A comparison of Bayesian, Hazard, and Mixed Logit model of bankruptcy prediction

Trabelsi, Samir; He, Roc; He, Lawrence; Kusý, Martin

doi:10.1007/s10287-013-0200-8

Cited by 12 publications

(13 citation statements)

References 20 publications

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“…For instance, in their meta-analyses, Kumar and Ravi (2007) explored 128 models, Bahrammirzaee (2010) discussed 278 models, Abdou and Pointon (2011) analyzed 214 models and, more recently, Opoku et al (2015) reviewed 137 prediction models of bankruptcy and financial distress. However, a prediction model is useful only if it forecasts financial health accurately as its incorrect development and application could have multiple negative outcomes (Trabelsi, He, He, & Kusy, 2015). Prior studies have attempted to develop accurate prediction models but many of the techniques and tools are criticized for their contextual and application perspectives (Balcaen & Ooghe, 2006;Farooq et al, 2018;Opoku et al, 2015).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria

Farooq

Qamar

2019

Journal of Forecasting

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This research proposes a prediction model of multistage financial distress (MSFD) after considering contextual and methodological issues regarding sampling, feature and model selection criteria. Financial distress is defined as a three‐stage process showing different nature and intensity of financial problems. It is argued that applied definition of distress is independent of legal framework and its predictability would provide more practical solutions. The final sample is selected after industry adjustments and oversampling the data. A wrapper subset data mining approach is applied to extract the most relevant features from financial statement and stock market indicators. An ensemble approach using a combination of DTNB (decision table and naïve base hybrid model), LMT (logistic model tree) and A2DE (alternative N dependence estimator) Bayesian models is used to develop the final prediction model. The performance of all the models is evaluated using a 10‐fold cross‐validation method. Results showed that the proposed model predicted MSFD with 84.06% accuracy. This accuracy increased to 89.57% when a 33.33% cut‐off value was considered. Hence the proposed model is accurate and reliable to identify the true nature and intensity of financial problems regardless of the contextual legal framework.

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Section: Theoretical Backgroundmentioning

confidence: 99%

“…Their primary focus was only data balancing methods. Similarly, Trabelsi et al (2015) compared the results for cut-off point and sampling procedures. Abellán and Mantas (2014) tried to explore the importance of bagging optimization only.…”

Section: Literature Gapmentioning

confidence: 99%

Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria

Farooq

Qamar

2019

Journal of Forecasting

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“…The accuracy is between 74.14% and 76.10%. Also, the authors concluded that Bayesian, Hazard, and Mixed Logit (Trabelsi et al, 2014). The accuracy and effectiveness was tested by Trabelsi et al (2014) in their paper, and they concluded cut of point was predicted on the learning sample.…”

Section: Evolution Of Bankruptcy Predictionmentioning

confidence: 99%

Bankruptcy Prediction: A Survey on Evolution, Critiques, and Solutions

Fejér-Király

2015

Acta Universitatis Sapientiae, Economics and Business

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After the economic crisis and the BASEL agreement, the bankruptcy prediction research has evolved substantially due to its importance in corporate finance. This paper summarizes the short history of bankruptcy prediction from the beginning until quite recently. First, it presents a short summary of bankruptcy prediction evolution pointing to the most used models. Then, it provides a summary of the most cited papers that discuss the evolution of bankruptcy prediction and of those papers that have contributed to bankruptcy prediction. Finally, it summarizes some critiques about bankruptcy prediction that the literature has formulated over time and provides some suggestions for future research on bankruptcy prediction.

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“…The second approach is to choose features that occur at bankruptcy and classify, if the bank looks like close to bankruptcy or not [8,12,15]. Gordini [8] used info from database containing bank accounts records for one, two, and three years before bankruptcy.…”

Section: Introductionmentioning

confidence: 99%

“…Division to bankrupt and non-bankrupt cases was done for the same year. Trabelsi [15] also used database info and examined the impact of cut-off points choosing and sampling procedures. Jeong [12] used a set of balance sheet samples for bankrupt and non-bankrupt organizations and applied the general additive model to select useful and less redundant variables for classification.…”

Section: Introductionmentioning

confidence: 99%

Influence of the External Environment Behaviour on the Banking System Stability

Guleva

Dukhanov

2015

Procedia Computer Science

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There are plenty of researches dedicated to financial system stability, which takes significant place in prevention of financial crisis and its consequences. However banking system and external environment interaction and customers behaviour influence on the banking system stability are poorly studied. Current paper propose agent-based model of banking system and its external environment. We show how customers behaviour characteristics affect a banking system stability. Optimal interval for total environmental funds towards banking system wealthy is performed.

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A comparison of Bayesian, Hazard, and Mixed Logit model of bankruptcy prediction

Cited by 12 publications

References 20 publications

Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria

Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria

Bankruptcy Prediction: A Survey on Evolution, Critiques, and Solutions

Influence of the External Environment Behaviour on the Banking System Stability

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