Building up Default Predicting Model based on Logistic Model and Misclassification Loss

Ma, Ruijuan; Chun-yang, Tang

doi:10.1016/s1874-8651(08)60049-6

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Delmar et al [8] use correlations and regression analysis to model company growth, whereas Geroski [12] uses static and dynamic optimizing models for company output choice, modelled production functions for corporate learning, modelled R&D competition and diversification, and examined their influence on corporate growth rates. Ma and Tang [19] use a stepwise logistic regression model to predict a default considering the misclassification loss. Logistic regression was also compared with and integrated into certain machine learning methods to predict company distress or bankruptcy.…”

Section: Review Of Previous Researchmentioning

confidence: 99%

Predicting company growth using logistic regression and neural networks

Zekić–Sušac

Šarlija

Has

et al. 2016

CRORR

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Abstract. The paper aims to establish an efficient model for predicting company growth by leveraging the strengths of logistic regression and neural networks. A real dataset of Croatian companies was used which described the relevant industry sector, financial ratios, income, and assets in the input space, with a dependent binomial variable indicating whether a company had high-growth if it had annualized growth in assets by more than 20% a year over a three-year period. Due to a large number of input variables, factor analysis was performed in the pre-processing stage in order to extract the most important input components. Building an efficient model with a high classification rate and explanatory ability required application of two data mining methods: logistic regression as a parametric and neural networks as a non-parametric method. The methods were tested on the models with and without variable reduction. The classification accuracy of the models was compared using statistical tests and ROC curves. The results showed that neural networks produce a significantly higher classification accuracy in the model when incorporating all available variables. The paper further discusses the advantages and disadvantages of both approaches, i.e. logistic regression and neural networks in modelling company growth. The suggested model is potentially of benefit to investors and economic policy makers as it provides support for recognizing companies with growth potential, especially during times of economic downturn.

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Section: Review Of Previous Researchmentioning

confidence: 99%

Predicting company growth using logistic regression and neural networks

Zekić–Sušac

Šarlija

Has

et al. 2016

CRORR

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show abstract

“…The conditional probability model becomes a popular technique in bankruptcy prediction domain [63]. The other researcher such as ( [29], [36], [40]- [41]) studied the corporation credit default based on Logistics regression or risk model. by using the fuzzy measurement and through the αcut process.…”

Section: Literature Reviewmentioning

confidence: 99%

Enterprise Credit Risk Evaluation models: A Review of Current Research Trends

Lee¹

2012

IJCA

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Enterprise Credit Risk becomes important issue in financial and accounting.It includes bankruptcy prediction, financial distress, corporate performance clustering / prediction and credit risk estimation. This research aims to provide a state-of-the art review of the relative literature and indicate relevant research opportunities. We found that the current research trends are necessary a method for reduction the feature subset, many hybrids SVM based model and rough model are proposed. Another consideration which requires future research is the evaluation of relative cost of TypeⅠand TypeⅡerrors.

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“…Some specified units, such as "poor units" or "special units", are expected to be compared objects. Generalized Data Envelopment Analysis (Generalized-DEA) method is an extension of DEA with the specified reference set based on DMUs or non-DMUs [7][8]. A group of generalized DEA models [9] was constructed where decision-makers choose the proper reference set according to their needs.…”

Section: Introductionmentioning

confidence: 99%

Cross-efficiencyBasedonGeneralized-DEAandItsApplication

Shu,

Cai

2023

J. Phys.: Conf. Ser.

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Traditional DEA selects automatically inner Decision-Making Units (DMUs) as the reference set. However, some applications might need specified units. Based on generalized DEA and cross-efficiency DEA, a cross-GDEA model is constructed in this paper, which not only considers any specified reference set in efficiency evaluation but also ranks distinguishably all DMUs. A numerical example and comparative analysis are given to illustrate the reasonableness of the cross-GDEA model. Comparative analysis with several established DEA models reveals the superior flexibility and generality of our method. Furthermore, an application aiming at measuring the multiple-factor carbon productivity of Western China is given to verify the applicability of the cross-GDEA model. Results show the proposed approach can contribute to advancing the theoretical and practical research of DEA.

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Building up Default Predicting Model based on Logistic Model and Misclassification Loss

Cited by 6 publications

References 11 publications

Predicting company growth using logistic regression and neural networks

Predicting company growth using logistic regression and neural networks

Enterprise Credit Risk Evaluation models: A Review of Current Research Trends

Cross-efficiencyBasedonGeneralized-DEAandItsApplication

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