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Using Sentiment and Momentum to Predict Stock Returns

Author

Listed:
  • Kevin J. Lansing
  • Michael Tubbs
Abstract
Studies that seek to forecast stock price movements often consider measures of market sentiment or stock return momentum as predictors. Recent research shows that a multiplicative combination of sentiment and momentum can help predict the return on the Standard & Poor?s 500 stock index over the next month. This predictive power derives mainly from periods when sentiment has been declining over the past year and recent return momentum is negative?periods that coincide with an increase in investor attention to the stock market as measured by a Google search volume index.

Suggested Citation

  • Kevin J. Lansing & Michael Tubbs, 2018. "Using Sentiment and Momentum to Predict Stock Returns," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    • Handle: RePEc:fip:fedfel:00180
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    File URL: https://www.frbsf.org/economic-research/files/el2018-29.pdf
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    References listed on IDEAS

    as
    1. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
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    Cited by:

    1. Lansing, Kevin J. & LeRoy, Stephen F. & Ma, Jun, 2022. "Examining the sources of excess return predictability: Stochastic volatility or market inefficiency?," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 50-72.
    2. Ngoc Bao Vuong, Yoshihisa Suzuki, 2020. "Does Fear has Stronger Impact than Confidence on Stock Returns?The Case of Asia-Pacific Developed Markets," Analele Stiintifice ale Universitatii "Alexandru Ioan Cuza" din Iasi - Stiinte Economice, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 67, pages 157-175, July.

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