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Predicting Earnings Surprises: Some Useful Tools for a High-Stakes Game Do Earnings Surprises Even Matter?

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Predicting Earnings Surprises: Some Useful Tools for a High-Stakes Game Do Earnings Surprises Even Matter? Rochester Cahan Yu Bai 212 803-7973 212 803-7919 September 13, 2019 Stock Selection: Research and Results September 2019 Predicting Earnings Surprises: Some Useful Tools for a High-Stakes Game Do Earnings Surprises Even Matter? Do earnings surprises even matter? Unfortunately, the answer is yes, which is good for purveyors of caffeine but bad for the stock pickers who have to struggle through late-night model updates and interminable earnings calls four times a year. In the post-Crisis era the median stock has generated about a fifth of its annual relative return on the four days it announces earnings, a statistic that’s higher than it has been in past decades. The re- sult holds irrespective of whether the stock outperformed or underperformed over the year. At the same time, the portion of a stock’s annual return that’s produced in the week after earnings (excluding the announcement day itself) hasn’t changed much this decade and remains fairly insignificant. That means earnings reports are more consequential to a stock’s longer-term performance, but most of the action occurs on the day itself and there’s very little post-announcement drift. Today’s market is frightfully efficient and new information gets discounted almost immediately. To profit from earnings these days one either needs to be the first to react (i.e., probably be a robot) or one has to call the surprise correctly ahead of time. No Country for Old Surprises To make matters worse, predicting earnings surprises has become harder because there’s been a complete breakdown in the autocorrelation of surprises. Autocorrelation is just a statistic that measures the likelihood that an earnings beat will be followed by another beat next quarter (or a miss by another miss). Over the past five years the average autocorrelation in earnings surprises across all stocks has been exactly zero. However, there is one cohort of stocks that’s bucked the trend: serial beaters. In the 1990s serial beaters – which we define as stocks beating earnings for eight consecutive quarters – produced about 20% of the earn- ings beats in a given quarter. Now they account for close to 40% of all beats market-wide. Autocorrelation has vanished for most stocks, but an elite cohort of winners just keeps on winning. Unsurprisingly many of these firms are drawn from the technology sector, so what we may be capturing here is the ongoing strength of de- mand rather than a behavioral anomaly. Some Tools for Spotting Earnings Surprises Luckily, the collapse in autocorrelation doesn’t mean earnings surprises are completely unpredictable, it just means we need to inject additional information into the problem beyond what happened in previous quarters. We spent some time digging through the wide range of factors that we keep in our toolbox, to see if there are any that can help call the direction of earnings surprises. We found a few that can improve our win rate in predicting earnings surprises. However, win rates aren’t alpha and what ultimately matters is returns, which means it’s important to find stocks that surprise and move in the direction of the surprise. It turns out our Failure Model, which has been in live use for 15 years now, is hard to beat in that regard. It’s been good at identifying earnings blowups ahead of time and its Failure Candidates are almost 70% more likely to suffer a really big earnings blowup compared to a random stock picked from the market. Each week in earnings season we circulate a list of stocks reporting that week that screen as Failure Candidates. Let your salesperson know if you’d like to get that. Overall, our work in this report has reaffirmed our view that avoiding disasters is one of the best ways to use quantitative tools in a fundamental process. These days the market’s response to an earnings miss is swift and devastating so even sidestepping a few of them can make a big difference. Sungsoo Yang (212) 803-7925 Nicole Price (212) 803-7935 Yi Liu (212) 803-7942 Iwona Scanzillo (212) 803-7915 © 2019, Empirical Research Partners LLC, 565 Fifth Avenue, New York, NY 10017. All rights reserved. The information contained in this report has been obtained from sources believed to be reliable, and its accuracy and completeness is not guaranteed. No representation or warranty, ex- press or implied, is made as to the fairness, accuracy, completeness or correctness of the information and opinions contained herein. The views and other information provided are subject to change without notice. This report is issued without regard to the specific investment objectives, fi- nancial situation or particular needs of any specific recipient and is not to be construed as a solicitation or an offer to buy or sell any securities or related financial instruments. Past performance is not necessarily a guide to future results. Stock Selection: Research and Results September 2019 Conclusions in Brief This decade more of a stock’s annual return has come from …And the increase has been larger for losers than winners: earnings announcement days… Large-Capitalization Outperformers¹ Large-Capitalization Outperformers and Underperformers¹ Median Share of Annual Relative Returns that Occured on Earnings Median Share of Annual Relative Returns that Announcement Days² Occured Around an Earnings Announcement² % 2002 Through Late-August 2019 % 2003 Through 2007 and 2009 Through Late-August 2019 25 20 15 20 10 5 15 0 10 (5) (10) 5 (15) Week Before Announcement Next Week Week Before Announcement Next Week Day Day Outperformers Underperformers 0 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 2003-07 2009-19 -to- Source: Empirical Research Partners Analysis. Date ¹ Stocks that outperformed or underperformed the equally-weighted market over the calendar year by more than ±1% Source: Empirical Research Partners Analysis. respectively. ¹ Stocks that outperformed the equally-weighted market over the calendar year by more than +1%. ² For earnings announcements outside of market hours the first trading day is used. Week before and next week exclude ² For earnings announcements outside of market hours the first trading day is used. the announcement-day return. Autocorrelation in earnings surprises has collapsed… …Except for an elite group of serial beaters: Large-Capitalization Stocks Large-Capitalization Stocks Average Autocorrelation of Quarterly Earnings Surprises¹ that Beat Earnings 1996 Through Late-August 2019 Share by Number of Consecutive Beats % % 1996 Through Late-August 2019 10 40 35 8 30 6 25 20 4 15 2 10 0 5 0 (2) 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 Two Quarters in a Row Eight or More Quarters in a Row Source: Empirical Research Partners Analysis. ¹ Autocorrelation based on earnings surprises over the prior 12 quarters. Source: Empirical Research Partners Analysis. Some quant factors, many of which capture sentiment, can …B ut the F ailure M odel is hard to beat: help us predict surprises… Large-Capitalization Failure Candidates Large-Capitalization Stocks Number of Big Earnings Misses Identified Ratio of Beats-to-Misses in the Best Quintile of Select Factors Relative to the Base Rate¹ Relative to that in the Worst Quintile: Top Ten x 1996 Through August 2019 x 1996 Through Early-September 2019 3.0 2.8 2.6 2.5 2.4 2.2 2.0 2.0 1.8 1.5 Parity 1.6 1.4 1.0 1.2 1.0 0.5 0.8 Earnings Earnings Market Nine- Media Free Growth ROE Short Failure Revisions Estimate Reaction Month Sentiment Cash Score Pressure Model Dispersion Super Price (since Flow (Since Factor Trends 2002) Margin 2007) 0.0 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 Whole Period Since 2010 Source: Empirical Research Partners Analysis. Source: Empirical Research Partners Analysis. ¹ Big misses are defined as those inthe worst decile of earnings surprises each quarter. 2 Stock Selection: Research and Results September 2019 Predicting Earnings Surprises: Some Useful Tools for a High-Stakes Game Do Earnings Surprises Even Matter? For as long as most of us can remember the rhythm of our industry – and indeed professional life (some might have to drop the professional qualifier) – has marched to the beat of quarterly earnings. Four times a year things cre- scendo in a caffeinated blur of conference calls, obscure footnotes, late-night model updates and cold pizza. Then, just when you’ve caught your breath – and some sleep – it’s time to start prepping for the next one. Does any of this make any sense? After all, technology has vastly increased the rate at which information can be digested and it’s not clear one really needs to listen to every word of the earnings call when the robots are already eavesdropping on the line.1 Unfortunately, the importance of the earnings announcement day to the performance of individual stocks has been rising, not declining. One way to see this is to plot how much of a stock’s annual return is generated on those days. Exhibit 1 shows the results for stocks that outperformed the market over a calendar year. In the past five years the median outperformer generated almost a fifth of its annual return on the four days when it released earnings. That’s significantly higher than what was seen in the prior cycle.
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