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Financial trading systems: Is recurrent reinforcement the via?

Francesco Bertoluzzo () and Marco Corazza ()
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Francesco Bertoluzzo: Consorzio Venezia Ricerche

No 141, Working Papers from Department of Applied Mathematics, Università Ca' Foscari Venezia

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.

Keywords: Financial trading system; recurrent reinforcement learning; no-hidden-layer perceptron model; returns weighted directional symmetry measure; gradient ascent technique; Italian stock market. (search for similar items in EconPapers)
JEL-codes: C45 C61 C63 G31 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2006-10
New Economics Papers: this item is included in nep-cmp, nep-mst and nep-rmg
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Citations: View citations in EconPapers (2)

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