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Financial trading systems: Is recurrent reinforcement the via?

Author

Listed:
  • Francesco Bertoluzzo

    () (Consorzio Venezia Ricerche)

  • Marco Corazza

    () (Department of Applied Mathematics, University of Venice)

Abstract

In this paper we propose a financial trading system whose trading strategy is developed by means of an artificial neural network approach based on a learning algorithm of recurrent reinforcement type. In general terms, this kind of approach consists: first, in directly specifying a trading policy based on some predetermined investorâs measure of profitability; second, in directly setting the financial trading system while using it. In particular, with respect to the prominent literature, in this contribution: first, we take into account as measure of profitability the reciprocal of the returns weighted direction symmetry index instead of the wide-spread Sharpe ratio; second, we obtain the differential version of the measure of profitability we consider, and obtain all the related learning relationships; third, we propose a simple procedure for the management of drawdown-like phenomena; finally, we apply our financial trading approach to some of the most prominent assets of the Italian stock market.

Suggested Citation

  • Francesco Bertoluzzo & Marco Corazza, 2006. "Financial trading systems: Is recurrent reinforcement the via?," Working Papers 141, Department of Applied Mathematics, Università Ca' Foscari Venezia.
  • Handle: RePEc:vnm:wpaper:141
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    References listed on IDEAS

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    1. Lettau, Martin, 1997. "Explaining the facts with adaptive agents: The case of mutual fund flows," Journal of Economic Dynamics and Control, Elsevier, vol. 21(7), pages 1117-1147, June.
    2. Myerson, Roger B. & Satterthwaite, Mark A., 1983. "Efficient mechanisms for bilateral trading," Journal of Economic Theory, Elsevier, vol. 29(2), pages 265-281, April.
    3. Mikhail Anufriev & Jasmina Arifovic & John Ledyard & Valentyn Panchenko, 2013. "Efficiency of continuous double auctions under individual evolutionary learning with full or limited information," Journal of Evolutionary Economics, Springer, pages 539-573.
    4. Mendelson, Haim, 1985. "Random competitive exchange: Price distributions and gains from trade," Journal of Economic Theory, Elsevier, vol. 37(2), pages 254-280, December.
    5. Zhan, Wenjie & Friedman, Daniel, 2007. "Markups in double auction markets," Journal of Economic Dynamics and Control, Elsevier, vol. 31(9), pages 2984-3005, September.
    6. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    7. Leininger, W. & Linhart, P. B. & Radner, R., 1989. "Equilibria of the sealed-bid mechanism for bargaining with incomplete information," Journal of Economic Theory, Elsevier, vol. 48(1), pages 63-106, June.
    8. Rustichini, Aldo & Satterthwaite, Mark A & Williams, Steven R, 1994. "Convergence to Efficiency in a Simple Market with Incomplete Information," Econometrica, Econometric Society, vol. 62(5), pages 1041-1063, September.
    9. Roberto Cervone & Stefano Galavotti & Marco LiCalzi, 2009. "Symmetric Equilibria in Double Auctions with Markdown Buyers and Markup Sellers," Working Papers 187, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    10. Mark A. Satterthwaite & Steven R. Williams, 2002. "The Optimality of a Simple Market Mechanism," Econometrica, Econometric Society, vol. 70(5), pages 1841-1863, September.
    11. Dawid, Herbert, 1999. "On the convergence of genetic learning in a double auction market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1545-1567, September.
    12. Shira Fano & Marco LiCalzi & Paolo Pellizzari, 2013. "Convergence of outcomes and evolution of strategic behavior in double auctions," Journal of Evolutionary Economics, Springer, pages 513-538.
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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. Marco Corazza & Francesco Bertoluzzo, 2014. "Q-Learning-based financial trading systems with applications," Working Papers 2014:15, Department of Economics, University of Venice "Ca' Foscari".

    More about this item

    Keywords

    Financial trading system; recurrent reinforcement learning; no-hidden-layer perceptron model; returns weighted directional symmetry measure; gradient ascent technique; Italian stock market.;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G31 - Financial Economics - - Corporate Finance and Governance - - - Capital Budgeting; Fixed Investment and Inventory Studies

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