Using Boosting for Financial Analysis and Performance Prediction: Application To S&P 500 Companies, Latin American ADRs and Banks

Computational Economics, Vol. 36, No. 2, pp. 133-151

10 Pages Posted: 15 May 2010 Last revised: 20 Feb 2013

Germán G. Creamer

Stevens Institute of Technology - Wesley J. Howe School of Technology Management

Yoav Freund

University of California, San Diego

Date Written: 2010

Abstract

This paper demonstrates how the boosting approach can support the financial analysis functions in two ways:

1. As a predictive tool to forecast corporate performance, and rank accounting and corporate variables according to their impact on performance, and

2. As an interpretative tool to generate alternating decision trees that capture the non-linear relationship among accounting and corporate governance variables that determine performance.

We compare our results using Adaboost with logistic regression, bagging, and random forests. We conduct 10-fold cross-validation experiments on one sample each of S&P 500 companies, American Depository Receipts (ADRs) of Latin American companies and Latin American banks.

Adaboost results indicate that large companies perform better than small companies, especially when these companies have a limited long-term assets to sales ratio. Performance improves for large LAADR companies when the country of residence is characterized by a weak rule of law. In the case of S&P 500 companies, performance increases when the compensation for top officers is mostly variable.

Keywords: Financial analysis, machine learning, adaboost, data mining

JEL Classification: C49, C63, G24

Suggested Citation

Creamer, Germán G. and Freund, Yoav, Using Boosting for Financial Analysis and Performance Prediction: Application To S&P 500 Companies, Latin American ADRs and Banks (2010). Computational Economics, Vol. 36, No. 2, pp. 133-151. Available at SSRN: https://ssrn.com/abstract=1604998

Germán G. Creamer (Contact Author)

Stevens Institute of Technology - Wesley J. Howe School of Technology Management ( email )

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

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

Yoav Freund

University of California, San Diego ( email )

9500 Gilman Drive
Mail Code 0502
La Jolla, CA 92093-0502
United States

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