Convolutional Neural Network Analysis of the Gramian Angular Field for Oil Services Exchange Traded Fund (ETF) Prediction

Prediction of oil prices is important for both countries and companies in terms of economic decisions to be made and financial policies to be created. However, due to the nature of financial price fluctuations, they are non-linear, complex, and uncertain. Because of this reasons, prediction of oil prices is a difficult problem. In the literature, statistical and machine learning methods have been used to predict oil prices. However, in most of these studies, oil prices were usually represented as time series. In this study, oil services Exchange-traded fund (ETF) data is represented as a 2D image using Gramian Angular Field (GAF) method, in order to benefit from the representation power of images and then AlexNet and VGG16 convolutional neural network (CNN) architectures are used to analyze this image datasets. To test the performances of existing and the proposed GAF-AlexNet and GAF-VGG16 models, a dataset covering period of 2016 and 2022 belonging to the VanEck Oil Services ETF (OIH), a fund that invests in energy companies, was used. Experimental evaluations show that the proposed models gave promising results. The findings suggest that integrating the predictive model into a trading system can provide valuable insights to researchers and investors as a decision support system.