematic risk, the firms with
extremely high betas or extremely low betas, relative
to the other firms, should react differently from the
sample as a whole. To isolate firms with extreme betas
relative to the mean of all the betas, the high beta
72 ' The 6l months used were the same months used to construct the parameter estimates for the market model. and the low beta groups contain only 75 firms. The remaining 585 firms make up the medium beta group.
The Unsystematic Risk Grouping. The logic for creating subsamples on the basis of the level of unsys tematic risk is the same as the logic used in the beta classification. If investors are unsure about what impact inflation has had on a particular firm, that firm will probably have a higher level of unsystematic risk. This unsystematic risk, by definition, will not result from general changes in inflation, but from the particular situations each firm faces with regard to the replacement of inventories and gross property, plant, and equipment.
The standard deviation of the returns of a firm is a measure of the total risk of that firm. Since total risk is a combination of systematic risk and unsystematic risk, a measure of unsystematic risk can be obtained by eliminating systematic risk from the total risk of each firm. The unsystematic risk measure was calculated in the following manner. The alpha and beta coefficients constructed for each firm during the
6l-month model building period were used to predict the returns of that firm during the 61-month model building periodo The predicted returns were subtracted from the actual returns to obtain error residuals. Since the predicted returns represent fluctuations in the return of the firm caused by the market factor, the error 4-9 residuals result primarily from unsystematic factors.
An unbiased estimate of the standard deviation of these error residuals (S ) can be calculated as:
RSS
where:
RSS = the sum of the squared error residuals,
and
n = the number of observations.^
This standard deviation of the error residuals was used as the measure of the level of unsystematic risk.
The firms were ranked according to the size of the standard deviation of the error residuals. The 75 firms with the highest level of unsystematic risk were designated the high sigma group, and the 75 firms with the lowest relative level of unsystematic risk were designated the low sigma group. The remaining 585 firms made up the medium sigma group.
Asset Ratio Grouping. The replacement cost figures provided as a result of ASR #190 give the esti mated replacement cost of inventory and gross property, plant, and equipment. If investors have incorrectly estimated the impact of inflation on these assets, the
73 '-'Richard W. Mensing and Bernard Ostle, Statistics in Research (Ames: The Iowa State University Press, 197577 p. 170. extent of their reaction to the replacement cost figures will probably be related to the percentage of total assets of a particular firm that are tied up in inven tory and gross property, plant, and equipment (asset ratio). If investors have underestimated the impact of inflation in general, there should be a larger neg ative reaction for firms with high asset ratios than for other firms in the primary sample.
The ratio of inventory and gross property, plant, and equipment to total assets (the asset ratio) was cal culated for each firm. The firms were ranked according to the asset ratios. As in the previous groupings, the
75 firms with the highest ratios were designated the high asset ratio group, the 75 firms with the lowest ratios were designated the low asset ratio group, and the other 585 firms made up the medium asset ratio group.
The Industry Grouping. Prices of various assets increase at different rates. Since firms within an in dustry group tend to possess similar assets and inven tories, they may be affected in a similar manner by infla tion. If investors have misjudged the impact of infla tion on a particular firm, they may have misjudged the impact of inflation on other firms in the same industry in the same manner.
To classify the firms by industry, the four digit SIC codes assigned by Investors Management Services were used. Since, the market model relies on naive diversification to eliminate unsystematic risk, industries with one or only a few firms in the primary sample were eliminated from consideration. Based on visual examination of the results of the research of
Evans and Archer^ (see Figure 1, page 12), seven was chosen, somewhat arbitrarily, as the cutoff point.
Industries with fewer than seven firms in the primary sample were eliminated. Table 3 lists the 29 industries examined and the number of firms represented by each.
The Current Impact of Inflation Grouping,, The difference between replacement cost pretax profit and historical cost pretax profit is one possible measure of the current impact of inflation. To the extent that investors rely on profit, any new information concerning the impact of inflation on the profit figure will be usefulc If inflation has been affecting profits to a greater extent than investors realized before the release of the replacement cost information, then the release of this information could trigger a negative reaction,, For this paper, replacement cost pretax profit is approx imated by substituting historical cost depreciation and cost of goods sold with replacement cost depreciation and replacement cost of goods soldo
^Evans and Archer, pp. 761 -767. 52
Table 3
List of Industries Analyzed
Observation Numbera Industry Firms
1 1000 Metals Miscellaneous 10 2 1311 Oil-Crude Producers 18 3 1621 Heavy Construction-Ex Hwy & St 8 4 2400 Forest Products 7 5 2600 Paper 15 6 2711 Publishing-Newspaper 7 7 2800 Chemicals 15 8 2835 Drugs-Ethi cal 13 9 2911 Petroleum Refining 9 10 2912 Oil-Integrated Domestic 18 11 2913 Oil-Integrated International 8 12 3221 Containers-Metal & Glass 9 13 3241 Building Materials-Cement 7 14 3310 Blast Furnaces & Steel Works 19 15 3330 Prim Smelt-Refin Nonfer Metal 8 16 3560 Machinery-Industrial 10 17 3570 Office & Business Equipment 8 18 363 0 Electric Household Appliances 7 19 3714 Auto Parts & Accessories 15 20 3999 Manufacturing Industries 7 21 4011 Railroads 16 22 4511 Air Transport 18 23 4811 Telephone Companies 13 24 4911 Electric Utilities-Flow Through 35 25 4912 Electric Utilities-Normalized 67 26 4922 Natural Gas Transmission 14 27 4924 Natural Gas Companies 21 28 5411 Retail Food Chains 7 29 9997 Conglomerates 14
aNumber is the Four Digit SIC Code Assigned by IMS 53
The Value Line Data Base tape^ provides the replacement cost information for some of the firms in the primary sample. Using the data available on this tape, replacement cost pretax profit was calculated for
288 of the firms in the primary sample.
Grouping of firms according to the difference
(usually negative) between replacement cost pretax profit and historical cost pretax profit would not be comparable because of the large differences in the sizes of the firms in the sample. To rank the firms according to the level of the current impact of inflation the difference was normalized by the market value of all the shares of out standing common stocks of each company on December 31»
1975. For each company, the difference between replace ment cost and historical cost pretax profit was divided by the market value of the firms' common stock. The resulting percentage is a measure of the decrease in return on investment that results from matching current replacement costs with current revenues.*7^
If the assumption were made that investors were not aware of inflation, then the expectation would be
75 Value Line Data Services, Value Line Data Base (New Yorks Value Line Data Services, 1978).
For 2k firms replacement cost pretax profit, computed in the manner described above was larger than historical cost pretax profit. Therefore, the change in return on investment computed in this manner was an increase rather than a decrease, and the resulting per centages for these 2k firms were positive. that investors' reactions to the release of the replace ment cost figures would be highly correlated with the relative impact of inflation on the individual firms.
However, it would be unrealistic to assume that inves tors had no knowledge that inflation existed. Therefore, the expectation for the current impact of inflation group ings is not based on the assumption that the size and nature of investors' reactions will be directly related to the size and nature of the impact of inflation on particular firms.
For firms whose inflation ratio was extremely large or extremely small relative to the mean ratio, the impact of inflation was classified as being sig nificantly different from the general impact of infla tion. Investors were probably least able to determine the true impact of inflation for firms that were affected by inflation in a manner significantly different from that for most firms. Therefore, in these extreme cases, investors may react differently to the release of the replacement cost figures.
The firms were ranked according to the percen tages obtained in the manner described above. The firms with the largest negative percentages were most adversely affected by inflation during the current period. The firms with the smallest negative percentages or positive percentages were least adversely affected by inflation. The 40 firms that were most adversely affected by 55 inflation were classified as the greatest impact group.
The ^0 firms that were the least adversely affected hy inflation were classified as the smallest impact group.
The medium group contains the other 208 firms. In this and in the following subsample groupings, the extreme groups are smaller than in the previous subsample group ings because the total number of firms being examined is smaller.
An alternative ranking of firms as to the current impact of inflation is by the percentage difference between replacement cost pretax profits and the historical cost pretax profits,This ranking was made because of the widespread use of the percentage decrease in profits terminology in the financial press with regard to the release of the replacement cost figures, As in the pre vious grouping, the ^0 firms with the smallest percen tages (greatest percentage decrease in profits) were designated the greatest impact group and the ^0 firms with the highest percentages were designated the small est impact group. The remaining 208 firms made up the medium group.
The Cumulative Impact of Inflation Grouping. The difference between replacement cost accumulated depreciation and historical cost accumulated depreciation is a measure
77 ''This method of ranking firms can be misleading if the historical cost pretax profit is very close to aero. Using a denominator which is very close to zero results in very large percentages. 56 of the cumulative impact of inflation. The size of this difference is influenced by two factors. The first influence is the rate of inflation on the fixed assets of the firm. The second is the age of the fixed assets of the firm. Although the relative size of the impact of these factors will be different across firms, both will have the same directional impact„ The total impact of these factors will (when the assets are replaced) represent the additional capital necessary to replace existing fixed assets.
To achieve a relative basis for ranking the firms according to the severity of this cumulative effect, the difference between replacement cost and historical cost accumulated depreciation was divided by stockholders' equity, a relative measure of the size of the firms.
Using the information available on the Value Line
Data Base tape,' 3^8 firms from the primary sample were ranked in the manner described above. To make the rank ing comparable, only firms that used primarily straight line depreciation were considered. The 40 firms with the largest percentages, who were apparently affected most by inflation over the years, were designated the greatest impact group. The ^0 firms with the lowest percentages made up the smallest impact group, and the other 268 firms made up the medium impact group.
^Value Line Data Services. 57 An alternative ranking is by the percentage difference between replacement cost and historical cost accumulated depreciation. This percentage difference was calculated by subtracting historical cost accumulated depreciation from replacement cost accumulated depreciation and dividing the difference by historical cost accumulated depreciation. In the previous ranking, the cumulative impact of inflation was related to the size of the firm.
A ranking made according to the percentage difference between replacement cost and historical cost accumulated depreciation would reveal the relative impact as a per centage of historical cost. The larger the percentage, the more adversely the replacement cost policies with respect to the particular assets have been affected by inflation. The 3^8 firms for which sufficient data were available were ranked in this manner and stratified into subsamples on the basis of this rankings The 40 firms with the highest percentage difference were designated the greatest impact group and the ^0 firms with the lowest percentage difference became the smallest impact group. The remaining 268 firms made up the medium impact group.
As was the case in the current impact groupings, the expectation in the cumulative impact groupings is that investors will react differently to the release of the replacement cost information for the greatest impact
and the smallest impact groups.
Summary
This chapter provided a foundation for the
current research hy reviewing some related literature.
The research methodology used in this study was detailed.
The results of the application of this methodology are presented in Chapter 3° Chapter 3
PRESENTATION AND ANALYSIS OF FINDINGS
Introduction
The purpose of this study is to determine whether investors found the replacement cost accounting figures provided as a result of ASR #190 useful in their evaluation of the impact of inflation. In making this determination the market residual analysis technique was used to measure investors' reactions to the replacement cost information in the primary sample and in 50 subsamples. The results of this analysis are described in this chapter.
The Primary Sample
The average residuals for the primary sample for each of the months in the test period (month t = -12 through month t = 8) are shown in Figure The negative residual for December, 1976 (month t = -3) is relatively large (-.03^2). Assuming that the residuals are normally distributed, the probability that the December, 1976
This, assumption was tested by applying the Chi- Square goodness of fit test to the error residuals within the model used to estimate the parameters. The result of the test indicates that at the alpha = .05 level the null hypothesis (the population is normally distributed) can not be rejected.
59 Average residual et ot nomto. paetyivsos determined investors Apparently information. cost ment from the replacement cost figures that they had underestimated had they that figures cost replacement the from public to limited not are investors that hypothesis niae ht netr ratdngtvl o h replace the to negatively reacted investors that indicate information. accounting h Jnayad eray 17 rsdas Tee residuals These residuals. 1977 February, and January the the with consistent is This figures. cost replacement et ot iue i ntraoal. h tmn f the of timing The reasonable. not is figures cost ment eiul niae ta tlat oe f h replacement the of some least at that indicates residual eiul eutdfo h oe erri sal (. small is error model the from resulted residual netr pirt te cul ulc icoue f the of disclosure public actual the to prior investors replace the to reactions investors’ from resulted residual cost information was obtained by a significant number of number significant a by obtained was information cost eas ti rsda i lre h asmto ht the that assumption the large is residual this Because -.10 .10 0. -15 h Dcme, 96 eiulws eaie s were as negative was residual 1976 December, The lt f h Aeae eiul for Residuals Average the of Plot ot rltv t anucmn date announcement to relative Month 9 h Piay Sample Primary the iue ^ Figure ■3 0 3
9 0322
).
60 15 the impact of inflation. Since these residuals are the average of the residuals for 735 firms, the indication is that investors generally underestimated the impact of inflation. Although the examination of individual residuals is useful in determining the timing of the impact of information in the market, the cumulative average resid uals technique more clearly reveals the lasting impact of the factor being analyzed. Cumulative average resid uals analysis also reveals the cumulative impact of a factor which investors react to over an extended period of time.
Investors could have reacted to the replacement cost numbers over an extended period of time. The replacement cost numbers were a new type of information with which investors had no experience. For this reason, investors may have required an extended period of time to incorporate the information into their decision making model. Second, not all firms used the same methods to calculate the replacement cost figures; as a result, the figures were not completely comparable from firm to firm. This may have also extended the time required for investors to interpret the replacement cost infor mation.
The cumulative average residuals for the primary sample are presented in Figure 5» The expected value of Cumulative average residual ur (ots -, = 2 adt= l idct that indicate -l) = t and -2, = t -3,= t (months Feb and ruary January, December, in reactions negative The reaction positive apparent an reveals residuals average to be provided in the months immediately following immediately months the in provided be to information. cost replacement the to attributed to f h cmltv aeae eiul fo eo is zero from residuals average cumulative the of ation h dt S 10ws none (ot t= 1, March, -12, = t (month announced was #190 ASR date the was information cost replacement the that fact the to 1976 there was apparently no reaction by investors. by reaction no apparently was there 1976 96. uigte ots ue 17 truh November, through 1976 June, months the During 1976). h smo h rsdas s eo Aysgiiat devi significant Any zero. is residuals the of sum the -.20 .10 -15 n xmnto f h lt fte cumulative the of plot the of examination An uuaie vrg Rsdas for Residuals Average Cumulative ot rltv t anucmn date announcement to relative Month -10 h Piay Sample Primary the iue 5 Figure 5 10 0
5
62 63 some investors may have become aware of the replacement cost information during that time and realized that they had generally underestimated the impact of infla tion.
Following the announcement date the cumulative average residuals continued to decline through September,
1977 (month t = 6). If investors did take an extended period of time to incorporate the replacement cost figures into their decision making models, their reaction could have continued for several months after the announce- 2 ment date.
The cumulative average residuals are the average of the cumulative residuals of the 735 different firms.
Conclusions drawn from the analysis of these residuals are generalizations about the average reactions of inves tors to the release of the replacement cost information.
No significant average residuals occurred at the announce ment date. This does not mean there were no reactions by investors at that date, this does mean the average of the
2 This continued decline is contrary to what would normally be expected in an efficient market. The semi strong form of the efficient market hypothesis states that market prices react immediately and unbiasedly to the release of new information. The term new apparently refers to the informational content of the data released. Replacement cost numbers are not only new in their infor mational content, but they are also new in their nature. They are a type of information with which investors have had no previous experience. This is apparently the reason that investors reacted to the information over an extended period of time. 64 reactions at that date was zero. Both positive and neg ative reactions could have occurred at the announcement date. The tests of the subsample portfolios help to clarify investors' reactions to the public release of the detailed replacement cost figures.
Tests to Determine the Stability of the Model Parameters
A limitation of analyzing the cumulative average residuals is that the residuals may result from instability of the model parameters. In the present research, because all firms announced the replacement cost information at essentially the same date, beta coefficients were cal culated for efficient portfolios rather than for indi vidual stocks. Beta coefficients of efficient portfolios demonstrate a high degree of stability in the long run and a particularly high degree of stability in the short run.J In their examination of the current research on this topic, Lorie and Hamilton wrote:
Those who questioned the usefulness of betas, either because of their instability or because they were not related to returns in exactly the way implied by Sharpe's model, should think again. Efficient portfolios have stable betas and the relationship between betas and returns, though more complex than implied by Sharpe, is still rational and observable and useful in managing money.4
•^Michael C. Jensen, "Risk, the Pricing of Capital Assets, and the Evaluation of Investment Portfolios," Journal of Business. XLII (April, 1969), 167-247. 4 James H. Lorie and Mary T. Hamilton, The Stock Market-Theories and Evidence (Homewood: Richard D. Irwin, Inc.,“1975),_P. 225. The possibility also exists that the model para meters may shift during the test period as a result of the systematic factor being examined. A shift of this nature would be an indication that the replacement cost information was useful information.
Despite the evidence indicating that portfolio betas are stable in the short run, an examination of the beta of the primary sample was made to determine if there were any indications, that beta might be changing. Two tests were conducted to examine the possibility that the portfolio beta was shifting. In the first test, esti mates of beta were made for six overlapping 30 month periods selected from the 6l-month model building period used originally in estimating beta. The first beta estimate was made for months t = -73 through t = -440
A second beta was estimated for months t = -67 through t = -380 The other four betas were estimated in the same manner, by moving the test period of 30 months forward six months each time. Using these estimates as the dependent variable, time as the independent var iable, and ordinary least squares regression a point estimate of beta for the test period was predicted.
The six estimates of beta obtained appear to be correlated over time (correlation coefficient of .90251).
The test indicates that beta was increasing slightly over time. These results are consistent with those of Blume,-^ that there is a tendency for portfolio betas to regress toward unity over time. The beta estimated in the manner described above (.778) was seven percent larger that the beta constructed originally (.727). In this first test of the stability of the parameters, the alpha coefficients were not correlated over time.
The beta estimate made in the first test is pro bably not as accurate as the original estimate of beta for two reasons. First, the R-square of the model used to predict the new estimate of beta was relatively low,
.48. Second, only six observations were used in the model. Because of the size of the sample, the general izations that can be drawn are limited. However, the beta of the portfolio appears to be increasing slightly over time.
In the second test estimates of beta were con structed for the 32 overlapping 30 month intervals dur ing the 6l-month model building period. These estimates of beta were used in ordinary least squares regression to predict a beta coefficient for each of the 19 test months t = -12 through t = 8. There appeared to be a slight correlation between beta and time (correlation coefficient of .4790). These predicted beta coefficients
■^Marshall E. Blume, "Beta and Their Regression Tendencies," Journal of Finance, XXX (June, 1975)» 785" 795. Cumulative average residual line and test, first the from meters s rgnly acltd Ln 2 s h lt f the of plot the is 2 Line calculated. originally as Line uuaie vrg rsdas banduigte para the using obtained residuals average cumulative suggested by the two tests is illustrated in Figure Figure in illustrated is tests two the by suggested then changing, is sample primary the for beta if that eai rbby increasing. probably is beta t = = t from range ofiins n tm a fud Bt et indicate tests Both found. was time and coefficients -.20 10 8 -15 1 N sgiiat orlto btente alpha the between correlation significant No . s h lt f h cmltv aeae residuals average cumulative the of plot the is uuaie vrg Rsdas o te Original the for Residuals Average Cumulative h oeta ipc o h icess n beta in increases the of impact potential The .757 Test Two Model Two Test Model One Test Model Original ot eaie o noneet date announcement to relative Month for period t = t period for -10 oe ad w Ts Models Test Two and Model
iue 6 Figure -505 -12 to 3 s h lt of plot the is .782 for period for
6
.
10 the cumulative residuals obtained using the parameter
estimates obtained in the second test. These plots
indicate that if the parameters do increase in the
manner suggested by the two tests, the results will
be biased. To the extent that a bias exists, the neg
ative residuals found are underestimated.
Another possible test to determine the stab
ility of beta is to construct another estimate of beta
after the test period. Because of the lack of post test observations, this was not possible.
The Asset Ratio Grouping
The asset ratio grouping was formed by ranking the 735 firms of the primary sample according to the
size of the percentage of assets which were revalued under ASR #190 to total assets. The assumption was that if investors reacted negatively to the replace- • ment cost figures, there would be a larger negative
reaction by investors to firms that had the largest percentage of their total equity tied up in assets that were revalued under ASR #190.
The results for the asset ratio grouping are presented in Figure 7« All three subsamples reacted in approximately the same manner up to one month before the announcement date. The cumulative average residuals indicate that detailed releases of the replacement cost information probably occurred during March, 1977. At that time the cumulative average residuals of the low group (those firms with the smallest percentage of their assets revalued) started moving in the opposite direction of the cumulative average residuals of the high group. The cumulative average residuals of the high group continue to decline as investors began to incorporate the replacement cost information into 6 their decision making models. The cumulative average residuals of the low group moved upward after the announcement date. Apparently the reaction that took place in December and January was to general information about the impact of inflation, based on limited early releases of the replacement cost information. When investors were given detailed figures for all of the firms involved, they modified their earlier reactions.
The medium group in this grouping and in the other groupings tested, contains 80 percent of the firms
The decline exhibited by the high group is smooth and continuous in one direction. A shift in beta will result in residuals that are different in direction for months where the market return is pos itive than for months where the market has a negative return. Of the eight monthly market returns after the announcement date, four were positive and four were negative. Therefore the continuous decline exhibited by the high group probably did not result from a beta shift. 70
in the primary sample. Therefore, the medium group
moves in about the same manner as the primary sample.
The results for the asset ratio grouping are
consistent with the hypothesis that investors found
the replacement cost figures to be new and useful infor
mation. The results of the asset ratio grouping indi
cate investors had generally underestimated the impact
of inflation.
.10.
(U ■2 -.io-
H = High Group
-15 -10 -5 0 5 Month relative to announcement date
Figure 7
Cumulative Average Residuals for the Asset Ratio Subsamples 71 The Systematic Risk Level Grouping
Figure 8 reveals the results of the level of
systematic risk grouping. In the months prior to the
announcement of the actual detailed replacement cost
figures, investors became aware of the fact that they
had generally underestimated the impact of inflation.
Analysis of the asset ratio grouping results indicated
that this reaction was based on general market wide
.10
a 3 T3 •H CO O u
CD > •H -f3 L = Low Group paH -.20 3 s 3 M = Medium Group O H = High Group
-.30 -15 -10 -5 o 5 Month relative to announcement date
Figure 8
Cumulative Average Residuals for the Systematic Risk Level Subsamples information. Prior to the announcement date, the high beta group reacted much more negatively to this general market wide information than the other two groups. The uncertainty about the details of the replacement cost information apparently had a greater impact on the high beta group than was anticipated by the parameters of the model.
The release of the detailed replacement cost figures apparently initiated a positive reaction by investors to the high beta stocks. This reaction is consistent with the hypothesis that the replacement cost numbers will be more useful to investors in high beta stocks. Since high beta stocks are affected more by systematic factors, information concerning how infla tion (one of the most important systematic factors) affects specific firms will be more useful to inves tors in high beta stocks.
The Unsystematic Risk Level Grouping
The results of the cumulative average residuals analysis for the unsystematic risk grouping are shown in Figure 9* The positive residuals that occurred after the announcement date (month t = 0) indicate that the high unsystematic risk level group reacted positively to the release of the specific replacement cost figures.
Apparently there is some connection between the level of unsystematic risk and uncertainty about the impact of 73 inflation. Information which helps to clear up this
uncertainty is therefore more useful to investors in
the high risk stocks (both systematic and unsystematic).
The initial positive reaction by investors to
the firms of the low unsystematic risk level group
apparently is not related to the release of the replace
ment cost information. Unsystematic risk is that por
tion of total risk that investors are able to diversify
away. Apparently, even though unsystematic risk does
.20
•H
•H
2 -. 10_ L = Low Sigma Group
M = Medium Sigma Group
H = High Sigma Group
-.20 -15 -10 5 o 5 10 Month relative to announcement date
Figure 9
Cumulative Average Residuals for the Unsystematic Risk Level Grouping not exist in efficient portfolios, the average level
of unsystematic risk of the individual- firms that made up a particular portfolio is a systematic factor that affects portfolio returns. The introduction of this new systematic factor apparently has resulted in resid uals' that are not related to the release of the replace ment cost figures.
Table ^ provides a cross sectional view of some of the statistics relating to the samples that have been discussed to this point. One consistency that should be noted is that the low risk groups, both systematic and unsystematic, appear to have slightly higher asset ratios than the other risk groups. This is not inconsistent with the previous conclusions concerning asset ratios and investors' ability to estimate the impact of infla tion. The replacement cost figures issued by the firms in the primary sample are the first figures issued under
ASR #190. These first figures provide investors infor mation which is useful in adjusting their estimates of the impact of inflation, not only for the year just ended, but for previous years as well. The asset ratio grouping is not based on the presumption that investors in the high asset firms are less successful predictors of the impact of inflation. Rather, the asset ratio grouping is based on the assumption that if investors in high asset ratio stocks have predicted the impact of inflation at least as poorly as investors in the Table 4
Statistics Relating to the Primary Sample and Twelve Subsamples
Number Model Statistics Mean of Individual Stocks 01.p Subsairrple Grouping R2 Beta Alpha Sigmaa Asset Ratio Sigma^ Firms
Primary Sample .916 .7 28 .0024 .0185 .9861 . 0809 735
Low Asset Ratio .9^1 .920 -.0014 o0194 o5^68 .09 75 75
Medium Asset Ratio .901 .701 .0027 .0195 .9920 .0785 585
High Asset Ratio .865 .75^ .0034 .0246 1.3789 .0832 75
Low Systematic Risk 0 235 .238 o0079 .0361 1.0304 .0705 75
Medium Systematic Risk .906 .705 .0028 .0196 .9831 o0779 585
High Systematic Risk .969 1.384 -.0064 o0207 .9651 .1143 75
Low Unsystematic Risk 0612 A 3 2 .0009 .0290 1.1111 .0460 75
Medium Unsystematic Risk .906 .723 o0025 .0196 .9713 .0779 58 5 1.056 .0288 High Unsystematic Risk .905 .0032 ...... 1*763 . .1390 , J 5
aThe model statistics sigma is the standard deviation of the error residuals for the ordinary least squares regression model used to construct the related beta and alpha parameters for the particular portfolio grouping.,
^The mean of individual stocks sigma is the mean of the standard deviations of the least squares regression error residuals calculated on a stock by stock basis„ 76 other groups, the cumulative impact of this first release of the replacement cost figures will he great est for the high asset ratio stocks„
High risk stocks would more likely correspond to firms for which investors are least able to estimate the current impact of inflation,, This inability to determine how inflation affects particular firms would cause investors to take more conservative positions in these stocks (the risk return trade off). There is no reason to believe there to be a positive correlation between the size of the percentage of assets being revalued and investors' ability to estimate the current impact of inflation on particular firms. The results of this research confirm this conclusion,,
The Industry Grouping
The industry grouping had perhaps the greatest potential of all of the subsample groupings for classify ing firms into subsample portfolios consisting of firms which investors would be expected to react to in a sim ilar manner upon the release of the replacement -cost numbers. Firms in a particular industry generally have similar assets. If investors seriously misjudged the impact of inflation on one firm in a particular industry, they probably misjudged the impact of inflation on other firms in that same industry in a similar manner. Like some of the other groupings, subsampling on the basis 77 of industry lines introduces another systematic differ ence "between the market index used and the sample "being examined. For example, when a large labor union of one industry goes on strike, the stocks of firms in that industry will probably all be poor performers until the strike is resolved. Although the industry factor has the potential for causing a significant impact on the firms of any particular industry, King showed that the industry factor explains only about 10 percent of the variation of stock returns on average. ?
In all, 29 industry subsamples were formed (see
Table 3» page 52). The plots of the cumulative average residuals of only 10 of these industries are analyzed in this chapter. The plots of the cumulative average residuals for the remaining 19 industries are presented in Appendix C.
In Figure 10, the plots of the cumulative average residuals for four industry subsamples for which investors apparently underestimated the impact of inflation are pre sented. In each, there is a significant negative reaction several months prior to the actual public release of the detailed replacement cost figures. This reaction is pro bably based on leaks of general information that occurred prior to the actual announcement date. After the detailed replacement cost figures are released, a large negative
'Benjamin F. King, "Market and Industry Factors in Stock Price Behavior," Journal of Business. XXIX (January, 1966), 139-190, Cumulative average residual -. -.20 .10 60 -15 uuaie vrg Rsdas o or Industries Four for Residuals Average Cumulative Industry # 3330 # Industry Industry # 2800 # Industry 1000 # Industry Industry # 2600 # Industry ot rltv t anucmn date announcement to relative Month ht ece Ngtvl t the to Negatively Reacted That elcmn Cs Information Cost Replacement -10
iue 10 Figure -5
0
5
78 Cumulative average residual residuals of three industries that apparently reacted apparently that industries three of residuals figures. cost replacement the of meaning full the mine tto netne eido tm frivsos o deter to investors for time of period extended that an took it indicating months, six about for continues reaction . - . .10 20 10 uuaie vrg Rsdas o Tre Industries Three for Residuals Average Cumulative -15 . - nFgr 1, h lt o te uuaie average cumulative the of plots the 11, Figure In Industry Industry Industry Industry ht ece Pstvl t the to Positively Reacted That ot eaie o noneet date announcement to relative Month -10 elcmn Cs Information Cost Replacement it it it 3241 4811 3570 iue 11 Figure ■5 o 5
79 10 Cumulative average residual oet ecin t te elcmn cs information. cost replacement the to reactions modest average residuals for three industries exhibiting only exhibiting industries three for residuals average ees o te elcmn cs figures. cost replacement the of release misleading apparently were leaks These negatively. -.10 50 i priual neetn. hspo indicates plot This interesting. particularly is 3570) # -.20 actual the to reaction positive strong a was there because reacted investors leaks, information early on based that h Ofc ad uies qimn Idsr (Industry Industry Equipment Business and Office the pre- are information cost replacement the to positively etd Tepo o h cmltv aeae eiul of residuals average cumulative the of plot The sentedo _ uuaie vrg Rsdas o Tre Industries Three for Residuals Average Cumulative -15 iue 2 rsns h lt o te cumulative the of plots the presents 12 Figure nuty // 3221 Industry Industry Industry nuty // 2711 Industry Mnh eaie o noneet date announcement to relative .Month xiiiga oet ecin to Reaction Modest a Exhibiting -10 elcmn Cs Information Cost Replacement
if 2911
-5 iue 12 Figure
5 0
10
80 Investors apparently were not surprised by the impact of inflation on the firms in these industries. The large negative reaction prior to month t = -6 for the Petroleum
Refining Industry (Industry # 2911) and the following pos itive reaction are probably a result of the other syste matic factor, the industry effect.
The names of the industries whose cumulative average residuals were plotted in Figures 10, 11, and
12 are presented in Table 5* Statistics relating to the models for each of the subsample groups are the asset ratios of each industry are also presented. Although the asset ratios of the four industries that revealed negative reactions by investors to the replacement cost information are higher than the asset ratios of the other industries, no clear stratification of industries can be based on the asset ratios.
A better stratification could be made on the nonquantitative basis of the general availability of replacement co-st figures prior to the release of the replacement cost figures. For firms that replace their assets frequently through purchases from outside suppliers like the Publishing-Newspaper Industry (Industry # 2711), there was little or no reaction. Other industries, like the Chemical Industry (Industry # 2800), who replace their fixed assets infrequently, and who often construct a majority of their fixed assets rather than purchasing them from outside suppliers, appear to have been the Table 5
Statistics Relating to Twelve of the Industry Subsamples
Model Statistics Mean Number Asset of Industry Industry Names R2 Beta Alpha Sifcma Ratio Firms
1000 Metals Miscellaneous o596 .672 .0051 . 0466 .8837 10
2600 Paper .598 .677 .0085 .0468 1.1597 15
2800 Chemicals .631 .673 .0089 .0428 101466 15
3330 Primary Smelt-Refin Non Fer Metal .42 7 .640 .0067 .0624 1.1106 8
3241 Building Materials-Cement o717 .860 -.0071 .0455 1.2820 7
3570 Office and Business Equipment . 696 .772 -.0068 .0430 .9354 8
4811 Telephone Companies .654 .534 -.0015 .0327 1.0988 13
2711 Publishing-Newspaper 0 721 ' .851 .0010 o0447 .6565 7
2911 Petroleum Refining .627 .789 .0009 .0513 1.0033 9
3221 Containers Metal and Glass .655 .698 -o0025 .0427 1.0717 ____ 9.
00 r\j object of the greatest negative reaction. Replacement cost figures for the latter group are not readily avail able.
Groupings Based on the Replacement Cost Figures
Introduction
The Value Line Data Base tape served as a basis for the remaining subsample groupings.® This tape con tains some replacement cost figures for some of the companies that were required to provide the information.
The replacement cost figures that were available were used to calculate measures of the current impact of inflation and of the cumulative impact of inflation.
The current impact of inflation (replacement cost pre tax profit less historical cost pretax profit) was measured as a percentage of the market value of the firm's common stock outstanding on December 31, 1976, and as a percentage of historical cost pretax profit.
The cumulative impact of inflation (replacement cost accumulated depreciation less historical cost accumu lated depreciation) was measured as a percentage of stockholders' equity and as a percentage of historical cost accumulated depreciation.
®Value Line Data Services, Value Line Data Base (New York: Value Line Data Services^ 1978)« Cumulative average residual The 13. Figure in shown are analysis the of results inflation, the expectation is that they underestimated they that is expectation the inflation, 1976. 31, Decemher on outstanding stock common firm's h ipc o nlto ot nte xrm gop. The groups. extreme the in most inflation of impact the of impact the underestimated generally investors If the of value market the to relative inflation of impact ruig, im wr rne codn o h current the to according ranked were firms groupings, Groupings Inflation of Impact Current The -.30 -.20 -.10 .10 0.
1 -0 5 5 10 5 0 -5 -10 -15 nte is o to urn ipc o inflation of impact current two of first the In M = Medium Impact Group Impact Medium = M mlet mat Group Imnact Smallest S= raetIpc Group Impact Greatest G = Cumulative Average Residuals for the for Residuals Average Cumulative Relative to Return Grouping Return to Relative Current Impact of Inflation of Impact Current ot eaie o noneet date announcement to relative Month iue 13 Figure
s —
8 ^ 85 larger than average negative reactions of investors to the stocks in the extreme groups are consistent with the hypothesis that the extreme groups were least able to determine the true impact of inflation,,
The current impact of inflation is also measured as a percentage of historical cost profits. Figure 1^ presents the results of the analysis. As in the previous
.10
H 3 TJ •H CO 0 u 0
2 -.10 - 0 >
0 > •H G = Greatest Impact Group -P Cti M = Medium Impact Group
S = Smallest Impact Group
-.30 -10 5 0 5 10 Month relative to announcement date
Figure 14
Cumulative Average Residuals for the Current Impact of Inflation as a Percentage Change in Profit Grouping grouping, the expectation is that the extreme groups
will react more negatively to the replacement cost
information than the medium group. Ranking firms in
this manner apparently isolated the extreme groups
better than the return related ranking used in the
previous grouping. The larger than average negative
residuals associated with the greatest impact group
indicate that investors may be using the percentage
decrease in profit as a measure of the impact of infla
tion.
The Cumulative Impact of Inflation Groupings
The cumulative impact of inflation was defined
for the purpose of this study as the difference between
replacement cost accumulated depreciation and histor
ical cost accumulated depreciation. In the first of
two groupings, firms were ranked according to the cum
ulative impact of inflation relative to stockholders’
equity., Since investors are not completely naive about
inflation, the extreme groups are expected to react more negatively than the medium groups. The results
of this first ranking of the firms according to the measure of the cumulative impact of inflation are pre
sented in Figure 15. This grouping is more successful
in subsetting the firms into portfolios based on the difference between investors’ expectations and the actual replacement cost numbers. The extreme groups 87 appear to contain most of the firms for which investors were least able to estimate the full extent of the impact of inflation.
The cumulative impact of inflation was also measured as a percentage of historical cost accumulated depreciation. In this subsample grouping, as in the three previous subsample groupings, the expectation is that investors misjudged the impact of inflation most
10 i—i cti T) •H W CD /\
CD bjO cti CD > cd ® - . 1 0 - •H +3 r—) 3 s 3 G = Greatest Impact Group o -. 20 _ M = Medium Impact Group
S = Smallest Impact Group
-.30 -15 -10 ■5 0 5 10 Month relative to announcement date
Figure 15
Cumulative Average Residuals for the Cumulative Impact of Inflation as a Percentage of Stockholders' Equity Grouping 88
for the firms in the extreme group. The results of
this analysis are plotted in Figure 16. Apparently
using historical cost accumulated depreciation as the
common denominator is less successful in segregating
firms into groups based on the size of the error of
investors expectations than using stockholders' equity.
However, the results are consistent with those of the "
previous grouping, and indicate that investors found the
replacement cost figures to be new and useful information.
.10
cti 3 -a •H ca a; f-i cti CD > •H G = Greatest Impact Group •P Cti rH 3 M = Medium Impact Group £ 3 a S = Smallest Impact Group
-10 -505 Month relative to announcement date
Figure 16
Cumulative Average Residuals for the^Cumulative Impact of- Inflation as a Percentage of Historical Cost Accumulated Depreciation Grouping Table 6
Statistics Relating to the Subsamples Based on the Replacement Cost Figures
Model Statistics Mean of Individual Stocks Number of Subsample Grouping R2 Beta Alpha Sigma Asset Ratio Sigma Firms
Current Impact/MVS Smallest .888 .781 .0039 .0233 .9105 .0858 4-0
Current Impact/MVS Medium .878 .727 .0039 .0228 .95^2 .0793 208
Current Impact/MVS Greatest .9^1 .994- -.0009 .0209 1,04-86 .0962 4-0
Current Impact/HP Smallest .887 .791 .0037 .0238 .9092 0O850 4-0
Current Impact/HP Medium .895 . 7 ^ .0035 .0214- o9530 .0800 208 CO 00 ON Current Impact/HP Greatest .884- • .0015 .0274- 1.0563 .0938 4-0
Change in AD/SE Smallest o 858 .84-7 .0051 .0290 . 8626 .0919 4-0
Change in AD/SE Medium .878 .690 .0016 .0217 .9857 .0760 268
Change in AD/SE Greatest .814- .817 .0038 .0329 1.24-27 .0851 4-0
Change in AD/HAD Smallest .7 86 .750 .004-1 .0330 .9827 .0883 4-0 CO CO ON Change in AD/HAD Medium • .718 .0013 .0204- .9904- .0769 268 CO CM Change in AD/HAD Greatest .808 • .0069 .0299 I.O878 .0827 4-0 MVS = The Market value of the common stock outstanding December 31, 1976.
HP = Historical Cost Pretax Profit.
SE = Stockholders0 Equity. Table 6 shows statistics related to the replace ment cost subsample groupings. Apparently the only con sistent relationship that exists between the portfolios that had the largest negative cumulative average resid uals (the extreme groups) is that the individual stocks that make up these portfolios have, on the average, higher levels of systematic risk.
The results of the subsample groupings based on the actual replacement cost figures are consistent with the results of the previous subsample groupings.
The results of the subsample groupings based on the actual replacement cost figures substantiate the con clusion that investors found the replacement cost figures to be new and useful information. These results offer particularly meaningful evidence because subsample groupings based on the actual replacement cost figures do not introduce another systematic element into this sample that is not directly related to inflation and the replacement cost accounting figures.
Summary
In this chapter the results of the statistical tests to determine whether investors reacted to the replacement cost figures were presented and analyzed.
In Chapter 4 these results are summarized and conclusions based on these results are given. Chapter 4
SUMMARY,. CONCLUSIONS, LIMITATIONS, AND RECOMMENDATIONS
The purposes of this final chapter are (1) to • summarize the objectives, methodology, and results of the study, (2 ) to present conclusions based on the find ings of the study, (3) to explain the study’s limitations, and (b ) to suggest related areas for further research.
Summary and Conclusions
The primary objective of this study was to determine whether replacement cost accounting figures provide inves tors with information which is useful in evaluating the impact of inflation. The intent of the study was to determine whether investors reacted to the replacement cost information provided by firms in the initial com- -1 pliance with Accounting Series Release No. 190. The results of the investigation form a basis for making conclusions about the general usefulness of replacement cost accounting figures0 The conclusions have implications for both suppliers and users of financial accounting infor mation.
•t Securities and Exchange Commission, Rule 3-17 of Regulation S-X, Accounting Series Release, NoT I^T~l^LrcK 2 3,1976 (Washingtons Government Printing Office, 1976).
91 92
The test of the primary sample indicated that investors did react to the information. The results of the test indicated that there was a negative reac tion to the replacement cost information and that the reaction began about three months before the detailed replacement cost figures were released. The results of this test were averages of the reactions of all investors. For this reason, the test could not be relied on to reveal the full extent of investor reac tion to the replacement cost information. Additional tests were made by subsetting the primary sample into portfolios consisting of firms which would likely be affected in a similar manner by the release of the replacement cost figures. A total of 50 subsample portfolios were examined to further substantiate and clarify the findings of the first test.
With the exception of the low unsystematic risk grouping, all the subsample groupings confirmed the results of the test of the primary sample. The results of the low unsystematic risk grouping were not inconsistent with the other results, but because of noise that resulted from a systematic factor not related to the replacement cost information, the impact of the release of the replace ment cost information was obscured.
In addition to confirming the conclusion that investors did find the replacement cost information to be new and useful, the subsample groupings also provided additional information about the impact of the replace ment cost information. An examination of the plots of the cumulative average residuals indicated that there were two distinct reactions to the replacement cost information. The first reaction took place about three months before the actual detailed replacement cost figure were released. This reaction was a general market reac tion, in which most firms that were going to provide the replacement cost information experienced negative resid uals. The subsample groupings indicated that detailed replacement cost figures were acquired by investors during March, 1977* This was the time that the second reaction began. While the first reaction related to leaks of information, the second reaction was based on the complete detailed replacement cost figures published in the 10-Ks and annual reports. Unlike the first reaction, the second reaction was not a general market reaction, but rather a number of individual reactions that resulted from investors refining their earlier estimates of the impact of inflation. Where the first reaction was generally negative for most portfolios tested, the second reaction varied widely in both direction and magnitude from one portfolio to another.
Statistics describing the various subsamples made it possible to draw conclusions about the nature of the portfolios that reacted to the replacement cost
information. The asset ratio grouping indicated that
the reactions of investors were negatively correlated with the percentage of assets revalued. Even though
the high asset ratio portfolio (the portfolio with
the highest ratio of revalued assets to total assets) had the largest negative reaction to the replacement
cost figures, the size of the negative reaction to port folios was not found to be linearly related to the size of the asset ratio of the portfolios. For some port folios with high asset ratios there was little reac tion by investors or there was a positive reaction by investors to the replacement cost figures.
The conclusions of this study can be summarized as follows s
1. Investors found the replacement cost figures provided as a result of ASR #190 to be new and useful information in their evaluation of the impact of infla tion.
2. Prior to the release of the replacement cost figures, investors had generally underestimated the impact of inflation. * 3. The general impact of the replacement cost information began three months before the detailed replacement cost figures were released.
While investors had generally underesti mated the impact of inflation, for some categories of firms investors had overestimated the impact of infla
tion.
5. Investors underestimated the impact of infla
tion most for firms that were either affected much more
or much less than average by inflation.
Implications of the Findings
There are many implications of these findings
for both the accounting profession and the financial
community. The accounting profession has for some time
been struggling with the problems brought about by the
historical cost accounting model's failure to deal with
the reality of inflation. Valuing assets on a replace
ment cost basis appears to be a significant step forward
in dealing with the impact of inflation. This valuation
method should be examined closely as a possible alter
native or supplement to historical cost valuation.
Even if the accounting profession decides that
the replacement cost figures should not be substituted
for historical cost figures, much work is needed in the
area of clarifying the meaning of the replacement cost
figures. Apparently, a significant number of investors have already determined what they think is an appropriate manner of interpreting the replacement cost figures.
This is evidenced by the reactions observed. Since
there is no indication that ASR #190 will be withdrawn, accountants who generate the replacement cost figures should make an effort to clarify the meaning of the figures for the benefit of all financial statement users.
The implications for the financial community are also many. While accountants should be concerned with providing accurate useful information, investors should be concerned with using this information in the most meaningful manner. Since a significant number of investors have apparently determined how the replacement cost figures can be incorporated into the decision mak ing process, other investors, educators, and regulators should study and evaluate the potential uses of this information.
Limitations of the Study
The results of this research did not determine how the replacement cost information was incorporated into investors' decision making models. Furthermore, since the market can fully digest new information if only a significant number of investors are aware of the information, no conclusion can be drawn as to how wide spread the use of the replacement cost information was.
Another limitation of this research was the availability pose-announcement date returns. If a sufficient number of post-announcement date returns
had been available the model parameters could have
been calculated using both pre-announcement date and
post-announcement date returns. A large number of
post-announcement date returns would also have been
helpful in evaluating the stability of the model para
meters.
A minor limitation was the limited availability
of the replacement cost figures. Since many of the
firms included little or no replacement cost information
in the annual reports, this may have been a major lim
itation for investors.
Recommendations for Further Research
This research indicates that the replacement
cost figures were used by investors. Further research
is needed to determine exactly how investors used the
specific replacement cost figures. This information
is necessary to determine how, if at all, the replace
ment cost information should be included in the account
ing information model.
Another extention of this research would be to
determine how the replacement cost figures affected the
risk levels assigned to individual firms. If replace ment cost information gives investors information that
is useful in reducing the uncertainties that surround
inflation, perceived risk should be reduced. Finally, research is needed to determine whether or not the replacement cost information alters or affects the policies of management with regard to the replacement of fixed assets and the retention of earnings for this purpose. . One of the conclusions of this research is that investors generally underestimated the impact of inflation. Since many managers are also investors in their respective firms, apparently management also under estimated the impact of inflation. The long run policies of managers with regard to the retention of earnings may be altered as a result of the replacement cost infor mation. BIBLIOGRAPHY 100
BIBLIOGRAPHY
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LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS.THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
1 3740 800 ACF INDS 2 3940 1688 AMF INC 3 3560 2080 A-T-0 INC 4 2835 2824 ABBOTT LABORATORIES 5 2820 10202 AKZONA 6 4922 11734 ALASKA INTERSTATE CO 7 3999 12347 ALBANY INTL CORP 8 3330 12716 ALCAN ALUMINUM LTD 9 4511 17248 ALLEGHENY AIRLINES INC 10 3310 17372 ALLEGHENY LUDLUM INDS 11 4911 17411 ALLEGHENY POWER SYSTEM 12 2800 19087 ALLIED CHEMICAL CORP 13 3449 19411 ALLIED PRODUCTS 14 3520 19645 ALLIS-CHALMERS CORP 15 1621 20771 ALPHA PORTLAND INDS 16 3330 22249 ALUMINUM CO OF AMERICA 17 1000 23127 AMAX INC 18 3670 23141 AMBAC INDS INC 19 3000 23519 AMERACE CORP 20 2912 23551 AMERADA HESS CORP 21 4511 23771 AMERICAN AIRLINES INC 22 3241 23904 AMCORD INC 23 2051 24069 AMERICAN BAKERIES CO 24 3069 24591 AMERICAN BILTRITE INC 25 2111 24703 AMERICAN BRANDS INC 26 4830 24735 AMERICAN BROADCASTING 27 3221 24843 AMERICAN CAN CO 28 2800 25321 AMERICAN CYANAMID 29 7393 25411 AMERICAN DISTRICT TELEGRAPH 30 4911 25537 AMERICAN ELECTRIC POWER 31 2935 26609 AMERICAN HOME PRODUCTS CORP 32 2837 26681 AMERICAN HOSPITAL SUPPLY 33 2046 27339 AMERICAN MAIZE-PRODUCTS 34 8061 27447 AMERICAN MEDICORP 35 4924 28609 AMERICAN NATURAL RESOURCES 36 1311 28861 AMERICAN PETROFINA 37 3430 29717 AMERICAN STANDARD INC 38 3811 31105 AMETEK INC 39 5311 31141 AMFAC INC 40 3670 31897 AMP INC 41 3221 33047 ANCHOR HOCKING CORP 42 2082 35231 ANHEUSER-BUSCH INC 43 3999 37411 APACHE CORP 44 1311 38402 AQUITAINE CO CANADA LTD 45 4911 40555 ARIZONA PUBLIC SERVICE CO 46 4924 40879 ARKANSAS LOUISIANA GAS 47 3310 42195 ARMCO STEEL CORP 48 2270 42321 ARMSTRONG CORK CO 49 3714 43339 ARVIN INDS INC 50 1000 43413 ASARCO INC 108
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
51 3999 47483 ATHLONE INDS 52 4912 48303 ATLANTIC CITY ELECTRIC 53 2912 48825 ATLANTIC RICHFIELD CO 54 3330 49249 ATLAS CONS MINING & DEV 55 2844 54303 AVON PRODUCTS 56 3221 58498 BALL CORP 57 4912 59165 BALTIMORE GAS & ELECTRIC 58 3560 67806 BARNES GROUP INC 59 3831 71707 BAUSCH & LOMB INC 60 2837 71892 BAXTER TRAVENOL LABORATORIES 61 4922 72612 BAY STREET GAS 62 2870 77266 BEKER INDS 63 2912 77419 BELCO PETROLEUM CORP 64 3861 77851 BELL & HOWELL CO 65 4811 78149 BELL TELEPHONE OF CANADA 66 2649 81437 BEMIS CO 67 3310 87509 BETHLEHEM STEEL CORP 68 3533 89671 BIG THREE INDS 69 3721 97023 BOEING CO 70 2400 97383 BOISE CASCADE CORP 71 2020 99599 BORDEN INC 72 3714 99725 BORG-WARNER CORP 73 4912 100599 BOSTON EDISON CO 74 2600 102187 BOWATER CORP LTD-ADR 75 4511 105425 BRANIFF INTL CORP 76 4912 105502 BRASCAM LTD-CL A 77 1621 105647 BRAUN (C.F.) & CO 78 100 107565 BREWER (C.) & CO 79 2836 110097 BRISTOL-MYERS CO 80 2913 110889 BRITISH PETROLEUM CO LTD 81 3221 111853 BROCKWAY GLASS CO 82 3940 117043 BRUNSWICK CORP 83 1000 117421 BRUSH WELLMAN INC 84 3531 118745 BUCYRUS-ERIE CO . 85 3610 120655 BUNKER RAMO CORP 86 4011 121897 BURLINGTON NORTHERN INC 87 3570 122781 BURROUGHS CORP 88 1311 124187 BUTTES GAS & OIL CO 89 4830 124845 CBS INC 90 7530 125615 CLC OF AMERICA 91 2046 126149 CPC INTL INC 92 2051 134449 CAMPBELL TAGGART INC 93 5980 136051 CANADIAN HYDROCARBONS LTD 94 2810 136420 CANADIAN OCCIDENTAL PETRO 95 4011 136440 CANADIAN PACIFIC LTD 96 1311 136645 CANADIAN SUPERIOR OIL 97 2020 143483 CARNATION CO 98 4912 144141 CAROLINA POWER & LIGHT 99 4924 147339 CASCADE NATURAL GAS CORP 100 2030 148429 CASTLE & COOKE INC 109
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
101 3531 149123 CATERPILLAR TRACTOR CO 102 3449 150033 CECO CORP 103 2800 150843 CELANESE CORP 104 4912 152357 CENTRAL & SOUTH WEST CORP 105 4911 153609 CENTRAL HUDSON GAS & ELEC 106 4912 153645 CENTRAL ILLINOIS LIGHT 107 4912 153663 CENTRAL ILL PUBLIC SERVICE 108 4912 153897 CENTRAL LOUISIANA ELECTRIC 109 4911 154051 CENTRAL MAINE POWER CO 110 4811 155447 CENTRAL TELEPHONE & UTIL 111 4911 155771 CENTRAL VERMONT PUB SERV 112 2950 156879 CENTRAIN-TEED CORP 113 2400 158525 CHAMPION INTL CORP 114 3714 158663 CHAMPION SPARK PLUG 115 2911 161177 CHARTER CO 116 2649 165159 CHESAPEAKE CORP OF VA 117 2844 165339 CHESEBROUGH-POND'S INC 118 4011 165496 CHESSIE SYSTEM INC 119 3449 167249 CHICAGO BRIDGE&IRON CO 120 3560 167898 CHICAGO PNEUMATIC TOOL CO 121 3390 171106 CHROMALLOY AMERICAN CORP 122 3711 171196 CHRYSLER CORP 123 4811 171870 CINCINNATI BELL INC 124 4911 172070 CINCINNATI GAS & ELECTRIC 125 3540 172172 CINCINNATI MILACRON INC 126 2912 173036 CITIES SERVICE CO 127 9997 177846 CITY INVESTING CO 128 3531 181396 CLARK EQUIPMENT CO 129 2911 181486 CLARK OIL & REFININF CORP 130 1000 186000 CLEVELAND-CLIFFS IRON CO 131 4912 186108 CLEVELAND ELECTRIC ILLUM 132 2300 189486 CLUETT, PEABODY & CO 133 4922 190556 COASTAL STATES GAS CORP 134 2086 191162 COCA-COLA BOTTLING CO OF NY 135 2086 181215 COCA-COLA CO 136 3940 193558 COLEMAN CO INC 137 2841 194162 COLGATE-PALMOLIVE CO 138 5411 196054 COLONIAL STORES INC 139 9997 196864 COLT INDS INC 140 4924 197648 COLUMBIA GAS SYSTEM 141 4911 198846 COLUMBUS & SOUTHERN OHIO 142 7311 200101 COMBINED COMMUNICATIONS 143 3510 200273 COMBUSTION ENGINEERING INC 144 1031 200435 COMINCO LTD 145 4912 202795 COMMONWEALTH EDISON 146 4811 203417 COMMUNICATIONS SATELLITE 147 4912 204021 COMMUNITY PUBLIC SERVICE 148 2200 206813 CONE MILLS CORP 149 2270 207192 CONGOLEUM CORP 150 4911 209111 CONSOLIDATED EDISON OF N.Y. 110
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
151 4210 209237 CONSOLIDATED FREIGHTWAYS INC 152 4924 209615 CONSOLIDATED NATURAL GAS CO 153 4912 210615 CONSUMERS POWER CO 154 4511 210795 CONTINENTAL AIR LINES INC 155 3221 211452 CONTINENTAL GROUP 156 2912 211813 CONTINENTAL OIL CO 157 4811 212093 CONTINENTAL TEL CORP 158 3570 212363 CONTROL DATA CORP 159 5312 216244 COOK UNITED INC 160 3560 216669 COOPER INDS INC 161 2082 217016 COORS (ADOLPH) CO 162 3310 217687 COPPERWELD CORP 163 3210 219327 CORNING GLASS WORKS 164 3494 224399 CRANE CO 165 3610 227813 CROUSE-HINDS CO 166 2912 228219 CROWN CENTRAL PETROLEUM CORP 167 3221 228255 CROWN CORK & SEAL CO INC 168 2600 228069 CROWN ZELLERBACH 169 2121 229890 CULBRO CORP 170 3713 231021 CUMMINS ENGINE 171 3560 231561 CURTISS-WRIGHT CORP 172 3622 232165 CUTLER-HAMMER INC 173 3310 232525 CYCLOPS CORP 174 1000 232813 CYPRUS MINES CORP 175 7370 233108 DCL INC 176 2200 235773 DAN RIVER INC 177 5999 237424 DART INDS 178 4911 240019 DAYTON POWER & LIGHT 179 4911 247109 DELMARVA POWER & LIGHT 180 6552 247883 DELTONA CORP 181 2649 248631 DENNISON MFG CO 182 2850 250595 DE SOTO INC 183 4912 250847 DETROIT EDISON CO 184 5140 252435 DI GIORGIO CORP 185 2650 252669 DIAMOND INTL CORP 186 2800 252741 DIAMOND SHAMROCK CORP 187 3570 253034 DICK (A.B.) CO 188 1621 254111 DILLINGHAM CORP 189 1311 257093 DOME PETROLEUM LTD 190 2600 257561 DOMTAR LTD 191 6200 257860 DONALDSON LUFKIN & JENRETTE 192 2750 257867 DONNELLEY (R. R.) & SONS CO 193 3221 258435 DORSEY CORP 194 3550 260003 DOVER CORP 195 2800 260543 DOW CHEMICAL 196 2711 260561 DOW JONES & CO INC 197 1621 261471 DRAVO CORP 198 2800 263534 DU PONT (E.I.) DE NEMOURS 199 4912 264399 DUKE POWER CO 200 7392 264830 DUN & BRADSTREET COS 111
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
201 3000 265720 DUNLOP HOLDING LTD 202 4911 266228 DUQUESNE LIGHT CO 203 3429 270330 EASCO CORP 204 4511 276191 EASTERN AIR LINES 205 1211 276461 EASTERN GAS & FUEL ASSOC 206 4912 277173 EASTERN UTILITIES ASSOC 207 3861 277461 EASTMAN KODAK CO 208 3714 278058 EATON CORP 209 5661 280875 EDISON BROTHERS STORES 210 4922 283362 EL PASO CO 211 4912 263677 EL PASO ELECTRIC CO 212 3550 291210 EMHART CORP 213 4912 291641 EMPIRE DISTRIC ELECTRIC CO 214 5050 292843 ENGELHARD MIN & CHEM 215 4924 293587 ENSERCH CORP 216 4924 294497 EQUITABLE GAS CO 217 2800 297659 ETHYL CORP 218 2400 299209 EVANS PRODUCTS CO 219 2913 302290 EXXON CORP 220 3531 302491 FMC CORP 221 3670 303693 FAIRCHILD CAMERS&INSTRUMENT 222 3720 303711 FAIRCHILD INDS INC 223 3714 313549 FEDERAL-MOGUL CORP 224 2650 313693 FEDERAL PAPER BOARD CO 225 2890 315405 FERRO CORP 226 2650 315711 FIBREBOARD CORP 227 2200 316549 FIELDCREST MILLS 228 5411 337819 FISHER FOODS INC 229 5140 339130 FLEMING CO'S, INC 230 7399 339376 FLEXI-VAN CORP 231 2950 339711 FLINTKOTE CO 232 4922 340693 FLORIDA GAS 233 4912 341081 FLORIDA POWER & LIGHT 234 4912 341099 FLORIDA POWER CORP 235 1000 344892 FOOTE MINERAL CO 236 3711 345352 FORD MOTOR CO OF CANADA LTD 237 3711 345370 FORD MOTOR CO 238 2649 347460 FORT HOWARD PAPER 239 3510 350244 FOSTER WHEELER CORP 240 3822 351604 FOXBORO CO 241 1499 356715 FREEPORT MINERALS CO 242 4511 359064 FRONTIER AIRLINES INC 243 3714 359370 FRUEHAUF CORP 244 3940 361028 FUQUA INDS INC 245 2950 361428 GAF CORP 246 3740 361448 GATX CORP 247 6500 361578 GDV INC 248 2711 364730 GANNETT CO 249 3560 365550 GARDNER-DENVER CO 250 3350 369298 GENERAL CABLE CORP 112
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
251 3730 369550 GENERAL DYNAMICS CORP 252 3600 369604 GENERAL ELECTRIC CO 253 2010 370064 GENERAL HOST CORP 254 3711 370442 GENERAL MOTORS CORP 255 3241 370514 GENERAL PORTLAND INC 256 4911 370550 GENERAL PUBLIC UTILITIES 257 3290 370622 GENERAL REFRACTORIES CO 258 3821 370838 GENERAL SIGNAL CORP 259 4811 371028 GENERAL TELEPHONE&ELECTRONICS 260 3270 372451 GENSTAR LTD 261 5012 372460 GENUINE PARTS CO 262 2400 373298 GEORGIA-PACIFIC CORP 263 2912 374280 GETTY OIL CO 264 3270 375149 GIFFORD-HILL & CO 265 2844 375766 GILLETTE CO 266 2600 377316 GLATFELTER (P H) CO 267 °714 377352 GLEASON WORKS 268 1381 379352 GLOBAL MARINE INC 269 3000 382388 GOODRICH (B.F.) CO 270 3000 382550 GOODYEAR TIRE & RUBBER CO 271 3610 383492 GOULD INC 272 2800 383883 GRACE (W.R.) & CO 273 5063 384802 GRAINGER (W.W.) INC 274 2200 387478 GRANITEVILLE CO 275 2600 391090 GREAT NORTHERN NEKOOSA CORP 276 2010 398028 GREYHOUND CORP 277 2731 398784 GROLIER INC 278 3760 400181 GRUMMAN CORP 279 2911 402442 GULF OIL OF CANADA 280 2913 402460 GULF OIL CORP 281 1031 402496 GULF RESOURCES & CHEMICAL 282 4912 402550 GULF STATES UTILITIES CO 283 3533 406216 HALLIBURTON CO 284 2600 408306 HAMMERMILL PAPER CO 285 3999 410306 HANDY & HARTMAN 286 2300 410342 HANES CORP 287 1000 410522 HANNA MINING CO 288 3341 415864 HARSCO CORP (DEL) 289 4912 419866 HAWAIIAN ELECTRIC CO 290 1000 422704 HECLA MINING CO 291 2800 427056 HERCULES INC 292 2065 427866 HERSHEY FOODS CORP 293 7011 432848 HILTON HOTELS CORP 294 3560 433728 HOBART CORP 295 7011 435081 HOLIDAY INNS INC 296 1311 437272 HOME OIL CO 297 1041 437814 HOMESTAKE MINING 298 3573 438506 HONEYWELL INC 299 3630 439272 HOOVER CO 300 8061 441065 HOSPITAL CORP OF AMERICA 113
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
301 6812 441074 HOST INTL INC 302 3714 441488 HOUDAILLE INDS INC 303 4912 442161 HOUSTON INDS 304 1311 442281 HOUSTON OIL & MINERALS CORP 305 5812 442672 HOWARD JOHNSON CO 306 1031 443654 HUDSON BAY MINING & SMELT-A 307 1311 444222 HUDSON BAY OIL & GAS CO 308 3533 444492 HUGHES TOOL CO 309 2911 448096 HUSKY OIL LTD 310 4011 449268 IC INDS 311 9997 450694 IU INTL CORP 312 4912 451380 IDAHO POWER CO 313 3241 451542 IDEAL BASIC INDS INC 314 4912 452092 ILLINOIS POWER CO 315 3452 452308 ILLINOIS TOOL WORKS 316 2911 453038 IMPERIAL OIL LTD-CL A 317 1000 453258 INCO LTD 318 4912 455434 INDIANAPOLIS POWER & LIGHT 319 1311 456623 INEXCO OIL 320 3560 456866 INGERSOLL-RAND CO 321 2650 457326 INLAND CONTAINER CORP 322 3310 457470 INLAND STEEL CO 323 3999 457659 INSILCO CORP 324 3330 457686 INSPIRATION CONS COPPER CO 325 3310 458702 INTERLAKE INC 326 3570 459200 INTL BUSINESS MACHINES CORP 327 2844 459506 INTL FLAVORS & FRAGRANCES 328 2600 460146 INTL PAPER CO 329 9997 460470 INTL TELEPHONE & TELEGRAPH 330 3270 460578 INTERPACE CORP 331 4912 461074 INTERSTATE POWER CO 332 7394 461143 INTERWAY CORP 333 4912 462416 IOWA ELECTRIC LIGHT & PWR 334 4912 462470 IOWA-ILLINOIS GAS & ELEC 335 4912 462506 IOWA POWER & LIGHT 336 4912 462524 IOWA PUBLIC SERVICE CO 337 4912 462542 IOWA SOUTHERN UTILITIES CO 338 3831 465632 ITEK CORP 339 7370 465640 ITEL CORP 340 2950 478124 JOHNS-MANVILLE CORP 341 2837 478160 JOHNSON & JOHNSON 342 3822 478366 JOHNSON CONTROLS INC 343 2300 479898 JONATHAN LOGAN INC 344 5050 480827 JORGENSEN (EARLY M.) CO 345 3330 483008 KAISER ALUMINUM & CHEM CORP 346 3241 483044 KAISER CEMENT & GYPSUM CORP 347 9997 483062 KAISER INDS CORP 348 3310 483098 KAISER STEEL CORP 349 2010 484098 KANE-MILLER CORP 350 4912 485134 KANSAS CITY POWER & LIGHT 114
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
351 4011 485170 KANSAS CITY SOUTHERN INDS 352 4912 485260 KANSAS GAS & ELECTRIC 353 4924 485278 KANSAS-NEBRASKA NATL GAS CO 354 4912 485314 KANSAS POWER & LIGHT 355 3560 487314 KEENE CORP 356 2000 487836 KELLOGG CO 357 3330 489314 KENNECOTT COPPER CORP 358 4912 491674 KENTUCKY UTILITIES CO 359 3210 492376 KERR GLASS MFG 360 2912 492386 KERR-MCGEE CORP 361 9997 493782 KIDDE (WALTER) & CO 362 2600 494368 KIMBERLY-CLARK CORP 363 2711 499040 KNIGHT-RIDDER NEWSPAPERS INC 364 2800 500602 KOPPERS CO 365 2020 500755 KRAFT, INC 366 5411 501044 KROGER CO 367 9997 502210 LTV CORP 368 4210 522066 LEASEWAY TRANS CORP 369 3210 530000 LIBBEY-OWNES-FORD CO 370 2111 532202 LIGGETT GROUP 371 2835 532457 LILLY (ELI)& CO 372 3720 539821 LOCKHEED AIRCRAFT CORP 373 6199 540424 LOEWS CORP 374 3241 542290 LONE STAR INDS 375 4911 542671 LONG ISLAND LIGHTING 376 1311 546268 LOUISIANA LAND & EXPLORATION 377 2400 546347 LOUISIANA PACIFIC 378 4912 546676 LOUISVILLE GAS & ELECTRIC 379 2200 547779 LOWENSTEIN (M.) & SONS INC 380 2890 549271 LUBRIZOL CORP 381 2510 549662 LUDLOW CORP 382 3310 549866 LUKENS STEEL CO 383 3310 550890 LYKES CORP 384 7810 552653 MCA INC 385 - 6199 552845 MGIC INVESTMENT CORP 386 2731 554790 MACMILLAN INC 387 4912 557497 MADISON GAS & ELECTRIC CO 388 3679 561246 MALLORY (P.R.) & CO 389 1311 565097 MAPCO INC 390 3533 565821 MARATHON MFG CO 391 2912 565845 MARATHON OIL CO 392 3714 566472 MAREMONT CORP 393 1021 568100 MARINDUQUE MINING-CL B 394 3760 573275 MARTIN MARIETTA CORP 395 3430 574599 MASCO CORP 396 3630 578592 MAYTAG CO 397 1311 579885 MCCULLOCH OIL 398 5812 580135 MCDONALD’S CORP 399 3721 580169 MCDONNELL DOUGLAS CORP 400 3610 580628 MCGRAW-EDISON CO 115
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
401 2731 580645 MCGRAW-HILL INC 402 1041 581238 MC INTYRE MINES LTD 403 3310 582273 MCLOUTH STEEL CORP 404 3550 582562 MCNEIL CORP 405 2600 582834 MEAD CORP 406 2711 584404 MEDIA GENERAL-CL A 407 3241 585072 MEDUSA CORP 408 3140 585745 MELVILLE CORP 409 3573 586005 MEMOREX CORP 410 2835 589331 MERCK & CO 411 6200 590188 MERRILL LYNCH & CO 412 1311 590655 MESA PETROLEUM 413 4830 591690 METROMEDIA INC 414 4811 595390 MID-CONTINENT TELEPHONE 415 4912 595832 MIDDLE SOUTH UTILITIES 416 3550 597715 MIDLAND-ROSS CORP 417 4924 604036 MINNESOTA GAS CO 418 3861 604059 MINNESOTA MINING & MFG CO 419 4912 604110 MINNESOTA POWER & LIGHT 420 4011 606191 MISSOURI PACIFIC CORP 421 4912 606249 MISSOURI PUBLIC SERVICE CO 422 2913 607059 MOBIL CORP 423 2510 608030 MOHASCO CORP 424 2800 611662 MONSANTO CO 425 4912 612017 MONTANA-DAKOTA UTILITIES 426 4912 612085 MONTANA POWER CO 427 2761 615785 MOORE CORP LTD 428 4400 615798 MOORE MCCORMACK RESOURCES 429 1511 618448 MORRISON-KNUDSEN 430 3662 620076 MOTOROLA INC 431 4922 624029 MOUNTAIN FUEL SUPPLY CO 432 5411 626144 MUNFORD INC 433 5331 626643 MURPHY (G.C.) CO 434 2912 626717 MURPHY OIL CORP 435 3570 628862 NCR CORP 436 2810 629156 N L INDS 437 3310 629449 NVF CORP 438 2052 629527 NABISCO INC 439 2860 629853 NALCO CHEMICAL CO 440 3570 631226 NASHUA CORP 441 3221 635128 NATIONAL CAN CORP 442 2085 635655 NATIONAL DISTILLERS &CHEMICL 443 2950 636316 NATIONAL GYPSUM CO 444 7349 636632 NATIONAL KINNEY CORP 445 2046 637776 NATIONAL STARCH & CHEMICAL 446 3310 637844 NATIONAL STEEL CORP 447 5411 638097 NATIONAL TEA CO 448 2913 638760 NATOMAS CO 449 4911 641423 NEVADA POWER CO 450 4912 644001 NEW ENGLAND ELECTRIC SYSTEM 116
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
451 4912 644052 NEW ENGLAND GAS & ELECTRIC 452 4911 649840 NEW YORK STATE ELEC & GAS 453 2711 650111 NEW YORK TIMES CO 454 1021 651639 NEWMONT MINING CORP 455 4911 653522 NIAGARA MOHAWK POWER 456 4924 654086 NICOR INC 457 4011 655694 NORFOLK & WESTERN RAILWAY 458 3630 656389 NORRIS INDS INC 459 1211 656780 NORTH AMERICAN COAL 460 3600 657045 NORTH AMERICAN PHILIPS CORP 461 4511 658408 NORTH CENTRAL AIRLINES INC 462 4911 664397 NORTHEAST UTILITIES 463 4912 665262 NORTHERN INDIANA PUBLIC SERV 464 4922 665500 NORTHERN NATURAL GAS 465 4912 665772 NORTHERN STATES POWER 466 3661 665815 NORTHERN TELECOM LTD 467 3720 666807 NORTHROP CORP 468 4511 667281 NORTHWEST AIRLINES INC 469 4922 667446 NORTHWEST ENERGY 470 9997 667528 NORTHWEST INDS 471 4924 667655 NORTHWEST NATURAL GAS CO 472 4912 668231 NORTHWESTERN PUBLIC SERV CO 473 3290 668605 NORTON CO 474 1311 674599 OCCIDENTAL PETROLEUM CORP 475 5093 676346 OGDEN CORP 476 4911 677347 OHIO EDISON CO 477 4912 678858 OKLAHOMA GAS & ELECTRIC 478 2800 680665 OLIN CORP 479 2600 680711 OLINKRAFT, INC 480 4911 684065 ORANGE & ROCKLAND UTILITIES 481 4912 689648 OTTER TAIL POWER CO 482 4400 690368 OVERSEAS SHIPHOLDING GROUP 483 3290 690734 OWENS-CORNING FIBERGLAS CORP 484 3221 690768 OWENS-ILLINOIS INC 485 4511 692515 OZARK AIR LINES INC 486 2810 693506 PPG INDS 487 4511 693602 PSA INC 488 2082 693715 PABST BREWING CO 489 4911 694308 PACIFIC GAS & ELECTRIC 490 4924 694478 PACIFIC LIGHTING CORP 491 2400 694529 PACIFIC LUMBER CO 492 2911 694750 PACIFIC PETROLEUMS LTD 493 4911 694784 PACIFIC POWER & LIGHT 494 4511 698057 PAN AMERICAN WORLD AIRWAYS 495 4922 698465 PANHANDLE EASTERN PIPE LINE 496 5999 699466 PARGAS INC 497 3310 707355 PENN-DIXIE INDS 498 4911 709051 PENNSYLVANIA POWER & LIGHT 499 2800 709317 PENNWALT CORP 500 1311 709903 PENNZOIL CO 117
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
501 2086 713448 PEPSICO INC 502 1621 713839 PERINI CORP 503 2835 717081 PFIZER INC 504 3330 717265 PHELPS DODGE CORP 505 4912 717537 PHILADELPHIA ELECTRIC CO 506 1700 718009 PHILADELPHIA SUBURBAN CORP 507 2111 718167 PHILIP MORRIS INC 508 4811 718252 PHILLIPINE L D TEL 509 2912 718507 PHILLIPS PETROLEUM CO 510 3350 719151 PHOENIX STEEL CORP 511 4511 720101 PIEDMONT AVIATION INC 512 4924 720186 PIEDMONT NATURAL GAS CO 513 4924 723645 PIONEER CORP 514 3570 724479 PITNEY-BOWES INC 515 1211 725701 PITTSTON CO 516 1021 726056 PLACER DEVELOPMENT LTD 517 3861 731095 POLAROID CORP 518 3999 736245 PORTER (H.K.) CO 519 4911 736508 PORTLAND GENERAL ELECTRIC CO 520 2600 737628 POTLATCH CORP 521 4912 737679 POTOMAC ELECTRIC POWER 522 4911 744448 PUBLIC SERVICE CO OF COLO 523 4912 744465 PUBLIC SERVICE CO OF IND 524 4911 744482 PUBLIC SERVICE CO OF N H 525 4912 744499 PUBLIC SERVICE CO OF N MEX 526 4924 744516 PUBLIC SERVICE CO OF N C 527 4912 744567 PUBLIC SERVICE ELEC & GAS 528 2085 744635 PUBLICKER INDS INC 529 3241 745075 PUERTO RICAN CEMENT CO INC 530 4911 745332 PUGET SOUND POWER & LIGHT 531 1621 745791 PULLMAN INC 532 7393 746384 PUROLATOR INC 533 2912 747419 QUAKER STATE OIL REFINING 534 3714 748369 QUESTOR CORP 535 3651 749285 RCA CORP 536 7011 751328 RAMADA INNS 537 3714 754586 RAYBESTOS-MANHATTAN INC 538 1621 754722 RAYMOND INTL INC 539 3662 755111 RAYTHEON CO 540 2820 759200 REICHHOLD CHEMICALS INC 541 6199 759466 RELIANCE GROUP INC 542 3480 759754 REMINGTON ARMS CO 5*3 3310 760779 REPUBLIC STEEL CORP 544 2912 761066 RESERVE OIL & GAS 545 3350 761406 REVERE COPPER & BRASS INC 546 2844 761525 REVLON INC 547 2111 761753 REYNOLDS (R.J.) INDS 548 3330 761763 REYNOLDS METALS CO 549 1021 766889 RIO ALGOM LTD 550 4011 767100 RIO GRANDE INDS 118
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
551 4210 769739 ROADWAY EXPRESS INC 552 3822 770519 ROBERTSHAW CONTROLS 553 2950 770553 ROBERTSON (H.H.) CO 554 2835 770706 ROBINS (A.H.) CO 555 4911 771367 ROCHESTER GAS & ELECTRIC 556 4811 771758 ROCHESTER TEL CO 557 2000 775371 ROHM & HAAS CO 558 3510 776678 ROPER CORP 559 2835 776755 RORER GROUP 560 1381 779382 ROWAN COS INC 561 2086 780240 ROYAL CROWN COLA CO 562 2913 780257 ROYAL DUTCH PETROLEUM CO 563 3000 781088 RUBBERMAID INC 564 7530 783549 RYDER SYSTEM INC 565 1382 785316 SABINE CORP 566 5411 786514 SAFEWAY STORES INC 567 1211 790155 ST. JOE MINERALS CORP 568 4011 791808 ST LOUIS-SAN FRAN RAILWAY 569 2600 793453 ST. REGIS PAPER CO 570 4911 797440 SAN DIEGO GAS & ELECTRIC 571 4011 802020 SANTA FE INDS 572 1621 802037 SANTA FE INTL 573 7530 804498 SAUNDERS LEASING SYSTEM INC 574 4912 804787 SAVANNAH ELEC & POWER 575 5199 805567 SAXON INDS 576 2082 806228 SCHAEFER (F. & M.) CORP 577 2835 806805 SCHERING-PLOUGH 578 2082 806823 SCHLITZ (JOS.) BREWING 579 3533 806857 SCHLUMBERGER LTD 580 2600 809877 SCOTT PAPER CO 581 3630 810640 SCOVILL MFG CO 582 4400 811369 SEA CONTAINERS 583 4011 811517 SEABOARD COAST LINE INDS 584 4511 811641 SEABOARD WORLD AIRLINES 585 3714 812132 SEALED POWER 586 2835 812302 SEARLE (G.D.) & CO 587 3310 819785 SHARON STEEL 588 2912 822635 SHELL OIL CO 589 1311 823118 SHENANDOAH OIL CORP 590 4912 826418 SIERRA PACIFIC POWER CO 591 9997 826622 SIGNAL COS 592 3499 826690 SIGNODE CORP 593 2510 828709 SIMMONS CO 594 3630 829302 SINGER CO 595 5912 830164 SKAGGS COMPANIES INC 596 3714 831865 SMITH (A.O.) CORP 597 3533 832110 SMITH INTL INC 598 2835 832377 SMITHKLINE CORP 599 4011 835716 SOO LINE RAILROAD 600 4912 837004 SOUTH CAROLINA ELEC & GAS 119
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS OBSINDUSTRYCONUMBER CORPNAME
601 4924 838518 SOUTH JERSEY INDS 602 2062 841297 SOUTHDOWN INC 603 4911 842400 SOUTHERN CALIF EDISON CO 604 4912 842587 SOUTHERN CO 605 4912 843163 SOUTHERN INDIANA GAS & ELEC 606 ' 4922 843456 SOUTHERN NAT RESOURCES 607 4811 843486 SOUTHERN NEW ENG TELEPHONE 608 4011 843571 SOUTHERN PACIFIC CO 609 4011 843673 SOUTHERN RAILWAY 610 4924 844028 SOUTHERN UNION CO 611 5411 844436 SOUTHLAND CORP 612 6790 844521 SOUTHLAND ROYALTY CO 613 2600 844861 SOUTHWEST FOREST INDS 614 4924 844895 SOUTHWEST GAS CORP 615 2510 848338 SPERRY & HUTCHINSON CO 616 2200 851783 SPRINGS MILLS INC 617 3622 852206 SQUARE D CO 618 2835 852245 SQUIBB CORP 619 2000 853139 STANDARD BRANDS INC 620 2913 853683 STANDARD OIL CO OF CALIF 621 2912 853700 STANDARD OIL CO (INDIANA) 622 2912 853734 STANDARD OIL CO (OHIO) 623 3429 854616 STANLEY WORKS 624 2810 857721 STAUFFER CHEMICAL CO 625 2836 859264 STERLING DRUG INC 626 3560 860486 STEWART-WARNER CORP 627 8911 861572 STONE & WEBSTER INC 628 2650 861589 STONE CONTAINER CORP 629 4830 862131 STORER BROADCASTING CO 630 9997 863863 STUDEBAKER-WORTHINGTON INC 631 2890 866645 SUN CHEMICAL CORP 632 2912 866762 SUN CO 633 3540 867323 SUNDSTRAND CORP 634 1311 868273 SUPERIOR OIL CO 635 2837 871140 SYBRON CORP 636 3714 872649 TRW INC 637 4912 875127 TAMPA ELECTRIC CO 638 3811 878542 TECHNICON CORP 639 3580 878895 TECUMSEH PRODUCTS CO 640 9997 879335 TELEDYNE INC 641 4890 879488 TELEPROMPTER CORP 642 9997 880370 TENNECO INC 643 2913 881694 TEXACO INC 644 4922 882387 TEXAS EASTERN CORP 645 4922 882440 TEXAS GAS TRANSMISSION 646 3670 882508 TEXAS INSTRUMENTS INC 647 1311 882534 TEXAS INTL CO 648 4912 882848 TEXAS UTILITIES CO 649 1000 882887 TEXASGULF INC 650 9997 883203 TEXTRON INC 120
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
651 3760 884102 THIOKOL CORP 652 6199 886348 TICOR 653 4511 886735 TIGER INTERNATIONAL 654 2721 887224 TIME INC 655 2711 887360 TIMES MIRROR CO 656 3714 887389 TIMKEN CO 657 4911 889175 TOLEDO EDISON COMPANY 658 2911 891508 TOTAL PETROLEUM OF NORTH AM 659 3580 892892 TRANE CO 660 9997 893341 TRANS UNION CORP 661 4511 893349 TRANS WORLD AIRLINES 662 6199 893485 TRANSAMERICA CORP 663 4922 893532 TRANSCO CO’S 664 4700 894015 TRANSWAY INTERNATIONAL CORP 665 3350 695861 TRIANGLE INDS 666 3310 898592 TUBOS DE ACERO DE MEXICO S A 667 4911 898813 TUCSON GAS & ELECTRIC 668 7810 901221 TWENTIETH CENTURY-FOX FILM 669 3494 902182 TYLER CORP 670 4511 902550 UAL INC 671 4924 902686 UGI CORP 672 3580 902878 UMC INDS 673 3610 903422 UV INDS INC 674 5140 904767 UNILEVER LTD-AMER SHRS 675 2841 904784 UNILEVER N V 676 2600 905530 UNION CAMP CORP 677 2800 905581 UNION CARBIDE CORP 678 4912 906548 UNION ELECTRIC CO 679 2912 907770 UNION OIL CO OF CALIFORNIA 680 4011 907818 UNION PACIFIC CORP 681 3000 909160 UNIROYAL INC 682 2010 909660 UNITED BRANDS 683 4922 910210 UNITED ENERGY RESOURCES 684 4911 910637 UNITED ILLUMINATING CO 685 2911 911358 UNITED REFINING CO 686 3270 912027 U.S. GYPSUM CO 687 3999 912078 U.S. INDS 688 3310 912656 U.S. STEEL CORP 689 2111 912775 U.S. TOBACCO CO 690 3728 913017 UNITED TECHNOLOGIES CORP 691 4811 913025 UNITED TELECOMMUNICATIONS 692 2835 915302 UPJOHN CO 693 4911 917508 UTAH POWER & LIGHT 694 2300 918204 V.F. CORP 695 4912 927804 VIRGINIA ELECTRIC & POWER 696 3290 929160 VULCAN MATERIALS CO 697 4811 929350 WUI INC 698 3430 932355 WALLACE-MURRAY CORP 699 2000 934051 WARD FOODS INC 700 2300 934391 WARNACO INC 121
LIST OF ALL COMPANIES IN THE PRIMARY SAMPLE
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS OBS INDUSTRYCONUMBER CORPNAME
701 3540 934408 WARNER & SWASEY 702 3652 934436 WARNER COMMUNICATIONS INC 703 2835 934488 WARNER-LAMBERT CO 704 4924 938837 WASHINGTON GAS LIGHT CO 705 2711 939640 WASHINGTON POST CO-CL 8 706 4911 940688 WASHINGTON WATER POWER 707 4950 941063 WASTE MANAGEMENT INC 708 3540 947015 WEAN UNITED INC 709 7011 947423 WEBB (DEL E.) CORP 710 4511 957586 WESTERN AIR LINES INC 711 1381 958043 WESTERN CO OF NORTH AMERICA 712 4011 959090 WESTERN PACIFIC INDS 713 2700 959265 WESTERN PUBLISHING 714 4811 959805 WESTERN UNION CORP 715 3600 960402 WESTINGHOUSE ELECTRIC CORP 716 1211 960878 WESTMORELAND COAL CO 717 2400 962166 WEYERHAEUSER CO 718 3568 962898 WHEELABRATOR-FRYE 719 3310 963150 WHEELING-PITTSBURGH STEEL 720 3630 963320 WHIRLPOOL CORP 721 3630 963626 WHITE CONSOLIDATED INDS INC 722 3713 964066 WHITE MOTOR CORP 723 2870 969457 WILLIAMS COS 724 4912 976656 WISCONSIN ELECTRIC POWER 725 4924 976707 WISCONSIN GAS CO 726 4912 976826 WISCONSIN POWER & LIGHT 727 4912 976843 WISCONSIN PUBLIC SERVICE 728 2911 977385 WITCO CHEMICAL CORP 729 2086 978165 WOMETCO ENTERPRISES INC 730 4511 981423 WORLD AIRWAYS INC 731 2065 982526 WRIGLEY (WM.) JR. CO 732 3580 983044 WYLAIN INC 733 3570 984121 XEROX CORP 734 4210 985514 YELLOW FREIGHT SYSTEM 735 3651 989399 ZENITH RADIO CORP APPENDIX B LIST OF ALL FIRMS IN THE MARKET INDEX 123 LIST OF ALL FIRMS IN THE MARKET INDEX
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS OBS INDUSTRY CONUMBER CORPNAME
1 200 2455 AZL RESOURCES INC 2 1311 7239 ADOBE OIL & GAS CORP 3 2711 8261 AFFILIATED PUBLICATIONS 4 3714 17634 ALLEN GROUP 5 3841 30087 AMERICAN STERILIZER CO 6 3310 32037 AMPCO-PITTSBURGH CORP 7 3560 36627 ANSUL CO 8 4210 40789 ARKANSAS BEST CORP 9 1311 52519 AUSTRAL OIL CO 10 7392 53213 AUTOMATION INDS 11 7393 57255 BAKER INDS INC 12 3999 58732 BALLY MFG CORP 13 3000 59815 BANDAG INC 14 4911 60077 BANGOR HYDRO-ELEC CO 15 3841 67383 BARD (C.R.) INC 16 3290 69869 BASIC INC 17 1211 70581 BATES MFG CO INC 18 3350 77455 BELDEN CORP 19 2200 77491 BELDING HEMINWAY 20 1041 81851 BENGUET CONS INC-CL 21 7399 84419 BERKEY PHOTO INC 22 2810 87779 BETZ LABORATORIES INC 23 3061 87851 BEVERLY ENTERPRISES 24 3951 88734 BIC PEN CORP 25 3310 93545 BLISS & LAUGHLIN INDS 26 3540 115223 BROWN & SHARPE MFG CO 27 3679 122205 BURNDY CORP 28 3531 125761 CMI CORP 29 3679 126501 CTS CORP 30 4830 139861 CAPITAL CITIES COMMUNICATION 31 3000 142339 CARLISLE CORP 32 4210 143897 CAROLINA FREIGHT CARRIERS 33 5050 148411 CASTLE (A.M.) & CO 34 3711 162789 CHECKER MOTORS CORP 35 5812 171583 CHURCHS FRIED CHICKEN 36 7830 172468 CINERAMA INC 37 3270 208374 CONROCK CO 38 2111 212867 CONWOOD CORP 39 3000 216831 COOPER TIRE & RUBBER 40 4830 224003 COX BROADCASTING CORP 41 2860 227111 CROMPTON & KNOWLES CORP 42 2912 229385 CRYSTAL OIL CO 43 3843 249028 DENTSPLY INTL INC 44 6552 251597 DEVELOPMENT CORP OF AMERICA 45 2890 252165 DEXTER CORP 46 3499 253651 DIEBOLD INC 47 3560 258363 DORR-OLIVER INC 48 3825 268457 EG&G INC 49 3662 269157 E-SYSTENMS INC 50 3573 285744 ELECTRONIC MEMORIES & MAGNET 124 LIST OF ALL FIRMS IN THE MARKET INDEX
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
51 2844 302808 FABERGE INC 52 1211 306084 FALCON SEABOARD INC 53 1311 314387 FELMONT OIL CO 54 2810 317315 FILTROL CORP 55 3821 337693 FISCHER & PORTER CO 56 3811 338027 FISHER SCIENTIFIC CO 57 1311 344074 FLYING DIAMOND OIL 58 3911 354010 FRANKLIN MINT CORP 59 2520 361606 GF BUSINESS EQUIPMENT 60 1211 369784 GENERAL EXPLORATION 61 3740 370856 GENERAL STEEL INDS 62 3540 375046 GIDDINGS & LEWIS INC 63 5812 376109 GINO’S INC 64 1821 390804 GREAT LAKES DREDGE & DOCK CO 65 4911 393154 GREEN MOUNTAIN POWER CORP 66 3210 401370 GUARDIAN INDS 67 4210 406306 HALL’S MOTOR TRANSIT 68 2731 411631 HARCOURT BRACE JOVANOVICH 69 2048 417404 HARTZ MOUNTAIN CORP 70 3670 421596 HAZELTINE CORP 71 2082 422884 HEILEMAN (G.) BREWING INC 72 8061 441061 HOSPITAL AFFILIATES INT'L 73 2731 441560 HOUGHTON MIFFLIN CO 74 7011 441900 HOSPITALITY MOTOR INNS 75 3699 443510 HUBBELL (HARVEY) INC 76 6200 448499 HUTTON (E.F.) GROUP 77 2200 448510 HUYCK CORP 78 6312 454002 INDEPENDENT LIFE & ACCIDENT 79 3670 458140 INTEL CORP 80 2750 459101 INTERNATIONAL BANKNOTE 81 3499 459550 INTL GENERAL INDS 82 9997 482058 JUPITER INDS 83 1211 484170 KANEB SERVICES INC 84 9997 486026 KATY INDS 85 5944 486429 KAY CORP 86 2510 501026 KROEHLER MFG CO 87 4912 510894 LAKE SUPERIOR DISTRICT CORP 88 3452 513696 LAMSON & SESSIONS CO 89 3550 524462 LEESONA CORP 90 3269 526264 LENOX INC 91 5999 536257 LIONEL CORP 92 3241 546642 LOUISVILLE CEMENT 93 2065 554205 MACANDREWS & FORBES 94 3000 564402 MANSFIELD TIRE & RUBBER CO 95 2270 574803 MASLAND (C.H.) & SONS 96 1511 581323 MCKEE (ARTHUR G.) & CO 97 7213 583393 MEANS (F W) & CO 98 4210 588602 MERCHANTS INC 99 3560 590825 MESTA MACHINE CO 100 4924 594508 MICHIGAN GAS UTILITIES CO 125 LIST OF ALL FIRMS IN THE MARKET INDEX
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRYCONUMBER CORPNAME
101 3449 594517 MICHIGAN GENERAL CORP 102 3940 601753 MILTON BRADLEY CO 103 3499 604739 MIRRO ALUMINUM CO 104 4912 606385 MISSOURI UTILITIES 105 3000 608302 MOHAWK RUBBER CO 106 5661 619075 MORSE SHOE INC 107 2200 623555 MOUNT VERNON MILLS INC 108 2300 626320 MUNSINGWEAR INC 109 3750 627151 MURRAY OHIO MFG CO 110 1621 628454 MYERS (L.E.) & CO 111 2450 636418 NATIONAL HOMES CORP 112 8050 636882 NATIONAL MEDICAL CARE 113 4950 640745 NEPTUNE INTL CORP 114 3931 656041 NORLIN CORP 115 3449 670346 NUCOR CORP 116 3679 671400 OAK INDS INC 117 3610 677194 OHIO BRASS CO 118 2082 681453 OLYMPIA BREWING 119 4210 690326 OVERNITE TRANSPORTATION 120 5912 711021 PEOPLES DRUG STORES INC 121 3449 725038 PITTSBURGH-DES MOINES STEEL 122 3740 725106 PITTSBURGH FORGINGS CO 123 7399 725786 PITTWAY CORP 124 2400 732327 POPE & TALBOT INC 125 2731 740648 PRENTICE-HALL INC 126 4924 743753 PROVIDENCE GAS CO 127 6552 746030 PUNTA GORDA ISLES INC 128 1311 762805 RANGER OIL (CANADA) LTD 129 3630 758114 REECE CORP 130 7011 761185 RESORTS INTL 131 2650 761686 REXHAM CORP 132 3069 763121 RICHARDSON CO 133 4210 774834 ROCOR INTL 134 3999 776338 RONSON CORP 135 1000 776806 ROSARIO RESOURCES CORP 136 2200 782352 RUSSELL CORP 137 1531 783498 RYAN HOMES INC 138 4912 790654 ST JOSEPH LIGHT & POWER 139 5812 795872 SAMBO'S RESTAURANTS 140 5912 804617 SAV-ON-DRUOS INC 141 7213 812370 SEARS INDS INC 142 2086 818036 SEVEN-UP CO 143 6552 819470 SHAPELL INDS 144 3540 830643 SKIL CORP 145 4210 832407 SMITH’S TRANSFER 146 7011 835438 SONESTA INTL HOTELS CORP 147 4511 842179 SOUTHERN AIRWAYS INC 148 4210 847541 SPECTOR INDS 149 2761 853887 STANDARD REGISTER CO 150 2860 858586 STEPAN CHEMICAL CO 126 LIST OF ALL FIRMS IN THE MARKET INDEX
CONUMBER IS THE 6 DIGIT CUSIP NUMBER INDUSTRY IS THE SIC CODE ASSIGNED BY IMS
OBS INDUSTRY CONUMBER CORPNAME
151 3573 852111 STORAGE TECHNOLOGY CORP 152 5063 868494 SUPERSCOPE INC 153 3270 869104 SUSQUEHANNA CORP 154 4210 872489 T.I.M.E.-DC INC 155 2836 975314 TAMPAX INC 156 3630 876043 TAPPAN CO 157 2870 880902 TERRA CHEMICALS INT'L 158 3679 884315 THOMAS & BETTS CORP 159 3642 884425 THOMAS INDS INC 160 3940 890278 TONKA CORP 161 4210 893553 TRANSCON LINES 162 5211 895895 TRIANGLE PACIFIC CORP 163 5312 901486 TWIN FAIR INC 164 3331 904274 UNARCO INDS INC 165 3568 911842 U.S. FILTER CORP 166 4912 916303 UPPER PENINSULA POWER 167 3221 921033 VAN DORN CO 168 4090 925526 VIACOM INTL 169 3270 934442 WARNER CO 170 5411 948849 WEIS MARKETS INC 171 4511 967630 WIEN AIR ALASKA 172 3140 978097 WOLVERINE WORLD WIDE 173 7213 981373 WORK WEAR CORP 174 7370 983079 WYLY CORP APPENDIX C
PLOTS OF THE CUMULATIVE AVERAGE RESIDUALS OF THE INDUSTRY SUBSAMPLES NOT INCLUDED IN THE TEXT 128
olO
rH 3Ctf T3 •H ra 0 U -„1 0 _ 0 ho J-l 0 > ctf S> -.20- •H -P Cti
-10 - 5 0 5 10 Month relative to announcement date
Figure 17
Plot of the Cumulative Average Residuals for the Oil-Crude Producers Industry
Industry # 1311 Sample Sizes 18 Asset Ratio: .982
Model Statistics
R-Square: .449 Betas 06752 Alpha: <,00 96 129
.30-
i—i aJ 3 •H CO a> .20 - U
10 - a> > •H ■P cti r-) 3 S 3 O
-.10 -15 -10 5 5 10o Month relative to announcement date
Figure 18
Plot of the Cumulative Average Residuals for the Heavy Construction - Ex Hwy & St Industry
Industry # 1621 Sample Sizes 8 Asset Ratios .854
Model Statistics
R-Squares .612 Betas .6978 Alphas .0046 -qae .2 Bt: 92 Apa .0019 Alpha: .9020 Beta: .971 Ratio: Asset .624 R-Square: 7 Size: Sample 2400 # Industry Cumulative average residual -.20 -.10 . 10 1 5 5 -15 lt f h Cmltv Aeae eiul for Residuals Average Cumulative the of Plot h Frs Pout Industry Products Forest the ot rltv t anucmn date announcement to relative Month -10 oe Statistics Model iue 19 Figure
130 100 131
.10
H cd 3 •H m
Month relative to announcement date
Figure 20 Plot of the Cumulative Average Residuals for the Drugs - Ethical Industry
Industry # 2835 Sample Size: 13 Asset Ratio: .689
Model Statistics
R-Square: .179 Beta: .3586 iilpha: .0042 -qae .1 Bt: 35 Apa .0072 Alpha: .3758 Beta: 1.126 Ratio: Asset .216 R-Square: 18 Size: Sample 2912 # Industry Cumulative average residual -.20 .10 -15 lt fte uuaieAeae eiul for Residuals Average Cumulative the of Plot h Ol Itgae Dmsi Industry Domestic Integrated - Oil the ot rltv t anucmn date announcement to relative Month -10 oe Statistics Model iue 21 Figure 5 0 5
132 10 133
20
rH cd 3 tJ •Hm CD .10 U
cd fcuo cd U CD > cd
rH3 6 O .10-
-.20 -15 -10 5 o 5 10 Month relative to announcement date
Figure 22
Plot of the Cumulative Average Residuals for the Oil - Integrated International Industry
Industry # 2913 Sample Size: 8 Asset Ratio: „907
Model Statistics
R-Square: .387 Beta: .^793 Alpha: .0039 ■O'* .10
o.
-.10
-.20
r-flj i T33 S -.30
ho0 -Ucd >0) •P cd
1 -.50
-.60
-.70
-.80 10 Month relative to announcement date
Figure 23
Plot of the Cumulative Average Residuals for the Blast Furnaces & Steel Works Industry
Industry # 3310 Sample Sizes 19 Asset Ratios 1.307
Model Statistics
R-Squares .659 Betas .7902 Alphas .0125 135
H cd
•H w aj cu bD cd Sh CD > ** -.20 0) i> •H ■p cd i—l 3 § ° -.30-
-15 -10 5 o 5 10 Month relative to announcement date
Figure 24
Plot of the Cumulative Average Residuals for the Machinery - Industrial Industry
Industry # 35^0 Sample Sizes 10 Asset Ratio: .843
Model Statistics
R-Square: .869 Beta: .8854 Alpha: .0034 -qae .7 Bt: 90 Apa .0006 Alpha: .9001 Beta: .85^+ Ratio: Asset .772 R-Square: 7 Size: Sample 3^30 # Industry Cumulative average residual -.30- -.20 -.10 .10 Plot of the Cumulative Average Residuals for the for Residuals Average Cumulative the of Plot * - -15 lcrcHueodApine Industry Appliances Household Electric ot rltv t anucmn date announcement to relative Month -10
oe Statistics Model iue 25 Figure -5
05
136 10 .20
i—i 3 10 - •H CO Q) U Q) tuO ctf U 0. d) > cd Q) > •H ■P Oj rH 3 -.10 - s 3 O
-.20 -10 -505 Month relative to announcement date
Figure 26
Plot of the Cumulative Average Residuals for the Auto Parts & Accessories Industry
Industry # 371^ Sample Size : 15 Asset Ratio: 985
Model Statistics
R-Squares .890 Betas .8783 Alpha: .0010 138
.20
r—) cd 3 .10 - Td •H W CU 5h
-.20 -15-105 o 5 10 Month relative to announcement date
Figure 27
Plot of the Cumulative Average Residuals for the Manufacturing Industries Industry
Industry # 3999 Sample Size: 7 Asset Ratio: .770
Model Statistics
R-Square: .7^1 Beta: .6587 Alpha: .0018 -qae .-2 ea .34 Apa .0052 Alpha: .5394- Beta: .4-52 R-Square: nuty#401 ape ie • Size: Sample 4-011# Industry Cumulative average residual -.'20 -.10 .10 .20 - | - lt f h Cmltv vrg Rsdas for Residuals Average Cumulative the of Plot -15 ot eaie o noneet date announcement to relative Month -10 h Riras Industry Railroads the
oe Statistics Model iue 28 Figure -5
16 se Rto 1.116 Ratio: Asset
0
5
139 10 1^0
20
10-
10
9 -10 5-15 o5 10 Month relative to announcement date
Figure 29
Plot of the Cumulative Average Residuals for the Air Transport Industry
Industry # ^511 Sample Size: 18 Asset Ratio: 1.113
Model Statistics
R-Square: .756 Beta: 1.3030 Alpha: -.006 k -qae .8 Bt: 45 Apa .0002 Alpha: .4653 Beta: 1.089 Ratio*. Asset .485 R-Square: 35 Size*. Sample 4911 # Industry Cumulative average residual -.10 .20 .20 10 - 10 Plot of the Cumulative Average Residuals for the for Residuals Average Cumulative the of Plot - 1 5 5 -15 lcrc tlte - lwTruh Industry Through Flow - Utilities Electric -10 ot rltv t anucmn date announcement to relative Month oe Statistics Model iue 30 Figure o
10 -qae .9 Bt: 42 Apa -.0001 Alpha: .4621 Beta: .491 R-Square: nuty#41 Sml Sz: 7 se Rto 1.312 Ratio: Asset 67 Size: Sample 4912 # Industry
Cumulative average residual -.10 Plot of the Cumulative Average Residuals for the for Residuals Average Cumulative the of Plot . .20- 0.- 10 - 1 -0 5 5 io 5 0 -5 -10 -15 - i i i i
lcrc tlte - omlzd Industry Normalized - Utilities Electric ot rltv t anucmn date announcement to relative Month Model Statistics Model iue 31 Figure \
142 3 A 3
30-
H cd 3 •H CO 0 20 - 0 hD cd U 0 > cd 0 10 > •H -P cd rH 1 3 O
-.10 -15-10 5 o 5 10 Month relative to announcement date
Figure 32
Plot of the Cumulative Average Residuals for the Natural Gas Transmission Industry
Industry # ^922 Sample Size: 1^ Asset Ratio: 1.129
Model Statistics
R-Square: .589 Beta: .6092 Alpha: .0021 -qae .9 Bt: 43 Apa .0022 Alpha: .4437 Beta: Ratio: Asset .594 R-Square: 21 Size: Sample 4924 # Industry Cumulative average residual -.20 .20 10 10 _ - lt f h Cmltv Aeae eiul for Residuals Average Cumulative the of Plot -15 h Ntrl a Cmais Industry Companies Gas Natural the ot rltv t anucmn date announcement to relative Month -10 oe Statistics Model iue 33 Figure 5
0 5
1.063 10 1^5
10
rHa 3 ■H W CD u 10 -
20h
i—i 3 S 3 O
-15 -10 5o 5 10 Month relative to announcement date
Figure 3k
Plot of the Cumulative Average Residuals for the Retail Food Chains Industry
Industry # 5k11 Sample Size: 7 Asset Ratio: 1.065
Model Statistics
R-Square: .769 Beta: .8526 Alpha: -.0033 146
rH Cd 10 _ 3 T3 •H ro (U
Q) > •H -P cd -.10 rH3 S 3 O
-15 -10 5 o 5 10 Month relative to announcement date
Figure 35 Plot of the Cumulative Average Residuals for the Conglomerates Industry
Industry # 9997 Sample Size: 1^ Asset Ratio: .821
Model Statistics
R-Square: .8^9 Beta: .977^ Alpha: .0019 APPENDIX D
MONTHLY RETURNS FOR THE MARKET INDEX Observation Number Datea Return
I -73 0.075 15 2 -72 0.05032 3 -71 -0.052 10 4 -70 -0.00568 5 -69 -0.04867 6 -68 0.06863 7 -6 7 -0.00988 8 -66 -0.04925 9 -65 -0.03236 10 — 64 0.135C9 11 -63 0.10142 12 -62 0.05350 13 -6 1 0.00353 14 -60 0.02030 15 -5 9 -0.00096 16 -53 -0.03194 17 -57 -0 .026 79
18 -56 - 0.0 02 01 19 -55 -0.03032 20 -54 -0.02076 21 -53 0.04530 22 -52 -0.00 769 23 -51 -0.0565 1 24 -50 -0.06862 25 -49 -0.0 1678 26 -48 —0.06379 27 -47 -0.079 13 Observation Number Datea Return
28 -46 -0.03239 29 -45 0.13460 30 -44 -0.047 18 31 -43 0.10833 32 ' -42 — 0.02091 33 -41 -0.194 12 34 -40 -0.05270 35 -39 0.14880 36 -38 0.0 1385 3 7 -37 0.00131 38 -36 -0.04346 39 -35 -0.069C1 40 -34 -0.03131 41 -33 -0.03349 42 -32 -0.03532 43 -31 -0.09357 44 -30 0.11386 45 -29 -0.05954 4 6 -28 —0,086 4o 47 -2 7 0.34146 48 -26 0.02526 49 -25 0.09604 50 -24 0.04764 5 1 -23 0.0664G 52 -22 0.06680 53 -21 -0.01526 54 -20 — 0.06405 150
Observation Number Date Return
55 -19 -0.0 38 16 56 -18 0.02649 57 -17 0.01926 58 -16 -0.01158 59 -15 0.21075 60 -14 0.10451 61 -13 0.00336 62 -12 -0.0143 5 63 -11 -0.02215 64 -10 0.03711 65 -9 0.0L101 66 -8 -0.01462 67 -7 0.03003 68 -6 -0.02317 69 -5 0.030 15 70 -4 0.09263 71 -3 0.03944 72 -2 -0.31126 73 -1 0.01245 74 0 0.0 1785 75 1 -0.00434 76 2 0.059 80 77 3 -0.00091 78 4 -0.02813 79 5 0.00496 80 6 -0.0 1682 81 7 0.07341 82 8 0.01684
aMonth relative to announcement date (March, 1977)° 151
VITA
NAME OF AUTHORj William Carlton Fleenor PLACE OF BIRTH: Bristol, Virginia DATE OF BIRTH: March 27, 1950 DEGREES AWARDED:
Bachelor of Business Administration, Loyola University, 1972 Master of Science in Accounting, University of New Orleans, 1975 PROFESSIONAL EXPERIENCE:
Staff Member, Charbonnet and Laporte, CPA's, New Orleans, Louisiana, 1972-197^ Part-time Instructor, Accounting Department, Southeastern Louisiana State University, Hammond, Louisiana, 1976-1978 Assistant Professor, Accounting Department, Southeastern Louisiana State University, Hammond, Louisiana, 1978 AWARDS AND HONORS:
Paul C. Taylor Award for Outstanding Manuscript, New Orleans Chapter, National Association of Accountants, 197^-1975 Certified Public Accountant, Louisiana, 1975 EXAMINATION AND THESIS REPORT
Candidate: William Carlton Fleenor
Major Field: Accounting
Title of Thesis: Investors’ Reaction to the Replacement Cost
Information Provided as a Result of ASR #190i
Some Empirical Results
Approved: AO. / Major Professor and Chairman
Dean) of the Graduate School
EXAMINING COMMITTEE:
Date of Examination: Novem ber16 , 1978