Making Financial Trading by Recurrent Reinforcement Learning
| Autor: | Francesco Bertoluzzo, Marco Corazza |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
No-hidden-layer perceptron model
Settore SECS-S/06 - Metodi mat. dell'economia e Scienze Attuariali e Finanziarie Financial trading system Artificial neural network Returns weighted directional symmetry measure Computer science business.industry Sharpe ratio Financial market Differential (mechanical device) computer.software_genre Recurrent reinforcement learning Gradient ascent technique World financial market indices Econometrics Reinforcement learning Trading strategy Profitability index Artificial intelligence Algorithmic trading business computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783540748267 KES (2) |
| DOI: | 10.1007/978-3-540-74827-4_78 |
| Popis: | In this paper we propose a financial trading system whose strategy is developed by means of an artificial neural network approach based on a recurrent reinforcement learning algorithm. In general terms, this kind of approach consists in specifying a trading policy based on some predetermined investor's measure of profitability, and in 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 this measure of profitability and obtain all the related learning relationships; third, we propose a procedure for the management of drawdown-like phenomena; finally, we apply our financial trading approach to some of the major world financial market indices. |
| Databáze: | OpenAIRE |
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