A review of the Post-Earnings-Announcement Drift

Fink, Josef

doi:10.1016/j.jbef.2020.100446

Cited by 31 publications

(16 citation statements)

References 216 publications

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“…By doing so, the slope coefficient can be interpreted as the return of a hedge portfolio that is long in the top decile and short in the lowest decile. 13 While existing studies have identified several potential drivers to explain PEAD (Fink, 2020), we choose this parsimonious model structure to make our conclusion general, which is also consistent with how Lerman et al (2007) implemented the empirical analyses. Both Jegadeesh and Livnat (2006) and Lerman et al (2007) performed the regression analyses using the Fama-MacBeth methodology.…”

Section: Superiority Of Explaining Peadmentioning

confidence: 74%

A re‐examination of analysts versus time‐series extrapolation of quarterly earnings: Superiority in accuracy and earning expectation proxy

Liu

2022

J Corp Accounting Finance

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We document findings that earning extrapolation based on seasonal random trend (SRT) model is not inferior to analysts’ quarterly earnings forecasts, which contradicts the belief that analysts are superior to time‐series models. Our findings suggest that while the frequency of analysts beating SRT extrapolations is greater than 50%, the marginal accuracy improvement is weak. Analysts’ forecasts contain larger absolute forecast error and significant pessimistic bias than SRT extrapolation. Prior studies attribute the superiority of analysts’ forecasts in proxying for earnings expectation to its higher accuracy. Given that the SRT extrapolations have lower average forecast error, we explore whether market participants use them to develop earnings expectations. Our findings indicate that the market anchors to analysts’ forecasts and treats SRT extrapolations as supplemental.

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Section: Superiority Of Explaining Peadmentioning

confidence: 74%

A re‐examination of analysts versus time‐series extrapolation of quarterly earnings: Superiority in accuracy and earning expectation proxy

Liu

2022

J Corp Accounting Finance

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“…Over the years, several anomalies have been observed, ranging from the January effect (Thaler, 1987;Branch and Chang, 1990;Kumar Das and Rao, 2011), size effect (Al-Rjoub et al, 2005), Book-to-market effect (Loughran, 1997;Cakici and Topyan, 2014), momentum effect (Ejaz and Polak, 2013;Zoghlami, 2013), post-earnings announcement drift (Bernard and Thomas, 1989;Jegadeesh and Livnat, 2006;Fink, 2021), asset growth effect (Lipson et al, 2011;Watanabe et al, 2012), price clustering , overreaction and underreaction . From the abovementioned, the debate on market efficiency and inefficiency still continues till date.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating Joint Market Hypothesis during Periods of Financial Distress and its Implications

Enow

2023

IJEFI

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Empirical finance is about building an understanding of security prices and financial markets so as to foster better decision making while avoiding anecdote biases. Successful investors are often cognisant of their risk exposures coupled with extreme discipline and leverage. Therefore, the aim of this study was to explore the concept of joint market hypothesis in five international stock indexes. This study used the Taleb’s ratio which is an extension of the Parkinson model. The financial distress periods were the financial crisis (December 1, 2007 to June 30, 2009) and the Covid-19 pandemic (January 1, 2020 to December 31, 2021). Joint hypothesis was evident in the JSE, CAC 40, the DAX and the Nikkei 225 while the Nasdaq displayed high levels of market efficiencies. Allocating more capital to the JSE, CAC 40, the DAX and the Nikkei 225 during periods of financial distress in conjunction with the Fama French five factor model will generate higher returns. As per the author’s knowledge, this study is the first to explore the concept of joint market hypothesis during periods of distress

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“…Known as PEAD, the tendency of stock prices to drift in the direction of an earnings surprise has attracted extensive attention since its discovery by Ball and Brown (1968). Its importance and relevance are revealed clearly by Fink (2020), who reviewed 216 published papers on the phenomenon. As emphasized by Fama (1998), PEAD is one of the most robust and persistent financial anomalies, and it has resulted in what we (in the spirit of Cochrane (2011)) term a "zoo of controls."…”

Section: Introductionmentioning

confidence: 99%

“…As emphasized by Fama (1998), PEAD is one of the most robust and persistent financial anomalies, and it has resulted in what we (in the spirit of Cochrane (2011)) term a "zoo of controls." The expansion of this zoo has been amplified by the reliance on high-dimensional empirical data, which are noisy and subject to omitted-variable bias (Fink (2020)). The PEAD literature is a classic example of a line of research that relies on massive sample sizes and a zoo of variables.…”

Section: Introductionmentioning

confidence: 99%

Double Machine Learning: Explaining the Post-Earnings Announcement Drift

Hansen¹,

Siggaard²

2023

J. Financ. Quant. Anal.

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We demonstrate the benefits of merging traditional hypothesis-driven research with new methods from machine learning that enable high-dimensional inference. Because the literature on post-earnings announcement drift (PEAD) is characterized by a “zoo” of explanations, limited academic consensus on model design, and reliance on massive data, it will serve as a leading example to demonstrate the challenges of high-dimensional analysis. We identify a small set of variables associated with momentum, liquidity, and limited arbitrage that explain PEAD directly and consistently, and the framework can be applied broadly in finance.

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A review of the Post-Earnings-Announcement Drift

Cited by 31 publications

References 216 publications

A re‐examination of analysts versus time‐series extrapolation of quarterly earnings: Superiority in accuracy and earning expectation proxy

A re‐examination of analysts versus time‐series extrapolation of quarterly earnings: Superiority in accuracy and earning expectation proxy

Investigating Joint Market Hypothesis during Periods of Financial Distress and its Implications

Double Machine Learning: Explaining the Post-Earnings Announcement Drift

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