Predicting Corporate Bankruptcy Using Failing Firms

Flagg, James C.; Giroux, Gary; Wiggins, Casper E.

doi:10.1002/j.1873-5924.1991.tb00543.x

Cited by 77 publications

(55 citation statements)

References 7 publications

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“…Since our dependent variable, financial distress, is dichotomous, we use a binary logit regression model, as it is the case in many studies regarding the occurrence of bankruptcy filing (Ohlson, 1980;Mensah, 1984;Zavgren, 1985;Aziz, Emmanuel, & Lawson, 1988;Burgstahle, Jiambalvo, & Noreen, 1989;Flagg, Giroux, & Wiggins, 1991;Platt & Platt, 1991;Bell, Mossman, Swartz, & Turtle, 1998;Premachandra, Bhabra, & Sueyoshi, 2009). A logit model describes the relationship between a dichotomous dependent variable that can take value 1 (bankrupt business) or value 0 (healthy), and k other explanatory variables x 1 , x 2 ... x k .…”

Section: Methodsmentioning

confidence: 99%

Financial Distress and Corporate Governance: The Impact of Board Configuration

Brédart¹

2014

IBR

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The number of filings for bankruptcy procedures has exploded since 2007 and governance has been pointed out as one of the causes. We took a dataset of 312 US firms and asked the following research question: does the board of directors configuration have an impact on financial distress? We used a matched-pair sample of US quoted firms with half of the sample filing for Chapter 7 (liquidation) or 11 (reorganization) of the United States Bankruptcy Code and conducted logit regression analysis. We found that some board size was significantly different for firms that opted for legal protection from those that did not. This study uses corporate governance perspective to analyse the configuration of the board and its impact on a the decision of a company to resort to a bankruptcy protection law. By demonstrating that corporate governance matters in terms of financial distress, this study offers guidance to shareholders and financial institutions.

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Section: Methodsmentioning

confidence: 99%

Financial Distress and Corporate Governance: The Impact of Board Configuration

Brédart¹

2014

IBR

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“…[59] in which a list of 72 causes is proposed, taking as reference the works of Refs. [8][9][10]30,31,52], DAFO analysis [50] and studies of failure prediction with non-financial variables ( [12][13][14]50,51], etc.). Furthermore, the relationship between causes and the key areas is presented in Refs.…”

Section: Definition Of Causes and Symptomsmentioning

confidence: 99%

“…In other models such as Refs. [11][12][13][14], qualitative and external variables are introduced to improve the results. subjectivity and uncertainty.…”

Section: Introductionmentioning

confidence: 99%

An application of OWA operators in fuzzy business diagnosis

Vigier

Scherger

Terceño

2017

Applied Soft Computing

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The paper aims to develop an adjustment index based on OWA operators to enrich the results of diagnostic fuzzy models of business failure. A proposal to verify the diseases prediction accuracy of the models is also added. This allows a reduction of the map of causes or diseases detected in strategic defined areas. At the same time, these key areas can be disaggregated when an alert indicator is identified, and shows which of the causes need special attention. This application of OWA can encourage the development of suitable computer systems for monitoring companies' problems, warn of failures and facilitate decision-making. In addition, taking Vigier and Terceño's 2008 model as a benchmark, causes aggregation operators are introduced to evaluate alternative groupings, and the adjustment measure using approximate solutions is proposed to test the model's prediction.The empirical estimation and the verification of the improvement proposals in a set of small and medium-sized enterprises (SMEs) in the construction industry are also presented. The functionality and the prediction capacity are thus measured and detected by monitoring key areas that warn about insolvency situations in the firm.

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“…Linear discriminant analysis is still intensively used in practice; however, it implies the respect of some strict statistical restrictions such as the normality of the distribution of the explanatory variables. As a consequence, bankruptcy prediction models using regressions on qualitative variables as logit (Ohlson, 1980;Mensah, 1984;Zavgren, 1985;Aziz, Emmanuel, & Lawson, 1988;Bardos, 1989;Burgstahler, Jiambalvo, & Noreen, 1989;Flagg, Giroux, & Wiggins, 1991;Platt & Platt, 1991;Bardos & Zhu, 1997;Bell, Mossman, Swartz, & Turtle, 1998;Premachandra, Bhabra, & Sueyoshi, 2009) and probit models (Zmijewsji, 1984;Gentry, Newblod, & Whiteford, 1985;Lennox, 1999) have been developed, depending on whether the residuals follow a logistic or normal distribution. Duration models, similar to previous ones but making it possible to consider several periods, have also been used (Shumway, 2001;Duffie, Saita, & Wang, 2007).…”

Section: Bankruptcy Predictionmentioning

confidence: 99%

Bankruptcy Prediction Model: The Case of the United States

Brédart¹

2014

IJEF

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United States has faced a growing number of corporate bankruptcies since the subprimes crisis. This paper aims to develop an econometric forecasting model constructed from three simple and easily available financial ratios. We used a matched-pair sample of US quoted firms with half of the sample filing for chapter 11 (reorganization procedure) of the United States Bankruptcy Code for the period 2000-2012 and conducted logit regression analysis. We found that this model using three simple, few correlated and easily available financial ratios as explanatory variables shows a prediction accuracy of more than 80 percent. Besides the academic contribution to the research relative to the bankruptcy prediction, the empirical results of this study may be useful for practitioners, particularly for financial institutions interested in the probability of default of their partners.

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Predicting Corporate Bankruptcy Using Failing Firms

Cited by 77 publications

References 7 publications

Financial Distress and Corporate Governance: The Impact of Board Configuration

Financial Distress and Corporate Governance: The Impact of Board Configuration

An application of OWA operators in fuzzy business diagnosis

Bankruptcy Prediction Model: The Case of the United States

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