Evolving Predictions for Executive Pay Features in Board Networks

• Main Authors: Ami Hauptman, Amit Benbassat, Rosit Rosenboim
• Format: Article
• Language: English
• Published: Brno University of Technology 2019-06-01
• Series: Mendel
• Subjects: finance; genetic algorithm; genetic programming; prediction; pattern recognition
• Online Access: https://mendel-journal.org/index.php/mendel/article/view/79
• ISSN: 1803-3814; 2571-3701

Numerous recent studies in finance literature have shown that board networks are an important inter-corporate setting, influencing corporate decisions made by the board of directors, for example the determination of executive pay features. In this paper, we evolve predictors for the existence and adoption of several important pay features among S&P1500 companies, over the period 2006--2012. We use data from five well-known financial databases, including hundreds of variables containing both director-level and firm-level data. We present two approaches for predicting executive pay features. The first approach is based on a Genetic Algorithm (GA) used to evolve predictors based on weighted vectors of the predicting variables, providing relatively easy to understand prediction rules. The second approach employs Genetic Programming (GP) with sets of functions and terminals we devised specifically for this domain, based on contemporary research in finance. Thus, the GP approach explores a wider problem space and allows for more complex feature combinations. Experiments using both methods attain high quality prediction results, when compared to previous results in finance research. Additionally, our model is capable of successfully predicting combinations of pay features, compared to standard empirical models in finance, under various experimental conditions.