Credit risk assessment: a comparison of the performances of the linear discriminant analysis and the logistic regression

Levy, Aldo; Baha, Riad

doi:10.1504/ijesb.2021.112265

Cited by 11 publications

(9 citation statements)

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“…(2) Some financial research has used score models (Beaver, 1966;Altman, 1968;Altman et al, 2022) or econometric models (Lennox, 1999;Huang et al, 2004;Avery et al, 2012;L evy and Baha, 2021) to predict the SMEs default risk in the short term and to inform creditors of the risk level to which they are exposed.…”

Section: Main Approaches Of Smes Failurementioning

confidence: 99%

“…, 2004; Avery et al. , 2012; Lévy and Baha, 2021) to predict the SMEs default risk in the short term and to inform creditors of the risk level to which they are exposed.The second is centered on the primacy of resources which links failure to the managerial and strategic characteristics of the SME (Wiklund and Shepherd, 2005). Failure is due to SME's endogenous factors, such as the managerial incompetence of the entrepreneur (Cardon et al.…”

Section: Choice Of Capital Structure and Smes Failurementioning

confidence: 99%

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Capital structure and default risk of small and medium enterprises: evidence from Algeria

Baha

Levy

Hasnaoui

2023

JRF

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PurposeThis study examines the existence of a causal relationship between the capital structure at the creation of the small and medium-sized enterprise (SME) and its viability after 3 years.Design/methodology/approachThe empirical strategy consists of proceeding in two stages: first, the use of the Logit model to regress the studied variable reflecting the state of an SME of being in default or not, on the variables likely to significantly explain its default risk. Second, the authors investigate the existence of a relationship between the capital structure at the time of SME creation and viability. The obtained results are analyzed to confirm the initial hypothesis.FindingsThe results obtained indicate that the Logit model performs well in terms of discriminating and classifying SMEs. These findings are consistent with previous studies and support their conclusions regarding the model's strong classification capability. Furthermore, the model demonstrates a noteworthy classification rate of 90% for capital SMEs, specifically joint-stock companies (SpA). Out of the 10 observed SMEs, 8 nonfailing SMEs were still operational three years after the observation period, resulting in a survival rate of 80%.Practical implicationsThe results allow bankers to better understand the main determinants of SME default risk and demonstrate the existence of a causal relationship between the capital structure of an SME and its viability. This study is conducted in the construction, public works, and hydraulics sector (second largest sector in Algeria after the services sector). In future works, the authors try to extend the results of this study to other sectors of activity.Originality/valueThe richness of the established Logit model is to consider both financial and non-financial and qualitative variables. Although the qualitative variables are not statistically significant in the results obtained, the authors used the “Legal form” variable to demonstrate the existence of a causal relationship between the capital structure of an SME and its viability.

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Section: Main Approaches Of Smes Failurementioning

confidence: 99%

Section: Choice Of Capital Structure and Smes Failurementioning

confidence: 99%

Capital structure and default risk of small and medium enterprises: evidence from Algeria

Baha

Levy

Hasnaoui

2023

JRF

Self Cite

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“…Logistic Regression is derived from linear regression [6,7]. Linear regression is generally used to predict continuous data, and rarely or even not used to classify discrete data.…”

Section: Employed Machine Learning Algorithms 221 Logistic Regressionmentioning

confidence: 99%

Credit Card Fraud Detection Using Feature Fusion-based Machine Learning Model

Meng¹

2022

HSET

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Credit card fraud is a very common means of financial fraud at present. In order to reduce social and personal economic property losses, this study aims to propose a model to predict credit card fraud, so as to detect possible fraudulent transactions in the transaction process. In this paper, the data set to simulate fraudulent transactions be used, and construct the model after processing the characteristic data and selecting the features. In the model construction part, four algorithms are used as the trained classifiers, which are K Nearest Neighbor algorithm, Bgging algorithm, Logistic Regression algorithm and Gaussian Bayesian algorithm. First, the four algorithms are used to train the data set respectively, and then the parameters of each classifier are adjusted respectively, so that each individual classifier achieves the optimal performance. On this basis, a new model is constructed by using the method of model fusion. The classifier constructed by K Nearest Neighbor algorithm, Bagging algorithm and Gaussian Bayesian algorithm is used as the primary learner, and the classifier constructed by logistic regression algorithm is used as the secondary classifier to build a fused model as a whole. The final experimental results will give the classification performance of the five models, including individual models. The results show that the performance of the model after integrating the four individual models is better than that of the single model, which can provide accurate feedback for credit card holders on whether there is fraud.

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“…e Logistic model is commonly used in the linear discriminant problems [11]. e dependent variable Y represents the probability of event occurrence.…”

Section: Basic Principle Analysis Of Logistic Modelmentioning

confidence: 99%

Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies

Chen

Zhu

2021

Mathematical Problems in Engineering

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The purpose is to avert the systematic financial risks from the Internet financial bubble and improve the efficiency of legal service companies’ credit risk assessment ability. Firstly, this study analyzes the commonly used classification model, Support Vector Machine (SVM), and linear regression model, Logistic model, and then puts forward the integrated SVM-Logistic + Fuzzy Multicriteria Decision-Making (FMCDM) to evaluate and analyze the credit risk level of listed companies. In the proposed integrated model, the SVM model classifies the data sample from listed companies, and the Logistic model is used for regression analysis on the credit risk assessment. Based on the credit risk indexes and weight uncertain factors of sample companies, FMCDM based on fuzzy set is applied to obtain the evaluation indexes. Then, the Analytic Hierarchy Process (AHP) is used to obtain the weight of key indexes. Finally, the fit analysis is carried out according to the existing risk status of the sample company and the risk status results of the proposed integrated model. The results show that the integrated SVM-Logistic model is complementary and has high intensive evaluation. According to the fitness value obtained by FMCDM, the company's credit risk status can be accurately evaluated, and the intermediate threshold of corporate credit default risk measurement is 0.56152; if Fit is lower than the threshold, the company’s credit is low, and if Fit is higher than the threshold, the company’s credit is high. Therefore, the data mining technology based on integrated SVM-Logistic model + FMCDM has high precision and feasible application in the credit risk assessment from legal service companies. This study creates a new method model for legal service companies in the field of corporate credit risk assessment and can provide references and ideas for corporate credit risk assessment.

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Credit risk assessment: a comparison of the performances of the linear discriminant analysis and the logistic regression

Cited by 11 publications

References 0 publications

Capital structure and default risk of small and medium enterprises: evidence from Algeria

Capital structure and default risk of small and medium enterprises: evidence from Algeria

Credit Card Fraud Detection Using Feature Fusion-based Machine Learning Model

Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies

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