DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment

Premachandra, I. M.; Chen, Yao; Watson, John

doi:10.1016/j.omega.2011.01.002

Cited by 88 publications

(57 citation statements)

References 25 publications

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“…The most popular static bankruptcy prediction models are based on statistical methodologies (e.g., Altman 1968Altman , 1983Taffler 1984), stochastic methodologies (e.g., Theodossiou 1991;Ohlson 1980;Zmijewski 1984), and artificial intelligence methodologies (e.g., Kim and Han 2003;Li and Sun 2011;Zhang et al 1999;Shin et al 2005). DEA methodologies are increasingly gaining popularity in bankruptcy prediction (e.g., Cielen et al 2004;Paradi et al 2004;Premachandra et al 2011;Shetty et al 2012). However, the issue of out-of-sample evaluation remains to be addressed when DEA is used as a classifier.…”

Section: Introductionmentioning

confidence: 99%

An out-of-sample evaluation framework for DEA with application in bankruptcy prediction

Ouenniche

Tone

2017

Ann Oper Res

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Nowadays, data envelopment analysis (DEA) is a well-established non-parametric methodology for performance evaluation and benchmarking. DEA has witnessed a widespread use in many application areas since the publication of the seminal paper by Charnes, Cooper and Rhodes in 1978. However, to the best of our knowledge, no published work formally addressed out-of-sample evaluation in DEA. In this paper, we fill this gap by proposing a framework for the out-of-sample evaluation of decision making units. We tested the performance of the proposed framework in risk assessment and bankruptcy prediction of companies listed on the London Stock Exchange. Numerical results demonstrate that the proposed out-of-sample evaluation framework for DEA is capable of delivering an outstanding performance and thus opens a new avenue for research and applications in risk modelling and analysis using DEA as a non-parametric frontier-based classifier and makes DEA a real contender in industry applications in banking and investment.

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Section: Introductionmentioning

confidence: 99%

An out-of-sample evaluation framework for DEA with application in bankruptcy prediction

Ouenniche

Tone

2017

Ann Oper Res

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“…Záverečným zistením bolo, že navrhovaný DEA prístup dosahuje porovnateľnú presnosť klasifikácie ako tradičné techniky (DA a lineárna regresia). Významný návrh riešenia problematiky poskytujú Premachandra et al (2011), ktorí kombinujú klasickú filozofiu PPF a filozofiu inverznej DEA. Pri zvolených vstupoch a výstupoch bolo v rámci každej filozofie skonštruované DEA skóre prostredníctvom ADD modelu superefektívnosti.…”

Section: Súčasný Stav Využívania Dea Pri Diagnostikovaní Finančného Zunclassified

“…Výsledky boli relatívne uspokojivé, avšak ukázalo sa, že tento prístup je slabší v klasifikácii bankrotujúcich podnikov v porovnaní s klasifikáciou nebankrotujúcich podnikov. Napriek tomu Premachandra et al (2011) zastávajú názor, že oproti štatistickým prístupom má DEA unikátne charakteristiky, ktoré z nej robia excelentný nástroj na predikciu úpadku podniku. Na výskum Premachandru et al (2011) nadviazali Araghi a Makvandi (2013), ktorí podľa vyššie spomínaného postupu zostrojili diskriminačný index a následne ho využili ako prediktor v modeli LR.…”

Section: Súčasný Stav Využívania Dea Pri Diagnostikovaní Finančného Zunclassified

Diagnosing of the Corporate Financial Health Using DEA: an Application to Companies in the Slovak Republic

Mendelová¹,

Bieliková²

2017

Politicka ekonomie

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Diagnosing of the Corporate Financial Health Using DEA: an Application to Companies in the Slovak RepublicThe paper deals with the examining the possibilities for diagnosing the corporate financial health using Data Envelopment Analysis (DEA) technique. The main aim of the paper is to present a new proposal for diagnosing the corporate financial health by DEA, to predict financial distress of Slovak manufacturing companies using the proposed procedure, and to assess the potential of DEA as a tool for predicting financial distress of the company. Due to the special input and output variables selection and the construction of the Corporate Distress Frontier, the proposed procedure is very different from the conventional use of DEA. The proposed two-step procedure results into the identification of three zones of corporate financial health with different stage of corporate distress risk. The application of the proposed procedure to Slovak manufacturing companies and its comparison with the logistic regression model and decision tree show relatively satisfactory results of the proposed methodology in terms of correct classification of non-bankrupt firms.Keywords: data envelopment analysis, company, corporate distress, diagnosing, financial health JEL Classification: C14, G30 ÚvodZa účelom udržania prosperity a konkurencieschopnosti podniku je pre manažment a ostatné záujmové strany dôležité dôkladne poznať v akej ekonomickej a finančnej situácii sa ich podnik nachádza. Predpokladom pre detailné poznanie finančnej situácie je venovanie dostatočnej pozornosti diagnostikovaniu finančného zdravia podniku a včasné identifikovanie potenciálnych problémov ohrozujúcich jeho činnosť. Potreba včasného diagnostikovania finančných problémov podnikov podnietila vznik rôznych diagnostických modelov založených na finančných ukazovateľoch. Vo vedeckých štúdiách sa obzvlášť vysoká pozornosť venuje aplikácii variantných nástrojov na konštrukciu diagnostických modelov. K najviac využívaným nástrojom v súčasnosti patrí diskriminačná analýza (DA), logistická regresia (LR), rozhodovacie stromy (RS) a rôzne typy neuró-nových sietí (NS). Potreba splnenia viacerých štatistických predpokladov, či pomerne striktné požiadavky na veľkosť dátovej množiny týchto tradičných nástrojov sa však stali stimulom na vývoj nových prístupov, ktorých ambíciou je tieto nevýhody tradičných nástrojov čiastočne eliminovať. Nakoľko diagnostikovanie finančného zdravia podnikov je jednou z pomerne často diskutovaných tém v manažérskej praxi i v kruhoch odbornej *1 Viera Mendelová, Tatiana Bieliková (viera.mendelova@umb.sk, tatiana.bielikova@umb.sk ), Univerzita Mateja Bela v Banskej Bystrici, Ekonomická fakulta. Článok bol vypracovaný v rámci riešenia projektu VEGA 1/0647/14 -Variantné metódy predikcie finančného zdravia podnikov v podmienkach dynamického ekonomického prostredia.

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“…In addition, some authors have investigated the applicability of other artificial intelligence methods to bankruptcy prediction, for example, the principal component analysis [16,17], support vector machines [18,19], decision trees [20,21], rough sets, [12,22], data envelopment analysis [23,24], and others.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling bankruptcy prediction models in Slovak companies

Kováčová

Klieštiková

2017

SHS Web Conf.

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Abstract. An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression) and early artificial intelligence models (e.g. artificial neural networks), there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest) to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

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DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment

Cited by 88 publications

References 25 publications

An out-of-sample evaluation framework for DEA with application in bankruptcy prediction

An out-of-sample evaluation framework for DEA with application in bankruptcy prediction

Diagnosing of the Corporate Financial Health Using DEA: an Application to Companies in the Slovak Republic

Modelling bankruptcy prediction models in Slovak companies

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