Predicting Performance and Quantifying Corporate Governance Risk for Latin American Adrs and Banks

Abstract:     
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 Classifications: C44, F21, G32, O54

Working Paper Series