Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You and Your Team.

Learn More →

A prediction model for the secure issuance scale of Chinese local government bonds

A prediction model for the secure issuance scale of Chinese local government bonds <jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>The purpose of this paper is to calculate the local guaranteed fiscal revenue with the local fiscal revenue of 31 provinces, and predict their guaranteed fiscal revenue in 2018 with the artificial neural network (ANN).</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>The principal components analysis (PCA), particle swarm optimization (PSO) and extreme learning machine (ELM) model was designed to produce the inputs of KMV model. Then the KMV model was used for obtaining the default probabilities under different issuance scales. Data were collected from Wind Database. MATLAB 2018b and SPSS 22 were used in the field of modeling and results analysis.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>This study’s findings show that PCA–PSO–ELM proposed in this research has the highest accuracy in terms of the prediction compared with ELM, back propagation neural network and auto regression. And PCA–PSO–ELM–KMV model can calculate the secure issuance scale of local government bonds effectively.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Practical implications</jats:title> <jats:p>The sustainability forecast in this study can help local governments effectively control the scale of debt issuance, strengthen the budget management of local debt and establish the corresponding risk warning mechanism, which could make local governments maintain good credit ratings.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>This study sheds new light on helping local governments avoid financial risks effectively, and it is conducive to establish a debt repayment reserve system for local governments and the proper arrangement for stock debt.</jats:p> </jats:sec> http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Kybernetes CrossRef

A prediction model for the secure issuance scale of Chinese local government bonds

Kybernetes , Volume ahead-of-print (ahead-of-print) – Apr 20, 2020

A prediction model for the secure issuance scale of Chinese local government bonds


Abstract

<jats:sec>
<jats:title content-type="abstract-subheading">Purpose</jats:title>
<jats:p>The purpose of this paper is to calculate the local guaranteed fiscal revenue with the local fiscal revenue of 31 provinces, and predict their guaranteed fiscal revenue in 2018 with the artificial neural network (ANN).</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title>
<jats:p>The principal components analysis (PCA), particle swarm optimization (PSO) and extreme learning machine (ELM) model was designed to produce the inputs of KMV model. Then the KMV model was used for obtaining the default probabilities under different issuance scales. Data were collected from Wind Database. MATLAB 2018b and SPSS 22 were used in the field of modeling and results analysis.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Findings</jats:title>
<jats:p>This study’s findings show that PCA–PSO–ELM proposed in this research has the highest accuracy in terms of the prediction compared with ELM, back propagation neural network and auto regression. And PCA–PSO–ELM–KMV model can calculate the secure issuance scale of local government bonds effectively.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Practical implications</jats:title>
<jats:p>The sustainability forecast in this study can help local governments effectively control the scale of debt issuance, strengthen the budget management of local debt and establish the corresponding risk warning mechanism, which could make local governments maintain good credit ratings.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Originality/value</jats:title>
<jats:p>This study sheds new light on helping local governments avoid financial risks effectively, and it is conducive to establish a debt repayment reserve system for local governments and the proper arrangement for stock debt.</jats:p>
</jats:sec>

Loading next page...
 
/lp/crossref/a-prediction-model-for-the-secure-issuance-scale-of-chinese-local-uFjinsd0ft
Publisher
CrossRef
ISSN
0368-492X
DOI
10.1108/k-10-2019-0699
Publisher site
See Article on Publisher Site

Abstract

<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>The purpose of this paper is to calculate the local guaranteed fiscal revenue with the local fiscal revenue of 31 provinces, and predict their guaranteed fiscal revenue in 2018 with the artificial neural network (ANN).</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>The principal components analysis (PCA), particle swarm optimization (PSO) and extreme learning machine (ELM) model was designed to produce the inputs of KMV model. Then the KMV model was used for obtaining the default probabilities under different issuance scales. Data were collected from Wind Database. MATLAB 2018b and SPSS 22 were used in the field of modeling and results analysis.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>This study’s findings show that PCA–PSO–ELM proposed in this research has the highest accuracy in terms of the prediction compared with ELM, back propagation neural network and auto regression. And PCA–PSO–ELM–KMV model can calculate the secure issuance scale of local government bonds effectively.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Practical implications</jats:title> <jats:p>The sustainability forecast in this study can help local governments effectively control the scale of debt issuance, strengthen the budget management of local debt and establish the corresponding risk warning mechanism, which could make local governments maintain good credit ratings.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>This study sheds new light on helping local governments avoid financial risks effectively, and it is conducive to establish a debt repayment reserve system for local governments and the proper arrangement for stock debt.</jats:p> </jats:sec>

Journal

KybernetesCrossRef

Published: Apr 20, 2020

References