Posted: 9 Sep 2003
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 downwards. 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.
Keywords: PE ratio, PEG ratio, Earnings forecasts, Earnings growth, Cost of capital
JEL Classification: C53, E43, G11, G12, G31, M41
Suggested Citation: Suggested Citation
Easton, Peter D., PE Ratios, PEG Ratios, and Estimating the Implied Expected Rate of Return on Equity Capital. Accounting Review, January 2004. Available at SSRN: https://ssrn.com/abstract=439764