Evolving Predictions for Executive Pay Features in Board Networks
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.References
Barnea, A. and Guedj, I. 2006. 'But, Mom, All the Other Kids Have One!'-CEO Compensation and Director Networks. In 1st Annual Conference on Empirical Legal Studies. SSRN Electronic Journal.
Benbassat, A. and Shafet, Y. 2017. A Simple Bucketing Based Approach to Diversity Maintenance. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '17. ACM, New York, NY, USA, pp. 1559-1564.
Bennett, B., Bettis, J. C., Gopalan, R., and Milbourn, T. 2017. Compensation goals and rm performance. Journal of Financial Economics 124, 2, pp. 307-330.
Biethahn, J. and Nissen, V. 1994. Combinations of simulation and evolutionary algorithms in management science and economics. Annals of Operations Research 52, 4, pp. 181-208.
Bizjak, J., Lemmon, M, and Whitby, R. 2009. Option backdating and board interlocks. The Review of Financial Studies 22, 11, pp. 4821-4847.
Bouwman, C. H. S. 2011. Corporate governance propagation through overlapping directors. The Review of Financial Studies 24, 7, pp. 2358-2394.
Cai, Y. and Sevilir, M. 2012. Board connections and M&A transactions. Journal of Financial Economics 103, 2, pp. 327-349.
Dahya, J. and McConnell, J. J. 2005. Outside directors and corporate board decisions. Journal of Corporate Finance 11, 1-2, pp. 37-60.
Dahya, J. and McConnell, J. J. 2007. Board composition, corporate performance, and the Cadbury committee recommendation. Journal of Financial and Quantitative Analysis 42, 3, pp. 535-564.
Dahya, J., McConnell, J. J., and Travlos, N. G. 2002. The Cadbury committee, corporate performance, and top management turnover. The Journal of Finance 57, 1, pp. 461-483.
Davis, G. F. 1991. Agents without principles? The spread of the poison pill through the intercorporate network. Administrative science quarterly, pp. 583-613.
Davis, G. F. and Greve, H. R. 1997. Corporate elite networks and governance changes in the 1980s. American journal of sociology 103, 1, pp. 1-37.
Deb, K. 2014. Multi-objective optimization. In Search methodologies. Springer, pp. 403-449.
Elton, E. J., Gruber, M. J., and Blake, C. R. 2001. A first look at the accuracy of the CRSP mutual fund database and a comparison of the CRSP and Morningstar mutual fund databases. The Journal of Finance 56, 6, pp. 2415-2430.
Fortin, F.-A., De Rainville, F.-M., Gardner, M.-A., Parizeau, M., and Gagne, C. 2012. DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research 13, Jul, pp. 2171-2175.
Fracassi, C. and Tate, G. 2012. External networking and internal rm governance. The Journal of Finance 67, 1, pp. 153-194.
Freeman, L. C. 1977. A set of measures of centrality based on betweenness. Sociometry, pp. 35-41.
Gygax, A., Hazledine, M., and Martin, J. S. 2016. Are Fat Cats Copycats? Evidence from Director and Consultant Network Dynamics. In 2nd Annual Financial Institutions, Regulation and Corporate Governance Conference. DOI: 10.2139/ssrn.2832531
Hallock, K. F. 1997. Reciprocally interlocking boards of directors and executive compensation. Journal of Financial and Quantitative Analysis 32, 3, pp. 331-344.
Harrald, P. G. and Kamstra, M. 1997. Evolving artificial neural networks to combine financial forecasts. IEEE Transactions on Evolutionary Computation 1, 1, pp. 40-52.
Horton, J., Millo, Y., and Serafeim, G. 2012. Resources or power? Implications of social networks on compensation and rm performance. Journal of Business Finance & Accounting 39, 3-4, pp. 399-426.
Kantardzic, M. 2011. Data mining: concepts, models, methods, and algorithms. John Wiley & Sons.
Martin, W. N., Lienig, J., and Cohoon, J. P. 1997. Island (Migration) models: evolutionary algorithms based on punctuated equilibria. In Handbook of Evolutionary Computation (Eds: T. Baeck, D.B Fogel, Z. Michalewicz). Taylor & Francis.
Murphy, K. P. 2012. Machine Learning: a probabilistic perspective. MIT Press.
Nissen, V. 2018. Applications of Evolutionary Algorithms to Management Problems. In Innovative Research Methodologies in Management. Springer, pp. 211-235.
Renneboog, L. and Zhao, Y. 2011. Us knows us in the UK: On director networks and CEO compensation. Journal of Corporate Finance 17, 4, pp. 1132-1157.
Ulbricht, N. and Weiner, C. 2005. Worldscope meets Compustat: A comparison of financial databases. DOI: 10.2139/ssrn.871169
Weisbach, M. S. 1998. Outside directors and CEO turnover. Journal of Financial Economics 20, pp. 431-460.
Wong, L. H. H., Gygax, A. F., and Wang, P. 2015. Board interlocking network and the design of executive compensation packages. Social Networks 41, pp. 85{100.
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