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Conference object . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Conference object . 2025
License: CC BY
Data sources: Datacite
ZENODO
Conference object . 2025
License: CC BY
Data sources: Datacite
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Classification of Return on Equity (ROE) using Machine Learning Techniques

descriptionPublicationkeyboard_double_arrow_right Conference object 16 Oct 2025 English Publisher:Zenodo
Authors: Olaomi, John Olutunji; Ntshani, Silas;

doi: 10.5281/zenodo.17368808 , 10.5281/zenodo.17368809

Classification of Return on Equity (ROE) using Machine Learning Techniques

Abstract

This is a JSM 2025 conference proceeding contribution. The study identifies the financial ratios that influence ROE and determines the best machine learning techniques for predicting ROE, using a binary form of ROE.

Related Organizations
Keywords

Machine Learning, Naive Bayes, Random Forest, Stock Exchange, K-Nearest Neighbor, Return on Equity (ROE), Logistic Regression, Classification

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green