Developing a multi stage predicting system for corporate credit rating in emerging markets: Jordanian case

Dana Al-Najjar, Basil Al-Najjar

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Purpose
– The purpose of this paper is to build a neural network system to predict corporate credit rating in Jordanian non-financial firms, using 19 different financial characteristics such as profitability, leverage ratios, liquidity, bankruptcy, and sales performance.

Design/methodology/approach
– The study adopts two neural network techniques namely, Kohonen network and Back Propagation Neural Network (BPNN). Our sample includes the manufacturing firms that have provided the required financial information for the period from 2000 to 2007.

Findings
– BPNN has successfully predicted firms with high performance gaining A rating and the bankrupted firms with D rating for the period from 2005 to 2007.

Originality/value
– This study is the first study to investigate credit rating in Jordan using Neural Network technique.
LanguageEnglish
Pages475-487
Number of pages13
JournalJournal of Enterprise Information Management
Volume27
Issue number4
DOIs
Publication statusPublished - 2014
Externally publishedYes

Fingerprint

neural network
credit
rating
Neural networks
market
firm
Backpropagation
liquidity
bankruptcy
Jordan
profitability
sales
performance
Profitability
Sales
manufacturing
Credit rating
Emerging markets
methodology
Back-propagation neural network

Cite this

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abstract = "Purpose– The purpose of this paper is to build a neural network system to predict corporate credit rating in Jordanian non-financial firms, using 19 different financial characteristics such as profitability, leverage ratios, liquidity, bankruptcy, and sales performance.Design/methodology/approach– The study adopts two neural network techniques namely, Kohonen network and Back Propagation Neural Network (BPNN). Our sample includes the manufacturing firms that have provided the required financial information for the period from 2000 to 2007.Findings– BPNN has successfully predicted firms with high performance gaining A rating and the bankrupted firms with D rating for the period from 2005 to 2007.Originality/value– This study is the first study to investigate credit rating in Jordan using Neural Network technique.",
keywords = "Neural network, Jordan, Credit ratings, Default risk",
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AU - Al-Najjar, Basil

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N2 - Purpose– The purpose of this paper is to build a neural network system to predict corporate credit rating in Jordanian non-financial firms, using 19 different financial characteristics such as profitability, leverage ratios, liquidity, bankruptcy, and sales performance.Design/methodology/approach– The study adopts two neural network techniques namely, Kohonen network and Back Propagation Neural Network (BPNN). Our sample includes the manufacturing firms that have provided the required financial information for the period from 2000 to 2007.Findings– BPNN has successfully predicted firms with high performance gaining A rating and the bankrupted firms with D rating for the period from 2005 to 2007.Originality/value– This study is the first study to investigate credit rating in Jordan using Neural Network technique.

AB - Purpose– The purpose of this paper is to build a neural network system to predict corporate credit rating in Jordanian non-financial firms, using 19 different financial characteristics such as profitability, leverage ratios, liquidity, bankruptcy, and sales performance.Design/methodology/approach– The study adopts two neural network techniques namely, Kohonen network and Back Propagation Neural Network (BPNN). Our sample includes the manufacturing firms that have provided the required financial information for the period from 2000 to 2007.Findings– BPNN has successfully predicted firms with high performance gaining A rating and the bankrupted firms with D rating for the period from 2005 to 2007.Originality/value– This study is the first study to investigate credit rating in Jordan using Neural Network technique.

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KW - Credit ratings

KW - Default risk

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