Post-Earnings Announcement Effect

Post-earnings announcement drift, or PEAD is the tendency for a stock’s cumulative abnormal returns to drift for several weeks (even several months) following positive earnings announcement. It is an academically well-documented anomaly first discovered by Ball and Brown in 1968 (we present links to several related academic research papers). Since then it has been studied and confirmed by countless academics in many international markets. There are a number of ways to define an earnings surprise (or ways to filter stocks with positive response to earnings) - earnings higher than analysts estimates, earnings higher than some average earnings or stock’s price appreciation during earnings announcement period higher than expected. Each factor shows strong prediction ability for the stock’s future returns, and it is good to use some combination of factors just to enhance the PEAD effect. We present one such strategy from the source paper related to this anomaly. This strategy is presented in its long-short form, but most of the returns come from the long side so it is not a problem to implement it as long only. Research also shows that the main perfmorance contributors are small capitalization stocks therefore caution is recommended during the strategy’s implementation.

Fundamental reason

This phenomenon can be explained with a number of hypotheses. The most widely accepted explanation for this effect is investors' under-reaction to earnings announcements. It is also widely believed that there is a strong connection between earnings momentum and price momentum. Several studies also show earnings momentum could be explained by liquidity risk as the Post-Earnings Announcement Effect appears to be strong in small cap stocks.

Markets traded
Confidence in anomaly's validity
Notes to Confidence in anomaly's validity
Period of rebalancing
Notes to Period of rebalancing
Number of traded instruments
Notes to Number of traded instruments
more or less, it depends on investor's need for diversification
Complexity evaluation
Complex strategy
Notes to Complexity evaluation
Strategy complexity depends on number of stocks investor wishes to include into his/her portfolio, as strategy could be much simpler for execution if investor picks less stocks.
Financial instruments
Backtest period from source paper
Indicative performance
Notes to Indicative performance
per annum, strategy’s performance calculated as annualized (geometrically) 60 days return (2.97%-(-0.58%)) for long short portfolio from table 7.
Estimated volatility
not stated
Notes to Estimated volatility
Maximum drawdown
not stated
Notes to Maximum drawdown
Sharpe Ratio
not stated


equity long short, earnings announcement, financial statements effect, stock picking

Simple trading strategy

The investment universe consists of all stocks from NYSE, AMEX and NASDAQ except financial and utility firms and stocks with prices less then $5. Two factors are used: EAR (Earnings Announcement Return) and SUE (Standardized Unexpected Earnings). SUE is constructed by dividing the earnings surprise (calculated as actual earnings minus expected earnings, expected earnings are computed using a seasonal random walk mode with drift) by the standard deviation of earnings surprises. EAR is the abnormal return for firm recorded over a three-day window centered on the last announcement date, in excess of the return of a portfolio of firms with similar risk exposures.
Stocks are sorted into quintiles based on the EAR and SUE. To avoid look-ahead bias, data from the previous quarter are used to sort stocks. Stocks are weighted equally in each quintile. The investor goes long stocks from intersection of top SUE and EAR quintiles and goes short stocks from the intersection of the bottom SUE and EAR quintiles the second day after actual earnings announcement and holds the portfolio one quarter (or 60 working days). The portfolio is rebalanced every quarter.

Source Paper

Brandt, Kishore, Santa-Clara, Venkatachalam: Earnings Announcements are Full of Surprises
We study the drift in returns of portfolios formed on the basis of the stock price reaction around earnings announcements. The Earnings Announcement Return (EAR) captures the market reaction to unexpected information contained in the company’s earnings release. Besides the actual earnings news, this includes unexpected information about sales, margins, investment, and other less tangible information communicated around the earnings announcement. A strategy that buys and sells companies sorted on EAR produces an average abnormal return of 7.55% per year, 1.3% more than a strategy based on the traditional measure of earnings surprise, SUE. The post earnings announcement drift for EAR strategy is stronger than post earnings announcement drift for SUE. More importantly, unlike SUE, the EAR strategy returns do not show a reversal after 3 quarters. The EAR and SUE strategies appear to be independent of each other. A strategy that exploits both pieces of information generates abnormal returns of about 12.5% on an annual basis.

Other Papers

Chan, Jagadeesh, Lakonishok: Momentum strategies
We relate the predictability of future returns from past returns to the market's underreaction to information, focusing on past earnings news. Past return and past earnings surprise each predict large drifts in future returns after controlling for the other. There is little evidence of subsequent reversals in the returns of stocks with high price and earnings momentum. Market risk, size and book-to- market effects do not explain the drifts. Security analysts' earnings forecasts also respond sluggishly to past news, especially in the case of stocks with the worst past performance. The results suggest a market that responds only gradually to new information.

Liu, Strong, Xiu: Post-earnings-announcement Drift in the UK
This paper fills a void in the market efficiency literature by testing for the presence of post-earnings announcement drift in the non-US market. We test for drift using alternative earnings surprise measures based on: (i) the time-series of earnings; (ii) market prices; and (iii) analyst forecasts. Using each of the measures we find evidence of significant post-earnings-announcement drift, robust to alternative controls for risk and market microstructure effects. Using a one-dimensional analysis, the price-based measure of earnings surprise gives the strongest drift, and using a two-dimensional analysis the drift associated with the price-based measure almost subsumes drift associated with the other two measures. Our conclusion is that the UK stock market is inefficient with respect to publicly available corporate earnings information. This evidence provides out-of-sample confirmation of the post-earnings-announcement drift documented in the US.

Sadka: Momentum and Post-Earnings-Announcement Drift Anomalies: The Role of Liquidity Risk
This paper investigates the components of liquidity risk that are important for asset-pricing anomalies. Firm-level liquidity is decomposed into variable and fixed price effects and estimated using intraday data for the period 1983-2001. Unexpected systematic (market-wide) variations of the variable component rather than the fixed component of liquidity are shown to be priced within the context of momentum and post-earnings-announcement drift (PEAD) portfolio returns. As the variable component is typically associated with private information (e.g., Kyle (1985)), the results suggest that a substantial part of momentum and PEAD returns can be viewed as compensation for the unexpected variations in the aggregate ratio of informed traders to noise traders.

Garginkel, Sokobin: Volume, Opinion Divergence and Returns: A Study of Post-Earnings Announcement Drift
This paper examines the relationship between post-earnings announcement returns and different measures of volume at the earnings date. We find that post-event returns are strictly increasing in the component of volume that is unexplained by prior trading activity. We interpret unexplained volume as an indicator of opinion divergence among investors and conclude that post-event returns are increasing in ex-ante opinion divergence. Our evidence is consistent with Varian (1985) who suggests that opinion divergence may be treated as an additional risk factor affecting asset prices.

Chordia, Shivakumar: Earnings and Price Momentum
This paper examines whether earnings momentum and price momentum are related. Both in time-series as well as in cross-sectional asset pricing tests, we find that price momentum is captured by the systematic component of earnings momentum. The predictive power of past returns is subsumed by a zero-investment portfolio that is long on stocks with high earnings surprises and short on stocks with low earnings surprises. Further, returns to the earnings-based zero-investment portfolio that is long on stocks with high earnings surprises. Further, returns to the earnings-based zero-investment portfolio are significantly related to future macroeconomic activities, including growth in GDP, industrial production, consumption, labor income, inflation, and T-bill returns. Our results have implications for the Carhart four-factor model and suggest that the price-momentum factor in the Carhart model is merely a noisy proxy for the earnings-momentum based hedge portfolio, PMN.

Barbosa: Differential Interpretation of Information and the Post-Announcement Drift: A Story of Consensus Learning
I show how a post-announcement drift can be generated in a model with fully rational investors who interpret public information differently. Differential interpretation of information transforms public raw information into private interpreted information. If investors recognize their limited ability to interpret information, they will look for other investors’ opinions in prices. Noise trading prevents investors from learning the market consensus interpretation of the announcement from the observation of a single price. But if noise trading follows a mean-reverting process, investors can gradually learn the market consensus from the observation of a series of prices. As investors become more confident about their interpretation of the announcement, they put more weight on it, and the announcement is gradually incorporated into prices, which generates a post-announcement drift. The model accounts for all salient empirical facts related to the post-announcement drift and delivers two new testable implications. If, in addition, investors make mistakes in extracting information from prices, the model also generates momentum.

Dechow, Sloan, Zha: Stock Prices & Earnings: A History of Research
Accounting earnings summarize periodic corporate financial performance and are a key determinant of stock prices. We review research on the usefulness of accounting earnings, including research on the link between accounting earnings and firm value and research on the usefulness of accounting earnings relative to other accounting and non-accounting information. We also review research on the features of accounting earnings that make it useful to investors, including the accrual accounting process, fair value accounting and the conservatism convention. We finish by summarizing research that identifies situations in which investors appear to misinterpret earnings and other accounting information leading to security mispricing.

Barinov, Park, Yildizhan: Firm Complexity and Post-Earnings-Announcement Drift
The paper shows that the post-earnings-announcement drift is stronger for conglomerates, despite conglomerates being larger, more liquid, and more actively researched by investors. We attribute this finding to slower information processing about complex firms and show that the post-earnings-announcement drift is positively related to measures of conglomerate complexity. We also find that the post-earnings-announcement drift is stronger for new conglomerates than it is for existing conglomerates and that investors are most confused about complicated firms that expand from within rather than firms that diversify into new business segments via mergers and acquisitions.

Alwathnani, Dubofsky: It's All Overreaction: The Post Earnings Announcement Drift
In this paper, we test whether the well-documented post-earnings-announcement drift is a manifestation of an investor overreaction to extremely good or bad earnings news rather than a market underreaction, as it is commonly interpreted. Using the market reaction to the extreme earnings surprises (i.e. SUE) in quarter Qt as a reference point, we show that firms reporting SUE in quarter Qt+1 that confirms their initial SUE rankings in the highest or lowest SUE quintiles for the second consecutive quarter generate an incremental price run that moves in the same direction as that of the initial SUE. However, the price impact of the confirming SUE signal is weaker than that of its initial SUE counterpart. On the other hand, firms reporting disconfirming SUE that fails these firms to keep (move out of) their positions in the top (bottom) SUE quintile experience a strong price reversal. Our findings are robust to the Fama-French three-factor daily regression extended by the momentum factor and a number of robustness tests. Evidence reported in this study is not consistent with prevailing view that investors underreact to earnings news. To the contrary, our finding suggests an investor overreaction to extreme SUE signals.

Kwon, Kim: Investment Horizon of Shareholders and Post-Earnings-Announcement Drift
We hypothesize that post-earnings-announcement drift (PEAD) is caused by underreaction of long-term investors since they do not pay much attention to short-term events. Consistent with the hypothesis, empirical observations show that stocks mostly held by long-term investors exhibit strong PEAD, while stocks mostly held by short-term investors does not. The results are still robust even after transaction costs, investor recognition, temporal inattention, and reversal in earnings surprises are controlled for.

Messias: Post Earnings Announcement Drift, a Price Signal?
This paper investigates the robustness of post-earnings-announcement-drift (PEAD) on a price signal perspective, unlike the traditional literature that focuses on fundamental signal. The studied period is 2003-2015, for four main US indices. The results suggest that some economic agents are too slow to integrate the information, although they still have a major market impact. We find a strong empirical evidence of the preeminence of this bias for Momentum stocks rather than blue-chips or non-Momentum small-caps. Even by challenging the strategy, the conclusion remains strong with abnormal returns linked to such market inefficiency, with better returns for positive signals than negative ones. We choose Nasdaq Composite as the backbone of our development as it is the closest index to Uncia’s field of expertise. For indices known as Momentum, we find strong predictability of the systematic net exposure, the latter being a consequence of the long and short positions implied by the earnings signals.

Daniel, Hirschleifer, Sun: Short and Long Horizon Behavioral Factors
Recent theories suggest that both risk and mispricing are associated with commonality in security returns, and that the loadings on characteristic-based factors can be used to predict future returns. We offer a parsimonious model which features: (1) a factor motivated by limited attention that is dominant in explaining short-horizon anomalies, and (2) a factor motivated by overconfidence that is dominant in explaining long-horizon anomalies. Our three-factor risk-and-behavioral composite model outperforms both standard models and recent prominent factor models in explaining a large set of robust return anomalies.

Hypothetical future performance