Indian Stock Market Prediction Using Neural Networks: A Comparative Analysis

Title

Indian Stock Market Prediction Using Neural Networks: A Comparative Analysis

Creator

Sreya, Pampati; Yashaswi, D.; Stephen, R.; Gobinath, R.; Ramkumar, S.

Description

Predicting stock prices remains a challenging problem due to the highly dynamic and nonlinear nature of financial markets. Traditional statistical models like ARIMA and GARCH often fail to capture the complexities inherent in stock market data. This paper investigates the use of deep learning techniques, focusing on Convolutional Neural Networks (CNNs) and a hybrid CNN-LSTM ensemble model for stock price prediction in the Indian stock market. The CNN model efficiently extracts temporal patterns from sequential data, while the CNN-LSTM ensemble leverages temporal dependencies for improved long-term prediction accuracy. Historical data from Tata motors, spanning over two decades, was used to train and evaluate the models. Experimental results highlight the CNN-LSTM ensemble's superior performance in capturing volatile trends and long-term dependencies, with a notable decrease in test loss compared to standalone CNN. This study underscores the effectiveness of hybrid deep learning architectures in enhancing prediction reliability, paving the way for more adaptive and robust financial forecasting systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

Source

Lecture Notes in Networks and Systems;Volume;1469 LNNS;pp.101-112

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Convolutional Neural Networks (CNNs); Deep learning; Ensemble model; Long Short-Term Memory (LSTM); Machine learning (ML); Stock price prediction

Coverage

Sreya P., CHRIST University, Bangalore, India; Yashaswi D., CHRIST University, Bangalore, India; Stephen R., CHRIST University, Bangalore, India; Gobinath R., CHRIST University, Bangalore, India; Ramkumar S., CHRIST University, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981967806-8;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-7807-5_10

https://www.scopus.com/pages/publications/105020263014?origin=resultslist