The Effect of Conference Calls on Analyst and Market Underreaction to Earnings Announcements

2005 ◽  
Vol 80 (1) ◽  
pp. 189-219 ◽  
Author(s):  
Michael D. Kimbrough
Keyword(s):  
Earnings Announcements ◽  
Analyst Forecast ◽  
Forecast Errors ◽  
Conference Calls ◽  
Conference Call ◽  
Earnings Announcement ◽  
Post Earnings Announcement Drift ◽  
Investor Sophistication ◽  
Two Measures ◽  
Matched Samples

I extend prior research on the information content of conference calls by examining whether they accelerate analysts' and investors' responses to the future implications of currently announced earnings. I find that the initiation of conference calls is associated with a significant reduction in the serial correlation in analyst forecast errors, a measure of initial analyst underreaction. I also find that the initiation of conference calls is associated with significant reductions in two measures of initial investor underreaction: (1) post-earnings announcement drift and (2) the proportion of the total market reaction to firms' earnings announcements that is “delayed” (i.e., that is attributable to post-earnings announcement drift). The reduction in post-earnings announcement drift surrounding conference call initiation is concentrated in the set of sample firms where drift is most severe (i.e., the smallest, least heavily traded sample firms) while the largest, most heavily traded sample firms do not exhibit significant drift either before or after conference call initiation. Robustness tests, including analyses of matched samples of nonconference call firms, indicate that the results are not driven by general increases in analyst and investor sophistication over time or by contemporaneous increases in the information and trading environments of conference call initiators.

scholarly journals Overconfident Institutions and Their Self-Attribution Bias: Evidence from Earnings Announcements

2020 ◽  
pp. 1-33 ◽  
Author(s):  
Hsin-I Chou ◽  
Mingyi Li ◽  
Xiangkang Yin ◽  
Jing Zhao
Keyword(s):  
Information Asymmetry ◽  
Earnings Announcements ◽  
Earnings Announcement ◽  
Behavioral Bias ◽  
Price Pressure ◽  
Post Earnings Announcement Drift ◽  
Institutional Demand ◽  
Attribution Bias

Abstract Institutional demand for a stock before its earnings announcement is negatively related to subsequent returns. The relation is not attributable to the price pressure of institutional demand and is stronger for stocks with higher information asymmetry and/or greater valuation difficulty. These findings support the notion that overconfident institutions misprice stocks. Following announcements, institutions’ behavior exhibits the outcome-dependent feature of self-attribution bias. Whether they become more overconfident and delay their mispricing correction depends on whether earnings news confirms their preannouncement trades. This behavioral bias also offers a new explanation for the well-known post-earnings-announcement drift.

scholarly journals The Effect of Ex Ante Management Forecast Accuracy on the Post-Earnings-Announcement Drift

2012 ◽  
Vol 87 (5) ◽  
pp. 1791-1818 ◽  
Author(s):  
Li Zhang
Keyword(s):  
Earnings Announcements ◽  
Forecast Accuracy ◽  
Data Availability ◽  
Management Forecasts ◽  
Composite Measure ◽  
Earnings Announcement ◽  
Post Earnings Announcement Drift ◽  
Management Forecast ◽  
Ex Ante

ABSTRACT I examine the effect of ex ante management forecast accuracy on the post-earnings-announcement drift when management forecasts about next quarter's earnings are bundled with current quarter's earnings announcements. I build a composite measure of ex ante management forecast accuracy that takes into account forecast ability, forecast difficulty, and forecast environment. The results show that the bundled forecasts with higher ex ante accuracy mitigate investors' under-reaction to current earnings and reduce the magnitude of the post-earnings-announcement drift. Data Availability: The data used in this paper are available from the sources listed in the text.

Does analyst forecast dispersion represent investors’ perceived uncertainty toward earnings?

2020 ◽  
Vol 19 (3) ◽  
pp. 289-312
Author(s):  
Jundong (Jeff) Wang
Keyword(s):  
Negative Correlation ◽  
Private Information ◽  
Implied Volatility ◽  
Earnings Announcements ◽  
Analyst Forecast ◽  
Forecast Dispersion ◽  
Earnings Announcement ◽  
Content Type ◽  
Analyst Forecast Dispersion ◽  
Forward Looking

Purpose This paper aims to investigate the association between analyst forecast dispersion and investors’ perceived uncertainty toward earnings. Design/methodology/approach A new measure for investors’ expectations of earnings announcement uncertainty is constructed, using changes in implied volatility of option contracts prior to earnings announcements. Unlike other proxies of uncertainty, this measure isolates the incremental uncertainty regarding the upcoming earnings announcement and is a forward-looking measure. Findings Using this new proxy, this paper finds a significant negative correlation between analyst forecast dispersion and investors’ uncertainty regarding the upcoming earnings announcements. Further tests show that this negative correlation is driven by analysts’ private information acquisition rather than analysts; uncertainty toward upcoming earnings announcements. Additional cross-sectional tests show that this negative relationship is more pronounced in the subsample with lower earnings quality. Social implications This paper helps to further the understanding of the information content of analyst forecast dispersion, particularly the ways in which they gather and produce private information and their incentives for so doing. Originality/value This paper introduces a new market-based and forward-looking proxy of earnings announcement uncertainty that should be useful in future research. This paper also provides original empirical evidence that analysts gather and produce an additional private information to the market when facing noisy signals and that their information reduces investors’ uncertainty toward upcoming earnings announcements.

Investor Trading and the Post-Earnings-Announcement Drift

2011 ◽  
Vol 86 (2) ◽  
pp. 385-416 ◽  
Author(s):  
Benjamin C. Ayers ◽  
Oliver Zhen Li ◽  
P. Eric Yeung
Keyword(s):  
Random Walk ◽  
Earnings Announcements ◽  
Discovery Process ◽  
Earnings Announcement ◽  
Post Earnings Announcement Drift ◽  
Earnings Surprises ◽  
Trading Activities ◽  
Investor Trading ◽  
Small Traders ◽  
Price Discovery Process

ABSTRACT: We examine whether the two distinct post-earnings-announcement drifts associated with seasonal random-walk-based and analyst-based earnings surprises are attributable to the trading activities of distinct sets of investors. We predict and find that small (large) traders continue to trade in the direction of seasonal random-walk-based (analyst-based) earnings surprises after earnings announcements. We also find that when small (large) traders react more thoroughly to seasonal random-walk- (analyst-) based earnings surprises at the earnings announcements, the respective drift attenuates. Further evidence suggests that delayed small trades associated with random-walk-based surprises are consistent with small traders’ failure to understand time-series properties of earnings, whereas delayed large trades associated with analyst-based surprises are more consistent with a longer price discovery process. We also find that the analyst-based drift has declined in recent years.

Investors' Trade Size and Trading Responses around Earnings Announcements: An Empirical Investigation

2001 ◽  
Vol 76 (2) ◽  
pp. 221-244 ◽  
Author(s):  
Nilabhra Bhattacharya
Keyword(s):  
Random Walk ◽  
Earnings Announcements ◽  
Random Walk Model ◽  
Analyst Forecast ◽  
Forecast Errors ◽  
Analyst Following ◽  
Earnings Expectations ◽  
Trading Activities ◽  
Trading Response ◽  
Small Traders

Prior research suggests that the earnings expectations of a segment of the market can be described by the seasonal random-walk model. Prior research also provides evidence that less wealthy and less informed investors tend to make smaller trades (small traders) than wealthier and betterinformed investors (large traders). I hypothesize that it is the earnings expectations of small traders that are associated with predictions from the seasonal random-walk model. By directly analyzing the trading activities of small and large traders, this study provides evidence that is largely consistent with the hypotheses. Specifically, small traders' trading response around earnings announcements is increasing in the magnitude of seasonal random-walk forecast errors, even after controlling for absolute analyst forecast errors, contemporaneous price changes, and market-wide trading. Supplementary analysis reveals that this effect is largely confined to firms with relatively impoverished information environments (i.e., smaller firms and firms with little to moderate analyst following).

Impact of Information Technology on Post-Earnings Announcement Drift

2003 ◽  
Vol 17 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Sharad Asthana
Keyword(s):  
Information Technology ◽  
Transaction Costs ◽  
Value Relevance ◽  
Forecast Errors ◽  
Informational Efficiency ◽  
Market Anomalies ◽  
Earnings Announcement ◽  
Earnings Information ◽  
Post Earnings Announcement Drift ◽  
Time Changes

Post-earnings announcement drift is among the most persistent market anomalies. Two possible causes of the drift are transaction costs that dissuade investors from trading on earnings information immediately, and the market's inability to fully interpret the implications of earnings information. This study hypothesizes that information technology advances have significantly reduced the transaction costs of trading equity stocks and improved the informational efficiency of the capital market. These two changes should, therefore, reduce drift. Using a sample of over 27,000 firm-quarter observations for more than 1,600 firms, the results support the hypothesis that drift has declined significantly with the growth of information technology. The results are robust after controlling for factors correlated with time, changes in the value-relevance of earnings, and previously identified variables that affect the drift, such as size, investor sophistication, and magnitude and sign of analysts' forecast errors. The study provides evidence that the information technology revolution may reduce trading friction and improve informational efficiency.

scholarly journals Rest in Peace Post-Earnings Announcement Drift

2021 ◽  
Author(s):  
Charles Martineau
Keyword(s):  
Financial Markets ◽  
Stock Prices ◽  
Earnings Announcements ◽  
Price Formation ◽  
Future Research ◽  
Earnings Announcement ◽  
Post Earnings Announcement Drift ◽  
Announcement Date ◽  
Earnings Surprises ◽  
Area Of Study

This paper revisits price formation following earnings announcements. In modern financial markets, stock prices fully reflect earnings surprises on the announcement date, leading to the disappearance of post-earnings announcement drifts (PEAD). For large stocks, PEAD have been non-existent since 2006 but has only disappeared recently for microcap stocks. PEAD remain a prevalent area of study in finance and accounting despite having largely disappeared. This paper concludes with a set of recommendations for researchers who conduct such studies to better assess the existence of PEAD and suggests future research avenues to examine price formation following earnings news.

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