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Investor sentiment and the prediction of stock returns: a quantile regression approach

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  • Chaoqun Ma
  • Shisong Xiao
  • Zonggang Ma

Abstract

We employ quantile regression to provide a detailed picture of the stock return forecasting ability of investor sentiment. We find that investor sentiment predicts aggregate stock returns at lower quantiles. However, the forecasting power is lost at upper quantiles. The results are robust after controlling for a comprehensive set of macroeconomic and financial predictors and for characteristic portfolios. We also show that investor sentiment consists mainly of cash flow news and contains little information about discount rate news. The ability to forecast cash flows increases gradually from the lower quantiles to upper quantiles. Our results do not support that the ability of investor sentiment to predict stock returns comes from a rational forecast of future cash flows.

Suggested Citation

  • Chaoqun Ma & Shisong Xiao & Zonggang Ma, 2018. "Investor sentiment and the prediction of stock returns: a quantile regression approach," Applied Economics, Taylor & Francis Journals, vol. 50(50), pages 5401-5415, October.
    • Handle: RePEc:taf:applec:v:50:y:2018:i:50:p:5401-5415
    DOI: 10.1080/00036846.2018.1486993
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    Citations

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    Cited by:

    1. Yousra Trichilli & Mouna Abdelhédi & Mouna Boujelbène Abbes, 2020. "The thermal optimal path model: Does Google search queries help to predict dynamic relationship between investor’s sentiment and indexes returns?," Journal of Asset Management, Palgrave Macmillan, vol. 21(3), pages 261-279, May.
    2. Shi, Jinyan & Yu, Conghui & Liu, Xiangkun & Li, Yanxi, 2020. "Predicting firm stock returns with customer stock returns: Moderating effects of customer characteristics," Research in International Business and Finance, Elsevier, vol. 54(C).
    3. He, Zhifang, 2022. "Asymmetric impacts of individual investor sentiment on the time-varying risk-return relation in stock market," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 177-194.
    4. 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.
    5. Pedro Manuel Nogueira Reis & Carlos Pinho, 2021. "A Reappraisal of the Causal Relationship between Sentiment Proxies and Stock Returns," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 22(4), pages 420-442, October.
    6. Yang, Ann Shawing, 2020. "Misinformation corrections of corporate news: Corporate clarification announcements," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    7. Emre Cevik & Buket Kirci Altinkeski & Emrah Ismail Cevik & Sel Dibooglu, 2022. "Investor sentiments and stock markets during the COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-34, December.
    8. Dias, Ishanka K. & Fernando, J.M. Ruwani & Fernando, P. Narada D., 2022. "Does investor sentiment predict bitcoin return and volatility? A quantile regression approach," International Review of Financial Analysis, Elsevier, vol. 84(C).
    9. He, Zhifang, 2023. "Geopolitical risks and investor sentiment: Causality and TVP-VAR analysis," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    10. Pham, Linh & Cepni, Oguzhan, 2022. "Extreme directional spillovers between investor attention and green bond markets," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 186-210.

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