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A bifurcation model of market returns

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  • David Nawrocki
  • Tonis Vaga

Abstract

We propose a bifurcation model of market returns to describe transitions between an 'over-reaction' mean regressive state and 'under-reaction' trend persistent states. Since July 1929, the Dow Jones Industrial Average has exhibited non-stationary state transition behavior, including: (1) mean regressive behavior during crisis situations during the Great Depression of the 1930s and again in the crisis of 2008 when the availability of credit was interrupted; (2) strongly bifurcated, or trend persistent behavior from the 1940s through 1975; and (3) more efficient behavior since 1975. The bifurcation dynamic evident in the pre-1975 era is somewhat enhanced by conditional volume and moderate volatility. The bifurcation model is used to develop a quantitative measure of the degree of market efficiency, which indicates that the market has become more efficient, i.e. less trend persistent, since 1975 with the advent of negotiated commissions and computerized trading techniques. Similar findings are presented for the S&P 500 index and the CRSP Value Weighted Index, which represent large capitalization markets. Results for the CRSP Equal Weight Index are found to be significantly less efficient and more trend persistent than the larger capitalization CRSP Value Weighted Index.

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  • David Nawrocki & Tonis Vaga, 2014. "A bifurcation model of market returns," Quantitative Finance, Taylor & Francis Journals, vol. 14(3), pages 509-528, March.
    • Handle: RePEc:taf:quantf:v:14:y:2014:i:3:p:509-528
    DOI: 10.1080/14697688.2013.772651
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    References listed on IDEAS

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    1. Gabriel Perez‐Quiros & Allan Timmermann, 2000. "Firm Size and Cyclical Variations in Stock Returns," Journal of Finance, American Finance Association, vol. 55(3), pages 1229-1262, June.
    2. Avramov, Doron & Chordia, Tarun, 2006. "Predicting stock returns," Journal of Financial Economics, Elsevier, vol. 82(2), pages 387-415, November.
    3. Perez-Quiros, Gabriel & Timmermann, Allan, 2001. "Business cycle asymmetries in stock returns: Evidence from higher order moments and conditional densities," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 259-306, July.
    4. Chauvet, Marcelle & Potter, Simon, 2000. "Coincident and leading indicators of the stock market," Journal of Empirical Finance, Elsevier, vol. 7(1), pages 87-111, May.
    5. Nawrocki, David N., 1995. "Expectations, technological change, information and the theory of financial markets," International Review of Financial Analysis, Elsevier, vol. 4(2-3), pages 85-105.
    6. Beja, Avraham & Goldman, M Barry, 1980. "On the Dynamic Behavior of Prices in Disequilibrium," Journal of Finance, American Finance Association, vol. 35(2), pages 235-248, May.
    7. John M. Cozzolino & Michael J. Zahner, 1973. "The Maximum-Entropy Distribution of the Future Market Price of a Stock," Operations Research, INFORMS, vol. 21(6), pages 1200-1211, December.
    8. Wyart, Matthieu & Bouchaud, Jean-Philippe, 2007. "Self-referential behaviour, overreaction and conventions in financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 63(1), pages 1-24, May.
    9. Nawrocki, David N, 1996. "Market Dependence and Economic Events," The Financial Review, Eastern Finance Association, vol. 31(2), pages 287-312, May.
    10. Morse, Dale, 1980. "Asymmetrical Information in Securities Markets and Trading Volume," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(5), pages 1129-1148, December.
    11. Whitelaw, Robert F, 1994. "Time Variations and Covariations in the Expectation and Volatility of Stock Market Returns," Journal of Finance, American Finance Association, vol. 49(2), pages 515-541, June.
    12. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-996, November.
    13. Harrison Hong & Jeremy C. Stein, 1999. "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets," Journal of Finance, American Finance Association, vol. 54(6), pages 2143-2184, December.
    14. Groth, John C., 1979. "Security–Relative Information Market Efficiency: Some Empirical Evidence," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 14(3), pages 573-593, September.
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    Cited by:

    1. Zhou, Hao & Elliott, Robert J. & Kalev, Petko S., 2019. "Information or noise: What does algorithmic trading incorporate into the stock prices?," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 27-39.
    2. Kozłowska, M. & Denys, M. & Wiliński, M. & Link, G. & Gubiec, T. & Werner, T.R. & Kutner, R. & Struzik, Z.R., 2016. "Dynamic bifurcations on financial markets," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 126-142.

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