Financial distress among SMES in Malaysia: An early warning signal

Predicting financial distress among SMEs can have a significant impact on the economy as it serves as an effective early warning signal. The study develops distress prediction models combining financial, non-financial and governance, variables and analyse the influence of major corporate governance...

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Main Author:	Ma'aji, Muhammad Muhammad
Format:	Thesis
Language:	eng eng
Published:	2014
Subjects:	HG Finance
Online Access:	https://etd.uum.edu.my/4719/1/s815698.pdf https://etd.uum.edu.my/4719/6/s815698_abstract.pdf
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id	my-uum-etd.4719
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Abdullah, Nur Adiana Hiau
topic	HG Finance
spellingShingle	HG Finance Ma'aji, Muhammad Muhammad Financial distress among SMES in Malaysia: An early warning signal
description	Predicting financial distress among SMEs can have a significant impact on the economy as it serves as an effective early warning signal. The study develops distress prediction models combining financial, non-financial and governance, variables and analyse the influence of major corporate governance characteristics, i.e., ownership and board structures, on the likelihood of financial distress. The two extensively documented approaches, MDA and logit methods were used. The final sample for the estimation model consists of 172 companies with 50 percent nonfailed cases and 50 percent failed cases for the period between 2000 to 2012. The prediction models perform relatively especially in the logit and MDA model that incorporate governance, financial and non-financial variables, with an overall accuracy rate of 93.6 percent and 90.7 percent respectively in the estimated sample. The accuracy rate in the holdout sample was 91.2 percent for the logit and MDA model. This evidence shows that the models serve as efficient early warning signals and can thus be beneficial for monitoring and evaluation. Controlling shareholder, number of directors and sex of managing director are found to be significant predictors of financially distressed SMEs
format	Thesis
qualification_name	other
qualification_level	Master's degree
author	Ma'aji, Muhammad Muhammad
author_facet	Ma'aji, Muhammad Muhammad
author_sort	Ma'aji, Muhammad Muhammad
title	Financial distress among SMES in Malaysia: An early warning signal
title_short	Financial distress among SMES in Malaysia: An early warning signal
title_full	Financial distress among SMES in Malaysia: An early warning signal
title_fullStr	Financial distress among SMES in Malaysia: An early warning signal
title_full_unstemmed	Financial distress among SMES in Malaysia: An early warning signal
title_sort	financial distress among smes in malaysia: an early warning signal
granting_institution	Universiti Utara Malaysia
granting_department	Othman Yeop Abdullah Graduate School of Business
publishDate	2014
url	https://etd.uum.edu.my/4719/1/s815698.pdf https://etd.uum.edu.my/4719/6/s815698_abstract.pdf
_version_	1747827787274649600
spelling	my-uum-etd.47192022-03-06T00:58:13Z Financial distress among SMES in Malaysia: An early warning signal 2014 Ma'aji, Muhammad Muhammad Abdullah, Nur Adiana Hiau Othman Yeop Abdullah Graduate School of Business Othman Yeop Abdullah Graduate School of Business HG Finance Predicting financial distress among SMEs can have a significant impact on the economy as it serves as an effective early warning signal. The study develops distress prediction models combining financial, non-financial and governance, variables and analyse the influence of major corporate governance characteristics, i.e., ownership and board structures, on the likelihood of financial distress. The two extensively documented approaches, MDA and logit methods were used. The final sample for the estimation model consists of 172 companies with 50 percent nonfailed cases and 50 percent failed cases for the period between 2000 to 2012. The prediction models perform relatively especially in the logit and MDA model that incorporate governance, financial and non-financial variables, with an overall accuracy rate of 93.6 percent and 90.7 percent respectively in the estimated sample. The accuracy rate in the holdout sample was 91.2 percent for the logit and MDA model. This evidence shows that the models serve as efficient early warning signals and can thus be beneficial for monitoring and evaluation. Controlling shareholder, number of directors and sex of managing director are found to be significant predictors of financially distressed SMEs 2014 Thesis https://etd.uum.edu.my/4719/ https://etd.uum.edu.my/4719/1/s815698.pdf text eng public https://etd.uum.edu.my/4719/6/s815698_abstract.pdf text eng public other masters Universiti Utara Malaysia Abdullah, N., Ahmad, A., Ahmad, H & Md. Rus, R. (2008). 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Financial distress among SMES in Malaysia: An early warning signal

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