Valuation, Dividends, Stock market reaction, Information content

I describe a model of earnings and earnings growth and I demonstrate how this model may be used to obtain estimates of the expected rate of return on equity capital. These estimates are compared with estimates of the expected rate of return implied by commonly used heuristics—viz., the PEG ratio and the PE ratio. Proponents of the PEG ratio (which is the price-earnings [PE] ratio divided by the short-term earnings growth rate) argue that this ratio takes account of differences in short-run earnings growth, providing a ranking that is superior to the ranking based on PE ratios. But even though the PEG ratio may provide an improvement over the PE ratio, it is arguably still too simplistic because it implicitly assumes that the short-run growth forecast also captures the long-run future. I provide a means of simultaneously estimating the expected rate of return and the rate of change in abnormal growth in earnings beyond the (short) forecast horizon—thereby refining the PEG ratio ranking. The method may also be used by researchers interested in determining the effects of various factors (such as disclosure quality, cross-listing, etc.) on the cost of equity capital. Although the correlation between the refined estimates and estimates of the expected rate of return implied by the PEG ratio is high, supporting the use of the PEG ratio as a parsimonious way to rank stocks, the estimates of the expected rate of return based on the PEG ratio are biased downward. This correlation is much lower and the downward bias is much larger for estimates of the expected rate of return based on the PE ratio. I provide evidence that stocks for which the downward bias is higher can be identified a priori.

Studies of the information content of accounting earnings typically assume earnings response coefficients do not vary across firms. Valuation models relating earnings to security prices, however, predict that earnings response coefficients are positively associated with revision coefficients (coefficients relating current earnings to future earnings) and negatively associated with expected rates of return. A random coefficient regression model provides evidence consistent with these predictions. This evidence has implications for interpreting multiple regression models that relate abnormal returns to unexpected earnings and other information variables.

Recent literature has used analysts' earnings forecasts, which are known to be optimistic, to estimate implied expected rates of return, yielding upwardly biased estimates. We estimate that the bias, computed as the difference between the estimates of the implied expected rate of return based on analysts' earnings forecasts and estimates based on current earnings realizations, is 2.84%. The importance of this bias is illustrated by the fact that several extant studies estimate an equity premium in the vicinity of 3%, which would be eliminated by the removal of the bias. We illustrate the point that cross‐sample differences in the bias may lead to the erroneous conclusion that cost of capital differs across these samples by showing that analysts' optimism, and hence, bias in the implied estimates of the expected rate of return, differs with firm size and with analysts' recommendation. As an important aside, we show that the bias in a value‐weighted estimate of the implied equity premium is 1.60% and that the unbiased value‐weighted estimate of this premium is 4.43%.

Reporting lags, Squared market model prediction errors, Information content, SEC Filings

In this paper we investigate whether the level of earnings divided by price at the beginning of the return period is relevant for evaluating earnings/returns associations.' The primary model motivating this research relies on the idea that book value (owners' equity) and market value are both stock variables indicating the wealth of the firm's equity holders. The related flow variables (after adjusting for dividends) are, respectively, earnings divided by price at the beginning of the return period (A/P-1) and market returns. It then follows that earnings divided by beginning of period price should be associated with returns. Although models based on a relation between market value and book value are used occasionally in the accounting research literature (see, for example, Landsman [1986], Harris and Ohlson [1987], and Barth

The paper analyzes the contemporaneous association between market returns and earnings for long return intervals. The research design exploits two fundamental accounting attributes: (i) earnings aggregate over periods, and (ii) expanding the interval over which earnings are determined, is likely to reduce ‘measurement errors’ in (aggregate) earnings. These concepts lead to the level of (aggregate) earnings as a natural earnings variable for explaining security returns. We hypothesize that the longer the interval over which earnings are aggregated, the higher the cross-sectional correlation between earnings and returns. The empirical findings support this hypothesis.

Peter D. Easton, Peter H. Eddey, Trevor S. Harris, An Investigation of Revaluations of Tangible Long-Lived Assets, Journal of Accounting Research, Vol. 31, Studies on International Accounting (1993), pp. 1-38

We develop an empirical method that allows us to evaluate the reliability of an expected return proxy via its association with realized returns even if realized returns are biased and noisy measures of expected returns. We use our approach to examine seven accounting-based proxies that are imputed from prices and contemporaneous analysts' earnings forecasts. Our results suggest that, for the entire crosssection of firms, these proxies are unreliable. None of them has a positive association with realized returns, even after controlling for the bias and noise in realized returns attributable to contemporaneous information surprises. Moreover, the simplest proxy, which is based on the least reasonable assumptions, contains no more measurement error than the remaining proxies. These results remain even after we attempt to purge the proxies of their measurement error via the use of instrumental variables and grouping. We provide additional evidence, however, that demonstrates that some proxies are reliable when the consensus long-term growth forecasts are low and/or when analysts' forecast accuracy is high.