Using Adaboost for Equity Investment Scorecards
Germán G. Creamer
Stevens Institute of Technology - Wesley J. Howe School of Technology Management
University of California, San Diego
Howe School Research Paper
NIPS Workshop Machine Learning in Finance, 2005, Whistler, British Columbia, Canada
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.
Number of Pages in PDF File: 25
Keywords: Boosting, machine learning, corporate governance, balanced scorecard, planning, performance management
JEL Classification: C49, C63, G38working papers series
Date posted: October 28, 2006 ; Last revised: January 26, 2014
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