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A Nonlinear Approach for Predicting Stock Returns and Volatility with the Use of Investor Sentiment Indices

Author

Listed:
  • Stelios Bekiros

    (European University Institute (EUI) and IPAG Business School)

  • Rangan Gupta

    (Department of Economics, University of Pretoria and IPAG Business School)

  • Clement Kyei

    (Department of Economics, University of Pretoria)

Abstract

The popular sentiment-based investor index SBW introduced by Baker and Wurgler (2006, 2007) is shown to have no predictive ability for stock returns. However, and Huang et al. (2015) developed a new investor sentiment index, SPLS, which they show can predict monthly stock returns based on a linear framework. However, the linear model may lead to misspecification and lack of robustness. We provide statistical evidence that the relationship between stock returns, SBW and SPLS is characterized by structural instability and inherent nonlinearity. Given this, using a nonparametric causality approach, we show that neither SBW or SPLS predicts stock market returns or even its volatility, as opposed to previous empirical evidence.

Suggested Citation

  • Stelios Bekiros & Rangan Gupta & Clement Kyei, 2015. "A Nonlinear Approach for Predicting Stock Returns and Volatility with the Use of Investor Sentiment Indices," Working Papers 201536, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201536
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    Cited by:

    1. Dash, Saumya Ranjan & Maitra, Debasish, 2018. "Does sentiment matter for stock returns? Evidence from Indian stock market using wavelet approach," Finance Research Letters, Elsevier, vol. 26(C), pages 32-39.
    2. 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.
    3. Naeem, Muhammad Abubakr & Farid, Saqib & Faruk, Balli & Shahzad, Syed Jawad Hussain, 2020. "Can happiness predict future volatility in stock markets?," Research in International Business and Finance, Elsevier, vol. 54(C).
    4. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2023. "Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(1), pages 111-122, January.
    5. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Business applications and state‐level stock market realized volatility: A forecasting experiment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 456-472, March.
    6. Nabila Jawadi & Fredj Jawadi & Abdoulkarim Idi Cheffou, 2020. "Computing the Time-Varying Effects of Investor Attention in Islamic Stock Returns," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 131-143, June.
    7. Chi-Wei Su & Xu-Yu Cai & Ran Tao, 2020. "Can Stock Investor Sentiment Be Contagious in China?," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    8. Rilwan Sakariyahu & Mohamed Sherif & Audrey Paterson & Eleni Chatzivgeri, 2021. "Sentiment‐Apt investors and UK sector returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3321-3351, July.
    9. Rameeza Andleeb & Arshad Hassan, 2023. "Impact of Investor Sentiment on Contemporaneous and Future Equity Returns in Emerging Markets," SAGE Open, , vol. 13(3), pages 21582440231, August.
    10. Zachary McGurk & Adam Nowak & Joshua C. Hall, 2020. "Stock returns and investor sentiment: textual analysis and social media," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 44(3), pages 458-485, July.
    11. 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.
    12. Deng, Chao & Zhou, Xiaoying & Peng, Cheng & Zhu, Huiming, 2022. "Going green: Insight from asymmetric risk spillover between investor attention and pro-environmental investment," Finance Research Letters, Elsevier, vol. 47(PA).
    13. Lao, Jiashun & Nie, He & Jiang, Yonghong, 2018. "Revisiting the investor sentiment–stock returns relationship: A multi-scale perspective using wavelets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 420-427.
    14. Ftiti, Zied & Hadhri, Sinda, 2019. "Can economic policy uncertainty, oil prices, and investor sentiment predict Islamic stock returns? A multi-scale perspective," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 40-55.
    15. Yamini Yadav & Pramod Kumar Naik, 2024. "Investors’ Irrational Sentiment and Stock Market Returns: A Quantile Regression Approach Using Indian Data," Business Perspectives and Research, , vol. 12(1), pages 45-64, January.

    More about this item

    Keywords

    Investor sentiment; stock markets; nonlinear dependence;
    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
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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