Being Surprised by the Unsurprising: Earnings Seasonality and Stock Returns

Tom Y. Chang is Assistant Professor of Finance and Business Economics at the University of Southern California Marshall School of Business. This post is based on a recent paper by Professor Chang; Samuel M. Hartzmark, Assistant Professor of Finance at University of Chicago Booth School of Business; David H. Solomon, Assistant Professor of Finance and Business Economics at the University of Southern California Marshall School of Business; and Eugene F. Soltes, Jakurski Family Associate Professor of Business Administration at Harvard Business School.

“Day-to-day fluctuations in the profits of existing investments, which are obviously of an ephemeral and non-significant character, tend to have an altogether excessive, and even an absurd, influence on the market. It is said, for example, that the shares of American companies which manufacture ice tend to sell at a higher price in summer when their profits are seasonally high than in winter when no one wants ice.”

– John Maynard Keynes (1936)

The fact that obvious information should be incorporated into the price of a stock seems…obvious. If information seems obvious at first glance, but in fact requires significant thought to understand, this information may not be reflected in a stock’s price. In this case the apparent obviousness of a given piece of information causes a person to feel as if they understand it and dismiss it without given the thought necessary to truly understand its content.

Being Surprised by the Unsurprising: Earnings Seasonality and Stock Returns shows that this simple oversight of seemingly obvious information leads to significant mispricing of US equities. Many firms have predictably greater earnings at certain points in the year, usually due to the underlying cyclical nature of the firm’s business. Little consideration has been given to how earnings seasonality itself is priced as earnings seasonality appears to be a straightforward concept. The fact that ice cream producers generate more earnings in summer and snow blower shops generate more earnings in winter would strike most people as obvious to the point of being trite. Investors have ample opportunities for learning about seasonality, as information on earnings is easily available and repeated frequently for each firm.

The apparent simplicity of earnings seasonality is deceptive: while identifying seasonal quarters may be easy, calculating a precise seasonal correction for a given firm is rather complicated. Models of seasonal adjustments impose significant structure and are sensitive to a number of ad hoc specification choices. Because earnings seasonality seems straightforward, investors, like academics, pay less attention to it and are unlikely to understand its complexity. A problem that seems easy to solve, but is actually quite difficult, is one that is likely to reveal behavioral biases.

This paper presents evidence of abnormal returns consistent with markets failing to properly price information contained in the seasonal patterns of earnings. Some companies have earnings that are consistently higher in one quarter of the year relative to others, which are termed a positive seasonality quarter. Companies earn significant abnormal returns in months when they are likely to announce earnings from a positive seasonality quarter.

Consider the example of Borders Books, which traded from 1995 to 2010. Borders had a highly seasonal business, with a large fraction of earnings in the fourth quarter. Out of Borders’ 63 quarterly earnings announcements, the 14 largest were all fourth-quarter earnings. Not only did these quarters have high levels of earnings, but they also had high earnings announcement returns. The average monthly market-adjusted return for Borders’ fourth-quarter announcements was 2.27%, compared with -3.40% for all other quarters. Borders’ earnings seasonality is a persistent property of its business due in part to increased sales over Christmas. Thus, an investor could easily forecast when these high returns would occur. The paper shows that the pattern in earnings announcements returns for Borders holds in general for seasonal firms: high earnings announcement returns can be forecast using past information about which quarters contain higher than usual earnings.

A portfolio of companies with expected earnings announcements in the highest quintile of earnings seasonality earns abnormal returns of 65 basis points per month relative to a four-factor model, compared with abnormal returns of 31 basis points per month for the lowest seasonality quintile. This difference is statistically significant at the 1% level, and, unlike most asset pricing anomalies, it becomes stronger (55 basis points) when the portfolio is value weighted.

The paper hypothesizes that the effects of seasonality are due to investors overweighting recent earnings when forming estimates of future earnings. If an upcoming quarter has positive seasonality, the level of earnings in the three most recent announcements was likely lower than the announcement four quarters prior. If investors suffer from a recency effect, whereby recent information is easier to recall than is old information, they will be more likely to overweight recent lower earnings compared to the higher earnings from the same quarter in the prior year. This would cause them to be overly pessimistic about the upcoming announcement and would lead to greater positive surprises. Investors who suffer from a recency effect will not completely ignore information from more than three quarters prior, but recent earnings will be overweighted if more recent events are more salient.

Overall, our results are consistent with investors having an excessive focus on recent events, leading to insufficient attention to longer-term patterns in earnings.

The complete paper is available for download here.

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