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The impacts of investor sentiment on returns and conditional volatility of international stock markets

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  • Utku Uygur
  • Oktay Taş

Abstract

One of the main arguments of behavioral finance is that some properties of asset prices are most probably regarded as deviations from fundamental value and they are generated by the participation of traders who are not fully rational, thus called noise traders. Noise trader theory postulates that sentiment traders have greater impact during high-sentiment periods than during low-sentiment periods, and sentiment traders miscalculate the variance of returns undermining the mean-variance relation. The main objective of this research is to construct a model to evaluate the returns and conditional volatility of various stock market indexes considering the changes in the investor sentiment by measuring the effects of noise trader demand shocks on returns and volatility. EGARCH model is used to determine 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 market index returns of U.S., Japan, Hong Kong, U.K., France, Germany, and Turkey. Weekly trading volumes of these indexes are regressed against a group of macroeconomic variables and the residuals are used as proxies 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. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • 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.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:3:p:1165-1179
    DOI: 10.1007/s11135-013-9827-3
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    3. Weiguo Zhang & Xue Gong & Chao Wang & Xin Ye, 2021. "Predicting stock market volatility based on textual sentiment: A nonlinear analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1479-1500, December.
    4. Mobeen Ur Rehman & Syed Muhammad Amir Shah, 2016. "Does Bilateral Market and Financial Integration Explains International Co-Movement Patterns 1," IJFS, MDPI, vol. 4(2), pages 1-13, May.
    5. Ramiah, Vikash & Xu, Xiaoming & Moosa, Imad A., 2015. "Neoclassical finance, behavioral finance and noise traders: A review and assessment of the literature," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 89-100.
    6. Kanzari, Dalel & Nakhli, Mohamed Sahbi & Gaies, Brahim & Sahut, Jean-Michel, 2023. "Predicting macro-financial instability – How relevant is sentiment? Evidence from long short-term memory networks," Research in International Business and Finance, Elsevier, vol. 65(C).
    7. Irene Cherono & Tobias Olweny & Tabitha Nasieku, 2019. "Investor Behavior Biases and Stock Market Reaction in Kenya," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 9(1), pages 1-6.
    8. Guo, Peng & Shi, Jing, 2024. "Geopolitical risks, investor sentiment and industry stock market volatility in China: Evidence from a quantile regression approach," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).

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    More about this item

    Keywords

    Noise trader theory; Investor sentiment; Conditional volatility; G02; G15;
    All these keywords.

    JEL classification:

    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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