Predicting Financial Distress among SMEs in Malaysia

Ma’aji, Muhammad M.; Abdullah, Nur Adiana Hiau; Khaw, Karren Lee-Hwei

doi:10.19044/esj.2018.v14n7p91

Cited by 9 publications

(17 citation statements)

References 12 publications

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“…A model that can predict financially distressed SMEs for a period up to three years prior to distress is essential so that the model developed for SMEs in the hospitality industry can be useful to SMEs to help in sustaining their businesses. Additionally, earlier studies had suggested that ownership concentration was significant in predicting the failure of SMEs (Ciampi, 2015;Ma'aji et al 2018). Since these additional variables are common in the SME ownership structure, these variables are expected to be significant predictors of failure for SMEs.…”

Section: Literature Reviewmentioning

confidence: 95%

“…In the context of Malaysian literature, most of the studies model SME failure in the manufacturing sector (Abdullah, Ahmad et al, 2016Ma'aji et al 2018Ma'aji et al , 2019 and the only study that has conducted a failure prediction investigation of SMEs in the hospitality industry was for a single period only, i.e. two years prior to failure (Zainol Abidin et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similarly, in the context of Malaysian literature, the majority of research on corporate failures has focused on public-listed firms (Abdullah, 2020;Abdullah & Ahmad, 2005;Alifiah & Tahir, 2018;Low et al, 2001;Yasser & Mamun, 2015). However, there are relatively limited studies looking into the SMEs and most of the researches predict financially distressed SMEs in the manufacturing sector (Abdullah, Ahmad et al, 2016Ma'aji et al, 2018Ma'aji et al, , 2019.…”

Section: Introductionmentioning

confidence: 99%

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Bankruptcy Prediction: Smes in the Hospitality Industry

Abidin

Abdullah

Khaw

2021

International Journal of Banking and Finance

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The objectives of this study are to predict bankruptcy risk among SMEs in the hospitality industry for a three-year horizon period and to investigate the factors that are significant in determining bankruptcy. The contribution of SMEs in the hospitality industry is essential as businesses in the hospitality industry are dominated by SME operators. However, the failure rate among SMEs is relatively high and almost 50 percent of hospitality establishments do not survive beyond five years of operation. The Stepwise logistic model was employed to determine significant predictors that could predict bankruptcy for the period of one year, two years and three years before bankruptcy. Return on assets and firm age were found to be significant in all periods while other variables were identified to be important at a specific period prior to bankruptcy. In addition to return on assets and firm age, debt ratio and total assets turnover were found to be significant predictors of bankruptcy one-year prior to bankruptcy. However, in the two years prior to bankruptcy, debt ratio and total assets turnover were no longer important but current ratio, ownership concentration and gender diversity were found to be significant. As for the three years prior to bankruptcy, additional variables namely debt-to-equity ratio and board size were found to be significant, but ownership concentration and gender diversity ceased to be important. The findings of this study contribute to the limited literature in predicting the bankruptcy risk of small firms for a three-year horizon period by providing empirical evidence from SMEs in the hospitality industry of Malaysia.

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Section: Literature Reviewmentioning

confidence: 95%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bankruptcy Prediction: Smes in the Hospitality Industry

Abidin

Abdullah

Khaw

2021

International Journal of Banking and Finance

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“…The finding is in line with hypothesis 1 and consistent with previous studies (Bhat et al, 2018;Hermalin & Weisbach, 2003;Khanchel, 2007;Ma'aji, Abdullah & Karen, 2019;Ma'aji, Abdullah & Karen, 2018). Smaller board sizes are better than larger ones that may be the plague with free rider and monitoring problem and therefore are expected to experience fewer communication and coordination problems, thus improving performance (Hermalin & Weisbach, 2003;Khanchel, 2007;Ma'aji et al, 2019;Ma'aji et al, 2018). From the sampled banks, some of the banks are having board member s ranging from 17 to 15 people.…”

Section: Descriptive Analysismentioning

confidence: 99%

The Relevance of Good Corporate Governance Practices to Bank Performance

Ma’aji¹,

Anderson²,

Colon³

2021

jeb

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The purpose of this paper is to examine how corporate governance instruments impact firm value in the context of Cambodian banks. This paper considers foreign and domestic-owned banks in Cambodia. This study opts for a balanced sample of foreign and domestic owned banks for the period 2014-2018. Panel data regression is adopted for estimation of main results. The suitable model, i.e. fixed and random effect model is selected using the Hausman specification test where the result shows that the random effect model using generalized least square (GLS) regression is more suitable for the analysis. The findings show that Cambodian banks are having a substantially higher percentage of NEDs on their board, high implementation of governance procedures on board committees where on average the banks are having more than the required two board committees (audit and risk committees) as required by the Prakas on the governance of banks by National Bank of Cambodia. The average board size is around 8 members of which at least 3 members are having a postgraduate degree or a professional qualification. Policymakers need to improve on their supervisory function as the majority of the domestic and some foreign banks do not disclose their annual reports on their company website as required by the Prakas on Corporate Governance of Banks operating in Cambodia. Moreover, amendments should be made to the current corporate governance code for financial institutions as there are no explanatory notes that guide companies and therefore, the current guideline is open to individual and subjective interpretation.

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“…Salah satu contoh instrument tersebut adalah model Altman Z-score agar para pelaku usaha lebih mudah dalam mengambil keputusan dan mempersiapkan tindakan sebelum, pada saat, maupun pasca kejadian jika terjadi gangguan usaha. Salah satu faktor yang mendukung usaha agar tetap beroperasi adalah faktor kondisi keuangan, sehingga banyak penelitian yang telah mengembangkan model prediksi ini (Marcinkevicius & Kanapickiene, 2014;Muminovic, 2013;Ma'aji, Abdullah, & Khaw, 2018;Ong, Yap, & Khong, 2011).…”

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Model altman z-score modification sebagai early warning system untuk memprediksi financial distress UMKM di kabupaten Banyuwangi Indonesia

Kurniawansyah¹,

Agustia²

2021

JAA

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The aim of this study is to predict financial distress in Micro, Small, and Medium Enterprises (MSME) in Banyuwangi by using the Altman Z-score modification model from 2016 to 2019. This study involves 100 MSMEs registered in Creative House of Banyuwangi Regency. The data is obtained from the 2016 to 2018 businesses' financial statement between 2016 until 2018. The data then was analysis by using a descriptive analysis. The results of this study show that the majority of MSMEs have financial distress. These results contribute to the mapping of the financial conditions of MSME, and hence, it becomes an early sign to mitigate bankcruptcy risk for business actors and local governments to maintain the financial performance of MSMEs. Financial stability of MSMEs might reduce the poverty gap, strengthen regional and national economies and create new job opportunities.

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Predicting Financial Distress among SMEs in Malaysia

Cited by 9 publications

References 12 publications

Bankruptcy Prediction: Smes in the Hospitality Industry

Bankruptcy Prediction: Smes in the Hospitality Industry

The Relevance of Good Corporate Governance Practices to Bank Performance

Model altman z-score modification sebagai early warning system untuk memprediksi financial distress UMKM di kabupaten Banyuwangi Indonesia

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