Predicting financially distressed small- and medium- sized enterprises in Malaysia
This study investigates factors that might predict financially distressed small- and medium-sized enterprises (SMEs) 4 years, 3 years, 2 years and 1 year prior to distress. We employ logistic regression to identify the predictors of distressed SMEs in the manufacturing sector. Debt ratio is found to...
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my.uum.repo.270472020-05-21T05:46:21Z http://repo.uum.edu.my/27047/ Predicting financially distressed small- and medium- sized enterprises in Malaysia Abdullah, Nur Adiana Hiau Ahmad, Abd Halim Zainudin, Nasruddin Md Rus, Rohani HG Finance This study investigates factors that might predict financially distressed small- and medium-sized enterprises (SMEs) 4 years, 3 years, 2 years and 1 year prior to distress. We employ logistic regression to identify the predictors of distressed SMEs in the manufacturing sector. Debt ratio is found to be consistently significant throughout the period of study. It is the main cause of failure among SMEs in Malaysia, as they rely heavily on debt to support their operations. The finding also shows that young companies have a higher probability of failure than established companies and that the bigger the size of a company, the higher the probability of that company entering distress. Other factors that are also found to be significant are current ratio, short-term liabilities to total liabilities, return on assets, sales to total assets and net income to share capital. In addition, the 1-year and 2-year prior to distress models provide the highest accuracy rate in detecting financially distressed SMEs. 2019 Article PeerReviewed Abdullah, Nur Adiana Hiau and Ahmad, Abd Halim and Zainudin, Nasruddin and Md Rus, Rohani (2019) Predicting financially distressed small- and medium- sized enterprises in Malaysia. Global Business Review, 20 (3). pp. 627-639. ISSN 0972-1509 http://doi.org/10.1177/0972150919837053 doi:10.1177/0972150919837053 |
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HG Finance Abdullah, Nur Adiana Hiau Ahmad, Abd Halim Zainudin, Nasruddin Md Rus, Rohani Predicting financially distressed small- and medium- sized enterprises in Malaysia |
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This study investigates factors that might predict financially distressed small- and medium-sized enterprises (SMEs) 4 years, 3 years, 2 years and 1 year prior to distress. We employ logistic regression to identify the predictors of distressed SMEs in the manufacturing sector. Debt ratio is found to be consistently significant throughout the period of study. It is the main cause of failure among SMEs in Malaysia, as they rely heavily on debt to support their operations. The finding also shows that young companies have a higher probability of failure than established companies and that the bigger the size of a company, the higher the probability of that company entering distress. Other factors that are also found to be significant are current ratio, short-term liabilities to total liabilities, return on assets, sales to total assets and net income to share capital. In addition, the 1-year and 2-year prior to distress models provide the highest accuracy rate in detecting financially distressed SMEs. |
format |
Article |
author |
Abdullah, Nur Adiana Hiau Ahmad, Abd Halim Zainudin, Nasruddin Md Rus, Rohani |
author_facet |
Abdullah, Nur Adiana Hiau Ahmad, Abd Halim Zainudin, Nasruddin Md Rus, Rohani |
author_sort |
Abdullah, Nur Adiana Hiau |
title |
Predicting financially distressed small- and medium- sized enterprises in Malaysia |
title_short |
Predicting financially distressed small- and medium- sized enterprises in Malaysia |
title_full |
Predicting financially distressed small- and medium- sized enterprises in Malaysia |
title_fullStr |
Predicting financially distressed small- and medium- sized enterprises in Malaysia |
title_full_unstemmed |
Predicting financially distressed small- and medium- sized enterprises in Malaysia |
title_sort |
predicting financially distressed small- and medium- sized enterprises in malaysia |
publishDate |
2019 |
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http://repo.uum.edu.my/27047/ http://doi.org/10.1177/0972150919837053 |
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1669009603160965120 |