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CEO Tenure and Debt: An Artificial Higher Order Neural Network Approach

CEO Tenure and Debt: An Artificial Higher Order Neural Network Approach
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Author(s): Jean X. Zhang (George Washington University, USA)
Copyright: 2009
Pages: 18
Source title: Artificial Higher Order Neural Networks for Economics and Business
Source Author(s)/Editor(s): Ming Zhang (Christopher Newport University, USA)
DOI: 10.4018/978-1-59904-897-0.ch015

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Abstract

This chapter proposes nonlinear models using artificial neural network models to study the relationship between chief elected official (CEO) tenure and debt. Using Higher Order Neural Network (HONN) simulator, this study analyzes debt of the municipalities as a function of population and CEO tenure, and compares the results with that from SAS. The linear models show that CEO tenure and the amount of debt vary inversely. Specifically, a longer length of CEO tenure leads to a decrease in debt, while a shorter tenure leads to an increase in debt. This chapter shows nonlinear model generated from HONN out performs linear models by 1%. The results from both models reveal that CEO tenure is negatively associated with the level of debt in local governments.

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