Learning a board Balanced Scorecard to improve corporate performance

a r t i c l e i n f o 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. Kaplan and Norton [20] introduced the Balanced Scorecard (BSC) as a management system that helps organizations define their vision and strategy, and translate them into specific actions. The BSC provides feedback on internal business processes, performance, and market conditions in order to review the strategy and future plans [21–24,28]. Large U.S. companies, such as General Electric and Federal Express, and non-profit and public organizations have implemented the BSC approach [2,36]. The strategy of an organization, its main objectives, and its key business drivers define the indicators of the BSC. However, the choice of indicators is, in general, highly subjective and is often driven by company management or industry practices. Youngblood and Collins [39] describe a method based on indicators using multi-attribute utility theory. Clinton et al. [6] base their method on Analytic Hierarchy Process; nevertheless, these methods still require a mix of quantitative measures with a qualitative evaluation by managers or experts. The main objective of this paper is to adapt a machine learning method, such as Adaboost, to define the core variables and the structure of the board BSC. The criterion used to design the board BSC is the firm performance. We compare the predictive capacity of Adaboost with several other algorithms such as logistic regression, and other decision trees. The rest of the paper is organized as follows: Section 2 presents the basic concepts of a board BSC; Section 3 presents the methods used in this paper; Section 4 introduces the data and variables used in this research; Section 5 explains in detail our experiments; Section 6 presents the results of our forecast; Section 7 examines the results and the transformation of a representative ADT to a board BSC, and Section 8 presents the conclusions. The BSC suggests that …