Sector influence aware stock trend prediction using 3D convolutional neural network

Stock price prediction is a difficult task. This article takes on this challenge and proposes a 3D Convolutional Neural Network based approach to classify the directional trends in a stock's price. To do that, five companies from a sector are grouped together, and the overall trend in each is predicted simultaneously. This is done to analyze the influence of one company on another. For each company, multiple technical indicators are chosen, and the stock prices are converted into a 3D image of size 15 x 15 x 5. To find the best features, we experiment with hierarchical clustering. To complement the 3D Convolutional Neural Network, we also examine the idea of ensemble learning. The proposed method and several existing models are combined to improve the performance of the system. Experimentation is performed on forty-five different companies of the National Stock Exchange. Compared to other similar techniques in literature, our work has achieved up to 35% annual returns for some stocks, with the average being 9.19%. Lastly, we also try to show that grouping companies together and making the prediction on a sector could serve as a new benchmark for stock trend classification. (C) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University.