Data mining in predicting firms failure: a comparative study using artificial neural networks and classification and regression tree / Norashikin Nasaruddin ...[et al.]

Financial Institutions and investors alike are very much interested in the accuracy of predicting the potential failures of firms. These financial institutions believe accurate prediction will lead to a low default rate in servicing their financial loans. The aim of this study is to find a better mo...

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Main Authors: Nasaruddin, Norashikin, Che-Hussain, Wan-Siti-Esah, Nayan, Asmahani, Ahmad, Abd-Razak
Format: Conference or Workshop Item
Language:English
Published: 2015
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/53991/1/53991.pdf
https://ir.uitm.edu.my/id/eprint/53991/
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spelling my.uitm.ir.539912023-02-22T07:29:20Z https://ir.uitm.edu.my/id/eprint/53991/ Data mining in predicting firms failure: a comparative study using artificial neural networks and classification and regression tree / Norashikin Nasaruddin ...[et al.] Nasaruddin, Norashikin Che-Hussain, Wan-Siti-Esah Nayan, Asmahani Ahmad, Abd-Razak Banking Financial management. Business finance. Corporation finance Financial Institutions and investors alike are very much interested in the accuracy of predicting the potential failures of firms. These financial institutions believe accurate prediction will lead to a low default rate in servicing their financial loans. The aim of this study is to find a better model to classify firms that is more likely to fail. Bad prediction model will lead to a high default rate. Using financial and non-financial information, this paper illustrates the construction and comparison of two models – artificial neural networks (NN) and classification and regression tree (CART) models to classify the failed from the non-failed firms. This study found that based on the training sample’s result (NN = 94.03% & CART = 94.69%) the overall accuracy result of CART is higher than the NN model. Similar result can be drawn for the validation sample with CART leading at 92.93% overall accuracy rate compared to NN’s 91.92%. 2015-11-04 Conference or Workshop Item PeerReviewed text en https://ir.uitm.edu.my/id/eprint/53991/1/53991.pdf Data mining in predicting firms failure: a comparative study using artificial neural networks and classification and regression tree / Norashikin Nasaruddin ...[et al.]. (2015) In: International Conference on Computing, Mathematics and Statistics (iCMS2015), 4-5 November 2015, Langkawi Lagoon Resort Langkawi Island, Kedah Malaysia. (Submitted)
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Banking
Financial management. Business finance. Corporation finance
spellingShingle Banking
Financial management. Business finance. Corporation finance
Nasaruddin, Norashikin
Che-Hussain, Wan-Siti-Esah
Nayan, Asmahani
Ahmad, Abd-Razak
Data mining in predicting firms failure: a comparative study using artificial neural networks and classification and regression tree / Norashikin Nasaruddin ...[et al.]
description Financial Institutions and investors alike are very much interested in the accuracy of predicting the potential failures of firms. These financial institutions believe accurate prediction will lead to a low default rate in servicing their financial loans. The aim of this study is to find a better model to classify firms that is more likely to fail. Bad prediction model will lead to a high default rate. Using financial and non-financial information, this paper illustrates the construction and comparison of two models – artificial neural networks (NN) and classification and regression tree (CART) models to classify the failed from the non-failed firms. This study found that based on the training sample’s result (NN = 94.03% & CART = 94.69%) the overall accuracy result of CART is higher than the NN model. Similar result can be drawn for the validation sample with CART leading at 92.93% overall accuracy rate compared to NN’s 91.92%.
format Conference or Workshop Item
author Nasaruddin, Norashikin
Che-Hussain, Wan-Siti-Esah
Nayan, Asmahani
Ahmad, Abd-Razak
author_facet Nasaruddin, Norashikin
Che-Hussain, Wan-Siti-Esah
Nayan, Asmahani
Ahmad, Abd-Razak
author_sort Nasaruddin, Norashikin
title Data mining in predicting firms failure: a comparative study using artificial neural networks and classification and regression tree / Norashikin Nasaruddin ...[et al.]
title_short Data mining in predicting firms failure: a comparative study using artificial neural networks and classification and regression tree / Norashikin Nasaruddin ...[et al.]
title_full Data mining in predicting firms failure: a comparative study using artificial neural networks and classification and regression tree / Norashikin Nasaruddin ...[et al.]
title_fullStr Data mining in predicting firms failure: a comparative study using artificial neural networks and classification and regression tree / Norashikin Nasaruddin ...[et al.]
title_full_unstemmed Data mining in predicting firms failure: a comparative study using artificial neural networks and classification and regression tree / Norashikin Nasaruddin ...[et al.]
title_sort data mining in predicting firms failure: a comparative study using artificial neural networks and classification and regression tree / norashikin nasaruddin ...[et al.]
publishDate 2015
url https://ir.uitm.edu.my/id/eprint/53991/1/53991.pdf
https://ir.uitm.edu.my/id/eprint/53991/
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score 13.211869