Deterministic and Stochastic Machine Learning Classification Models: A Comparative Study Applied to Companies’ Capital Structures

Joseph F. Hair, Luiz Paulo Fávero, Wilson Tarantin Junior () and Alexandre Duarte
Additional contact information
Joseph F. Hair: Marketing & Quantitative Methods, Mitchell College of Business, University of South Alabama, Mobile, AL 36688, USA
Luiz Paulo Fávero: Faculty of Economics, Administration, and Accounting, University of Sao Paulo, Sao Paulo 05508-900, Brazil
Wilson Tarantin Junior: Faculty of Economics, Administration, and Accounting, University of Sao Paulo, Sao Paulo 05508-900, Brazil
Alexandre Duarte: Polytechnic School, University of Sao Paulo, Sao Paulo 05508-010, Brazil

Mathematics, 2025, vol. 13, issue 3, 1-24

Abstract: Corporate financing decisions, particularly the choice between equity and debt, significantly impact a company’s financial health and value. This study predicts binary corporate debt levels (high or low) using supervised machine learning (ML) models and firms’ characteristics as predictive variables. Key features include companies’ size, tangibility, profitability, liquidity, growth opportunities, risk, and industry. Deterministic models, represented by logistic regression and multilevel logistic regression, and stochastic approaches that incorporate a certain degree of randomness or probability, including decision trees, random forests, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks, were evaluated using usual metrics. The results indicate that decision trees, random forest, and XGBoost excelled in the training phase but showed higher overfitting when evaluated in the test sample. Deterministic models, in contrast, were less prone to overfitting. Notably, all models delivered statistically similar results in the test sample, emphasizing the need to balance performance, simplicity, and interpretability. These findings provide actionable insights for managers to benchmark their company’s debt level and improve financing strategies. Furthermore, this study contributes to ML applications in corporate finance by comparing deterministic and stochastic models in predicting capital structure, offering a robust tool to enhance managerial decision-making and optimize financial strategies.

Keywords: corporate financing; capital structure; debt classification; machine learning models; managerial decision-making; predictive analytics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/3/411/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/3/411/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:3:p:411-:d:1577621

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().