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Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search

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  • Joseph, Kissan
  • Babajide Wintoki, M.
  • Zhang, Zelin
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    Abstract

    We examine the ability of online ticker searches (e.g. XOM for Exxon Mobil) to forecast abnormal stock returns and trading volumes. Specifically, we argue that online ticker searches serve as a valid proxy for investor sentiment -- a set of beliefs about cash flows and investment risks that are not necessarily justified by the facts at hand -- which is generally associated with less sophisticated, retail investors. Based on prior research on investor sentiment, we expect online search intensity to forecast stock returns and trading volume, and also expect that highly volatile stocks, which are more difficult to arbitrage, will be more sensitive to search intensity than less volatile stocks. In a sample of S&P 500 firms over the period 2005-2008, we find that, over a weekly horizon, online search intensity reliably predicts abnormal stock returns and trading volumes, and that the sensitivity of returns to search intensity is positively related to the difficulty of a stock being arbitraged. More broadly, our study highlights the potential of employing online search data for other forecasting applications.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 27 (2011)
    Issue (Month): 4 (October)
    Pages: 1116-1127

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    Handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:1116-1127

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Investor sentiment Finance Fama-French model Portfolio tests Marketing;

    References

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    1. Schmeling, Maik, 2006. "Institutional and Individual Sentiment: Smart Money and Noise Trader Risk," Hannover Economic Papers (HEP) dp-337, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    2. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," NBER Working Papers 13189, National Bureau of Economic Research, Inc.
    3. George J. Stigler, 1961. "The Economics of Information," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 69, pages 213.
    4. Brad M. Barber & Terrance Odean & Ning Zhu, 2009. "Do Retail Trades Move Markets?," Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 151-186, January.
    5. Malcolm Baker & Jeffrey Wurgler, 2004. "Investor Sentiment and the Cross-Section of Stock Returns," NBER Working Papers 10449, National Bureau of Economic Research, Inc.
    6. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    7. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, Elsevier, vol. 33(1), pages 3-56, February.
    8. Carhart, Mark M, 1997. " On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    9. Beatty, Sharon E & Smith, Scott M, 1987. " External Search Effort: An Investigation across Several Product Categories," Journal of Consumer Research, University of Chicago Press, University of Chicago Press, vol. 14(1), pages 83-95, June.
    10. Shleifer, Andrei & Summers, Lawrence H, 1990. "The Noise Trader Approach to Finance," Journal of Economic Perspectives, American Economic Association, vol. 4(2), pages 19-33, Spring.
    11. Barber, Brad M. & Odean, Terrance & Zhu, Ning, 2009. "Systematic noise," Journal of Financial Markets, Elsevier, vol. 12(4), pages 547-569, November.
    12. Gustavo Grullon, 2004. "Advertising, Breadth of Ownership, and Liquidity," Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 439-461.
    13. Gregory W. Brown & Michael T. Cliff, 2005. "Investor Sentiment and Asset Valuation," The Journal of Business, University of Chicago Press, vol. 78(2), pages 405-440, March.
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    Cited by:
    1. Sofía B. Ramos & Helena Veiga & Pedro Latoeiro, 2013. "Predictability of stock market activity using Google search queries," Statistics and Econometrics Working Papers, Universidad Carlos III, Departamento de Estadística y Econometría ws130605, Universidad Carlos III, Departamento de Estadística y Econometría.
    2. Theologos Dergiades & Costas Milas & Theodore Panagiotidis, 2013. "Tweets, Google Trends and Sovereign Spreads in the GIIPS," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe, Hellenic Observatory, LSE 78, Hellenic Observatory, LSE.
    3. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.

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