Fear and greed are deeply ingrained

Transcription

1 IVO PH. JANSEN is an associate professor of accounting at Rutgers University in Camden, NJ. ANDREI L. NIKIFOROV is an assistant professor of finance at Rutgers University in Camden, NJ. Fear and Greed: A Returns-Based Trading Strategy around Earnings Announcements IVO PH. JANSEN AND ANDREI L. NIKIFOROV Be fearful when others are greedy and greedy when others are fearful Warren Buffett Fear and greed are deeply ingrained in life, and they are fundamental attributes to the survival of man. Without the right dose of fear, we would expose ourselves to unreasonable threats; and without the right dose of greed, we would forego opportunities to secure the resources that we need to live. However, too much of either fear or greed is often harmful. For example, individuals who are overcome by fear will not be able to pursue opportunities that will help them secure the resources they need; and individuals who are overcome by greed will not recognize and avoid the threats to their existence. In other words, for optimum survival, fear and greed must be balanced. In financial markets, where prices are set by the interactions of thousands of individuals, greed for profits and fear of losses drive investors to make value assessments as carefully as possible, thus contributing to market efficiency. But in this setting also, there is the danger that excessive or unbalanced fear or greed can be harmful. Warren Buffett s quote above succinctly speaks to the dual nature of fear and greed in investing: In a market with too many greedy or fearful investors, aggressive buying or selling may cause an overreaction, at which point it is profitable to take a contrarian position. Many investors understand this, and the history of markets has countless examples of asset prices becoming irrationally high or low because of greed and fear. The challenge in trying to profit from this mispricing, of course, lies in timing. That is, it is unclear when the market will come to its senses and prices will revert to levels justified by fundamentals. It is difficult, therefore, to profitably implement Warren Buffett s advice, especially in the short-term. 1 In this article, we develop a trading strategy around earnings announcements that seeks to profit from predictable reversals of fear- and greed-driven price development in individual stocks. We argue that earnings announcements are logical events around which to center such a trading strategy, because they convey fundamental information about asset prices and thus have the potential to break irrational price development. Moreover, because of heightened information asymmetry in the period just before an earnings announcement, price development is likely to be particularly susceptible to excessive fear or greed. That is, if uninformed investors observe sharp price changes just before an earnings announcement, they may attribute these changes to the informed trading of insiders and start to trade excessively in the same direction themselves, thus causing an overreaction. 88 Fear and Greed: A RETURNS-BASED TRADING STRATEGY AROUND EARNINGS ANNOUNCEMENTS Summer 2016

2 We therefore predict in the spirit of Warren Buffett s advice that stocks that experience sharp price changes just before an earnings announcement will experience price reversal at the time of the announcement itself. We test this prediction with a trading strategy that on the earnings announcement date takes 1) a long position in stocks that experienced extreme negative abnormal returns in the week prior, and 2) a short position in stocks that experienced extreme positive abnormal returns in the week prior. We find that, over the two-day window of the earnings announcement date and the day following, both positions are highly profitable. On average, the long position earns abnormal returns of 1.49%, and the short position earns 1.20%. We furthermore show that these return reversals are about 60% larger than those around non earnings announcement dates, and thus are significantly more pronounced than short-term return reversals documented in the prior literature (e.g., Lehmann [1990] and Figelman [2007]). We also show that our strategy 1) is profitable in 40 of the 42 years in our sample; 2) is similarly profitable in bear and bull markets; and 3) is significantly profitable for both large firms and high volume stocks. According to our findings, since the year 2000 using a conservative transactions costs estimate of 70 bps for a round-trip trade our strategy generates abnormal returns of 0.76% after transaction costs, or 95% on an annualized basis. We therefore conclude that prices are subject to sentiment-driven price development in the period of elevated information asymmetry just before earnings announcements, and that the announcements themselves serve as a reality check on that price development. THEORY AND BACKGROUND The history of financial markets includes countless examples of asset prices rising or falling rapidly, and then suddenly reversing. This has been true for individual securities, certain industries, and entire asset classes. Unbalanced fear or greed in financial markets most commonly manifests itself as price momentum which according to Fama [1998] is one of the two most robust and persistent anomalies posing a challenge to the efficient market paradigm. 2 Although it is usually argued that price momentum is attributable to underreaction ( Jegadeesh and Titman [1993]), it often ultimately produces an overreaction (Lehmann [1990], Hong and Stein [1999], Hirschey [2003], and Figelman [2007]). This could be an overreaction to underlying fundamentals for example, Zarowin [1989] documents that firms with a string of good (bad) earnings news tend to become overpriced (underpriced); or it could be an overreaction to price/investor behavior for example, the indiscriminate selling of stocks in the wake of the financial crisis in The price momentum can occur over a period of months (e.g., Jegadeesh and Titman [1993]), or within a single day (e.g. Fabozzi et al. [1995], Schulmeister [2009]). The defining feature of unbalanced fear- and greed-driven price momentum, however, is the reversal that occurs afterwards. This price reversal thus offers an investment opportunity for contrarian investors, as captured in Warren Buffett s advice to be fearful when others are greedy and greedy when others are fearful. However, timing a price reversal is very difficult, and potentially very costly, even when investors are correct in their assessment of mispricing. Theoretical papers indeed demonstrate that even when rational investors are aware of mispricing in the market and take positions to exploit it, the mispricing can persist and may, in fact, get worse (De Long et al. [1990], and Shleifer and Vishny [1997]). 3 In this article, we argue that earnings announcements are natural candidates for the implementation of a contrarian trading strategy that seeks to exploit the reversal of fear- and greed-driven price momentum. First, the increase in information asymmetry preceding the earnings announcement makes it more likely that excessive sentiment affects pricing during that time. Second, the earnings announcement, by conveying fundamental information, serves as a reality check on that sentiment, making it more likely that price reversal will occur (if there is indeed mispricing). 4 Third, the earnings announcement date is known ex ante, which makes it possible to anticipate the price reversal and thus implement a trading strategy. These last two arguments are obvious. In this article, we elaborate on the first argument. The anticipation of earnings announcements makes investors wary about informed traders taking advantage of their inside knowledge. Market makers, for example, protect themselves by increasing bid ask spreads and reducing depths in the period before the earnings announcement (Lee, Mucklow, and Ready [1993]). Summer 2016 The Journal of Portfolio Management 89

3 Because there is usually very little public information about a stock in the week before an earnings announcement, large price movements are more likely attributable to the trading activities of investors with private information. Individual investors who tend to watch their portfolios very closely and over trade in them (Barber and Odean [2001], [2002a], and [2002b]) are probably particularly sensitive to the increase in information asymmetry in the pre-announcement period. We propose that individual investors, in particular, may respond to large price movements at this time by trading in the same direction, thus contributing to price momentum. Specifically, we argue that when individual investors observe a large price increase, they may infer that insiders know that the upcoming earnings announcement will reveal unexpected good news. Greedy to profit from this inference, the individual investor will jump on board, pushing the price up even further. Similarly, when individual investors observe a large price decrease, they may infer that insiders know that the upcoming announcement will reveal unexpected bad news. Fearful to suffer the corresponding losses, the individual investor will jump ship, pushing the price down even further. We argue that the resulting sentiment-driven price momentum produces an overreaction thus setting the stage for our contrarian trading strategy. TRADING STRATEGY AND METHODOLOGY We implement our strategy as follows. We first select stocks with extreme abnormal returns in the week before the earnings announcement (from Day 5 to 1, relative to the announcement date reported on Compustat). We use three different benchmarks for extreme abnormal returns during this window: 5%, 10%, and 15%. Note that we do not argue that all stocks in our trading strategy have experienced sentiment-driven price momentum in the pre-announcement window. We simply rely on the argument that among the stocks with large price increases, some have experienced greeddriven price momentum such that, on average, stocks with large price increases are overpriced; and among the stocks with large price decreases, some have experienced fear-driven price momentum such that, on average, stocks with large price decreases are underpriced. Then, on the day of the earnings announcement, we take a long position in stocks that experienced extreme negative abnormal returns, and a short position in stocks that experienced extreme positive abnormal returns. We continue to hold both positions until the next day in order to capture the abnormal returns for stocks that announce their earnings after trading hours. We estimate buy-and-hold abnormal returns (BHARs) using, as our benchmark, the contemporaneous return from the portfolio of firms in the same sizedecile. We use size-adjusted abnormal returns because firm size is often found to be a significant determinant of the profitability of trading strategies and the magnitude of anomalies. In sensitivity analyses, we also examined abnormal returns estimated relative to the market model and to the three-factor Fama French [1993] model. The results from those analyses are qualitatively similar to those reported in this study. DATA AND RESULTS We obtain quarterly earnings announcement dates and earnings information from Compustat (from the second half of 1971 to 2012) and volume, price, and firm-size information from CRSP. We focus only on common shares and eliminate all observations with missing data, as well as those securities not traded on the NYSE, AMEX, or NASDAQ. Because of restrictions that brokers impose on trading low-priced stocks (e.g., restricting short sales and/or buying on margin), we eliminate all stocks priced under $2 per share. Our total sample consists of 643,669 observations. Trading Strategy Abnormal Returns Exhibit 1 reports the abnormal returns from our trading strategy. The results are reported in three panels, corresponding to the three abnormal returns benchmarks we use during our screen window: 5%, 10% and 15%. The results in all three panels confirm the economic and statistical significance (p-value < in all cases) of the profitability of our trading strategy. For brevity, we focus our discussion on the results in panel B, where we screen for firms with abnormal returns in excess of ±10% in the week before the earnings announcement. There are 25,226 observations (about 3.9% of the total) that experience abnormal returns below negative 10%, and 37,568 observations (about 5.8% of the total) that experience abnormal returns above positive 10%. During the ( 5, 1) screen window, these stocks have average BHAR of 15.01% and 18.08%, respectively. 90 Fear and Greed: A RETURNS-BASED TRADING STRATEGY AROUND EARNINGS ANNOUNCEMENTS Summer 2016

4 E XHIBIT 1 Trading Strategy Abnormal Returns Notes: This exhibit reports buy-and-hold, size-adjusted abnormal returns. The screen window runs from Day 5 through Day 1, relative to the earnings announcement. The holding window runs from Day 0 through Day +1. Stocks priced under $2 are deleted. All reported returns in this exhibit are significant at the level. Consistent with our argument that this reflects, on average, at least some overreaction, the stocks indeed experience very significant return reversals in the subsequent (0,1) holding window. And so the long position in our trading strategy generates a profit of 1.49%, while the short position generates a profit of 1.20%. A weighted average between the two positions equals 1.32% which, on an annualized basis, translates into abnormal returns in excess of 160% before transaction costs. 5 E XHIBIT 2 Trading Strategy Abnormal Returns over Time Panels A and C further show that the profitability of our trading strategy is increasing in the magnitude of the abnormal returns benchmark during the screen window. The abnormal returns for the long positions increase from 0.80% in Panel A, to 1.49% in Panel B, reaching 2.26% in Panel C. The abnormal returns for the short positions similarly increase across the three panels from 0.68%, to 1.20% and, finally, to 1.74%. Trading Strategy Abnormal Returns over Time To investigate whether our trading strategy profits are driven by just a few stellar years, we calculate the average holding-window abnormal returns for each year in our sample for the stocks that experienced at least ±10% abnormal returns during the screen window. The results reported in Exhibit 2 show that the long and short positions are profitable in 40 and 38 years, respectively, of the 42 years in our sample period. Moreover, the weighted average of the two legs of the strategy is profitable in 40 of the 42 years. Although the preceding results suggest that the trading strategy is profitable in both bull and bear markets, we explicitly investigate its profitability as a function of market sentiment in Panel A of Exhibit 3. 6 This investigation is relevant not only because many anomalies show different returns depending on market sentiment, but also because our study is a specific investigation of sentiment-driven price development. We find Notes: This exhibit reports buy-and-hold, size-adjusted abnormal returns by year. The stocks in this figure experienced screen-window abnormal returns in excess of 10% positive/negative (cf., Panel B in Exhibit 1). Stocks with prices less than $2 are deleted. Summer 2016 The Journal of Portfolio Management 91

5 E XHIBIT 3 Robustness of the Trading Strategy Abnormal Returns Notes: This exhibit reports buy-and-hold, size-adjusted abnormal returns over a (0,+1) holding window relative to the earnings announcement. The stocks in this exhibit experienced screen-window abnormal returns in excess of 10% positive/negative (cf., Panel B in Exhibit 1). In Panel A, we assess the robustness of the trading strategy abnormal returns to periods of bull-versus-bear markets. We define bear markets consistent with J.P. Morgan Asset Management. They identify the following bear markets during our sample period: January 5, 1973 October 3, 1974; November 28, 1980 August 12, 1982; August 25, 1987 December 4, 1987; March 24, 2000 October 9, 2002; and October 9, 2007 March 9, All other dates are bull markets. In Panel B, we assess the robustness of the trading strategy abnormal returns to firm size. Stocks are assigned by year to three size portfolios, in which the portfolio cutoffs correspond to the 33rd percentile and 67th percentile of NYSE market caps. Finally, in Panel C, we assess the robustness of the trading strategy abnormal returns to liquidity, as measured by $-trading volume. Stocks are assigned by year to three equal-sized $-Volume portfolios, based on total dollar volume during the holding window. Stocks with prices less than $2 are deleted. All reported returns in this exhibit are significant at the level, except for the 0.48% in Panel B, which is significant at the level. that the long position generates slightly higher profits in bull markets (1.63% versus 1.12%), but the short position generates slightly higher profits in bear markets (1.63% versus 1.08%). A weighted-average abnormal return between the long and short positions (not reported) shows that the trading strategy s profitability is actually a little bit higher in bear than in bull markets (1.40% versus 1.29%). This finding compares very favorably with the profitability of most well-known trading strategies, which earn positive returns in bull markets but tend to earn negative returns in bear markets. Trading Strategy Abnormal Returns, Firm Size, and Liquidity To address the concern that our strategy s returns might be driven by small and/or illiquid firms, we investigate the profitability of our trading strategy after partitioning the sample into size terciles and dollar-volume terciles. We do so, once again, after limiting our investigation to those stocks that experienced at least ±10% abnormal returns during the screen window. Panel B of Exhibit 3 presents the results of our trading strategy for size terciles based on NYSE breakpoints. Small firms indeed generate the highest holding-period returns, but the returns for medium and large firms are also statistically and economically significant. For example, the long position for large firms generates a profit of 1.40%, while the short position earns a profit of 0.48% over two days. Thus, Panel B shows that the trading strategy profits are not simply driven by small firms. Panel C of Exhibit 3 presents the results of our trading strategy across dollar-volume terciles. To create these terciles, we first divided our original sample into three equal-sized dollar-volume portfolios based on total dollar volume during the holding window, after which we identified stocks that experienced at least ±10% abnormal returns during the screen window. Not surprisingly, the least liquid stocks experienced the greatest return reversals during the holding window; 92 Fear and Greed: A RETURNS-BASED TRADING STRATEGY AROUND EARNINGS ANNOUNCEMENTS Summer 2016

6 but, once again, even for the most liquid stocks, our trading strategy is highly profitable, generating a 1.31% profit in the long, and a 0.4% profit in the short position. Thus, liquidity concerns do not prevent a profitable implementation of the trading strategy. Trading Strategy Profitability and Transaction Costs The consistent profitability of our trading strategy over the last four decades clearly suggests that fear and greed affects pricing in the week preceding earnings announcements. To assess implementability, however, we need to consider transaction costs. 7 We do so by focusing on the strategy s profitability since Estimates of transaction costs vary across sources. For example, Stoll [2006] estimates transaction costs at 28 bps for a round-trip trade in Elkins McSherry, a global expert in trade-cost analysis, estimates the average costs at 66 basis points in 2007 and at 53 basis points in (Pollin and Heintz [2011]). Finally, ITG Global Cost Review estimates that transaction costs range from 43 to 55 basis points between 2009 and In comparison, we use a relatively conservative estimate of transaction costs of 70 basis points for a round-trip trade to assess the implementability of our trading strategy. From 2000 through 2012, the net profit of our strategy that is, after subtracting transaction costs of 0.7% is 0.49% for the long position and 0.96% for the short position. These abnormal returns are statistically significant at the level. Moreover, the annualized abnormal returns would range from 60% for the long to more than 120% for the short position. Based on a weighted average of the long and short positions, the strategy would 1) generate a net profit of 0.76% from 2000 through 2012, for an annualized return of 95%, and 2) be profitable on a net basis in every single year. Additional Robustness Tests We discuss the results of several additional robustness tests in this section. They are not tabulated here, but are available online. First, we investigate whether our results are different from the generic, weekly return reversal patterns documented in Lehmann [1990]. To this end, we estimate return reversals from implementing our trading strategy around five equally spaced, non earnings announcement dates in the same fiscal quarter: 2, 4, 6, 8, and 10 weeks before the actual earnings announcement. Our results show that the return reversal around the actual earnings announcement date is about 60% higher than around non earnings announcement dates. These differences are highly statistically and economically significant. Second, we document the robustness of our trading strategy returns to alternative screen and holding windows. We find that the trading strategy becomes slightly more profitable when extending the holding window up to Day +5. However, we also find that further extending the holding window to Day +10 decreases profitability. When we extend the screen window to start on Day 7, 10, or 20, instead of on Day 5, we find that the trading strategy becomes less profitable the further we extend the screen window. Finally, we shift both the screen and holding windows, by moving Day 1 from the former to the latter. We do so to allow for the possibility that inside information leakage on the day immediately before the earnings announcement has already caused return reversal at that time. The results indicate that this is the case. Using a ( 5, 2) screen, and a ( 1,1) holding window, significantly increases the strategy s profitability: from 1.49% to 2.36% for the long, and from 1.20% to 1.59% for the short position. 8 Moreover, based on these windows, both legs of the trading strategy are profitable in each of the last 42 years. This is a remarkable finding that first of all speaks to the statistical significance of the trading strategy s profitability, but especially speaks to its economic significance. To our knowledge, there is no anomaly or other trading strategy including post-earnings-announcement drift that has been consistently profitable for such a long time. CONCLUSION This study documents that a contrarian trading strategy around earnings announcements, in stocks that experienced extreme abnormal returns in the week preceding the announcements, is highly profitable. We attribute the significant price reversal to a sentimentdriven overreaction in stock prices in the pre-announcement period. Specifically, we argue that because of heightened information asymmetry just before earnings announcements, individual investors in particular trade in the direction of observed price changes, contributing to price-momentum that ultimately results in Summer 2016 The Journal of Portfolio Management 93

7 overreaction. Then, when the earnings are announced and information asymmetry is reduced, the overreaction leads to price reversal. We find that our trading strategy generates abnormal returns of 1.3% over a two-day window, which is more than 160% on an annualized basis. After transaction costs, and since the year 2000, the annualized abnormal returns equal 95%. We also document that our trading strategy is similarly profitable in bull and bear markets, and is robust to firm size and liquidity. ENDNOTES We thank an anonymous referee, Brandon Cline, Lee Sanning, and Uzi Yaari for helpful comments and suggestions. 1 Buffett s advice is clearly intended as an investment strategy for the long-term; not the short-term. However, the reality is that most investors face powerful short-term performance pressures. For example, Stan Druckenmiller, the lead money manager at George Soros Quantum Fund, closed all his long positions in dot-com stocks in February 2000 based on the (correct) belief that dot-com prices reflected a bubble. Weeks later, however, after prices had continued their run-up and his performance was significantly lagging behind that of his colleagues, Druckenmiller reclaimed those long positions. A few weeks later, the bubble burst. 2 The other one is post-earnings announcement drift that is, the tendency of stock prices to drift upward (downward) after surprisingly good (bad) earnings news. 3 An instructive example is the case of Long-Term Capital Management (LTCM), which bet on the convergence of Royal Dutch Petroleum and Shell Transport. These two companies co-own Royal Dutch/Shell and receive their income from the same sources. In other words, there is no theoretical justification for their prices to diverge. LTCM took a position to exploit this mispricing when the divergence was 8%, but was later forced to liquidate the position when divergence had increased to 22% (Lamont and Thaler [2003]). 4 Many studies in accounting research indeed document that at the time of an earnings announcement, stock prices move in the same direction as the earnings surprise (e.g., Wilson [1987], Collins and Kothari [1989]). 5 We annualize abnormal returns by multiplying the two-day holding window return by 126, because a typical year has 126 such windows (i.e., 252 trading days). 6 We define bull and bear markets consistent with J.P. Morgan Asset Management. cm/blobserver/guide_to_the_markets_4q_2011.pdf. 7 Recall that we eliminate all stocks priced under $2 from our analyses. Transaction costs (as well as returns) are often particularly high for small-priced stocks. Those stocks, however, are not included in this study. 8 A further shift to include Day 2 in the holding window as well, leads to a significant decline in the strategy s profitability. REFERENCES Barber, B., and T. Odean. Boys will be Boys: Gender, Overconfidence, and Common Stock Investment. Quarterly Journal of Economics, Vol. 116, No. 1 (2001), pp Trading is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors. Journal of Finance, Vol. 55, No. 2 (2002a), pp Online Investors: Do the Slow Die First? Review of Financial Studies, Vol. 15, No. 2 (2002b), pp Collins, D.W., and S.P. Kothari. An Analysis of Intertemporal and Cross-sectional Determinants of Earnings Response Coefficients. Journal of Accounting and Economics, Vol. 11, No. 2-3 (1989), pp De Long, J.B., A. Shleifer, L. Summers, and R. Waldmann. Noise Trader Risk in Financial Markets. Journal of Political Economy, 98 (1990), pp Fabozzi, F.J., C.K. Ma, W.T. Chittenden, and R.D. Pace. Predicting Intraday Price Reversals. The Journal of Portfolio Management, Vol. 21, No. 2 (1995), pp Fama, E.F. Market Efficiency, Long-Term Returns, and Behavioral Finance. Journal of Financial Economics, 49 (1998), pp Fama, E.F., and K.R. French. Common Risk Factors in the Returns on Bonds and Stocks. Journal of Financial Economics, 33 (1993), pp Figelman, I. Stock Return Momentum and Reversal. The Journal of Portfolio Management, Vol. 34, No. 1 (2007), pp Hirschey, M. Extreme Return Reversal in the Stock Market. The Journal of Portfolio Management, Vol. 29, No. 3 (2003), pp Hong, H., and J. Stein. A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets. Journal of Finance, 54 (1999), pp Fear and Greed: A RETURNS-BASED TRADING STRATEGY AROUND EARNINGS ANNOUNCEMENTS Summer 2016

8 Investment Technology Group. Global Cost Review Q1/2013. ITG Peer Analysis, Jegadeesh, N., and S. Titman. Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48 (1993), pp Lamont, O., and R. Thaler. Can the Market Add and Subtract? Mispricing in Tech Stock Carve-Outs. Journal of Political Economy, 111 (2003), pp Lee, C., B. Mucklow, and M. Ready. Spreads, Depths, and the Impact of Earnings Information: an Intraday Analysis. Review of Financial Studies, 6 (1993), pp Lehmann, B. Fads, Martingales, and Market Efficiency. Quarterly Journal of Economics, Vol. 105, No. 1 (1990), pp Pollin, R., and J. Heintz. Transaction Costs, Trading Elasticities and the Revenue Potential of Financial Transaction Taxes for the United States. PERI Research Brief, University of Massachusetts Amherst, fileadmin/pdf/research_brief/peri_ftt_research_brief. pdf. Schulmeister, S. Profitability of Technical Stock Trading: Has It Moved From Daily to Intraday Data? Review of Financial Economics, Vol. 18, No. 4 (2009), pp Shleifer, A. and R.W. Vishny. The Limits of Arbitrage. Journal of Finance, 52 (1997), pp Stoll, H.R. Electronic Trading in Stock Markets. Journal of Economic Perspectives, Vol. 20, No. 1 (2006), pp Wilson, G.P. The Relative Information Content of the Accrual and Funds Component of Earnings after Controlling for Earnings. Accounting Review, 62 (1987), pp Zarowin, P. Does the Stock Market Overreact to Corporate Earnings Information? Journal of Finance, 44 (1989), pp To order reprints of this article, please contact Dewey Palmieri at dpalmieri@iijournals.com or Summer 2016 The Journal of Portfolio Management 95