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Modeling the effects of investor sentiment and conditional volatility in international stock markets

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  • Utku Uygur
  • Oktay TaÅŸ

Abstract

Behavioral finance argues that some properties of asset prices are most reasonably considered as deviations from fundamental value and they are caused by the presence of traders who are not fully rational hence called noise traders. Noise trader approach assumes that sentiment traders exert greater influence during highsentiment periods than during low-sentiment periods, and sentiment traders misestimate the variance of returns weakening the mean-variance relation. This study’s main objective is to provide a framework to model conditional volatility regarding the changes in the investor sentiment by measuring the effect of noise trader demand shocks on the volatility of stock market indexes of the various countries. GARCH, TARCH, and EGARCH models are used to test whether earning shocks have more influence on the conditional volatility in high sentiment periods weakening the mean-variance relation. This paper takes an international approach using weekly and daily returns of Nasdaq, Dow, S&P500, Nikkei225, HangSeng, FTSE100, CAC40, DAX, and ISE indexes. Weekly and daily trading volume changes of these indexes are used as a proxy for investor sentiment and significant evidence is found that there is asymmetric volatility in these market indexes and earning shocks have more influence on conditional volatility when the sentiment is high.

Suggested Citation

  • Utku Uygur & Oktay TaÅŸ, 2012. "Modeling the effects of investor sentiment and conditional volatility in international stock markets," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 2(5), pages 1-15.
    • Handle: RePEc:spt:apfiba:v:2:y:2012:i:5:f:2_5_15
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    Cited by:

    1. Bahloul, Walid & Bouri, Abdelfettah, 2016. "The impact of investor sentiment on returns and conditional volatility in U.S. futures markets," Journal of Multinational Financial Management, Elsevier, vol. 36(C), pages 89-102.
    2. Xue Gong & Weiguo Zhang & Yuan Zhao & Xin Ye, 2023. "Forecasting stock volatility with a large set of predictors: A new forecast combination method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1622-1647, November.
    3. Song, Ziyu & Gong, Xiaomin & Zhang, Cheng & Yu, Changrui, 2023. "Investor sentiment based on scaled PCA method: A powerful predictor of realized volatility in the Chinese stock market," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 528-545.
    4. Yu, Xing & Li, Yanyan & Gong, Xue & Zhang, Nan, 2022. "Evaluating the performance of futures hedging using factors-driven realized volatility," International Review of Financial Analysis, Elsevier, vol. 84(C).
    5. Gong, Xue & Zhang, Weiguo & Wang, Junbo & Wang, Chao, 2022. "Investor sentiment and stock volatility: New evidence," International Review of Financial Analysis, Elsevier, vol. 80(C).
    6. Zheng, Yao & Osmer, Eric, 2021. "Housing price dynamics: The impact of stock market sentiment and the spillover effect," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 854-867.
    7. Yang, Cai & Gong, Xu & Zhang, Hongwei, 2019. "Volatility forecasting of crude oil futures: The role of investor sentiment and leverage effect," Resources Policy, Elsevier, vol. 61(C), pages 548-563.
    8. Batsirai Winmore Mazviona, 2015. "Measuring Investor Sentiment on the Zimbabwe Stock Exchange," Asian Journal of Economic Modelling, Asian Economic and Social Society, vol. 3(2), pages 21-32, June.
    9. Shaista Wasiuzzaman, 2022. "Impact of COVID-19 on the Saudi stock market: analysis of return, volatility and trading volume," Journal of Asset Management, Palgrave Macmillan, vol. 23(4), pages 350-363, July.
    10. Utku Uygur & Oktay Taş, 2014. "The impacts of investor sentiment on returns and conditional volatility of international stock markets," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(3), pages 1165-1179, May.
    11. Zheng, Yao, 2015. "The linkage between aggregate investor sentiment and metal futures returns: A nonlinear approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 58(C), pages 128-142.
    12. Chen, Rongda & Bao, Weiwei & Jin, Chenglu, 2021. "Investor sentiment and predictability for volatility on energy futures Markets: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 112-129.
    13. Kamini Solanki & Yudhvir Seetharam, 2014. "Is consumer confidence an indicator of JSE performance?," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 8(3), September.

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