The influence of systematic risk factors and econometric adjustments in catastrophic event studies

Cam, Marie-Anne; Ramiah, Vikash

doi:10.1007/s11156-012-0338-4

Cited by 16 publications

(14 citation statements)

References 27 publications

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“…Previous studies on the stock price reaction to profit warnings are based on the standard event study methodology, which ignores the potential impact of news on stock betas and the residual variance (Brown et al 1988;Corrado and Jordan 1997;Cyree and DeGennaro 2002;Lui et al 2009;Savickas 2003;Zolotoy 2011;Cam and Ramiah 2014), a deficiency that may hinder the testing of the efficient market hypothesis (EMH).…”

Section: Introductionmentioning

confidence: 99%

Stock price reaction to profit warnings: the role of time-varying betas

Yin

Mazouz

Benamraoui

et al. 2017

Rev Quant Finan Acc

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This study investigates the role of time-varying betas, event-induced variance and conditional heteroskedasticity in the estimation of abnormal returns around important news announcements. Our analysis is based on the stock price reaction to profit warnings issued by a sample of firms listed on the Hong Kong Stock Exchange. The standard event study methodology indicates the presence of price reversal patterns following both positive and negative warnings. However, incorporating time-varying betas, event-induced variance and conditional heteroskedasticity in the modelling process results in post-negative-warning price patterns that are consistent with the predictions of the efficient market hypothesis. These adjustments also cause the statistical significance of some post-positive-warning cumulative abnormal returns to disappear and their magnitude to drop to an extent that minor transaction costs would eliminate the profitability of the contrarian strategy.

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Section: Introductionmentioning

confidence: 99%

Stock price reaction to profit warnings: the role of time-varying betas

Yin

Mazouz

Benamraoui

et al. 2017

Rev Quant Finan Acc

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Section: Effect Of Large-scale Unanticipated Incidents On Stock Marketsmentioning

confidence: 99%

“…This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The COVID-19 pandemic is conceptualized as an instance of large-scale unanticipated incidents which include natural disasters such as hurricanes and earthquakes (Lee et al 2018 ; Shelor et al 1992 ) or major incidents such as aviation accidents and terrorist attacks (Barrett et al 1987 ; Cam and Ramiah 2014 ). Thus, the first sub-section of this literature review is based on the effects of large-scale unanticipated incidents on stock markets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This results in a steep fall in stock prices on adverse news. Previously, various studies have examined investor behavior amidst unprecedented large-scale disasters, such as earthquakes or terrorist attacks (Barrett et al 1987 ; Cam and Ramiah 2014 ; Lee et al 2018 ; Shelor et al 1992 ). They find significant stock price overreactions, which are consistent with an availability bias known from the behavioral finance literature.…”

Section: Literature Reviewmentioning

confidence: 99%

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Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic

Ding

Guan

Chan

et al. 2020

Front. Bus. Res. China

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As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance (SBD) measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression (VAR). The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

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“…Cam and Ramiah (2014) also discuss the possibility that researchers may reach different results depending on the financial econometrics adjustments and asset pricing model used when calculating expected returns.4 Although we focus our analysis on M&As as a major corporate event, our inferences could easily be generalized for any other corporate decisions that exhibit similar market performance, such as season equity offerings, share repurchase, goodwill write offs, cross-listings, etc.…”

mentioning

confidence: 99%

Short-Horizon Event Study Estimation with a Star Model and Real Contaminated Events

2014

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The influence of systematic risk factors and econometric adjustments in catastrophic event studies

Cited by 16 publications

References 27 publications

Stock price reaction to profit warnings: the role of time-varying betas

Stock price reaction to profit warnings: the role of time-varying betas

Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic

Short-Horizon Event Study Estimation with a Star Model and Real Contaminated Events

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