Earnings Seasonality and Stock Returns

Earnings Seasonality and Stock Returns

November 19, 2014 Research Insights
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(Last Updated On: January 18, 2017)

Being Surprised by the Unsurprising: Earnings Seasonality and Stock Returns


We present evidence that markets fail to properly price information in seasonal earnings patterns. Firms whose earnings are historically larger in one quarter of the year (“high seasonality quarters”) have higher returns when those earnings are usually announced. Analyst forecast errors are more positive in high seasonality quarters, consistent with the returns being driven by mistaken earnings estimates. We show that investors appear to overweight recent lower earnings following a high seasonality quarter, leading to pessimistic forecasts in the subsequent high seasonality quarter. The returns are not explained by announcement risk, firm-specific information, increased volume, earnings management, or calendar effects.

Alpha Highlight:

Investors sometimes suffer from “limited attention,” which refers to how investors are unable to pay attention to all information at all times, and are selective with their attention. News that is more sensational or dramatic, and thus more psychologically “available,” tends to attract focused attention. An example would be a situation where stock XYZ is highlighted on CNBC as “the worst stock in the history of the world” by Jim Cramer. Because Cramer is so famous, rants so loudly, and gesticulates so wildly, his announcement might attract attention and investors might react to this information, even if Cramer’s announcement is irrelevant to the stock’s fundamental value. Meanwhile, other information that may be a better signal of the stock’s fundamental value is given less weight when making a judgment, since investor attention is limited to Jim Cramer.

If this framework sounds plausible to you, you might be curious as to what kinds of reliable fundamental signals exist that could be systematically overridden by limited attention.

What about seasonality?

Seasonality describes predictable, repeating variations in financial data over time, and so is arguably a reliable signal.

The authors investigate limited attention in the context of earnings announcement seasonality. We already know that earnings numbers will vary based on the season. For example, retailers usually have stronger profits in the 4th quarter than they do in Q1-Q3 because of the holiday season. Thus, since it is reliable, this variation should be fully anticipated by the markets. But is this true? Does the market fully appreciate this predictable seasonality or does it systematically always get “surprised” by the better than average results in quarters that benefit from seasonality?

To assess the seasonality hypothesis, the authors rank a company’s past 20 quarters (5 years) of earnings data from largest to smallest. The “earnings rank variable” is the average rank of the past 5 announcements from the same fiscal quarter. For example, if the rankings from the 4th quarter (over last 5 years) were 2, 4, 5, 7, and 12, then the “earnings rank variable” would be 6.  We would expect earnings rank values to cluster around 10, which would be average. If earnings rank was higher, this would indicate that the particular quarter examined probably had a relatively lower earnings number relative to other quarters. On average, our hypothetical 4th quarter earnings rank of 6 indicates it has higher earnings than other quarters.

Once we have established seasonality, we can examine whether stocks exhibit any abnormal returns around the earnings season. If we observe no abnormal returns, then the market has appropriately anticipated seasonality (after adjusting for the well-known “earnings announcement premium,” whereby owning portfolios of stocks that are announcing earnings generates positive abnormal returns). If we do observe abnormal returns, once we control for the earnings announcement effect itself, then the market has failed to fully account for seasonality.

Key Findings: 

  • The table below shows that portfolios of companies with expected earnings announcements in the highest quintile of earnings seasonality do indeed earn abnormal monthly returns of .91% for value-weight portfolios. Note: the lowest rank firms also earn slight abnormal returns, but this stems from the earnings announcement premium discussed above (i.e., owning portfolios of stocks that are announcing earnings generates positive abnormal returns).
2014-09-25 15_35_07-Being Surprise by the Unsurprising.pdf - Adobe Reader
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Where does the “seasonality effect” come from?

Why should this be? Why should the market systematically ignore this predictable signal? The paper suggests that the “seasonality effect” is likely due to mistaken estimates of earnings.

To be specific:

“…the effect is linked to the tendency of investors to underreact to predictable information in earning seasonality. We hypothesize that investors who suffer from a tendency to overweight recent data may place too much weight on the lower average earnings that follow a high seasonal quarter, causing them to be too pessimistic by the time the high seasonal quarter comes around again.”

Even though we know the seasonality of earnings, and that the upcoming 4th quarter is likely to strong, because we have recently moved through the other quarters and an environment containing their lower earnings, which are highly available to us, our attention has become limited to these recent quarters, and we fail to account for the reliable seasonal signal.

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Definitions of common statistics used in our analysis are available here (towards the bottom)

About the Author

Wesley R. Gray, Ph.D.

After serving as a Captain in the United States Marine Corps, Dr. Gray received a PhD, and was a finance professor at Drexel University. Dr. Gray’s interest in entrepreneurship and behavioral finance led him to found Alpha Architect. Dr. Gray has published three books: EMBEDDED: A Marine Corps Adviser Inside the Iraqi Army, QUANTITATIVE VALUE: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors, and DIY FINANCIAL ADVISOR: A Simple Solution to Build and Protect Your Wealth. His numerous published works has been highlighted on CBNC, CNN, NPR, Motley Fool, WSJ Market Watch, CFA Institute, Institutional Investor, and CBS News. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.

  • Michael Milburn

    re: “Investors sometimes suffer from “limited attention,” which refers to how
    investors are unable to pay attention to all information at all times,”

    Wes, I’ll share a similar example that I’m prone to – I’m not sure what type of bias/error this is called – but I find I historically have dismissed some stocks that prove to be very good performers “out-of-hand.” One example to my chagrin is Priceline, which for many years (year after year) showed up near the top of my spreadsheets for consideration. Looking back I tried to understand why I never bought it (I know I never seriously investigated it) – and I think I just dismissed it out of hand because it’s “one of those high flying internet stocks” and that surely meant it was going to blow up. Another more recent example has been the airlines. I had a mental bucket that simply said “airlines are terrible businesses. period.” Multiple airlines started showing up on some screens and I found myself not even giving them consideration. Ultimately I realized my bias (almost hit over the head with the observation of “wow, there’s a lot of airlines showing up – that’s weird” and belatedly made some money on LUV and currently JBLU, but it is difficult to short-circuit these types of thinking (I think researchers call it heuristics). When I look at individual stocks (this is in the non-mechanical portion of the portfolio), I try to more sensitive to those type of reactions as they might point to opportunity.

  • knowing your own faults is half the battle. The other half–arguably the hardest part–is going against your intuition…even when you know your intuition is incorrect. fascinating.