Learning a Board Balanced Scorecard to Improve Corporate Performance
Decision Support Systems 49 (4): 365-385
45 Pages Posted: 17 Jun 2013
Date Written: 2010
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 S&P 500 companies. Using Adaboost, we can generate alternating decision trees (ADTs) that explain the relationship between corporate governance variables, and firm performance.
We also propose an algorithm to build a representative ADT based on cross-validation experiments. The representative ADT selects the most important indicators for the board BSC. As a final result, we propose a partially automated strategic planning system combining Adaboost with the board BSC for board-level or investment decisions.
Keywords: Boosting, machine learning, corporate governance, balanced scorecard, planning
JEL Classification: C44, C53, C63, G17, M15
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