Using Adaboost for Equity Investment Scorecards
Stevens Institute of Technology - Wesley J. Howe School of Technology Management
University of California, San Diego
Decision Support Systems, Vol. 49, No. 4, pp. 365-385
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, G38Accepted Paper Series
Date posted: October 28, 2006 ; Last revised: February 20, 2013
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