Evolving Predictions for Executive Pay Features in Board Networks

Hauptman, Ami; Benbassat, Amit; Rosenboim, Rosit

Evolving Predictions for Executive Pay Features in Board Networks

Files

2019-1-057.pdf(994.59 KB)

Date

2019-06-24

Authors

Hauptman, Ami

Benbassat, Amit

Rosenboim, Rosit

Publisher

Institute of Automation and Computer Science, Brno University of Technology

Altmetrics

Abstract

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.

Keywords

finance, genetic algorithm, genetic programming, prediction, pattern recognition

Citation

Mendel. 2018 vol. 25, č. 1, s. 57-64. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/79

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license
http://creativecommons.org/licenses/by-nc-sa/4.0