In the paper, "Driven to Distraction: Extraneous Events and Underreaction to Earnings News" (Hirshleifer, Lim, Teoh, 2009, Journal of Finance 64, 2289-2325), the authors compare Post Earnings Announcement Drift for stocks that announce earnings during peak earnings season and stocks that announce earnings when there are fewer other competing announcements. Post Earnings Announcement Drift (PEAD) refers to the anomaly that when a company beats earnings, not only does the stock rise on the earnings announcement (which isn’t surprising but is difficult to predict), but also drifts higher for weeks after the announcement. Similarly, when a company misses earnings, the stock price drifts lower for weeks after the announcement (for one of the early papers documenting this phenomenon, see Bernard and Thomas here). The authors argue that for companies that report earnings when there are fewer other companies reporting at the same time, there is a strong price reaction on the earnings announcement, but only a small PEAD. But for companies that announce during periods when there are many other simultaneous announcements, the earnings announcement effect is muted and there is a much stronger PEAD. Their argument is that investors have limited attention and get distracted by the competing earnings announcements, which would explain both the muted announcement effect and the strong post announcement effect. Of course, because it's an academic paper, they give many references to show that people get distracted, including a reference to a study showing that British drivers get distracted when seeing semi-naked models on billboards (and the paper's title, "Driven to Distraction:..." is a play on this).
Discussion and Digressions
I don't find the explanation of limited attention particularly appealing (for example, it seems implausible that limited attention to an earnings release would last for weeks), but I do believe the results could be driven by limited capital, not limited attention. For example, if you are a trader in the utilities sector at a hedge fund (and most portfolio managers specialize in a particular sector) and one day during peak earnings season, four utilities that you follow all report earnings on the same day, you may not have the capital to buy two or three of the stocks.
Limited capital may be an explanation for other anomalies too. For example, there is a cottage industry in the hedge fund world of trading stocks in front of expected large index fund flows. In the early 2000's, when S&P announced that a company was added to the S&P500 index, the stock would, on average, go up 5% on the announcement, and go up another 3% between the announcement and when it was added (usually, the announcement precedes the "effective date" by a week). See, for example, Chen, Noronha, Singal, "Index Changes and Losses to Index Fund Investors". But this anomaly of one-off adds to the S&P500 has largely gone away - there is a much smaller announcement effect nowadays and the movement on the effective day is close to zero. However, for large index rebalances like the annual Russell rebalance, the anomaly, although much smaller than it used to be, still seems to exist. One explanation is that because of limited capital by hedge funds, they can take the other side of index fund flows for a one-off S&P500 add for several billion dollars, but in aggregate don't have the capital to take the other side when index funds are trading hundreds of billions of dollars on a single day.
To digress a little further, another anomaly that doesn't seem to work anymore, but maybe can be revived with this idea of limited capital, is the reversal from extreme one-day price drops. A much older study (see Bremer and Sweeney "The Reversal of Large Stock-Price Decreases"), finds that following a stock price drop of 10% or more, stocks reversed by about 2% the next day. This anomaly has gone away, and in fact, a very simple Quantopian backtest shows that it not only has gone away but performs horribly since 2007 (this has interesting implications, incidentally, for how to trade a typical five-day mean reversion strategy). But if you look at only days where there are numerous stocks dropping more than 10%, the strategy works better (unfortunately, almost all the trades are in one time period, during the financial crisis, so it's not very robust).
For the "Driven to Distraction" paper, I backtested their strategy out of sample from 2007-2016. For stocks that announced earnings on days when many other companies announced, I went long PEAD by buying stocks that had large earnings beats and shorting stocks that had large earnings misses. On low earnings announcement days, I do the opposite PEAD trade: I shorted large earnings beaters and went long large earnings missers. I used the Q500US Universe (and also screening, of course, by companies that have estimates of earnings), and used the quintile of earnings beats and misses, and the quintile of high and low announcement companies. The authors define beats and misses in what I think is a very sensible way: (actual earnings-expected earnings)/Price (Zacks has a measure that is normalized by earnings instead of price: (actual earnings-expected earnings)/(expected earnings), which I don't think makes as much sense). I also put together a short notebook that explains how I got the quintile cutoffs that I used in the algo. I’ll post that shortly under the same thread.
I would love to start a tradition on Quantopian where people post at the end of their strategies a section that addresses data mining issues, including things they tried that may not have worked. Also, with any strategy, there are many other choices that must be made: start date, universe, beta neutral vs. dollar neutral, decile vs. quintile sorts, holding periods, equal vs. non-equal weighting, sector, industry, or other factor limits, time of day for execution, ... These are all essentially parameters that can lead to data mining, so a discussion about how these choices were made would be useful also. In academia, the goal is to get something published, which often involves data mining, but in trading, the goal is to find strategies that will make money out of sample. As a Portfolio Manager at my previous hedge funds, I would certainly ask these same questions to anyone that worked for me and backtested a trading strategy.
Along those lines, in the case of this model, the biggest area of data mining, and deviation from the original paper, was in the holding period for PEAD. The paper argues that a 60 day PEAD holding period was optimal, and I tried one other holding period, 20 days, and got significantly better results (the Sharpe Ratio was about a third lower when I used a 60 day PEAD holding period). I tried other cutoffs, including decile cutoffs – the results were consistently positive but can vary considerably, even for small changes. Since the algo holds a relatively small number of positions, small changes in the threshold for earnings beats or the threshold for high announcement days can change the composition of the portfolio considerably. The strategy I tested was dollar neutral rather than beta neutral, and the beta turned out to be negative, so making it beta neutral (in a rising market) might slightly improve the results. One thing that disappointed me with the results is that, although you make money on both the long PEAD side on high announcement days and the short PEAD side on low announcement days, the results are much more driven by the short PEAD side.
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|Sortino||1 Month||3 Month||6 Month||12 Month|
|Volatility||1 Month||3 Month||6 Month||12 Month|
|Max Drawdown||1 Month||3 Month||6 Month||12 Month|