Using Adaboost for Equity Investment Scorecards

Howe School Research Paper

NIPS Workshop Machine Learning in Finance, 2005, Whistler, British Columbia, Canada

25 Pages Posted: 28 Oct 2006 Last revised: 26 Jan 2014

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: 2005

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, performance management

JEL Classification: C49, C63, G38

Suggested Citation

Creamer, Germán G. and Freund, Yoav, Using Adaboost for Equity Investment Scorecards (2005). Howe School Research Paper, NIPS Workshop Machine Learning in Finance, 2005, Whistler, British Columbia, Canada, Available at SSRN: https://ssrn.com/abstract=940729 or http://dx.doi.org/10.2139/ssrn.940729

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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