PDF Ebook Stock Returns, Aggregate Earnings Surprises, and Behavioral Finance
This paper studies the relation between stock returns and aggregate earnings surprises. An extensive literature investigates the stock market reaction to individual companies’ earnings announcements (e.g., Ball and Brown, 1968; Watts, 1978; Bernard and Thomas, 1989). At the firm level, stock prices react positively to earnings news but require several quarters to fully reflect the information in earnings. Our goal is to test whether post-earnings announcement drift extends to aggregate data, and more broadly, to understand the connection between stock returns and aggregate earnings surprises.
The motivation for our study is two-fold. First, we test for post-announcement drift in market returns as a simple ‘out-of-sample’ test of recent behavioral models. At the firm level, Fama (1998, p. 304) describes post-earnings announcement drift as an ‘anomaly above suspicion.’ Bernard and Thomas (1990), Barberis, Shleifer, and Vishny (1998), and Daniel, Hirshleifer, and Subrahmanyam (1998) all cite it as a prime example of market inefficiency, helping to motivate their behavioral theories. Our reading of the theories suggests that, although they are motivated by firm level evidence, the biases they describe should also affect aggregate stock returns. As discussed further below, we do not view our study as a strict test of the models, but our investigation is in the spirit of testing whether the theories can ‘explain the big picture’ (Fama 1998, p. 291). More generally, comparing how the stock market reacts to firm and aggregate earnings should help theorists refine models of price behavior.
Second, we study the market’s reaction to aggregate earnings news to help understand the connections among earnings, stock prices, and discount rates. A large literature in finance seeks to explain price movements using cashflow and discount-rate proxies. Economists initially believed that prices follow a random walk, and research focused mostly on cashflow news (e.g., Shiller, 1981). It is now recognized that discount rates fluctuate over time, and researchers have attempted to (1) find good proxies for discount rates, and (2) understand the connection between discount rates, business conditions, and cashflows (e.g., Campbell and Shiller, 1988; Fama and French, 1989; Fama, 1990; Campbell, 1991). We provide direct evidence on the correlation between earnings surprises and discount rates. Further, we argue that the market’s reaction to earnings news provides interesting indirect evidence.
Our initial tests mirror studies of firm-level returns and earnings. We begin by studying the time-series properties of aggregate earnings. Bernard and Thomas (1990) show that firms’ quarterly earnings changes are positively autocorrelated, and the pattern of autocorrelation helps explain the market’s reaction to future earnings announcements. They conclude that investors do not fully understand the time-series properties of earnings (see also Barberis, Shleifer, and Vishny, 1998). Our first key result is that aggregate earnings are more persistent than individual firms’ earnings, yet we find no relation between aggregate returns and past earnings surprises. Thus, unlike at the firm level, there is no evidence of delayed reaction to aggregate earnings news. It is important to note that, although aggregate earnings changes are positively autocorrelated, they exhibit substantial volatility and appear to be quite unpredictable. From 1970 – 2000, the growth rate of seasonally differenced quarterly earnings has a standard deviation of 18.6%, about half of which can be explained by a simple time-series model of earnings growth (asmeasured by the regression R2).
Earnings surprises seem to be large, so our tests should have reasonable power to detect post earnings announcement drift.
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PDF Ebook Stock Returns, Aggregate Earnings Surprises, and Behavioral Finance
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