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Forecasting Daily Highs and Lows of Liquid Assets with Neural Networks

In: Operations Research Proceedings 2012

Author

Listed:
  • Hans-Jörg Mettenheim

    (Leibniz Universität Hannover, Institut für Wirtschaftsinformatik)

  • Michael H. Breitner

    (Leibniz Universität Hannover, Institut für Wirtschaftsinformatik)

Abstract

We use Historically Consistent Neural Networks (HCNN) to forecast intraday highs and lows of liquid and volatile stocks. To build our forecast model we only use easily available open-high-low-close (OHLC) data. This is a novel application of HCNN to intraday data. It is important to note that model performance evaluation does not need tick data, which is more difficult to obtain and to handle. However, there is only few academic literature on forecasting intraday high-lows with neural networks. The present study aims at closing this gap. We measure the economic performance of a strategy using forecast high-low data. The strategy is intraday. It exits all positions at the close. This reduces the risk of being caught in abrupt price moves without the ability to exit the position. We test the strategy on a sample of S&P500 stocks. It turns out that profit and reward to risk ratios are attractive and confirm the good results of previous studies on an emerging market.

Suggested Citation

  • Hans-Jörg Mettenheim & Michael H. Breitner, 2014. "Forecasting Daily Highs and Lows of Liquid Assets with Neural Networks," Operations Research Proceedings, in: Stefan Helber & Michael Breitner & Daniel Rösch & Cornelia Schön & Johann-Matthias Graf von der Schu (ed.), Operations Research Proceedings 2012, edition 127, pages 253-258, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-00795-3_37
    DOI: 10.1007/978-3-319-00795-3_37
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    Cited by:

    1. Huiwen Wang & Wenyang Huang & Shanshan Wang, 2021. "Forecasting open-high-low-close data contained in candlestick chart," Papers 2104.00581, arXiv.org.

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