Computational Economics, Vol. 36, No. 2, pp. 133-151
10 Pages Posted: 15 May 2010 Last revised: 20 Feb 2013
Date Written: 2010
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: 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