The Value of Governance Variables in predicting Financial Distress among Small and Medium-Sized Enterprises in Malaysia

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

doi:10.21315/aamjaf2016.11.s1.4

Cited by 3 publications

(15 citation statements)

References 18 publications

(32 reference statements)

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“…Kim (2018) suggests that generating adequate cash flow is critical for the hospitality industry to service debt given the nature of this industry is capital-intensive. The results of this study are consistent to Abdullah et al [16,17], Altman et al [41], Bredart [42], Ciampi and Gordini [44], Terdpaopong [48], Wellalage and Locke [49], and Yazdanfar and Nilsson [52] that record a negative relationship. Board size is also found to be a significant predictor.…”

Section: Resultssupporting

confidence: 91%

“…As for non-financial variables, age is found to be significant in differentiating between failed (10 years) and healthy SMEs (12 years), but size is not. Furthermore, it is found that healthy SMEs have larger board size than failed SMEs, which concurs to the study of [17].…”

Section: Resultssupporting

confidence: 89%

“…Where P is the probability of failure, β_n are parameter estimates of the model and X_n are the predictor variables which are: current assets to current liabilities [41,42,43,44]; total liabilities to total assets and total liabilities to total equity (Abdullah et al, 2016a, 2016b; Charalambakis, 2014; Goo and Shih, 2015; Pacheco, 2015; Ciampi and Gordini, 2008); net income to total assets and net income to sales [17,24,41,42,49]; sales to total assets [47,48]; firm size (total assets) and firm age [16,17,44,49,54]; ownership concentration [45]; board size and board diversity [17,42,45]. Since there is no theoretical foundation for selecting potential variables [29], the variables identified in this study are based on the ratios used in previous studies and data availability in the CCM database.…”

Section: Methodsmentioning

confidence: 99%

“…Only recently, Pacheco [15] conducts a bankruptcy study for SMEs in the hospitality industry of Portugal. Studies that model small business failure in Malaysia focus on the manufacturing sector [16,17]. Thus far, there is lack of research in modeling business failure in the hospitality industry in Malaysia, which instigates us to look into this topic.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting Business Failure for Malaysia SMEs in the Hospitality Industry

Abidin¹,

Abdullah²,

Khaw³

2021

Proceedings of the Conference on International Issues in Business and Economics Research (CIIBER 2019)

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SMEs are an important segment of the Malaysian economy and contribute significantly to the country's economic growth. Nonetheless, SMEs are riskier and associated with a high failure rate. Hence, the aim of this study is to develop a failure prediction model for SMEs in the hospitality industry by using the logit and artificial neural network (ANN) approach for 82 SMEs over the period 2000 to 2016. The findings show that the ANN model predicts better than the logit model in both the estimation and holdout sample with a predictive accuracy rate of 98.2% and 92%, respectively, while the logit model provides overall accuracy rates of 86% and 80%, respectively. This study also finds that both models identify return on assets and board size as an important signal of business failure. The models could be used to assist investors, creditors and lenders to screen out failing SMEs, and the authorities could decide on policies to improve SMEs in the hospitality industry.

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Section: Resultssupporting

confidence: 91%

Section: Resultssupporting

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Business Failure for Malaysia SMEs in the Hospitality Industry

Abidin¹,

Abdullah²,

Khaw³

2021

Proceedings of the Conference on International Issues in Business and Economics Research (CIIBER 2019)

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“…Many companies’ managers display the behavior of moral hazard due to information asymmetry, which also reflects that corporate governance has not been properly enforced internally (Abdullah, Ma’aji, & Khaw, 2016; Berger, Imbierowicz, & Rauch, 2016; Lee & Yeh, 2004). Following the financial frauds of Enron, Tyco International, and WorldCom, the US government enacted the Sarbanes–Oxley Act of 2002 (SOX) and related Securities and Exchange Commission (SEC) regulations in response to these and other major corporate and accounting scandals to emphasize the importance of corporate governance.…”

Section: Introductionmentioning

confidence: 99%

Financial distress prediction model: The effects of corporate governance indicators

Chen

Lien

2020

Journal of Forecasting

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This paper constructs a financial distress prediction model that includes not only traditional financial variables, but also several important corporate governance variables. Using data from Taiwan, the empirical results show that the best in-sample and out-of-sample prediction models should combine the financial variables with the corporate governance variables. Moreover, the prediction accuracy is higher for the models using dynamic distress threshold values than those with tradition threshold values. Most financial ratios, except for the debt ratio, are higher in financially sound companies than in financial distressed ones. With regard to the corporate governance variables, we find that the CEO/Chairman duality may not result in the outbreak of financial distress, but higher equity pledge ratios of managers (shareholding ratios by board members and insiders) positively (negatively) correlate with financial distress.

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Modelling business failure among small businesses in Nigeria

Ma’aji¹

2019

Pressacademia

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Purpose -Despite the reported high bankruptcy rate among small businesses (SMEs) in Nigeria, this study is the first to develop failure prediction models specifically for SMEs using financial and non-financial variables. Methodology -The study employed logistic regression to a sample of 344 SMEs during the period 2000-2014. Findings-The increased in the predictive accuracy of the model shows that data relating to the age of business and location make a significant contribution. Additionally, the study finds that high leverage and operational expenses and low profitability are associated with SMEs failure. The prediction accuracy rate was 92.1 and 93.8 percent for model 1 and model 2 respectively. Conclusion-The findings will serve as an early warning signal for management to take proactive measures to overcome the threats of failure. Financial institutions such as banks will benefit from this study as it will help them set their internal control systems and procedures to manage credit risk for SMEs.

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The Value of Governance Variables in predicting Financial Distress among Small and Medium-Sized Enterprises in Malaysia

Abstract: Predicting financial distress among

Cited by 3 publications

References 18 publications

Predicting Business Failure for Malaysia SMEs in the Hospitality Industry

Predicting Business Failure for Malaysia SMEs in the Hospitality Industry

Financial distress prediction model: The effects of corporate governance indicators

Modelling business failure among small businesses in Nigeria

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