Using Adaboost for Equity Investment Scorecards

Abstract:     
The objective of this paper is to demonstrate how the boosting approach can be used to define a data-driven board Balanced Scorecard (BSC) with applications to Latin American markets and S&P 500 companies. 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), on another sample of Latin American banks, and on the S&P 500 companies. 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 generate alternating decision trees (ADTs) that explained the relationship between corporate governance variables, performance and efficiency. We also proposed an algorithm to build a representative ADT based on cross-validation experiments. The representative ADT selected the most important indicators for the board BSC. Additionally, the thresholds of the representative ADT established targets or ranges of values to be used in the board BSC. As a final result, we propose a quantitative strategic management system combining Adaboost with the BSC for board-level or investment decisions.

Keywords: Boosting, balanced scorecard, automated planning, performance management

JEL Classifications: C49, C63, G38

Working Paper Series