Corporate failure for manufacturing industries using firms specifics and economic environment with logit analysis

Darayseh, Musa; Waples, Elaine; Tsoukalas, Dimitrios

doi:10.1108/03074350310768409

Cited by 11 publications

(10 citation statements)

References 11 publications

(9 reference statements)

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“…However, his study was conducted in the mixed sector only. GDP was also found to be significant in predicting financial distress companies in other previous studies (Al-Darayseh, 1990;Bunn & Redwood, 2003;Hol, 2007;Kritzer, 1985).…”

Section: Literature Reviewsupporting

confidence: 63%

Predicting financial distress companies in the manufacturing and non-manufacturing sectors in Malaysia using macroeconomic variables

Alifiah¹,

Tahir²

2018

10.5267/j.msl

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This paper attempts to predict financial distress companies in the manufacturing and non-manufacturing sectors in Malaysia using financial distress companies as the dependent variable and financial ratios and macroeconomic variables as the independent variables. Logit Analysis was used as the analysis procedure because ratios do not have to be normal if it is used. It is also suitable when the dependent variable is binary in nature. Furthermore, it can also provide the probability of a company being financially distressed. This study found that the independent variables that can be used to predict financial distress companies in the manufacturing sector in Malaysia were total assets turnover ratio, current ratio, net income to total assets ratio and money supply (M2). However, the independent variables that can be used to predict financial distress companies in the nonmanufacturing sector in Malaysia were debt ratio, working capital ratio, net income to total assets ratio and money supply (M2). This study provides the prediction models of financial distress companies in the manufacturing and non-manufacturing sectors in Malaysia using financial ratios and macroeconomic variables as its independent variables.

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Section: Literature Reviewsupporting

confidence: 63%

Predicting financial distress companies in the manufacturing and non-manufacturing sectors in Malaysia using macroeconomic variables

Alifiah¹,

Tahir²

2018

10.5267/j.msl

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“…Lo (1986) studied corporate bankruptcies, comparing the logit model to MDA, and concluded that the logit model was more robust than MDA. Darayseh, Waples, and Tsoukalas (2003) conducted a study using a logit model to predict corporate bankruptcy and obtained 88 % accuracy for in-sample and holdout sample tests. Chi and Tang (2006) collected a sample of firms in seven Asia-Pacific capital markets to exam trade credit risk using a logit model.…”

Section: Bankruptcy Prediction Studies In Financial Literaturementioning

confidence: 99%

A Comparative Study of Logit and Artificial Neural Networks in Predicting Bankruptcy in the Hospitality Industry

Park

Hançer

2012

Tourism Economics

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Taking financial ratios as independent variables, this study used the framework of a neural network applied to hospitality firm bankruptcy, comparing the results to those of a logit model. Based on the empirical results of the two methodologies, the neural network obtained a higher accuracy rate than the logit model in an in-sample test. However, when tested with a holdout sample for verification, both models achieved a 100% accuracy rate. The study found that 'total liabilities to total assets' was a significant variable based on the results of both the t-test and logit analysis. Since hospitality firms are known for being highly leveraged, the conclusion can be drawn that extensive debt financing, when not accompanied by the competitive market value of equity, could play a pivotal role in forcing firms to file for bankruptcy.

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“…For instance, a study on corporate Failure conducted in the US for the manufacturing Industries by Darayseh, Waples and Tsoukalas (2003) revealed some against theory fi ndings. They found that net profi t margin has a positive impact on the failure risk.…”

Section: The Third Reason Regarding the Sign Of The Coeffi Cientsmentioning

confidence: 99%

Failure prediction from the investors’ view by using financial ratios. Lesson from Romania

Achim¹,

Borlea²,

Găban³

2016

E+M

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Corporate failure for manufacturing industries using firms specifics and economic environment with logit analysis

Cited by 11 publications

References 11 publications

Predicting financial distress companies in the manufacturing and non-manufacturing sectors in Malaysia using macroeconomic variables

Predicting financial distress companies in the manufacturing and non-manufacturing sectors in Malaysia using macroeconomic variables

A Comparative Study of Logit and Artificial Neural Networks in Predicting Bankruptcy in the Hospitality Industry

Failure prediction from the investors’ view by using financial ratios. Lesson from Romania

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