Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Journal of CENTRUM Cathedra: The Business and Economics Research Journal
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.