Learning a Board Balanced Scorecard to Improve Corporate Performance

Decision Support Systems 49 (4): 365-385

45 Pages Posted: 17 Jun 2013

See all articles by Germán G. Creamer

Germán G. Creamer

Stevens Institute of Technology, School of Business; Columbia University - Department of Computer Science

Yoav Freund

University of California, San Diego

Date Written: 2010

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 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

Suggested Citation

Creamer, Germán G. and Freund, Yoav, Learning a Board Balanced Scorecard to Improve Corporate Performance (2010). Decision Support Systems 49 (4): 365-385. Available at SSRN: https://ssrn.com/abstract=2280201

Germán G. Creamer (Contact Author)

Stevens Institute of Technology, School of Business ( email )

1 Castle Point on Hudson
Hoboken, NJ 07030
United States
2012168986 (Phone)

HOME PAGE: http://www.creamer-co.com

Columbia University - Department of Computer Science ( email )

New York, NY 10027
United States

Yoav Freund

University of California, San Diego ( email )

9500 Gilman Drive
Mail Code 0502
La Jolla, CA 92093-0502
United States

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