Comparative Analysis of Deep Learning Models for Stock Price Prediction in the Indian Market

Moumita Barua (), Teerath Kumar (), Kislay Raj and Arunabha M. Roy
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Moumita Barua: Department of Business Analytics, Dublin Business School, 13/14 Aungier St, D02 WC04 Dublin, Ireland
Teerath Kumar: Department of Business Analytics, Dublin Business School, 13/14 Aungier St, D02 WC04 Dublin, Ireland
Kislay Raj: School of Computing, Dublin City University, D09 V209 Dublin, Ireland
Arunabha M. Roy: Aerospace Engineering Department, University of Michigan, Ann Arbor, MI 48109, USA

FinTech, 2024, vol. 3, issue 4, 1-18

Abstract: This research presents a comparative analysis of various deep learning models—including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Attention LSTM—in predicting stock prices of major companies in the Indian stock market, specifically HDFC, TCS, ICICI, Reliance, and Nifty. The study evaluates model performance using key regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-Squared (R²). The results indicate that CNN and GRU models generally outperform the others, depending on the specific stock, and demonstrate superior capabilities in forecasting stock price movements. This investigation provides insights into the strengths and limitations of each model while highlighting potential avenues for improvement through feature engineering and hyperparameter optimization.

Keywords: stock prediction; deep learning; recurrent neural networks; long short-term memory; convolutional neural networks; Indian stock market (search for similar items in EconPapers)
JEL-codes: C6 F3 G O3 (search for similar items in EconPapers)
Date: 2024
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