Analysis of Bankruptcy using Data Mining Approach

This study involves the development of neural network prediction model to predict the stage of bankruptcy of a company. A total of 367 data was attained from the Registrar of Business and Companies, Kuala Lumpur Stock Exchange (KLSE) and Bank Negara Malaysia (Central Bank of Malaysia). The data was...

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Main Author:	Ong, Ai Ping
Format:	Thesis
Language:	eng eng
Published:	2009
Subjects:	QA299.6-433 Analysis
Online Access:	https://etd.uum.edu.my/1570/1/Ong_Ai_Ping_%28801972%29_2009.pdf https://etd.uum.edu.my/1570/2/1.Ong_Ai_Ping_%28801972%29_2009.pdf
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id	my-uum-etd.1570
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
topic	QA299.6-433 Analysis
spellingShingle	QA299.6-433 Analysis Ong, Ai Ping Analysis of Bankruptcy using Data Mining Approach
description	This study involves the development of neural network prediction model to predict the stage of bankruptcy of a company. A total of 367 data was attained from the Registrar of Business and Companies, Kuala Lumpur Stock Exchange (KLSE) and Bank Negara Malaysia (Central Bank of Malaysia). The data was then analyzed by considering the basic statistics, frequency and cross tabulation in order to get more information about the data. Initially, the data was classified using logistic regression.In addition, it was also trained using neural network in order to obtain the bankruptcy model. The findings show that the most suitable prediction model consist of 12 nodes of input , hidden layer 6 node and one output layer. The generalization performance of the selected model is100%. This methodology should be able to provide some new insight into the type of pattern that exists in the data. Thus, neural network has a great potential in supporting for predicting bankruptcy.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Ong, Ai Ping
author_facet	Ong, Ai Ping
author_sort	Ong, Ai Ping
title	Analysis of Bankruptcy using Data Mining Approach
title_short	Analysis of Bankruptcy using Data Mining Approach
title_full	Analysis of Bankruptcy using Data Mining Approach
title_fullStr	Analysis of Bankruptcy using Data Mining Approach
title_full_unstemmed	Analysis of Bankruptcy using Data Mining Approach
title_sort	analysis of bankruptcy using data mining approach
granting_institution	Universiti Utara Malaysia
granting_department	College of Arts and Sciences (CAS)
publishDate	2009
url	https://etd.uum.edu.my/1570/1/Ong_Ai_Ping_%28801972%29_2009.pdf https://etd.uum.edu.my/1570/2/1.Ong_Ai_Ping_%28801972%29_2009.pdf
_version_	1747827168351617024
spelling	my-uum-etd.15702013-07-24T12:12:21Z Analysis of Bankruptcy using Data Mining Approach 2009 Ong, Ai Ping College of Arts and Sciences (CAS) College of Arts and Sciences QA299.6-433 Analysis This study involves the development of neural network prediction model to predict the stage of bankruptcy of a company. A total of 367 data was attained from the Registrar of Business and Companies, Kuala Lumpur Stock Exchange (KLSE) and Bank Negara Malaysia (Central Bank of Malaysia). The data was then analyzed by considering the basic statistics, frequency and cross tabulation in order to get more information about the data. Initially, the data was classified using logistic regression.In addition, it was also trained using neural network in order to obtain the bankruptcy model. The findings show that the most suitable prediction model consist of 12 nodes of input , hidden layer 6 node and one output layer. The generalization performance of the selected model is100%. This methodology should be able to provide some new insight into the type of pattern that exists in the data. Thus, neural network has a great potential in supporting for predicting bankruptcy. 2009 Thesis https://etd.uum.edu.my/1570/ https://etd.uum.edu.my/1570/1/Ong_Ai_Ping_%28801972%29_2009.pdf application/pdf eng validuser https://etd.uum.edu.my/1570/2/1.Ong_Ai_Ping_%28801972%29_2009.pdf application/pdf eng public masters masters Universiti Utara Malaysia Ahmed, K. M., El-Makky, N. M. and Taha, Y. (1998).Effective data mining: a data warehouse-backboned architecture Abdelwahed, T. and Amir, M. (2005). New evolutionary bankruptcy forecasting model based on genetic algorithms and neural networks Agarwal, S., Ambrose, B. W., & Chomsisengphet, S. (2005). Asymmetric information and the automobile loan market.Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Preceding of The Journal of Finance, 23, 589-609.Altman, E. (1983). 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Analysis of Bankruptcy using Data Mining Approach

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