The Incremental Informativeness of Stock Prices for Future Accounting Earnings*

Abstract

AbstractThis study extends previous research that documents a stock price reaction leading accounting earnings. The primary issue is that prior studies use a naive earnings expectation model (random walk) as the benchmark for the information content of lagged returns and do not adequately address the “incremental” information content of lagged returns. This study identifies and estimates firm‐specific models of earnings to control directly for the autocorrelation in earnings. The explanatory power of lagged prices with respect to this earnings residual is investigated using both a multiple regression model of lagged returns and a multiple time‐series vector autoregressive model.In‐sample estimation of the models provides clear evidence that stock prices impound information about future earnings incremental to the information contained in historical earnings data. Holdout period analysis of the earnings forecasts from these lagged return models finds that both models outperform the naive seasonal random walk expectation, but neither model outperforms the more sophisticated Box‐Jenkins forecasts. On an individual firm basis, earnings forecasts supplemented with the lagged return data tend to be less precise than the Box‐Jenkins forecasts, but the price‐based models demonstrate an ability to rank the earnings forecast errors from the time‐series models. The analysis helps to characterize the limitations of lagged returns as a means of predicting future earnings innovations.