Version 1
: Received: 18 October 2024 / Approved: 18 October 2024 / Online: 18 October 2024 (12:56:36 CEST)
How to cite:
Barua, M.; Kumar, T.; Raj, K.; Roy, A. M. Comparative Analysis of Deep Learning Models for Stock Price Prediction in the Indian Market. Preprints2024, 2024101497. https://doi.org/10.20944/preprints202410.1497.v1
Barua, M.; Kumar, T.; Raj, K.; Roy, A. M. Comparative Analysis of Deep Learning Models for Stock Price Prediction in the Indian Market. Preprints 2024, 2024101497. https://doi.org/10.20944/preprints202410.1497.v1
Barua, M.; Kumar, T.; Raj, K.; Roy, A. M. Comparative Analysis of Deep Learning Models for Stock Price Prediction in the Indian Market. Preprints2024, 2024101497. https://doi.org/10.20944/preprints202410.1497.v1
APA Style
Barua, M., Kumar, T., Raj, K., & Roy, A. M. (2024). Comparative Analysis of Deep Learning Models for Stock Price Prediction in the Indian Market. Preprints. https://doi.org/10.20944/preprints202410.1497.v1
Chicago/Turabian Style
Barua, M., Kislay Raj and Arunabha M Roy. 2024 "Comparative Analysis of Deep Learning Models for Stock Price Prediction in the Indian Market" Preprints. https://doi.org/10.20944/preprints202410.1497.v1
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
Subject
Business, Economics and Management, Finance
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
