G. Creamer and Y. Freund (USA)
Corporate governance risk analysis, machine learning, Ad aboost, data mining
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 re gression, bagging, and random forests. We conduct tenfold cross-validation experiments on one sample of Latin Amer ican Depository Receipts (ADRs), and on another sample of Latin American banks. We find that if the dataset is uni form (similar types of companies and same source of in formation), 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 re sults. 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 ex plained the relationship between the corporate variables that determined performance and efficiency.
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