NIPS Workshop Machine Learning in Finance, 2005, Whistler, British Columbia, Canada
25 Pages Posted: 28 Oct 2006 Last revised: 26 Jan 2014
Date Written: 2005
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: 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