City Manager Compensation and Performance: An Artificial Intelligence Approach

City Manager Compensation and Performance: An Artificial Intelligence Approach

Jean X. Zhang (Virginia Commonwealth University, USA)
DOI: 10.4018/978-1-4666-2175-6.ch015
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Abstract

This chapter proposes a nonlinear artificial Higher Order Neural Network (HONN) model to study the relation between manager compensation and performance in the governmental sector. Using a HONN simulator, this study analyzes city manager compensation as a function of local government performance, and compares the results with those from a linear regression model. This chapter shows that the nonlinear model generated from HONN has a smaller Root Mean Squared Error (Root MSE) of 0.0020 as compared to 0.06598 from a linear regression model. This study shows that artificial HONN is an effective tool in modeling city manager compensation.
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2. Background: Neural Network In Accounting Research

Artificial neural network is widely used in accounting literature. Previous researchers use neural networks to predict takeover targets (Cheh, et al., 1999), earnings (Charitou & Charalambous, 1996), and pricing of initial public offerings (Jain & Nag, 1995). More recently, using 178 mergers from 1996-2001, Shawver (2005) developed and tested neural network models for predicting bank merger premiums. The evidence shows that a neural network methodology provides more explanation between bank merger premiums and financial variables in the model than a traditional regression model. The author attributes the results to the ability of a neural network model to recognize patterns in complicated financial relationships.

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