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The "CAPS" Prediction System and Stock Market Returns

Author

Listed:
  • Avery, Christopher N.
  • Chevalier, Judith
  • Zeckhauser, Richard Jay
Abstract
We study the predictive power of approximately 2.5 million stock picks submitted by individual users to the “CAPS†website run by the Motley Fool company (www.caps.fool.com). These picks prove to be surprisingly informative about future stock prices. Indeed, a strategy of shorting stocks with a disproportionate number of negative picks on the site and buying stocks with a disproportionate number of positive picks produces a return of over nine percent per annum over the sample period. These results are mostly driven by the fact that negative picks on the site strongly predict future stock price declines; positive picks on the site produce returns that are statistically indistinguishable from the market. A Fama French decomposition suggests that these results are largely due to stock-picking rather than style factors or market timing.

Suggested Citation

  • Avery, Christopher N. & Chevalier, Judith & Zeckhauser, Richard Jay, 2011. "The "CAPS" Prediction System and Stock Market Returns," Scholarly Articles 5098427, Harvard Kennedy School of Government.
    • Handle: RePEc:hrv:hksfac:5098427
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    Cited by:

    1. Cathy Yi-Hsuan Chen & Christian M. Hafner, 2019. "Sentiment-Induced Bubbles in the Cryptocurrency Market," JRFM, MDPI, vol. 12(2), pages 1-12, April.
    2. He, Xue-Zhong & Treich, Nicolas, 2017. "Prediction market prices under risk aversion and heterogeneous beliefs," Journal of Mathematical Economics, Elsevier, vol. 70(C), pages 105-114.
    3. Kommel, Karl Arnold & Sillasoo, Martin & Lublóy, Ágnes, 2019. "Could crowdsourced financial analysis replace the equity research by investment banks?," Finance Research Letters, Elsevier, vol. 29(C), pages 280-284.
    4. Sergey Nasekin & Cathy Yi-Hsuan Chen, 2020. "Deep learning-based cryptocurrency sentiment construction," Digital Finance, Springer, vol. 2(1), pages 39-67, September.
    5. Breitmayer, Bastian & Massari, Filippo & Pelster, Matthias, 2019. "Swarm intelligence? Stock opinions of the crowd and stock returns," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 443-464.
    6. Alasdair Brown & Dooruj Rambaccussing & James Reade & Giambattista Rossi, 2016. "Using Social Media to Identify Market Inefficiencies: Evidence from Twitter and Betfair," Economics Discussion Papers em-dp2016-01, Department of Economics, University of Reading.
    7. Chen, Cathy Yi-Hsuan & Després, Roméo & Guo, Li & Renault, Thomas, 2019. "What makes cryptocurrencies special? Investor sentiment and return predictability during the bubble," IRTG 1792 Discussion Papers 2019-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. André Betzer & Jan Philipp Harries, 2022. "How online discussion board activity affects stock trading: the case of GameStop," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(4), pages 443-472, December.
    9. Magnus Dahlquist & José Vicente Martinez & Paul Söderlind, 2017. "Individual Investor Activity and Performance," The Review of Financial Studies, Society for Financial Studies, vol. 30(3), pages 866-899.
    10. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    11. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    12. Ramirez, Philip & Reade, J. James & Singleton, Carl, 2023. "Betting on a buzz: Mispricing and inefficiency in online sportsbooks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1413-1423.
    13. Alasdair Brown & Dooruj Rambaccussing & J. James Reade & Giambattista Rossi, 2016. "Using Social Media to Identify Market Ine!ciencies: Evidence from Twitter and Betfair," Working Papers 2016-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    14. Michael Nofer & Oliver Hinz, 2015. "Using Twitter to Predict the Stock Market," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 57(4), pages 229-242, August.
    15. Godfrey Charles-Cadogan, 2012. "Alpha Representation For Active Portfolio Management and High Frequency Trading In Seemingly Efficient Markets," Papers 1206.2662, arXiv.org.
    16. Dushmanta Kumar Padhi & Neelamadhab Padhy & Akash Kumar Bhoi & Jana Shafi & Muhammad Fazal Ijaz, 2021. "A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators," Mathematics, MDPI, vol. 9(21), pages 1-31, October.
    17. Alasdair Brown & Dooruj Rambaccussing & J. James Reade & Giambattista Rossi, 2018. "Forecasting With Social Media: Evidence From Tweets On Soccer Matches," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1748-1763, July.
    18. Manuel Ammann & Nic Schaub, 2021. "Do Individual Investors Trade on Investment-Related Internet Postings?," Management Science, INFORMS, vol. 67(9), pages 5679-5702, September.
    19. Michael Weba, 2024. "Investment strategies based on forecasts are (almost) useless," Papers 2408.01772, arXiv.org.

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    More about this item

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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