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Financial trading systems: Is recurrent reinforcement the via?

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Author Info

  • 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.

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Bibliographic Info

Paper provided by Department of Applied Mathematics, Università Ca' Foscari Venezia in its series Working Papers with number 141.

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Length: 17 pages
Date of creation: Oct 2006
Date of revision:
Handle: RePEc:vnm:wpaper:141

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Related research

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

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