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

13 Pages Posted: 13 Aug 2009 Last revised: 20 Feb 2013

See all articles by Germán G. Creamer

Germán G. Creamer

Stevens Institute of Technology, School of Business; Columbia University - Department of Computer Science

Date Written: August 12, 2009

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.

Keywords: financial analysis, machine learning, random forests, logistic regression, data mining

Suggested Citation

Creamer, Germán G., Using Random Forests and Logistic Regression for Performance Prediction of Latin American ADRS and Banks (August 12, 2009). Journal of CENTRUM Cathedra, Vol. 2, Issue 1, pp. 24-36, 2009. Available at SSRN: https://ssrn.com/abstract=1447907

Germán G. Creamer (Contact Author)

Stevens Institute of Technology, School of Business ( email )

1 Castle Point on Hudson
Hoboken, NJ 07030
United States
2012168986 (Phone)

HOME PAGE: http://www.creamer-co.com

Columbia University - Department of Computer Science ( email )

New York, NY 10027
United States

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