A Neural Network Model for Executive Compensation and Firm Performance
Date Issued
2010
Date
2010
Author(s)
Yu, Kuan-Lun
Abstract
The conflict of interest between the shareholders and the executives is known as the principal-agent problem. If the shareholders have complete information, they can easily design a contract (or incentive plan) that encourages the actions they want. However, the literature suggests weak or statistically insignificant relation between executive compensation and firm performance. In order to overcome the limitation in prior empirical or analytical studies, this paper investigates the association between executive incentive plans and firm performance by using an artificial neural network. Our results show that, overall, we can accurately associate the executives'' incentive plan with the firm''s performance 63% of the time. For the best and the worst performing firms, the accuracy rate is about 70%. Our findings also suggest that (1) the importance of the component of the incentive plan changes over time, (2) accounting-based performance measure is associated with EPS while market-based performance measure is associated with the market-to-book ratio, and (3) when firms with higher uncertainty, they rely less on stock/option incentives. Finally, the simplicity of the model can help firms better design or change the compensation scheme of the executives.
Subjects
firm performance
artificial neural network
Type
thesis
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