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An Investigation of the Firm Effects Influence in the Analysis of Earnings to Price Ratios of Industrial Common Stocks

Published online by Cambridge University Press:  19 October 2009

Peter S. Chung
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Extract

In the past most of the studies whose aim was to explain earnings-to-price ratios (or alternatively, price-earnings ratios) of common stocks attempted to answer the much-debated question raised in both investment and academic circles: Why does a given common stock consistently command a higher price relative to its earnings vis-a-vis other stocks? Success in answering this question has been limited, largely due to (1) researchers' inability to incorporate expected earnings (E) correctly into the empirical measurement of earnings-to-price ratio and/or (2) inadequate treatment of risk variables to explain variability of earnings-to-price ratio.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 9 , Issue 6 , December 1974 , pp. 1009 - 1029
Copyright
Copyright © School of Business Administration, University of Washington 1974

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References

1 For a comprehensive literature on variability of earnings-to-price ratio, see Chung, Peter S., “Measurement and Explanation of Expected Earnings to Price Ratios of Common Stocks,” an unpublished doctoral dissertation (Evanston, Ill.: Northwestern University, 1972), Chapter IIGoogle Scholar.

2 For a similar definition used by others, see Benishay, H., “Market Preferences for Characteristics of Common Stocks,” The Economic Journal, vol. 83 (March 1973), pp. 173–191CrossRefGoogle Scholar.

3 Benishay, H., “Measurement of Market Yields on Equity Capital,” The National Banking Review, vol. 3 (December 1965), pp. 237–243Google Scholar.

4 Friend, I. and Puckett, M., “Dividends and Stock Prices,” The American Economic Review, vol. 54 (September 1964), pp. 656–682Google Scholar. Bower, R. S. and Bower, D. H., “Risk and Valuation of Common Stock,” The Journal of Political Economy, vol. 77 (May–June 1969), pp. 349–362CrossRefGoogle Scholar.

5 For the justification of corrective mission by these variables for measured expected earnings, see the live exchange between Benishay, H. and Gordon, M. in “Variability in Earnings-Price Ratios: Comment and Reply,” The American Economic Review, vol. 52 (March 1962), pp. 203–216Google Scholar.

6 The model presented here in Section II is one of several models proposed in the author's dissertation and deemed to be one of the most adequate valuation models developed so far. See, Peter S. Chung, “Measurement and Explanation,” Chapter III.

7 For definitions of variables, see Appendix II. The expected relations between the dependent and independent variables are well established in the literature and hence there is no need for long justification here. For interested readers, see Peter S. Chung, “Measurement and Explanation,” Chapter III, pp. 88–94, as well as Benishay, H., “Attitudes toward Characteristics of Common Stocks,” Proceedings of the American Statistical Association, (Pittsburgh, August 1968), pp. 318–337Google Scholar. Our regression experiment showed that model (2–4) with logarithmic transformation performed better statistically than without, and that some variables are not transformed in log due to some zero or negative data involved.

8 Kuh, E., “The Validity of Cross-Sectionally Estimated Behavior Equations in Time Series Applications,” Econometrica, vol. 27 (1959), pp. 200–202CrossRefGoogle Scholar.

9 Refer to: Friend and Puckett, “Dividends and Stock Prices,” and Bower and Bower, “Risk and Valuation.”

10 One cross-section year (1959) is lost due to the introduction of FIR into the model, and data for FIR in 1960 are the residuals of model (2–4) in 1959. Again the signs on the top of each variable are expected ones.

11 The rationale for selection of 220 firms, cross-section years, and the source of financial information are presented in Appendix I.

12 See Chow, G. C., “Tests of Equality between Sets of Coefficients in Two Linear Regressions,” Econometrics, vol. 28 (July 1960), pp. 591–605CrossRefGoogle Scholar. and Zellner, A., “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias,” The Journal of American Statistical Association, vol. 57 (June 1962), pp. 348–368CrossRefGoogle Scholar. The Chow-Zellner's equality test is essentially a more refined version of the analysis of covariance test. The operational testing statistic suggested by them is a F-ratio represented as:

where Q1 = the error sum of squares (SSE) of the pooled regression, Q2 = the sum of SEE of each cross-section regression (1959–1968.or 1960–1968.in this study), m = the number of cross-section years (10,9), q = the number of variables excluded for the equality test (1-regression intercept), n = the number of observations in each cross-section (220), p = the number of variables employed (10, 9). Thus the statistic is distributed as F with (m-l). (p-q) and (mn-mp) degress of freedom in the numerator and denominator, respectively.

13 See Chung, “Measurement and Explanation,” pp. 1–3. and 172–185.

14 Regression coefficients between residuals and t-ratios are not reported in Table 2 here, since as one of referees suggested, they do not seem to have any particular interest. For interested readers, refer to: Chung, “Measurement and Explanation,” pp. 140–143.and Appendix D and E.