Authors:
Humberto O. Bragança
1
;
Rafael Alceste Berri
1
;
Bruno Dalmazo
1
;
Eduardo N. Borges
1
;
Viviane L. D. de Mattos
1
;
Richard F. Pinto
1
;
Fabian C. Cardoso
2
and
Giancarlo Lucca
3
Affiliations:
1
Federal University of Rio Grande (FURG), Rio Grande, Brazil
;
2
University of Rio Verde (UniRV), Rio Verde, Brazil
;
3
Catholic University of Pelotas (UCPel), Pelotas, Brazil
Keyword(s):
Machine Learning, Feature Selection, Stocks, Technical Analysis, Financial Market.
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.