A Comparative Analysis of Various Soft Computing Techniques for Indian Stock Market Prediction

Title

A Comparative Analysis of Various Soft Computing Techniques for Indian Stock Market Prediction

Creator

Balaji, Lavanya; Anita, H.B.; Kumar, Balaji Ashok

Description

Soft computing techniques have been increasingly used for stock market analysis in the past few years because they can capture nonlinear aspects which traditional econometric models do not adequately capture. With different techniques like Artificial Neural Networks, Deep Neural Networks and Stacked Autoencoders available, in this paper, the author tries to determine which of the above methods can model the Indian stock market with higher accuracy. In this study, high-frequency data from Nifty 50 is used, and various feature selection techniques such as PCA and linear regression are used for each of the above machine learning models to predict the Nifty 50 data. Finally, all predictions from the different techniques are compared with the actual index movement and the best method for Nifty 50 is suggested. 2025 Seventh Sense Research Group

Source

International Journal of Engineering Trends and Technology;Volume;73;Issue;3;pp.95-103

Date

01-01-2025

Publisher

Seventh Sense Research Group

Subject

Artificial Neural Network; Deep Neural Network; Nifty 50; Principal Component Analysis (PCA); Stock market

Coverage

Balaji L., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, India; Anita H.B., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, India; Kumar B.A., Department of Commerce, Government First Grade College Vemagal, Karnataka, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23490918;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.14445/22315381/IJETT-V73I3P107

https://www.scopus.com/pages/publications/105001300504?origin=resultslist