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The "CAPS" Prediction System and Stock Market Returns

Author

Listed:
  • Avery, Christopher

    (Harvard University)

  • Chevalier, Judith

    (Yale University)

  • Zeckhauser, Richard

    (Harvard University)

Abstract

We analyze the informational content of more than 1.2 million stock picks provided by more than 60,000 individuals from November 1, 2006 to October 31, 2007 on the CAPS open access website created by the Motley Fool company (www.caps.fool.com). On average, an individual pick in CAPS outperformed the S&P 500 index by 4 percentage points in the twelve months after the pick. We use a four-factor regression framework to estimate the excess returns associated with portfolios that aggregate these picks; a portfolio of the most popular CAPS stocks yielded excess returns of more than 18 percentage points annually relative to the portfolio of the least popular stocks.

Suggested Citation

  • Avery, Christopher & Chevalier, Judith & Zeckhauser, Richard, 2009. "The "CAPS" Prediction System and Stock Market Returns," Working Paper Series rwp09-011, Harvard University, John F. Kennedy School of Government.
    • Handle: RePEc:ecl:harjfk:rwp09-011
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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. 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.
    3. Sergey Nasekin & Cathy Yi-Hsuan Chen, 2020. "Deep learning-based cryptocurrency sentiment construction," Digital Finance, Springer, vol. 2(1), pages 39-67, September.
    4. 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.
    5. 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".
    6. 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.
    7. 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.
    8. 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.
    9. Godfrey Charles-Cadogan, 2012. "Alpha Representation For Active Portfolio Management and High Frequency Trading In Seemingly Efficient Markets," Papers 1206.2662, arXiv.org.
    10. 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.
    11. Manuel Ammann & Nic Schaub, 2021. "Do Individual Investors Trade on Investment-Related Internet Postings?," Management Science, INFORMS, vol. 67(9), pages 5679-5702, September.
    12. Michael Weba, 2024. "Investment strategies based on forecasts are (almost) useless," Papers 2408.01772, arXiv.org.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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, The Center for Economic Research.
    18. 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.
    19. 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.

    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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