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Trading futures spread portfolios: applications of higher order and recurrent networks

Author

Listed:
  • Christian Dunis
  • Jason Laws
  • Ben Evans

Abstract

This paper investigates the modelling and trading of oil futures spreads in the context of a portfolio of contracts. A portfolio of six spreads is constructed and each spread forecasted using a variety of modelling techniques, namely, a cointegration fair value model and three different types of neural network (NN), such as multi-layer perceptron (MLP), recurrent, and higher order NN models. In addition, a number of trading filters are employed to further improve the trading statistics of the models. Three different filters are optimized on an in-sample measure of down side risk-adjusted return, and these are then fixed out-of-sample. The filters employed are the threshold filter, correlation filter, and the transitive filter. The results show that the best in-sample model is the MLP with a transitive filter. This model is the best performer out-of-sample and also returns good out-of-sample statistics.

Suggested Citation

  • Christian Dunis & Jason Laws & Ben Evans, 2008. "Trading futures spread portfolios: applications of higher order and recurrent networks," The European Journal of Finance, Taylor & Francis Journals, vol. 14(6), pages 503-521.
    • Handle: RePEc:taf:eurjfi:v:14:y:2008:i:6:p:503-521
    DOI: 10.1080/13518470801890834
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    Citations

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    Cited by:

    1. Dongdong Lv & Zhenhua Huang & Meizi Li & Yang Xiang, 2019. "Selection of the optimal trading model for stock investment in different industries," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-20, February.
    2. Lubnau, Thorben & Todorova, Neda, 2015. "Trading on mean-reversion in energy futures markets," Energy Economics, Elsevier, vol. 51(C), pages 312-319.
    3. Alexander, Carol & Prokopczuk, Marcel & Sumawong, Anannit, 2013. "The (de)merits of minimum-variance hedging: Application to the crack spread," Energy Economics, Elsevier, vol. 36(C), pages 698-707.
    4. Christian L Dunis & Spiros D Likothanassis & Andreas S Karathanasopoulos & Georgios S Sermpinis & Konstantinos A Theofilatos, 2013. "A hybrid genetic algorithm–support vector machine approach in the task of forecasting and trading," Journal of Asset Management, Palgrave Macmillan, vol. 14(1), pages 52-71, February.
    5. Vitor Azevedo & Christopher Hoegner, 2023. "Enhancing stock market anomalies with machine learning," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 195-230, January.
    6. Krauss, Christopher, 2015. "Statistical arbitrage pairs trading strategies: Review and outlook," FAU Discussion Papers in Economics 09/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    7. Erhard Reschenhofer & Werner Ploberger & Georg Lehecka, 2014. "Detecting fuzzy periodic patterns in futures spreads," Statistical Papers, Springer, vol. 55(2), pages 487-496, May.

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