Does Beta Move with News? Firm-Specific Information Flows and Learning About Profitability

Patton, Andrew J.; Verardo, Michela

doi:10.2139/ssrn.1361813

Cited by 43 publications

(57 citation statements)

References 124 publications

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“…Specifically, the figures show that betas are relatively stable before warning announcements, increase substantially on the announcement dates and stabilise thereafter. In contrast to Patton and Verardo (2012), our results suggest that betas do not revert back to the pre-announcement average over the 10-day window following warning announcements.…”

Section: Time-varying Betascontrasting

confidence: 96%

“…In untabulated results, we find that the changes in the betas of the control stocks around the warning events are not significantly different from zero, implying that the increase in the post-warning betas is more likely to be caused by the arrival of news than the systematic time-varying nature of stock betas. This evidence is consistent with Kalay and Lowenstein (1985), Brown et al (1988) and Patton and Verardo (2012), who show that, as surprises increase uncertainty, systematic risk increases significantly in the aftermath of both positive and negative news. Hence, ignoring the time-varying nature of the betas may result in biased abnormal return estimates.…”

Section: Time-varying Betassupporting

confidence: 88%

“…Using a sample of 302 US firms for the period 1962-1980, they show that stock betas exhibit a significant increase in the aftermath of dividend announcements. More recently, Patton and Verardo (2012) propose a simple learning model, which suggest that investors use firm-specific news to extract information on the aggregate economy. They argue that since earnings are affected by both marketwide and firm-specific conditions, investors can use earnings of the announcing firms to revise their expectations about the profitability of other firms in the economy.…”

Section: Introductionmentioning

confidence: 99%

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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: Time-varying Betascontrasting

confidence: 96%

Section: Time-varying Betassupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Yin

Mazouz

Benamraoui

et al. 2017

Rev Quant Finan Acc

View full text Add to dashboard Cite

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“…Although this evidence indicates that non-diversifiable price risk increases at earnings announcements, these studies do not establish a link between the increase in betas and realized announcement returns. Ball and Kothari (1991) and Hsieh, Jerris, and Kross (1999) test for such a link but do not find one; Patton and Verardo (2012) do not test for such a link. Thus, similar to other tests of the CAPM, these studies do not provide evidence that investors pay premiums to hedge increases in non-diversifiable price risk at earnings announcements.…”

Section: Non-diversifiable Volatility Risk and Risk Premiums At Earnimentioning

confidence: 99%

“…These studies focus on price risk and examine whether CAPM betas increase at earnings announcements. For example, Ball and Kothari (1991), Hsieh, Jerris, and Kross (1999), and Patton and Verardo (2012) provide evidence that CAPM betas increase at earnings announcements. Although this evidence indicates that non-diversifiable price risk increases at earnings announcements, these studies do not establish a link between the increase in betas and realized announcement returns.…”

Section: Non-diversifiable Volatility Risk and Risk Premiums At Earnimentioning

confidence: 99%

Non-Diversifiable Volatility Risk and Risk Premiums at Earnings Announcements

Barth

2014

The Accounting Review

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This study seeks to determine whether earnings announcements pose non-diversifiable volatility risk that commands a risk premium. We find that investors anticipate some earnings announcements to convey news that increases market return volatility and pay a premium to hedge this non-diversifiable risk. In particular, we find evidence of risk premiums embedded in prices of firms' traded options that are significantly positively associated with the extent to which the firms' earnings announcements pose non-diversifiable volatility risk. In addition, we find that volatility risk premiums are concentrated among bellwether firms and result in predictable variation in option straddle returns around earnings announcements. Taken together, our findings show that some earnings announcements pose non-diversifiable volatility risk that commands a risk premium. JEL Classifications: M41; G12; G13; G14

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Realized Beta Garch: A Multivariate Garch Model With Realized Measures of Volatility

Hansen

Lunde

Voev

2014

J of Applied Econometrics

134

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We introduce a multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model that incorporates realized measures of variances and covariances. Realized measures extract information about the current levels of volatilities and correlations from high-frequency data, which is particularly useful for modeling financial returns during periods of rapid changes in the underlying covariance structure. When applied to market returns in conjunction with returns on an individual asset, the model yields a dynamic model specification of the conditional regression coefficient that is known as the beta. We apply the model to a large set of assets and find the conditional betas to be far more variable than usually found with rolling-window regressions based exclusively on daily returns. In the empirical part of the paper, we examine the cross-sectional as well as the time variation of the conditional beta series during the financial crises.REALIZED BETA GARCH 775 we avoid such issues by incorporating realized measures and the use of measurement equations. The measurement equations tie realized measures to latent volatilities and correlations, and this leads to a useful regularization of the model. This particular structure was chosen for a number of reasons. First, the model provides a good empirical fit for the wide range of assets used in our empirical study; second, the structure of the model is amenable to a deeper analysis of secondary quantities such as betas; third, the model is simple to estimate, which is particularly important when a large set of assets are to be analyzed, as is the case in our empirical analysis.The proposed model has a hierarchical structure in which the market return and a corresponding realized measure form the core of the model. The model can be extended to an arbitrarily large set of individual returns, by adding a conditional model for an individual return and two realized measures, one being a realized measure of return volatility, and the other a realized measure of the correlation between the individual return and the market. This yields a flexible model with a dynamic covariance structure that is constantly revised by using the information contained in the realized measures.The concept of realized betas is not new. Bollerslev and Zhang (2003) carried out a large-scale estimation of the Fama-French three-factor model using high-frequency (5-minute) data on 6400 stocks over a period of 7 years. Their analysis showed that high-frequency data can improve the pricing accuracy of asset pricing models. Their approach differs from ours in important ways. For instance, they model raw realized factor loadings and use simple time series processes to forecast these. Thus there is no explicit link between realized and conditional moments of returns in their framework. Nor do they explicitly account for the measurement error (sampling error) in the realized quantities. Another related paper is Andersen et al. (2006), who study the time variation in realized variances, covariances an...

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Does Beta Move with News? Firm-Specific Information Flows and Learning About Profitability

Cited by 43 publications

References 124 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

Non-Diversifiable Volatility Risk and Risk Premiums at Earnings Announcements

Realized Beta Garch: A Multivariate Garch Model With Realized Measures of Volatility

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