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

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

Jean X. Zhang (George Washington University, USA)
Copyright: © 2009 |Pages: 18
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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Introduction

Reducing debt costs through investment in financial control systems is important to the municipalities. Several theoretical and empirical studies examine the determinants of borrowing costs on tax-exempt bond issues (Benson 1979; Benson, Marks, and Raman 1991). In the early eighties and nineties of the last century, some early studies examine state imposed disclosure requirements. For example, Ingram and Copeland (1982), Benson, Mark, and Raman (1984), and Fairchild and Kock (1998) consider state imposed disclosure requirements in the context of municipal debt costs. Benson, Mark, and Raman (1991) estimate the magnitude of the interest cost savings on general obligation bonds as a potential benefit from differential GAAP compliance. Their study suggests that bond prices incorporate the effects of differential GAAP compliance.

More recently, Downing and Zhang (2004) posit municipal bond markets are less liquid. In addition, Harris and Piwowar (2004) show higher transaction costs are associated with municipal bond markets. Most recently, Baber and Gore (2005) compare municipal debt costs in states that mandate the adoption of GAAP disclosure with debt costs in states that do not regulate municipal accounting methods. The result shows that municipal debt costs in states that impose GAAP are lower by 15 basis points.

Several studies examine the effect of audit variables and accounting variables on the borrowing costs on new bond issues for local governments. Wallace (1981) suggests that lower interest costs and higher bond ratings are associated with compliance with GAAFR, hiring a national auditor, and having a clean audit report. Employing a national sample, Wilson and Howard (1984) find poorer financial operating performance and substandard reporting practices are associated with lower bond ratings and higher borrowing costs. Most existing studies in the government sector examine the determinants of cost of debt, determinants other than CEO tenure; an important goal for this chapter is to extend the current literature and shed light on the issue of debt in the nonprofit area.

Debt is studied extensively in the private sector. According to prior research, debt is associated with accounting methods and accounting conservatism (Beatty and Weber 2003; Ahmed et al. 2002). Beatty and Weber (2003) show that borrowers with bank debt contracts that allow accounting method changes to affect contract calculations are more likely to make income-increasing rather than income-decreasing changes. On the other side, accounting conservatism plays an important role in reducing firms’ debt costs. Ahmed et al. (2002) provide the evidence that accounting conservatism is associated with a lower cost of debt after controlling for other determinants of firms’ debt costs.

Complete Chapter List

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Dedication
Table of Contents
Acknowledgment
Ming Zhang
Chapter 1
Ming Zhang
This chapter delivers general format of Higher Order Neural Networks (HONNs) for nonlinear data analysis and six different HONN models. This chapter... Sample PDF
Artificial Higher Order Neural Network Nonlinear Models: SAS NLIN or HONNs?
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Chapter 2
Adam Knowles, Abir Hussain, Wael El Deredy, Paulo G.J. Lisboa, Christian L. Dunis
Multi-Layer Perceptrons (MLP) are the most common type of neural network in use, and their ability to perform complex nonlinear mappings and... Sample PDF
Higher Order Neural Networks with Bayesian Confidence Measure for the Prediction of the EUR/USD Exchange Rate
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Chapter 3
Da Shi, Shaohua Tan, Shuzhi Sam Ge
Real-world financial systems are often nonlinear, do not follow any regular probability distribution, and comprise a large amount of financial... Sample PDF
Automatically Identifying Predictor Variables for Stock Return Prediction
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Chapter 4
John Seiffertt, Donald C. Wunsch II
As the study of agent-based computational economics and finance grows, so does the need for appropriate techniques for the modeling of complex... Sample PDF
Higher Order Neural Network Architectures for Agent-Based Computational Economics and Finance
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Chapter 5
Yuehui Chen, Peng Wu, Qiang Wu
Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and... Sample PDF
Foreign Exchange Rate Forecasting Using Higher Order Flexible Neural Tree
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Chapter 6
Yuehui Chen, Peng Wu, Qiang Wu
Artificial Neural Networks (ANNs) have become very important in making stock market predictions. Much research on the applications of ANNs has... Sample PDF
Higher Order Neural Networks for Stock Index Modeling
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Chapter 7
Ming Zhang
This chapter develops a new nonlinear model, Ultra high frequency Trigonometric Higher Order Neural Networks (UTHONN), for time series data... Sample PDF
Ultra High Frequency Trigonometric Higher Order Neural Networks for Time Series Data Analysis
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Chapter 8
Panos Liatsis, Abir Hussain, Efstathios Milonidis
The research described in this chapter is concerned with the development of a novel artificial higher order neural networks architecture called the... Sample PDF
Artificial Higher Order Pipeline Recurrent Neural Networks for Financial Time Series Prediction
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Chapter 9
Abir Hussain, Panos Liatsis
The research described in this chapter is concerned with the development of a novel artificial higherorder neural networks architecture called the... Sample PDF
A Novel Recurrent Polynomial Neural Network for Financial Time Series Prediction
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Chapter 10
David R. Selviah, Janti Shawash
Generalized correlation higher order neural network designs are developed. Their performance is compared with that of first order networks... Sample PDF
Generalized Correlation Higher Order Neural Networks for Financial Time Series Prediction
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Chapter 11
Godfrey C. Onwubolu
Real world problems are described by nonlinear and chaotic processes, which makes them hard to model and predict. This chapter first compares the... Sample PDF
Artificial Higher Order Neural Networks in Time Series Prediction
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Chapter 12
Rozaida Ghazali, Dhiya Al-Jumeily
This chapter discusses the use of two artificial Higher Order Neural Networks (HONNs) models; the Pi- Sigma Neural Networks and the Ridge Polynomial... Sample PDF
Application of Pi-Sigma Neural Networks and Ridge Polynomial Neural Networks to Financial Time Series Prediction
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Chapter 13
Edgar N. Sanchez, Alma Y. Alanis, Jesús Rico
In this chapter, we propose the use of Higher Order Neural Networks (HONNs) trained with an extended Kalman filter based algorithm to predict the... Sample PDF
Electric Load Demand and Electricity Prices ForecastingUsing Higher Order Neural Networks Trained by Kalman Filtering
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Chapter 14
Shuxiang Xu
Business is a diversified field with general areas of specialisation such as accounting, taxation, stock market, and other financial analysis.... Sample PDF
Adaptive Higher Order Neural Network Models and Their Applications in Business
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Chapter 15
Jean X. Zhang
This chapter proposes nonlinear models using artificial neural network models to study the relationship between chief elected official (CEO) tenure... Sample PDF
CEO Tenure and Debt: An Artificial Higher Order Neural Network Approach
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Chapter 16
Christian L. Dunis, Jason Laws, Ben Evans
This chapter investigates the soybean-oil “crush” spread, that is the profit margin gained by processing soybeans into soyoil. Soybeans form a large... Sample PDF
Modelling and Trading the Soybean-Oil Crush Spread with Recurrent and Higher Order Networks: A Comparative Analysis
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Chapter 17
Madan M. Gupta, Noriyasu Homma, Zeng-Guang Hou, Ashu M. G. Solo, Takakuni Goto
In this chapter, we aim to describe fundamental principles of artificial higher order neural units (AHONUs) and networks (AHONNs). An essential core... Sample PDF
Fundamental Theory of Artificial Higher Order Neural Networks
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Chapter 18
Jinde Cao, Fengli Ren, Jinling Liang
This chapter concentrates on studying the dynamics of artificial higher order neural networks (HONNs) with delays. Both stability analysis and... Sample PDF
Dynamics in Artificial Higher Order Neural Networks with Delays
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Chapter 19
Zhao Lu, Leang-san Shieh, Guanrong Chen
Aiming to develop a systematic approach for optimizing the structure of artificial higher order neural networks (HONN) for system modeling and... Sample PDF
A New Topology for Artificial Higher Order Neural Networks: Polynomial Kernel Networks
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Chapter 20
David R. Selviah
This chapter describes the progress in using optical technology to construct high-speed artificial higher order neural network systems. The chapter... Sample PDF
High Speed Optical Higher Order Neural Networks for Discovering Data Trends and Patterns in Very Large Databases
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Chapter 21
Zidong Wang, Yurong Liu, Xiaohui Liu
This chapter deals with the analysis problem of the global exponential stability for a general class of stochastic artificial higher order neural... Sample PDF
On Complex Artificial Higher Order Neural Networks: Dealing with Stochasticity, Jumps and Delays
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Chapter 22
Lei Zhang, Simeon J. Simoff, Jing Chun Zhang
This chapter introduces trigonometric polynomial higher order neural network models. In the area of financial data simulation and prediction, there... Sample PDF
Trigonometric Polynomial Higher Order Neural Network Group Models and Weighted Kernel Models for Financial Data Simulation and Prediction
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