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Stock market efficiency: an autopsy?

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  • Peter Fortune

Abstract

This article assesses the current state of the efficient market hypothesis, which was the conventional wisdom among academic economists in the 1970s and most of the 1980s. It concludes that empirical evidence provides an overwhelming case against the efficient market hypothesis. The evidence exists in the form of a number of well-established anomalies--the small firm effect, the closed-end fund puzzle, the Value Line enigma, the loser’s blessing and winner’s curse, and the January and weekend effects. ; These anomalies can be explained by resorting to a model of "noise trading," in which markets are segmented with the "smart money" enforcing a high degree of efficiency in the pricing of stocks of large firms while less informed traders dominate the market for small firms. This model can generate cycles in stock prices similar to those observed in the real world. The evidence suggests that in an inefficient market, policies designed to mitigate price changes might be appropriate.

Suggested Citation

  • Peter Fortune, 1991. "Stock market efficiency: an autopsy?," New England Economic Review, Federal Reserve Bank of Boston, issue Mar, pages 17-40.
  • Handle: RePEc:fip:fedbne:y:1991:i:mar:p:17-40
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    File URL: http://www.bostonfed.org/economic/neer/neer1991/neer291b.pdf
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    References listed on IDEAS

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    Cited by:

    1. Richard W. Kopcke, 1997. "Are stocks overvalued?," New England Economic Review, Federal Reserve Bank of Boston, issue Sep, pages 21-40.
    2. Francesco Guidi & Rakesh Gupta & Suneel Maheshwari, 2011. "Weak-form Market Efficiency and Calendar Anomalies for Eastern Europe Equity Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 10(3), pages 337-389, December.
    3. Georgios Bampinas & Stilianos Fountas & Theodore Panagiotidis, 2016. "The day-of-the-week effect is weak: Evidence from the European real estate sector," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 40(3), pages 549-567, July.
    4. Qin Xiao & Gee Kwang Randolph Tan, 2007. "Signal Extraction with Kalman Filter: A Study of the Hong Kong Property Price Bubbles," Urban Studies, Urban Studies Journal Limited, vol. 44(4), pages 865-888, April.
    5. Kohers, Theodor & Pandey, Vivek & Kohers, Gerald, 1997. "Using nonlinear dynamics to test for market efficiency among the major U.S. stock exchanges," The Quarterly Review of Economics and Finance, Elsevier, vol. 37(2), pages 523-545.
    6. Baur, Robert Frederick, 1992. "Overreaction in futures markets," ISU General Staff Papers 1992010108000010973, Iowa State University, Department of Economics.
    7. Lim, Kian-Ping & Brooks, Robert D. & Kim, Jae H., 2008. "Financial crisis and stock market efficiency: Empirical evidence from Asian countries," International Review of Financial Analysis, Elsevier, vol. 17(3), pages 571-591, June.
    8. Mookerjee, Rajen & Yu, Qiao, 1999. "An empirical analysis of the equity markets in China," Review of Financial Economics, Elsevier, vol. 8(1), pages 41-60, June.
    9. Graham, Michael & Peltomäki, Jarkko & Sturludóttir, Hildur, 2015. "Do capital controls affect stock market efficiency? Lessons from Iceland," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 82-88.
    10. Plastun, Alex & Plastun, Vyacheslav, 2013. "Force-majeure events and financial market’s behavior," MPRA Paper 58975, University Library of Munich, Germany.
    11. Christopher J. Neely, 1997. "Technical analysis in the foreign exchange market: a layman's guide," Review, Federal Reserve Bank of St. Louis, issue Sep, pages 23-38.

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