Title: Predicting stock price movement using a stack of multi-sized filter maps and convolutional neural networks

Authors: Yash Thesia; Vidhey Oza; Priyank Thakkar

Addresses: Computer Science and Engineering Department, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India ' Computer Science and Engineering Department, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India ' Computer Science and Engineering Department, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

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.

Keywords: convolutional neural network; CNN; stock market; stock price movement prediction; inception networks; technical indicators.

DOI: 10.1504/IJCSE.2022.120784

International Journal of Computational Science and Engineering, 2022 Vol.25 No.1, pp.22 - 33

Received: 14 Jun 2020
Accepted: 06 Mar 2021
Published online: 08 Feb 2022 *

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