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Manager Sentiment and Stock Market Volatility

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  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

Abstract

This paper hypothesizes that corporate managers’ sentiment can predict aggregate stock market volatility. Using a k-th order nonparametric causality-in-quantiles test, we show that manager sentiment is a stronger predictor for volatility than stock return, especially when one accommodates for misspecification in the linear predictive model via a nonparametric data-driven approach. But, predictability is completely absent at extreme ends of the conditional distribution of return, and at the upper end of the same for volatility.

Suggested Citation

  • Rangan Gupta, 2018. "Manager Sentiment and Stock Market Volatility," Working Papers 201853, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201853
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    References listed on IDEAS

    as
    1. Kumari, Jyoti & Mahakud, Jitendra, 2015. "Does investor sentiment predict the asset volatility? Evidence from emerging stock market India," Journal of Behavioral and Experimental Finance, Elsevier, vol. 8(C), pages 25-39.
    2. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch & Mark E. Wohar, 2018. "Terror attacks and stock-market fluctuations: evidence based on a nonparametric causality-in-quantiles test for the G7 countries," The European Journal of Finance, Taylor & Francis Journals, vol. 24(4), pages 333-346, March.
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    6. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    7. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    8. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    9. Mehmet Balcilar & Rangan Gupta & Duc Khuong Nguyen & Mark E. Wohar, 2018. "Causal effects of the United States and Japan on Pacific-Rim stock markets: nonparametric quantile causality approach," Applied Economics, Taylor & Francis Journals, vol. 50(53), pages 5712-5727, November.
    10. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch & Mark Wohar, 2016. "Do Terror Attacks Affect the Dollar-Pound Exchange Rate? A Nonparametric Causality-in-Quantiles Analysis," Working Papers 201615, University of Pretoria, Department of Economics.
    11. Jeong, Kiho & Härdle, Wolfgang K. & Song, Song, 2012. "A Consistent Nonparametric Test For Causality In Quantile," Econometric Theory, Cambridge University Press, vol. 28(4), pages 861-887, August.
    12. Nishiyama, Yoshihiko & Hitomi, Kohtaro & Kawasaki, Yoshinori & Jeong, Kiho, 2011. "A consistent nonparametric test for nonlinear causality—Specification in time series regression," Journal of Econometrics, Elsevier, vol. 165(1), pages 112-127.
    13. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    14. Mehmet Balcilar & Riza Demirer & Rangan Gupta & Mark E. Wohar, 2018. "Differences of opinion and stock market volatility: evidence from a nonparametric causality-in-quantiles approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 42(2), pages 339-351, April.
    15. Mehmet Balcilar & Rangan Gupta & Clement Kyei, 2018. "Predicting Stock Returns And Volatility With Investor Sentiment Indices: A Reconsideration Using A Nonparametric Causality†In†Quantiles Test," Bulletin of Economic Research, Wiley Blackwell, vol. 70(1), pages 74-87, January.
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    Cited by:

    1. Rangan Gupta & Chi Keung Marco Lau & Wendy Nyakabawo, 2018. "Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment," Working Papers 201866, University of Pretoria, Department of Economics.

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    More about this item

    Keywords

    Manager Sentiment; Asset Pricing; Return and Volatility Predictability;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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