DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique

Premachandra, I. M.; Bhabra, Gurmeet S.; Sueyoshi, Toshiyuki

doi:10.1016/j.ejor.2007.11.036

Cited by 143 publications

(116 citation statements)

References 53 publications

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“…The DEA score method performed less well than a linear discriminant function. Premachandra et al (2009) estimated an additive DEA, which is invariant to data translation (and so can deal with negative data) with varying Returns to Scale. On the training sample DEA had an inferior predictive performance whereas out of sample it was superior.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A limitation of many studies that have used DEA efficiency in bankruptcy prediction is that they have estimated TE across a range of industries that use heterogeneous technologies (Cielen et al, 2004;Premachandra et al, 2009;Premachandra et al, 2011). If the technology used by the DMUs in the sample is different then the weights on the inputs and outputs will be different and the concept of efficiency will be somewhat meaningless.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Different DEA studies have used different inputs and outputs, which is a shortcoming of DEA (Premachandra et al, 2009). First of all, inputs and outputs have to be meaningful within the framework of the competitive environment (Oral and Yolalan, 1990).…”

Section: Selecting Inputs and Outputsmentioning

confidence: 99%

“…Premachandra et al (2009) propose that the smaller (inferior) values in the financial ratios, which could possibly cause financial distress, are considered to be inputs whereas the larger (superior) values in those ratios, which could cause financial distress, are considered as outputs. Xu and Wang (2009) Our choice of variables has been influenced by the following considerations.…”

Section: Selecting Inputs and Outputsmentioning

confidence: 99%

See 3 more Smart Citations

Chinese companies distress prediction: an application of data envelopment analysis

Crook

Andreeva

2014

Journal of the Operational Research Society

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Bankruptcy prediction is a key part in corporate credit risk management. Traditional bankruptcy prediction models employ financial ratios or market prices to predict bankruptcy or financial distress prior to its occurrence. We investigate the predictive accuracy of corporate efficiency measures along with standard financial ratios in predicting corporate distress in Chinese companies. Data Envelopment Analysis (DEA) is used to measure corporate efficiency.In contrast to previous applications of DEA in credit risk modelling where it was used to generate a single efficiency -Technical Efficiency, we assume Variable Returns to Scale, and decompose Technical Efficiency into Pure Technical Efficiency and Scale Efficiency. These measures are introduced into Logistic Regression to predict the probability of distress, along with the levels of Returns to Scale. Effects of efficiency variables are allowed to vary across industries through the use of interaction terms, whilst the financial ratios are assumed to have the same effects across all sectors. The results show that the predictive power is improved by this corporate efficiency information.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Selecting Inputs and Outputsmentioning

confidence: 99%

Section: Selecting Inputs and Outputsmentioning

confidence: 99%

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Chinese companies distress prediction: an application of data envelopment analysis

Crook

Andreeva

2014

Journal of the Operational Research Society

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“…Therefore, we use a binary logit regression model as it is in many studies of that kind (Ohlson, 1980;Platt & Platt, 1991;Premachandra, Bhabra, & Sueyoshi, 2009). A logit model represents the relationship between a binary dependent variable that takes value 1 (financial distress) or value 0 (no financial distress), and k explanatory variables x 1 , x 2 ... x k .…”

Section: Methodsmentioning

confidence: 99%

Financial Distress and Corporate Governance around Lehman Brothers Bankruptcy

Brédart¹

2014

IBR

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United States faced a growing number of corporate bankruptcies during the subprime crisis. This article is intended to determine the effectiveness of some governance mechanisms on the financial distress probability of companies over the period [2007][2008][2009]. This period is divided into two parts; we take the date of filing for chapter 11 of the company Lehman Brothers (15 December, 2008) as the cutting point between the mortgage and the financial phases of the crisis. Aiming to highlight the influence of corporate governance mechanisms on financial distress probability, we then use, for each sub period, logit models on paired samples of firms which half opted for legal protection due to financial and commercial difficulties. Our results indicate that, during the first sub period, combining the functions of CEO and chairman of the board of directors is beneficial to the survival of the firm, whereas, during the second sub period, capital ownership by the CEO appears to be an incentive to keep the firm on float. This paper seeks to add to the corporate governance literature by considering how certain corporate governance mechanisms impacted the perennity of companies during the subprime crisis.

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Ranking of Bankruptcy Prediction Models Under Multiple Criteria

Ouenniche

Mousavi

et al. 2017

Advances in DEA Theory and Applications

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DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique

Cited by 143 publications

References 53 publications

Chinese companies distress prediction: an application of data envelopment analysis

Chinese companies distress prediction: an application of data envelopment analysis

Financial Distress and Corporate Governance around Lehman Brothers Bankruptcy

Ranking of Bankruptcy Prediction Models Under Multiple Criteria

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