They combine insights from multiple financial analysts, offering a benchmark based on average earnings per share (EPS) estimates. When companies surpass these forecasts, stock prices typically rise, reflecting investor optimism. The economic rationale for using SUE is that earnings surprises are not immediately fully reflected in the stock price. This implies that investors can earn excess returns by buying stocks that have positive stock surprises. This behavior of stock prices drifting upward after a positive announcement is referred to as the post-earnings announcement drift.
This metric gauges a corporation’s ability to fulfill financial expectations, thus significantly impacting investor sentiment and stock values in either direction. For instance, when a tech giant like Apple surpasses earnings forecasts, it often leads to a surge in stock price. Interestingly, studies show that companies with positive earnings surprises tend to experience more prolonged stock performance increases compared to those that fall short of estimates. The “surprise” aspect of the earnings means that the price of a stock can spike up or fall dramatically over the course of a single day.
Macro trends and industry dynamics significantly influence earnings expectations and can enhance your ability to foresee surprises. To compare across companies, analysts exclude nonrecurring items and normalize accruals—focusing on sustainable earnings from operations. We use a coarse selection filter to narrow down the universe to 1000 stocks at the beginning of each month according to dollar volume, price and whether the stock has fundamental data in our data library. They compare a stock’s performance during a period with its past performance or with the performance of a group of stocks. This indicator is often scaled to 1 in the beginning period for ease of interpretation.
- SUE measures the earnings surprise in terms of the number ofstandard deviation above or below the consensus earnings estimate.
- Macro trends and industry dynamics significantly influence earnings expectations and can enhance your ability to foresee surprises.
- Keep an eye on analyst coverage; companies with limited analyst attention may offer unique opportunities, as their stock prices react more dramatically to information changes.
- Interestingly, research shows that a firm with less than five analysts covering it can see stock price movements twice as volatile compared to more widely followed firms.
5. Different earnings surprises measures: PEAD
By analyzing these expectations, you can uncover differences between predicted and actual earnings. Steady positive cash flow suggests that a company is well-positioned to exceed earnings forecasts. Historical data shows that stocks with robust margins and cash flow metrics frequently enjoy significant price increases following earnings announcements. Earnings Exceeding Market Expectations After an earnings announcement, investors express their views through buy or sell decisions.
Standardised unexpected earnings interpretation
An institution that downloaded an insider trading filing by a given firm last quarter increases its likelihood of downloading an insider trading filing on the same firm by more than 41.3 percentage points this quarter. Moreover, the average tracked stock that an institution buys generates annualized alphas of over 12% relative to the purchase of an average non tracked stock. When actual earnings per share (EPS) differ greatly from analysts’ expectations, stock prices either rise or fall. A positive surprise generally results in a price increase, while a negative surprise leads to a decrease. SUE measures the earnings surprise in terms of the number ofstandard deviation above or below the consensus earnings estimate.
Considering standardized earnings surprise the robustness measure of the excess return, we use various methods to calculate the excess return. The whole market reaction attributed to the earnings report, measured from 60 days before and after the earnings release, is estimated at 18%, which means that about a third of the whole market response is delayed. In the semi-strong form of market efficiency, all the publicly available information regarding the firms must be reflected already in the stock price.
This concise explanation helps students and practitioners alike understand how to interpret earnings surprises—and when to treat them with skepticism or opportunity. The SUE formula enables a trader or analyst to get an understanding of where the current pricing on a stock falls, whether it is within a single standard deviation of the expected price or not. Since the middle of the ’90s, Post Earnings Announcement Drift returns fluctuated and became lower than in the previous two decades.
Interestingly, the evidence shows that investors usually trade using FOM rather than ERROR. The similar results are also found in subsample, thus indicating that FOM is a better proxy for earnings surprises. Before the earnings release, the investors set an expectation while the company prepares its forward-looking statement (guidance).
SUE in Q and SUE in Q+1
You may exercise your right to consent or object to a legitimate interest, based on a specific purpose below or at a partner level in the link under each purpose. These choices will be signaled to our vendors participating in the Transparency and Consent Framework. High-frequency traders supply liquidity and mitigate market inefficiency, which implies the HFT actions lower the magnitude of the PEAD.
Within this phenomenon, stocks demonstrating the most pronounced earnings surprises continue to ascend, while those with the weakest earnings surprises persistently decline post-announcement. The conclusions in Table 5 show that even the star analysts with stronger individual ability, the mean (the latest) of their earnings surprises are still not as effective as FOM. Considering that FOM can covers more companies, using FOM as a proxy for earnings surprises is better than those calculated based on the mean (the latest) of star analysts’ errors. To predict earnings surprises, analyze key metrics such as earnings per share (EPS) and standardized unexpected earnings (SUE).
This is calculated by dividing the percentage earnings surprise by the standard deviation of analyst earnings forecasts. An earnings announcement is an official public statement of a company’s profitability for a specific time period, typically a quarter or a year. A positive surprise will often lead to a sharp increase in the company’s stock price, while a negative surprise to a rapid decline. Finally, the study suggests that trading on the basis of previous quarters SUE is profitable as it is directly correlated with the SUE in the subsequent quarter. An expected surprise prediction estimates how much a company’s actual earnings per share (EPS) could vary from analysts’ forecasts.
What Are Some Factors That Can Influence Post Earnings Announcement Drift Movements?
Analysts attribute it to the broader academic research in the field and broader recognition of the phenomenon among investors. At the same time, most academic researchers focussed on the magnitude and direction of the surprise and the price changes immediately after the announcement. Post Earnings Announcement Drift is a form of trend trading that leverages breakouts or technical momentum price signals of higher highs.
Research by Ball in 1993 states that betas rise for firms with high unexpected earnings and decline for firms with low unexpected earnings. A rise or a fall in beta (or risk) results from the seemingly abnormal returns after earnings announcements. Also, the Post Earnings Announcement Drift was more intense in subsequent announcement windows. Many analysts use forecasting models, management guidance, and additional fundamental information to derive an EPS estimate.
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Trading frictions like transaction costs or liquidity are positively related to Post Earnings Announcement Drift. They can be direct transaction costs (bid-ask spreads, commissions) and indirect transaction costs (illiquidity, market impact costs). It is debatable whether such frictions allow for profitable arbitrage opportunities, depending on trading strategy specifications and risk factors. This article takes a deep look at the PEAD strategy, its evolution, and how investors can take advantage of the post-earnings drift while portfolio building. Chip Stapleton is a Series 7 and Series 66 license holder, CFA Level 1 exam holder, and currently holds a Life, Accident, and Health License in Indiana.
Table 4 shows that the fraction of misses on the same side outperforms earnings surprises calculated based on the mean (the latest) of analysts’ forecasts errors. Some studies have shown that there is heterogeneity among analysts, star analysts demonstrate superior personal and information-processing capabilities compared to non-star analysts 32. Consequently, the earnings forecasts disseminated by star analysts tend to be more precise. To focus on this narrowed sample, we include only companies that have earnings forecasts from star analysts, and FOM is calculated using the earnings forecasts from all analysts. Given the inherent biases in CAR, the results are debatable when CAR is employed to assess the validity of various earnings surprises measures. Neither ERROR1 nor ERROR2 can accurately proxy for earnings surprises, and the fraction of misses on the same side (FOM) is a better proxy for earnings surprises.
- The regression coefficients for earnings surprises are notably positive when each of the three earnings surprises measures are individually examined using CAR as an indicator of investors’ reactions to earnings information.
- As we mentioned, Post Earnings Announcement Drift is an inefficiency that investors can capitalize on if they buy stocks with high earnings surprises and hold them for nearly two months.
- A strategy that buys stocks with a positive surprise and sells stocks with a negative surprise generally generates alpha.
- Consequently, the earnings forecasts disseminated by star analysts tend to be more precise.
- This behavior of stock prices drifting upward after a positive announcement is referred to as the post-earnings announcement drift.
- Theoretical models have shown that both phenomena find potential explanations in cognitive biases, that is, investor irrationality.
The investment implications of the size and sign of the unexpected earnings in global equity markets are well addressed in recent years. For example, Sultan finds that the unexpected earnings can be used as a discriminator between stocks that performed relatively well and stocks that performed relative poorly in Japan. Brown and Jeong show that an earnings surprise predictor is effective in selecting stocks from S&P 500 firms. Dische and Zimmermann report that abnormal returns can be earned from the portfolio of the Swiss stocks exhibiting the most positive earnings revision. Conroy, Eades and Harris find that stock prices are significantly affected by earnings surprises in Japan.
This indicates the stock return in the time window is dominated by other non-earnings factors when there is no earnings shock. According to the Efficient Market Hypothesis, stock prices follow a random walk and exhibit price jumps in response to earnings shocks. Reference from Jiang and Zhu 21, we introduce an innovative revision to CAR, denoted as CAR_NEW, incorporating stock price jumps, and we validate that CAR_NEW is an effective proxy for investors’ reactions to earnings correction (EPS_PUB). We investigate the robustness of earnings surprise measures in the context of a revised market reaction. Specifically, we introduce an innovative adjustment to CAR using stock price jumps, and prove that the fraction of misses on the same side (FOM) provides a superior measure of earnings surprises. Furthermore, we find that investor trading patterns align with FOM, and the post-earnings announcement drift (PEAD) strategy based on FOM outperforms that based on analysts’ forecast error.
We also calculate earnings surprises using the median of the latest analysts’ earnings errors. These earnings surprises are denoted as ERROR2, replacing the average of analysts’ earnings forecasts with the most recent analysts’ earnings forecasts. On the one hand, if earnings surprises reflect the actual earnings shock for investors, there should exist a significantly positive relation between earnings surprises and investor trading behavior. Thus, we use high-frequency data to track the trading behavior of investors and examine whether earnings surprises are in line with investors trading behavior around earnings announcement.
This strategy exploits the observed phenomenon that the stock price tends to drift in the days after the earnings announcement. A strategy that buys stocks with a positive surprise and sells stocks with a negative surprise generally generates alpha. The above results still show that FOM is a more effective measure of earnings surprises, FOM provides more information on earnings. Table 9 shows that PEAD_FOM is capable of explaining PEAD_ERROR1, implying that FOM reflects more earnings surprises information.
The company’s last two quarterly SUE scores were very high at 7.8 and 2.6, and analysts’ mean forecast for the fourth quarter had been upgraded about 14% over the last three months. They use the models to forecast what the company can reasonably expect to generate in earnings during the upcoming accounting period. The involvement of more analysts enhances the accuracy of consensus forecasts, decreasing forecast errors and improving your ability to anticipate earnings surprises. Interestingly, a study found that roughly 60% of all reported earnings tend to fall below analyst expectations, highlighting the importance of skepticism in this analysis.