Using Random Forests and Logistic Regression for Performance Prediction of Latin American ADRS and Banks

• Autor: Creamer, Germán G.
• Formato: artículo
• Fecha de Publicación: 2009
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: PUCP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.pucp.edu.pe:20.500.14657/194760
• Enlace del recurso: https://repositorio.pucp.edu.pe/index/handle/123456789/194760
• Nivel de acceso: acceso abierto
• Materia: Data mining; Financial analysis; Logistic regression; Machine learning; Random forests; https://purl.org/pe-repo/ocde/ford#5.02.04

In the paper, random forests and logistic regressions’ support of financial analysis functions’ predictive tool to forecast corporate performance and rank accounting and corporate variables according to their impact on performance is demonstrated. Ten-fold cross-validation experiments are conducted on one sample each of Latin American depository receipts (ADRs) and Latin American banks. Random forests indicate that the most important variables that affect ADRs performance are size and the law-and-order tradition; the most important variables that affect banks are size, long-term assets to deposits, number of directors, and efficiency of the legal system. The interpretation of predictive models for a small sample improved when the capacity of random forests to rank and predict with the parameters of a logistic regression were combined.