Volatility expectations and the reaction to analyst recommendations

Kliger, Doron; Kudryavtsev, Andrey

doi:10.1016/j.joep.2013.04.003

Cited by 17 publications

(23 citation statements)

References 26 publications

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“…Moreover, our regression results are in line with recent findings of Kliger and Kudryavtsev (2013). They investigate the effects of market analysts' recommendations on VIX changes and found that positive (negative) excess returns following recommendation upgrades (downgrades) are stronger when accompanied by daily VIX decreases (increases).…”

Section: Please Insert Table 2 About Heresupporting

confidence: 91%

Unravelling the asymmetric volatility puzzle: A novel explanation of volatility through anchoring

Ormos¹,

Timotity²

2016

Economic Systems

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This paper discusses a novel explanation for asymmetric volatility based on the anchoring behavioral pattern. Anchoring as a heuristic bias causes investors focusing on recent price changes and price levels, which two lead to a belief in continuing trend and mean-reversion respectively. The empirical results support our theoretical explanation through an analysis of large price fluctuations in the S&P 500 and the resulting effects on implied and realized volatility. These results indicate that asymmetry (a negative relationship) between shocks and volatility in the subsequent period indeed exists. Moreover, contrary to previous research, our empirical tests also suggest that implied volatility is not simply an upward biased predictor of future deviation compensating for the variance of the volatility but rather, due to investors' systematic anchoring to losses and gains in their volatility forecasts, it is a cointegrated yet asymmetric over/under estimated financial instrument. We also provide results indicating that the medium-term implied volatility (measured by the VIX Index) is an unbiased though inefficient estimation of realized volatility, while in contrast, the short-term volatility (measured by the recently introduced VXST Index representing the 9-day implied volatility) is also unbiased and yet efficient.Keywords: Anchoring; Implied volatility; Realized volatility; Asymmetric volatility; VIX; VXST JEL classification: G02; G14; C53; C58; Acknowledgements: We are grateful to the conference participants at the Workshop on Behavioural Economics and Industrial Organization at Corvinus University, 2014. We would like to gratefully acknowledge the valuable comments and suggestions of the anonymous referee that contribute to a substantially improved paper. Mihály Ormos acknowledges the support by the János Bolyai Research Scholarship of the Hugarian Academy of Sciences. Dusán Timotity acknowledges the support by the Fundation of Pallas Athéné Domus Scientiae.This paper is appearing in the Economic Systems. Please cite this article as: Ormos, M., Timotity, D., Unravelling the asymmetric volatility puzzle: A novel explanation of volatility through anchoring, Economic Systems (2016), DOI: 10.1016DOI: 10. /j.ecosys.2015 This is the pre-print version of our accepted paper before typesetting. 2 Unravelling the Asymmetric Volatility Puzzle A Novel Explanation of Volatility Through Anchoring AbstractThis paper discusses a novel explanation for asymmetric volatility based on the anchoring behavioral pattern. Anchoring as a heuristic bias causes investors focusing on recent price changes and price levels, which two lead to a belief in continuing trend and mean-reversion respectively. The empirical results support our theoretical explanation through an analysis of large price fluctuations in the S&P 500 and the resulting effects on implied and realized volatility. These results indicate that asymmetry (a negative relationship) between shocks and volatility in the subsequent period indeed exists. Moreover, contrar...

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Section: Please Insert Table 2 About Heresupporting

confidence: 91%

Unravelling the asymmetric volatility puzzle: A novel explanation of volatility through anchoring

Ormos¹,

Timotity²

2016

Economic Systems

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“…In other words, I expect that if the direction of a company-specific shock, either public or unobserved, corresponds to the quality of the contemporaneous investors' mood, proxied by the daily changes in VIX, then investors may consider the shock to have a greater subjective probability of leading to stock returns of the respective sign, which increases the magnitude of the shock, creating overreaction. This hypothesis is consistent with the findings by Kliger and Kudryavtsev (2013), suggesting that abnormal stock price reactions to positive (negative) company-specific news are more pronounced if the latter are accompanied by decreases (increases) in the daily value of VIX.…”

Section: Research Hypothesissupporting

confidence: 90%

“…Following the psychological "risk-as-feelings" model by Loewenstein et al (2001), claiming that good (bad) mood may cause people to perceive positive (negative) future outcomes as more probable, Kliger and Kudryavtsev (2013) suggest that the changes in the value of VIX may be negatively correlated with contemporaneous investors' mood, and empirically document that positive (negative) excess stock returns following analyst recommendation upgrades (downgrades) are stronger when accompanied by decreases (increases) in the daily value of VIX, the latter serving as a proxy for relatively good (bad) contemporaneous investors' mood. Following the same logic, I expect that if a major positive stock price move takes place on a day when the value of VIX falls, then its magnitude may be amplified by whose prices declined by at least 10% exhibit reversals and significantly outperform the market as a whole.…”

Section: Introductionmentioning

confidence: 99%

“…Following the "risk-as-feelings" model by Loewenstein et al (2001), indicating that decision-makers' feelings may affect their way of treating risk and uncertainty, Kliger and Kudryavtsev (2013) suggest that the changes in the value of VIX may be negatively correlated with contemporaneous investors' mood. They find supportive empirical evidence, documenting that positive (negative) excess stock returns following analyst recommendation upgrades (downgrades) are stronger when accompanied by decreases (increases) in the daily value of VIX.…”

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The Effect of Volatility Expectations on Large Stock Price Changes

Kudryavtsev¹

2018

IJAFS

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<p><em>My study explores the effect of future volatility expectations, embedded in VIX index, on large daily stock price changes and on subsequent stock returns. Following both psychological and financial literature claiming that good (bad) mood may cause people to perceive positive (negative) future outcomes as more probable and that the changes in the value of VIX may be negatively correlated with contemporaneous investors’ mood, I hypothesize that if a major positive (negative) stock price move takes place on a day when the value of VIX falls (rises), then its magnitude may be amplified by positive (negative) investors’ mood, creating price overreaction to the initial company-specific shock, which may result in subsequent price reversal. In line with my hypothesis, I document that both positive and negative large price moves accompanied by the opposite-sign contemporaneous changes in VIX are followed by significant reversals on the next two trading days and over five- and twenty-day intervals following the event, the magnitude of the reversals increasing over longer post-event windows, while large stock price changes taking place on the days when the value of VIX moves in the same direction are followed by non-significant price drifts. The results remain robust after accounting for additional company (size, beta, historical volatility) and event-specific (stock’s return and trading volume on the event day) factors, and are stronger for small and volatile stocks.</em></p>

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“…In addition, research has confirmed that a negative sentiment has a greater impact on return of the index than a positive sentiment. Kliger and Kudryavtsev (2013) focused on VIX. The aim of their study was to investigate the relationship between investors' market volatility expectations and their reaction to analyze stock returns.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Artificial Intelligence for Sensitivity Analysis in the Stock Market

Janková

Dostál

2019

Acta Univ. Agric. Silvic. Mendelianae Brun.

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The main contribution of this paper is to perform sensitivity analysis using artificial intelligence methods on the US stock market using alternative psychological indicators. The Takagi-Sugeno fuzzy model applies investor sentiment represented by VIX index and monitors the impact of economic optimism, political stability and control of the corruption index on the S&P 500 stock index. Alternative psychological indicators have been chosen that have not been explored in the context of stock index performance sensitivity. Investors primarily use fundamental and technical analysis as a source to determine when and what to buy into an investment portfolio. However, psychological factors that may indicate the strength of reaction to the market are often neglected. Fuzzy rules are determined and tested using a neuro-fuzzy inference system and then the rules are reduced by fuzzy clustering to improve performance of ANFIS. The membership function is defined as a Gaussian function because it has the least RMSE value. The sensitivity analysis confirmed that there is a significant impact of the political stability index and the economic optimism index on the S&P 500 performance. Conversely, the sensitivity analysis, unlike the previous study, did not confirm the strong impact of VIX on equity index performance. Results indicate that incorporating psychological indicators in macroeconomic models leads to better supervision and control of the financial markets.

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Volatility expectations and the reaction to analyst recommendations

Cited by 17 publications

References 26 publications

Unravelling the asymmetric volatility puzzle: A novel explanation of volatility through anchoring

Unravelling the asymmetric volatility puzzle: A novel explanation of volatility through anchoring

The Effect of Volatility Expectations on Large Stock Price Changes

Utilization of Artificial Intelligence for Sensitivity Analysis in the Stock Market

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