Predicting stock price movement using a stack of multi-sized filter maps and convolutional neural networks
by Yash Thesia; Vidhey Oza; Priyank Thakkar
International Journal of Computational Science and Engineering (IJCSE), Vol. 25, No. 1, 2022

Abstract: The paper explores the use of convolutional neural networks (CNNs) to predict the movement of the stock market from a classification perspective. Standard classification methods yield results with very low confidence and precision. We therefore propose a CNN enhanced by multi-size feature maps and spatial mapping that will provide a more accurate two-way classification for the collection of stocks. We also propose transforming stock indicators and data into a spatial map/image so that they can be processed using CNN. Our model and mapping fairs at an average of 80% weighted F1-score for a two-way classification of market movement. A trading strategy is also used and returns are compared to the benchmarks. The return of the proposed trading strategy for the period 2017 to 2020 is above the previous benchmarks.

Online publication date: Tue, 08-Feb-2022

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