Feature Selection for Stock Market Prediction: A Comparison of Relief and Information Gain Methods

Humberto O. Bragança, Rafael Alceste Berri, Bruno Dalmazo, Eduardo N. Borges, Viviane L. D. de Mattos, Richard F. Pinto, Fabian C. Cardoso, Giancarlo Lucca

2025

Abstract

This study explores an approach to predictive analysis in the financial market, using a data set composed of financial information from different companies listed on the stock market, which provides a more detailed and contextualized view of the behavior of shares. Based on these indicators, feature selection methods, such as Relief and Information Gain, are applied to identify the most relevant variables for building predictive models. One of the main contributions of this work is the use of cross-validation to evaluate attribute selection, a technique that has not yet been explored in this context with this dataset. The results show that the combination of new financial indicators and cross-validation offers a solid basis for more accurate analysis, with important implications for investors, financial analysts and policymakers in the stock market. This work expands the boundaries of the literature on feature selection and opens possibilities for future research in emerging markets.

Paper Citation

in Harvard Style

Bragança H., Berri R., Dalmazo B., Borges E., D. de Mattos V., Pinto R., Cardoso F. and Lucca G. (2025). Feature Selection for Stock Market Prediction: A Comparison of Relief and Information Gain Methods. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 996-1003. DOI: 10.5220/0013481300003929

in Bibtex Style

@conference{iceis25,
author={Humberto Bragança and Rafael Berri and Bruno Dalmazo and Eduardo Borges and Viviane D. de Mattos and Richard Pinto and Fabian Cardoso and Giancarlo Lucca},
title={Feature Selection for Stock Market Prediction: A Comparison of Relief and Information Gain Methods},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={996-1003},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013481300003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Feature Selection for Stock Market Prediction: A Comparison of Relief and Information Gain Methods
SN - 978-989-758-749-8
AU - Bragança H.
AU - Berri R.
AU - Dalmazo B.
AU - Borges E.
AU - D. de Mattos V.
AU - Pinto R.
AU - Cardoso F.
AU - Lucca G.
PY - 2025
SP - 996
EP - 1003
DO - 10.5220/0013481300003929
PB - SciTePress