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A non-linear approach for predicting stock returns and volatility with the use of investor sentiment indices

Author

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  • Stelios Bekiros
  • Rangan Gupta
  • Clement Kyei

Abstract

The popular sentiment-based investor index S -super-BW introduced by Baker and Wurgler (2006, 2007) is shown to have no predictive ability for stock returns. However, Huang et al. (2015) developed a new investor sentiment index, S -super-PLS, which 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, S -super-BW and S -super-PLS is characterized by structural instability and inherent nonlinearity. Given this, using a nonparametric causality approach, we show that neither S -super-BW nor S -super-PLS predicts stock market returns or even its volatility, as opposed to previous empirical evidence.

Suggested Citation

  • Stelios Bekiros & Rangan Gupta & Clement Kyei, 2016. "A non-linear approach for predicting stock returns and volatility with the use of investor sentiment indices," Applied Economics, Taylor & Francis Journals, vol. 48(31), pages 2895-2898, July.
    • Handle: RePEc:taf:applec:v:48:y:2016:i:31:p:2895-2898
    DOI: 10.1080/00036846.2015.1130793
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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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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).
    15. 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.

    More about this item

    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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