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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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    Citations

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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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