Post-earnings Announcement Drift Research Articles

Multi-factor quantification strategy of LGBM based on post-earnings announcement drift

Multi-factor quantification strategy of LGBM based on post-earnings announcement drift

Asymmetric post earnings announcement drift and order flow imbalance: The impact on stock market returns

We conduct an event study around the earning announcement to examine the asymmetric post earnings announcement drift, and its relationship with the bid-ask bias, order follow imbalance on post earnings announcement period for UK stock market from 2000 2021. The earning drift is significantly asymmetric in the post earnings announcement period, stocks with good news have less drift, and it (at least) partly due to the order flow imbalance during the event period. The earning announcement attracts more sell than buy orders, and an overall negative reaction in the market. The difference between transaction price return and quote price returns shows that the bid-ask bias is also a possible explanation of post earnings announcement drift. Our results are robust across different estimations and robustness tests.

The impact of insider ownership and institutional ownership on post-earnings-announcement-drift: Evidence from Vietnam

In this study, we first document the existence of post-earnings-announcement-drift (PEAD) in Vietnam, consistent with investors’ under-reaction to earnings surprises. We then investigate whether and how institutional ownership and insider ownership affect PEAD. We find that the magnitude of PEAD effect is stronger among firms with higher insider ownership. We argue that higher insider ownership may increase information opacity and impede efficient processing of earnings news among investors. We provide supporting empirical evidence for this argument. Furthermore, in contrast to the conventional view that institutional investors exploit PEAD and accelerate the speed of price discovery, we find that institutional investors do not have significant impacts on PEAD. We argue that this result is consistent with recent findings in the US and China that some institutional investors may herd, hence, not necessarily help to mitigate PEAD. Our findings provide useful insights about the effect of insider investors and institutional investors on the delayed response to earnings news in the context of Vietnam.

ECHO-GL: Earnings Calls-Driven Heterogeneous Graph Learning for Stock Movement Prediction

Stock movement prediction serves an important role in quantitative trading. Despite advances in existing models that enhance stock movement prediction by incorporating stock relations, these prediction models face two limitations, i.e., constructing either insufficient or static stock relations, which fail to effectively capture the complex dynamic stock relations because such complex dynamic stock relations are influenced by various factors in the ever-changing financial market. To tackle the above limitations, we propose a novel stock movement prediction model ECHO-GL based on stock relations derived from earnings calls. ECHO-GL not only constructs comprehensive stock relations by exploiting the rich semantic information in the earnings calls but also captures the movement signals between related stocks based on multimodal and heterogeneous graph learning. Moreover, ECHO-GL customizes learnable stock stochastic processes based on the post earnings announcement drift (PEAD) phenomenon to generate the temporal stock price trajectory, which can be easily plugged into any investment strategy with different time horizons to meet investment demands. Extensive experiments on two financial datasets demonstrate the effectiveness of ECHO-GL on stock price movement prediction tasks together with high prediction accuracy and trading profitability.

Exploring the Influence of Post-Earnings Announcement Drift and Investor Sentiments on Investor Trading Behavior: A Qualitative Analysis

"Post-Earnings-Announcement Drift" is a market phenomenon, which refers to investors’ tendency to overreact to the earnings announcements in conducting their investment decisions. However, this effect shows up gradually over the whole period that stock of the company is being sold out following a bullet earnings surprise. Form radically different treatments from the efficient market hypothesis (EMH), where prices of stocks will be able to quickly and fully adjust to any new coming information, it takes time for the drift to reveal itself. This happens as a slow and eventual process. This research study investigates the impact of investor sentiments and post earnings announcements drift on investor trading behavior within the Pakistan stock market context. Twelve participants that comprises of institutional and individual investors, were selected using the convenience sampling methods. Semi-structured interviews were conducted to explore the fluctuations in stock prices, investment strategies, and the market conditions. NVivo software was employed to analyze the interview transcripts, revealing themes related to the fundamental analysis, dividend importance, decision-making strategies, sources of information, investor sentiments, earnings announcements, stock pricing, information leakage, and the market anomalies. The findings highlight the significance of both the rational analysis and emotional responses in investment decisions, the importance of timely earnings disclosures, and the presence of market anomalies and the information leakage.

Investor-listed company interaction and post-earnings announcement drift for individual stocks

This study explores listed companies in China's capital market as research subjects to investigate the impact of online interaction between investors and listed companies on stock price drift following earnings announcements. The results show that the online interaction between investors and listed companies can significantly reduce the stock price drift following earnings announcements, with this effect being more pronounced in nonstate-owned enterprises and companies with a higher proportion of institutional investor holdings. The findings provide useful information for improving information efficiency in the securities market and optimizing the market environment.

Non-Deal Roadshows, Post-Earnings Announcement Drift, & the Implications of Private Meetings

Non-Deal Roadshows (NDRs) are among the most valuable channels of management access; however, little is known about the implications of the information conveyed by NDRs. Using a novel dataset of NDR meetings, I investigate how they affect post-earnings announcement drift (PEAD). I find that PEAD declines after NDR activity when the most recent NDR occurs within one month before the earnings announcement. This decline is greatest among smaller firms, and firms with high idiosyncratic volatility, less concurrent earnings announcements, Friday earnings announcements, and infrequent NDRs. These findings suggest that NDRs promote market efficiency and convey information regarding upcoming earnings announcements.

Post-Earnings Announcement Drift, Systemic Shock, and Limited Attention: Evidence from COVID-19 Pandemic

Post-Earnings Announcement Drift, Systemic Shock, and Limited Attention: Evidence from COVID-19 Pandemic

Overnight Post-Earnings Announcement Drift and SEC Form 8-K Disclosures

Overnight Post-Earnings Announcement Drift and SEC Form 8-K Disclosures

Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

How to measure earnings surprises: Based on revised market reaction.

We investigate the robustness of earnings surprise measures in the context of a revised market reaction. While existing literature suggests that financial anomalies may distort cumulative abnormal returns (CAR) during annual announcements, our research proves that a revised market reaction offers a more accurate reflection of investor reactions to earnings correction. 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.

Impact of human resource characteristics of internal accounting system on post-earnings announcement drift: Evidence from Korea

When companies invest in their internal accounting personnel, investors place greater trust in disclosed earnings information and highly regard a company’s information transparency. This results in prompt investment decisions regarding the company. Consequently, earnings information will be immediately reflected in stock prices, thereby reducing stock price drift. The purpose of this study is to examine the impact of investments in establishing and operating internal accounting systems on investors’ responses to the mitigation of stock price drift. The study focused on firms listed on the Korea Exchange from 2011 to 2018 and constructed a regression model using the cumulative abnormal return following earnings disclosure dates for 30, 60, and 120 days as the dependent variable, with the characteristics of internal accounting personnel as independent variables. The analysis reveals that companies with many internal accounting personnel and position experts, such as accountants, within their internal accounting control systems, experience a significantly lower stock price drift. The coefficients of the interaction terms between internal accounting personnel characteristics and standardized unexpected earnings are positive and significant at the 1% level for all cumulative abnormal return values. The findings of the study indicate that as efficiency is secured, stemming from the scale of personnel managing internal accounting control systems and their expertise, market investors’ understanding and trust of accounting information also increase. Investors, as information users, react promptly to the earnings information disclosed by the company, leading to a decrease in stock price drift. AcknowledgmentsThis work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A5A8070518).

Post-Earnings Announcement Drift, Momentum, and Contrarian Strategies in the Saudi Stock Market: Risk Explanation vs. Behavioral Explanation

Post-Earnings Announcement Drift, Momentum, and Contrarian Strategies in the Saudi Stock Market: Risk Explanation vs. Behavioral Explanation

Do psychological factors exert greater influence on investment decisions than physiological factors? Evidence from Borsa Istanbul

We investigate the impact of Ramadan, the Holy month of fasting, on the phenomenon of post-earnings announcement drift (PEAD) in Borsa Istanbul. Utilizing quarterly earnings announcements and employing both the analyst forecast method and time series forecast method as surprise measures, our study spans the earnings period from the first quarter of 2007 to the last quarter of 2018. Our objective is to bridge the gap between studies highlighting the negative impacts of physiological factors and the positive impacts of psychological factors on economic decision-making. Given the anticipated opposing effects of physiological and psychological factors on PEAD during Ramadan, our findings offer a unique opportunity to compare the influences of physiology and psychology on investment decisions. Contrary to the expected positive impact resulting from physiological factors causing attention deficit, our research reveals that earnings announcements made during Ramadan exhibit, on average, a 16–25% lower drift in the subsequent 60-day period, depending on the method used. We attribute this unexpected outcome to the dominance of psychological factors, such as heightened sentiment, self-awareness, and spirituality, over physiological factors. This finding holds significance for policymakers, as activities that may seem to have a harmful impact on the economy due to visible physiological or physical factors can unexpectedly yield positive effects due to less tangible psychological factors. Additionally, our results hold importance for investors, as a hedge strategy using earnings surprises results in a negative return if the announcements are made during Ramadan. Our findings remain robust across different measures of PEAD and various intensities of physiological factors.

When Beliefs Influence the Perceived Signal Precision: The Impact of News on Reinforcement-Oriented Agents

In a world of increasingly extensive information, rational investors can make better decisions. However, reinforcement-oriented investors are also more likely to observe preferred signals close to their own perception. A focus on these signals distorts the perceived aggregate signal in the direction of the prior estimate. This reduces belief adaptation. Hence, the empirically well-documented selective exposure/reinforcement theory reduces the positive impact of greater information availability on price efficiency. Additional information can sometimes even decrease perception correctness. In a market with biased investors, managers have an incentive to announce more diffuse (fewer precise) signals in case of negative (positive) information. This results in postearnings-announcement drift and dispersion anomaly. Also, the distribution shape matters for information processing. For unimodal, symmetric distributions, agents’ perceptions converge to the fundamental—even though at a reduced speed. For multimodal signal distributions, the estimate can diverge from the fundamental. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4941 .

Measuring Portfolio Gains: The Case of Earnings Announcement Trading Signals

ABSTRACT This study offers solutions to help address practical issues researchers generally overlook when assessing gains from a trading signal. Specifically, the methodologies used in previous research generally ignore investor risk aversion when forming portfolios, do not update portfolios as signals arrive, exploit look-ahead biases, do not assess the incremental gains of a new signal, and do not consider market frictions. We examine trading signals based on post-earnings announcement drift (PEAD), the earnings announcement premium (EAP), and earnings announcement rescheduling (RES). Using our proposed approach, we find that portfolios that incorporate the individual signals produce higher Sharpe ratios than equal-weighted portfolios; however, the gains for each signal are concentrated to a few days around the announcement. The EAP and RES signals do not provide incremental portfolio gains over the PEAD signal. After considering market frictions, portfolio performance rapidly attenuates and becomes similar to the SPY ETF as the portfolio size increases. JEL Classifications: G12; G14; G17.

Seasonal patterns of earnings releases and post-earnings announcement drift

In line with SEC regulations, U.S. firms tend to announce their earnings in specific weeks, resulting in clustered earnings releases. Our study examines whether this pattern of earnings releases leads to delayed market responses. We observe that firms announcing earnings in the two busy weeks of each season exhibit a stronger post-earnings announcement drift (PEAD) compared to those in non-busy weeks. Additionally, we find that firms with fewer institutional investors, financial analysts, and smaller sizes are more susceptible to market under-reaction. These findings support the hypothesis that under-reaction plays a role in the occurrence of PEAD. We attribute this under-reaction mainly to investors' limited capacity to promptly process a large volume of earnings news simultaneously.

Insider trading, future earnings, and post-earnings announcement drift

This paper examines the association between insider trading prior to quarterly earnings announcements and the magnitude of the post-earnings announcement drift (PEAD). We conjecture and find that insider trades reflect insiders’ private information about the persistence of earnings news. Thus, insider trades can help investors better understand and incorporate the time-series properties of quarterly earnings into stock prices in a timely and unbiased manner, thereby mitigating PEAD. As predicted, PEAD is significantly lower when earnings announcements are preceded by insider trading. The reduction in PEAD is driven by contradictory insider trades (i.e., net buys before large negative earnings news or net sells before large positive earnings news) and is more pronounced in the presence of more sophisticated market participants. Consistent with investors extracting and trading on insiders’ private information, pre-announcement insider trading is associated with smaller market reactions to future earnings news in each of the four subsequent quarters. Overall, our findings indicate insider trading contributes to stock price efficiency by conveying insiders’ private information about future earnings and especially the persistence of earnings news.

Integrating Factor Models

ABSTRACTThis paper develops a comprehensive framework to address uncertainty about the correct factor model. Asset pricing inferences draw on a composite model that integrates over competing factor models weighted by posterior probabilities. Evidence shows that unconditional models record near‐zero probabilities, while postearnings announcement drift, quality‐minus‐junk, and intermediary capital are potent factors in conditional asset pricing. Out‐of‐sample, the integrated model performs well, tilting away from subsequently underperforming factors. Model uncertainty makes equities appear considerably riskier, while model disagreement about expected returns spikes during crash episodes. Disagreement spans all return components involving mispricing, factor loadings, and risk premia.

Do Differences in Analyst Quality Matter for Investors Relying on Consensus Information?

This study investigates whether investors can reap economic benefits from analyzing differences in analyst quality. Although high-quality analysts’ average forecast is more accurate than the consensus forecast for firms with a large analyst following, the benefits of using high-quality analysts’ average forecasts are not economically significant. In contrast, the value of analyst quality differentiation exists in the second moment of forecasts. High-quality analysts’ forecast dispersion gives investors an advantage in dealing with uncertainty by predicting return volatility and providing opportunities for economically significant returns using option straddle and post-earnings announcement drift investment strategies. This paper was accepted by Suraj Srinivassan, accounting. Funding: A. Rubin and A. Vedrashko thank the financial support of the Social Sciences and Humanities Research Council of Canada (SSHRC). Supplemental Material: The data are available at https://doi.org/10.1287/mnsc.2023.4699 .