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Forecasting and Trading the High-Low Range of Stocks and ETFs with Neural Networks

  • Hans-Jörg von Mettenheim
  • Michael H. Breitner
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)

Abstract

Intraday trading has some appealing characteristics. For example, overnight gap risks are greatly reduced. Intraday trading strategies tend to achieve better risk adjusted returns. However, academic literature on intraday trading strategies is relatively scarce compared to a significant amount of literature based on daily closing data. This may be partly related to the increased difficulty of dealing with intraday data. In the present paper we expand on a novel approach that builds an intraday trading strategy on open-high-low-close (OHLC) data. OHLC data is easily available from most database vendors. We use OHLC data to train neural networks that forecast the day’s high and low of liquid US stocks and ETFs. The resulting long-short strategy tries to take advantage of the daily trading range of a security and exits all positions at the close. A volatility filter further improves risk-adjusted returns.

Keywords

Neural networks intraday trading open-high-low-close data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hans-Jörg von Mettenheim
    • 1
  • Michael H. Breitner
    • 1
  1. 1.Information Systems ResearchLeibniz Universität HannoverHannoverGermany

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