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On the Profitability of Scalping Strategies Based on Neural Networks

  • Marina Resta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4253)

Abstract

We analyze the potential of unsupervised neural networks when they are employed to support intraday trading activity on financial markets. Several time frequencies have been considered: from five minutes to daily trades. At the current stage our major findings may be summarized as follows: a) unsupervised neural networks are helpful to localize profitable intraday patterns, and they make possible to achieve higher performances than common trading rules; b) trading strategies based on neural networks make exploitable with profits almost continuous trades (i.e. scalping), until transaction costs maintain below proper thresholds.

Keywords

Neural Network Transaction Cost Trading Strategy Trading System Time Frequency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marina Resta
    • 1
  1. 1.DIEM sez. di Matematica FinanziariaUniversity of GenovaGenovaItaly

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