FINANCIAL ENGINEERING AND APPLICATIONS, MIT, Cambridge, 2004
11 Pages Posted: 20 Jun 2005 Last revised: 20 Feb 2013
Date Written: November 1, 2004
The objective of this paper is to demonstrate how the boosting approach can be used to quantify the corporate governance risk in the case of Latin American markets. We compare our results using Adaboost with logistic regression, bagging, and random forests. We conduct tenfold cross-validation experiments on one sample of Latin American Depository Receipts (ADRs), and on another sample of Latin American banks. We find that if the dataset is uniform (similar types of companies and same source of information), as is the case with the Latin American ADRs dataset, the results of Adaboost are similar to the results of bagging and random forests. Only when the dataset shows significant non-uniformity does bagging improve the results. Additionally, the uniformity of the dataset affects the interpretability of the results. Using Adaboost, we were able to select an alternating decision tree (ADT) that explained the relationship between the corporate variables that determined performance and efficiency.
Keywords: Corporate governance, machine learning, Adaboost, data mining
JEL Classification: C44, F21, G32, O54
Suggested Citation: Suggested Citation
Creamer, Germán G. and Freund, Yoav, Predicting Performance and Quantifying Corporate Governance Risk for Latin American Adrs and Banks (November 1, 2004). FINANCIAL ENGINEERING AND APPLICATIONS, MIT, Cambridge, 2004. Available at SSRN: https://ssrn.com/abstract=743209