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

Radosław Puka and Bartosz Łamasz
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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

Energies, 2020, vol. 13, issue 17, 1-20

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.

Keywords: price risk; WTI crude oil options; artificial neural networks (ANN); support decision-making (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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