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Using Artificial Neural Networks to Find Buy Signals for WTI Crude Oil Call Options

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  • Radosław Puka

    (Faculty of Management, AGH University of Science and Technology, 30-059 Cracow, Poland)

  • Bartosz Łamasz

    (Faculty of Management, AGH University of Science and Technology, 30-059 Cracow, Poland)

Abstract

Oil price changes significantly influence proper functioning of the entire world economy, which entails the risk of losses. One of the possible ways to reduce this risk is to use some dedicated risk management tools, such as options contracts. In this paper we investigate the possibility of using multilayer perceptron neural networks to provide signals of long positions to take in the European call options. The experiments conducted on the West Texas Intermediate (WTI) oil prices (2630 observations coming from 16 June 2009 until 14 February 2020) allowed the selection of the network parameters, such as the activation function or the network error measure, giving the highest return on options contracts. Despite the fact that about 2/3 call options produced losses, the buying signals provided by the network for the test set allowed it to reach a positive return value. This indicates that neural networks can be a useful tool supporting the process of managing the risk of changes in oil prices using option contracts.

Suggested Citation

  • Radosław Puka & Bartosz Łamasz, 2020. "Using Artificial Neural Networks to Find Buy Signals for WTI Crude Oil Call Options," Energies, MDPI, vol. 13(17), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4359-:d:403221
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    Cited by:

    1. Radosław Puka & Bartosz Łamasz & Iwona Skalna & Beata Basiura & Jerzy Duda, 2023. "Knowledge Discovery to Support WTI Crude Oil Price Risk Management," Energies, MDPI, vol. 16(8), pages 1-14, April.
    2. Radosław Puka & Bartosz Łamasz & Marek Michalski, 2021. "Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk," Energies, MDPI, vol. 14(11), pages 1-26, June.

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