Comparing Logit, Probit and Multiple Discriminant Analysis Models in Predicting Bankruptcy of Companies

Araghi, Maryam Khalili; Makvandi, Sara

doi:10.5296/ajfa.v5i1.2977

Cited by 5 publications

(5 citation statements)

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“…More importantly, their relative values indicate their relative influence on the binary variable (Roncek, ). The final, and clearest, reason to apply logit methodology to our propensity score‐matched samples is provided by Khalili Araghi, and Makvandi (): Using the same sample, the authors compare the accuracy of logit, probit, and MDA models and give evidence for the superiority of the first when predicting bankruptcies. All in all, the combination of propensity score matching and logit methodology ensures that the results are not biased by self‐selection, as described by Heckman (), and therefore increases their accuracy and verifiability compared to solely matching on industry and size.…”

Section: Methodsmentioning

confidence: 99%

Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10‐K Filings

Lopatta

Gloger

Jaeschke

2017

Accounting Perspectives

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We examine whether the language used in 10-K filings reflects a firm's risk of bankruptcy. Our sample contains 424 bankrupt U.S. companies in the period 1994-2015 and we use propensity score matching to find healthy matches. Based on a logit model of failing and vital firms, our findings indicate that firms at risk of bankruptcy use significantly more negative words in their 10-K filings than comparable vital companies. This relationship holds up until three years prior to the actual bankruptcy filing. With our investigation, we confirm the results from previous accounting and finance research. 10-K filings contain valuable information beyond the reported financials. Additionally, we show that 10-Ks filed in the year of a firm's collapse contain an increased number of litigious words relative to healthy businesses. This indicates that the management of failing firms is already dealing with legal issues when reporting financials prior to bankruptcy. Our results suggest that analysts ought to include the presentation of financials in their assessment of bankruptcy risk as it contains explanatory and predictive power beyond the financial ratios.Les auteurs se demandent si le langage utilis e dans les d eclarations 10-K r ev ele l'existence d'un risque de faillite de l'entreprise. Ils analysent un echantillon de 424 soci et es des Etats-Unis ayant fait faillite entre 1994 et 2015 et utilisent l'appariement des coefficients de propension pour d eterminer quelles sont les correspondances d'entreprises saines. En appliquant un mod ele logit aux entreprises d efaillantes et aux entreprises saines, ils constatent que celles qui pr esentent un risque de d efaillance font un usage sensiblement plus abondant de termes n egatifs dans leurs d eclarations 10-K que les entreprises saines comparables. Cette relation perdure jusqu' a trois ans avant le r eel d epôt de bilan. L' etude confirme les * Accepted by Pascale Lapointe-Antunes.

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Section: Methodsmentioning

confidence: 99%

Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10‐K Filings

Lopatta

Gloger

Jaeschke

2017

Accounting Perspectives

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“…He found that the proposed DEA approach achieved comparable classification accuracy to traditional techniques (DA and LR). Araghi and Makvandi (2013) used not only conventional DEA but also inverse DEA as predictors in LR. They quantified 72% overall accuracy of DEA, which is less compared to LR.…”

Section: Discussionmentioning

confidence: 99%

Risk of Bankruptcy, Its Determinants and Models

Horváthová

Mokrišová

2018

Risks

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In this paper, the following research problem was addressed: Is DEA (Data Envelopment Analysis) method a suitable alternative to Altman model in predicting the risk of bankruptcy? Based on the above-mentioned research problem, we formulated the aim of the paper: To apply DEA method for predicting the risk of bankruptcy and to compare its results with the results of Altman model. The research problem and the aim of the paper follow the research of authors aimed at the application of methods which are appropriate for measuring business financial health, performance and competitiveness as well as for predicting the risk of bankruptcy. To address the problem, the following methods were applied: financial ratios, Altman model for private non-manufacturing firms and DEA method. When applying DEA method, we formulated input-oriented DEA CCR model. We found that DEA method is an appropriate alternative to Altman model in predicting the risk of possible business bankruptcy. The important conclusion is that DEA allows us to apply not only outputs but also inputs. Since prediction models do not include these indicators, DEA method appears to be the right choice. We recommend, especially for Slovak companies, to apply cost ratio when calculating risk of bankruptcy.

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“…Credit scoring modeling can be built through various methods, including Logistic Regression (RS), Discriminant Analysis, The k-nearest Neighbors (k-NN), Bootstrap Aggregation, Boosting, Random Forest (RF), The Support Vector Machine (SVM), An artificial neural network, Multivariate Adaptive Regression Splines (MARS), and Variable Neighborhood Search (VNS) Although Logistic Regression is the most popular method in credit scoring modeling and is specifically developed when the output variable is binary (Lessmann et al, 2015), with the consideration that this method can produce a cut-off point value that can become a threshold (Araghi & Maryam, 2013;Mihalovič, 2016). This threshold value becomes the basis for consideration in assessing the feasibility of corporate credit.…”

Section: Figure 1 Research Frameworkmentioning

confidence: 99%

“…Logistic regression is a predictive modeling approach used to study the relationship of one or more independent variables with one dependent variable on a dichotomous (binary) scale, which is a nominal data scale with two categories. Logistic regression emphasizes finding linear combinations of two or more predictors that are able to predict among groups of companies that are eligible or ineligible for corporate credit at banks (Araghi & Maryam, 2013;Hosmer et al, 2013) through the assessment of the company's credit rating published by the Indonesian Securities Rating Agency, which is worth one if the corporate credit application is eligible and worth 0 if the corporate credit application is not eligible. The companies included in the 1-value criteria are companies with credit ratings of AAA to B, while companies included in the 0 criteria are companies with credit ratings lower than B to unrated companies (Hu & Su, 2022).…”

Section: Figure 1 Research Frameworkmentioning

confidence: 99%

Credit Scoring Modelling For Corporate Banking Institutions

Purbayati,

Muflih,

Pakpahan

2024

JIMS

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This research aims to build a credit scoring modeling simulation of bank corporate loans. The credit scoring model is used in assessing creditworthiness in credit decisions. This model determines whether or not a company is eligible for the corporate credit facility it proposes. Observations were made of 100 companies included in the list of Kompas100 Index formers on the Indonesia Stock Exchange (IDX) that have the potential to apply for loans/credits to Bank Financial Institutions (IKB) in optimizing the corporate capital structure through bank debt facilities in the period 2022. Analysis was conducted on five financial aspects consisting of 14 research variables, including (i) liquidity aspects, including current ratio and quick ratio variables; (ii) solvency aspects, including debt asset ratio and equity ratio variables; (iii) profitability aspects including return on net assets, operating profit ratio, price to earnings ratio variables, (iv) activity aspects including total asset turnover, accounts receivable turnover, inventory turnover, current assets turnover, and (v) growth aspects including operating income growth rate, total assets growth rate, and operating profit growth rate variables. The analysis tool uses Logistic Regression through an assessment conducted on the company's credit rating as a proxy for the dependent variable, worth one if the credit application is feasible and worth 0 if the credit application is not feasible with a cut-off point of 0.5. The results show that credit scoring modeling for corporate credit is significantly formed from liquidity (CR) and solvency (DER) aspects. Out of 61 companies classified as not eligible for credit facilities, 58 companies were classified correctly, and out of 39 companies classified as eligible, 29 companies were classified correctly. The overall percentage shows 68.0, meaning that the logistic regression model has an accuracy of 68%.

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Comparing Logit, Probit and Multiple Discriminant Analysis Models in Predicting Bankruptcy of Companies

Cited by 5 publications

References 0 publications

Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10‐K Filings

Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10‐K Filings

Risk of Bankruptcy, Its Determinants and Models

Credit Scoring Modelling For Corporate Banking Institutions

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