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Regime-switching recurrent reinforcement learning for investment decision making

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  • Dietmar Maringer

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  • Tikesh Ramtohul

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  • Dietmar Maringer & Tikesh Ramtohul, 2012. "Regime-switching recurrent reinforcement learning for investment decision making," Computational Management Science, Springer, vol. 9(1), pages 89-107, February.
  • Handle: RePEc:spr:comgts:v:9:y:2012:i:1:p:89-107
    DOI: 10.1007/s10287-011-0131-1
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    File URL: http://hdl.handle.net/10.1007/s10287-011-0131-1
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    References listed on IDEAS

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    1. LeBaron, Blake, 1992. "Some Relations between Volatility and Serial Correlations in Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 65(2), pages 199-219, April.
    2. Sentana, Enrique & Wadhwani, Sushil B, 1992. "Feedback Traders and Stock Return Autocorrelations: Evidence from a Century of Daily Data," Economic Journal, Royal Economic Society, vol. 102(411), pages 415-425, March.
    3. Koutmos, Gregory, 1997. "Feedback trading and the autocorrelation pattern of stock returns: further empirical evidence," Journal of International Money and Finance, Elsevier, vol. 16(4), pages 625-636, August.
    4. Michael D. McKenzie & Robert W. Faff, 2003. "The Determinants of Conditional Autocorrelation in Stock Returns," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 26(2), pages 259-274.
    5. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
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    Cited by:

    1. Jin Zhang & Dietmar Maringer, 2016. "Using a Genetic Algorithm to Improve Recurrent Reinforcement Learning for Equity Trading," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 551-567, April.
    2. L.-F. Pau, 2014. "Discovering the dynamics of smart business networks," Computational Management Science, Springer, vol. 11(4), pages 445-458, October.

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