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Bankruptcy prediction using Partial Least Squares Logistic Regression · Study

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ArticleBankruptcy prediction using Partial Least Squares Logistic Regression

Sami Ben Jabeur2017

Article2017

Bankruptcy prediction using Partial Least Squares Logistic Regression

Sami Ben Jabeur

100 citations·0/0 files stored·Journal of Retailing and Consumer Services

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Financial Distress and Bankruptcy Prediction Corporate Insolvency and Governance Forecasting Techniques and Applications

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References

1986Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers 1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1958The Cost of Capital, Corporation Finance and the Theory of Investment 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1966Financial Ratios As Predictors of Failure 1984Methodological Issues Related to the Estimation of Financial Distress Prediction Models 1994Financial Distress and Corporate Performance 1972A Discriminant Analysis of Predictors of Business Failure

Cited by

2021The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning 2019A bibliometric study on intelligent techniques of bankruptcy prediction for corporate firms 2022Predicting the changes in the WTI crude oil price dynamics using machine learning models 2020Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods 2018A Survey on Machine Learning and Statistical Techniques in Bankruptcy Prediction

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Details

Year2017

Typearticle

Published2017-03-10

VenueJournal of Retailing and Consumer Services

ISSN-L0969-6989

Languageen

Citations100

Files0 public / 0

ShelfFinancial Distress and Bankruptcy Prediction

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doi10.1016/j.jretconser.2017.02.005

openalex_workW2592858280

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2019Modeling corporate financial distress using financial and non-financial variables

2021Bankruptcy prediction using fuzzy convolutional neural networks

2022Systematic Review of Financial Distress Identification using Artificial Intelligence Methods