A semiparametric method for predicting bankruptcy

Hwang, Ruey-Bing; Cheng, Kuang Fu; Lee, Jack C.

doi:10.1002/for.1027

Cited by 40 publications

(19 citation statements)

References 30 publications

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“…Therefore, researchers proposed new techniques to overcome the problems associated with static models. Hwang et al (2007) propose a robust semi-parametric logit model with smaller hold-out sample error rates.…”

Section: The Hazard Modelmentioning

confidence: 99%

The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model

Kalak

Hudson

2016

International Review of Financial Analysis

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(2016) The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model. AbstractThis paper investigates the extent to which the size affects the SMEs probabilities of bankruptcy.Using a dataset of (11,117) US non-financial firms, of which (465) filed for insolvency under chapters 7/11 between 1980 and 2013. We forecast the bankruptcy probabilities by developing four discretetime duration-dependant hazard models namely SMEs, Micro, Small, and Medium. A comparison of the default prediction models for medium firms and SMEs suggest that almost an identical set of explanatory variables affect the default probabilities leading us to believe that there is no material impact on the decision-making process by treating each of these groups separately. However, comparisons between the micro and small firms with the SMEs firms strongly suggest that they need to be considered separately while modelling credit risk for them.JEL classification:

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Section: The Hazard Modelmentioning

confidence: 99%

The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model

Kalak

Hudson

2016

International Review of Financial Analysis

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“…In the development of bankruptcy prediction models, few researchers have addressed the issue of missing feature values and most of them have simply deleted the observations with missing feature values and consider those observations with complete predictor values (Cheng et al 2010;Hwang et al 2007). However, Shumway (2001) pointed out that a complete set of explanatory variables is not always observable for each firm year, and he substituted variable values from past years for missing values in some cases.…”

Section: Literature Reviewmentioning

confidence: 99%

AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data

Zhou

Lai

2016

Comput Econ

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Very little existing research in corporate bankruptcy prediction discusses modeling where there are missing values. This paper investigates AdaBoost models for corporate bankruptcy prediction with missing data. Three AdaBoost models integrated with different imputation methods are tested on two data sets with very different sample sizes. The experimental results show that the AdaBoost algorithm combined with imputation methods has strong predictive accuracy in both data sets and it is a useful alternative for bankruptcy prediction with missing data.

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“…Many statistical and intelligent methods have been proposed [2,3]. For example, discriminant analysis [4,5], logistic regression (LR) [6], probit regression [7], the case-based reasoning (CBR) method [8], neural networks [9], the genetic algorithm [10], rough sets [11], decision trees [12], the semi-parametric method [13], the discrete-time duration method [14], support vector machines (SVMs) [15], and the minimal optimization technique [16] were used for BFP.…”

Section: Introductionmentioning

confidence: 99%

Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction

2015

J Inf Process Syst

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This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

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A semiparametric method for predicting bankruptcy

Cited by 40 publications

References 30 publications

The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model

The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model

AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data

Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction

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