The Evolution of Market Efficiency: 103 Years Daily Data of the Dow

  • Anthony Yanxiang Gu
  • Joseph Finnerty
Article

Abstract

Autocorrelation in daily returns of the Dow 30 Index fluctuates significantly over time and reveals a declining trend after World War II. The relation between autocorrelation and volatility is negative and nonlinear. The relation between autocorrelation and volume is also negative and nonlinear. Returns exhibit positive autocorrelation during years with higher autocorrelation, and negative autocorrelation during years with lower autocorrelation. Positive autocorrelation appears more frequently during periods of low volatility, while negative autocorrelation appears more frequently during periods of high volatility. Current period's autocorrelation is related to previous period's autocorrelation and to both the previous and the current period's volatility and rate of return, which implies that investors incorporate previous period's pattern of market behavior into their trading strategy.

autocorrelation evolution market efficiency 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alabiso, V., K. S. Tunney and C. Zoeller, Flash! The Associated Press Covers the World, The Associated Press and Harry N. Abrams, Inc. Publishers, 1998.Google Scholar
  2. Albright, S. C., Statistics for Business and Economics, New York: Macmillan, 1987.Google Scholar
  3. Bollerslev, T., R. Engle and J. Wooldridge, “A Capital Asset Pricing Model with Time Varying Variance.” Journal of Political Economy 96, February, 116-131, (1988).Google Scholar
  4. Campbell, J. Y. and L. Hentschel, “No News is Good News.” Journal of Financial Economics 31, 281-318, (1992).Google Scholar
  5. Campbell, J. Y., S. J. Grossman and J. Wang, “Trading Volume and Serial Correlation in Stock Returns.” Quarterly Journal of Economics 108(3), 905-934, (1993).Google Scholar
  6. Chiang, T. C., “Stock Returns and Conditional Variance-Covariance: Evidence from Asian Stock Markets,” in J. Jay Choi and John Doukas (Eds), Emerging Markets: Finance and Investments, Greenwood Publishing Group Inc., Westport, CT, 1998.Google Scholar
  7. Cochrane, J., “How Big is the Random Walk in GNP?” Journal of Political Economy 96, 893-920, (1998).Google Scholar
  8. Congress of the United States Office of Technology Assessment, Trading Around the Clock: Global Securities Markets and Information Technology, 1990.Google Scholar
  9. Conrad, J. and G. Kaul, “Time-Variation in Expected Returns.” Journal of Business 61, 409-425, (1988).Google Scholar
  10. Crowley, D. and P. Heyer, Communication in History: Technology, Culture, Society, New York: Longman, 1991.Google Scholar
  11. Fama, E. F., “The Behavior of Stock Prices.” Journal of Business 38, January, 34-105, (1965).Google Scholar
  12. Fama, E. F., “Efficient Capital Markets: A Review of Theory and Empirical Work.” Journal of Finance 25(2), 383-417, (1970).Google Scholar
  13. Fama, E. F. and K. R. French, “Permanent and Temporary Components of Stock Prices.” Journal of Political Economy 96, 246-273, (1988).Google Scholar
  14. Fama, E. F., “Efficient Capital Markets II.” Journal of Finance 46(5), 1575-1617, (1991).Google Scholar
  15. Fatemi, A. and J. Park, “Seasonal Patterns in Japanese ADR Returns and the US Stock Market Influence.” Japan and the World Economy 8, 65-79, (1996).Google Scholar
  16. French, K., R. G.W. Schwert and R. F. Stambaugh, “Expected Stock Returns and Volatility.” Journal of Financial Economics 19, 3-29, (1987).Google Scholar
  17. Griffith, R. J., Relative Strength-An Indicator for Investment in the Equity Market. Thesis, Department of Statistics, Cranfield College, Cambridge, England, 1970.Google Scholar
  18. Gu, A.Y., “Index Size, Autocorrelation and Evolution of Market Efficiency.” Academy of Accounting and Financial Studies Journal, December, 2001, forthcoming.Google Scholar
  19. Gu, A. Y., “Investors' Maturity and Stock Market Behavior.” Review of the Academy of Finance 1(1), 76-85, (2001).Google Scholar
  20. Gu, A. Y., “Information Speed and Stock Market Behavior.” Review of the Academy of Finance, 2(1), 137-146, (2002).Google Scholar
  21. Gu, A. Y., “Information Frequency and Stock Return Behavior.” Proceedings of the Academy of Accounting and Financial Studies Journal, forthcoming.Google Scholar
  22. Hagerman, R. L. and R. D. Richmond, “Random Walks, Martingales and the OTC.” Journal of Finance 2, 897-909, (1973).Google Scholar
  23. Huizinga, J., “An Empirical Investigation of the Long-run Behavior of Real Exchange Rates.” Carnegie-Rochester Conference Series on Public Policy 27, 149-214, (1987).Google Scholar
  24. Karpoff, J. M., “The Relation between Price Changes and Trading Volume: A Survey.” Journal of Financial and Quantitative Analysis 22, March, 109-126, (1987).Google Scholar
  25. LeBaron, B., “Some Relations between Volatility and Serial Correlation in Stock Market Returns.” Journal of Business LXV, 199-219, (1992).Google Scholar
  26. Liu, C. Y. and J. He., “A Variance-Ratio Test of Random Walks in Foreign Exchange Rates.” Journal of Finance 46, 773-785, (1991).Google Scholar
  27. Lo, A. W. and A. C. MacKinlay, “Stock Market prices Do Not Follow Random Walks: Evidence From a Simple Specification Test.” The Review of Financial Studies 1(1), Spring, 41-66, (1988).Google Scholar
  28. Lucas, H. C. and R. A. Schwartz, (Eds), The Challenge of Information Technology for the Securities Markets: Liquidity, Volatility, and Global Trading. Homewood, Illinois: Dow Jones-Irwin, 1989.Google Scholar
  29. Sentana, E. and S. Wadhwani, “Feedback Traders and Stock Return Autocorrelations: Evidence from a Century of Daily Data.” Economic Journal CII, 415-425, (1992).Google Scholar
  30. Urrutia, J. L., “Tests of Random walk and Market Efficiency for Latin American Emerging Equity Markets.” The Journal of Financial Research 18(3), 299-309, (1995).Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Anthony Yanxiang Gu
    • 1
  • Joseph Finnerty
    • 2
  1. 1.State University of New YorkGeneseo
  2. 2.The University of Illinois at Urbana-ChampaignUSA

Personalised recommendations