ItemConference Paper

Artificial Intelligence-Powered Stock Market Forecasting with Metaheuristic Feature Selection Techniques

Title
Artificial Intelligence-Powered Stock Market Forecasting with Metaheuristic Feature Selection Techniques
Creator
Hasan, Mohammed Faez; Sherin, K.; Champaneria, Tushar; Sidhu, Kawerinder Singh; Raja Mannar, B.; Acharjee, Purnendu Bikash
Description
This study proposed a hybrid stock market forecasting model which consists of Artificial Intelligence (AI) and metaheuristic feature selection algorithms to improve the accuracy in prediction and efficiency of the prototypical. It uses PSO (Particle Swarm Optimization) algorithm to pick the most relevant feature out of a pool of technical indicators and sentiment data and temporally learns the pattern using the LSTM (Long Short-Term Memory) network. The model yields better learning by diminishing noise and dimensionality and prevents over fitting. The efficiency of the anticipated system is seen through comparative analysis with such baseline models as SVM (Support Vector Machine), RF (Random Forest), and standard LSTM. This prototypical obtained MAE of 11.2RMSE of 18.18, and the mean absolute percentage error (MAPE) of 5.36 percent, with R2 of 0.91 and directional accuracy of 86.4 percent. The above results confirm the effectiveness of the suggested method, providing a solid and generalizable solution in terms of intelligent stock market prediction and investment decision support. 2025 IEEE.
Source
Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.740-745
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Artificial Intelligence; Feature Selection; Financial Prediction; Metaheuristic Algorithms; Stock Market Forecasting; Time Series Examination
Coverage
Hasan M.F., Kerbala University, Finance and Banking Department, Iraq; Sherin K., St.Joseph's Institute of Technology, Dept of CSE, India; Champaneria T., Computer Engineering, L. D. College of Engineering, Ahmedabad, India; Sidhu K.S., Uttaranchal Institute of Management, Uttaranchal University, Uttarakhand, Dehradun, India; Raja Mannar B., Graham International University, (Online), Viet Nam; Acharjee P.B., Computer Science, CHRIST University, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155787-4;
Format
online
Language
English
Type
Conference paper

Citation

Hasan, Mohammed Faez; Sherin, K.; Champaneria, Tushar; Sidhu, Kawerinder Singh; Raja Mannar, B.; Acharjee, Purnendu Bikash, “Artificial Intelligence-Powered Stock Market Forecasting with Metaheuristic Feature Selection Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26207.

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