Évaluation du risque de défaillance de solvabilité des PME : une application du modèle de la régression logistique

Ahlem  BOUAZZARA; Riad BAHA; Fatiha BEKTACHE

doi:10.34118/djei.v11i2.724

Ahlem BOUAZZARA Université Alger 3
Riad BAHA EHEC
Fatiha BEKTACHE Université Alger 3

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Résumé

La prévision du risque de crédit des PME a été largement abordée par la littérature financière et comptable. Plusieurs travaux de recherches ont permis d’aboutir à modèles et techniques de prédiction qui peuvent être adaptés à différents pays et secteurs d’activité.

Dans cette étude, nous visons à évaluer le risque de défaut de solvabilité des PME en utilisant un modèle de la régression logistique, sur un échantillon de PME du secteur du bâtiment, travaux publiques et hydraulique (BTPH) privé algérien.

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Références

Altman, E.I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance, 23 (4), pp.589–609.

Altman, E.I., Marco, G. and Varetto, F. (1994). Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the Italian experience), Journal of Banking and Finance, 18 (3), pp.505–529.

Altman, E.I., Margaine, M., Schlosser, M. and Vernimmen, P. (1974). Financial and statistical analysis for commercial loan evaluation: a French experience, Journal of Financial and Quantitative Analysis, 9 (2), pp.195–211.

Avery, R.B., Brevoort, K.P. and Canner, G. (2012). Does credit scoring produce a disparate impact?, Real Estate Economics, 40, (S1), pp.65–114.

Bardos, M. (2005). Les scores de la banque de France: leur développement leur développement, leurs applications, Bulletin de la Banque de France, (144), pp.63–73.

Bardos, M. and Zhu, W.H. (1997). Comparaison de l’analyse discriminante linéaire et des réseaux neuronaux: application à la détection de défaillance d’entreprises, Revue Statistique Appliquée, 45 (4), pp.65–92.

Beaver, W.H. (1966). Financial ratios as predictors of failure, Empirical research in accounting: selected studies, Supplement of Journal of Accounting Research, 4, pp.71–111.

Blum, M. (1974). Failing company discriminant analysis, Journal of Accounting Research, 12 (1), pp.1–25.

Collongues, Y. (1977). Ratios financiers et prévision des faillites des petites et moyennes entreprises, Revue Banque, September, (365), pp.963–970.

Conan, J. and Holder, M. (1979), Variables explicatives de performances et contrôle de gestion dans les PMI. Thèse non publiée, Université Paris IX, Paris, France.

Dambolena, I.G. and Khoury, S.J. (1980) . Ratio stability and corporate failure, Journal of Finance, 35 (4), pp.1017–1026.

Dietsch, M. and Petey, J. (2008). Mesure et gestion du risque de crédit dans les institutions financières. Paris : Revue Banque.

Frydman, H., Altman, E. and Kao, D.L. (1985). Introducing recursive partitioning for financial classification: the case of financial distress, Journal of Finance, 40 (1), pp.269–291.

Gan, C., Li, Z., Wang, W. and Kao, B. (2012). Credit scoring in mortgage lending: evidence from China, International Journal of Housing Markets and Analysis, 5 (4), pp.334–350.

Gentry, J.A., Newbold, P. and Whiteford, D.T. (1985). Classifying bankrupt firms with funds flow components, Journal of Accounting Research, 23 (1), pp.146–160.

Hamer, M.M. (1983). Failure prediction: sensitivity of classification accuracy to alternative statistical methods and variable sets, Journal of Accounting and Public Policy, 2 (4), pp.289–307.

Huang, Z., Chen, H., Hsu, C.J., Chen, W.H. and Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: a market comparative study, Decision Support Systems, 37 (4), pp.543–558.

Kharoubi, C. and Thomas, P. (2013). Analyse du risque de crédit: Banque et Marchés. Paris : Revue Banque.

Kira, D.S., Doreen, D. and Nguyen, D. (1997). An application of artificial neutral networks and statistical methods in qualitative evaluation of small business loans, Papier présenté en titre de conférence ASMDA Seminar, Italy: Capri.

Kuritzkes, A. and Harris S. (1999). Re-thinking the credit process, Banking Strategies, 75 (5), pp.6–12.

Lennox, C. (1999). Identifying failing companies: a revaluation of the logit, probit and DA approaches, Journal of Economics and Business, 51 (4), pp.347–364.

Levy, A., Ben Bouheni, F. and Ammi, C. (2016). Financial Management in French vs IFRS and US GAAP Standard, (ISTE Ltd)., Hoboken, NJ: London and John Wiley & Sons.

Li, X. and Zhong, Y. (2012). An overview of personal credit scoring: techniques and future work, International Journal of Intelligence Science, 2 (4A), pp.181–189.

Lo, A.W. (1986). Logit versus discriminant analysis: a specification test and application to corporate bankruptcies, Journal of Econometrics, 31 (2), pp.151–178.

Micha, B. (1984). Analysis of business failures in France, Journal of Banking and Finance, 8 (2), pp.281–291.

Nguyen, H.T. (2016). Credit Scoring et ses applications dans la gestion du risque de crédit, thèse non publiée, Université Paris-Nanterre, Paris, France.

Ohlson, J. (1980). Financial ratios and ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, 18 (1), pp.109–131.

Platt, H.D and Platt, M.B. (1990). Improving ex ante bankruptcy classification using stable predictive variables, Journal of Business Finance and Accounting, 17, pp.31–51.

Refait, C. (2004). La prévention de la faillite fondée sur l’analyse financière de l’entreprise: état de lieu, Économie et Prévision, 162 (1), pp.129–147.

Shin, K-S. and Lee, Y.J. (2002). A genetic algorithm application in bankruptcy prediction modelling, System Experts with Applications, 23 (3), pp.637–646.

Tamari, M. (1966). Financial ratios as a means of forecasting bankruptcy, Management International Review, 6 (4), pp.15–21.

Varetto, F. (1998). Genetic algorithms applications in the analysis of insolvency risk, Journal of Banking and Finance, 22 (10–11), pp.1421–1439.

Wu, W., Lee, V.C.S. and Tan, T.Y. (2006). Data preprocessing and data parsimony in corporate failure forecast models: evidence from Australian materials industry, Accounting and Finance, 46, (2), pp.327–345.

Zavgren, C.V. (1985). Assessing the vulnerability to failure of American industrial firms: a logistic analysis, Journal of Business Finance and Accounting, 12 (1), pp.19–45.

Zmijewski, M.E. (1984). Methodological issues related to the estimation of financial distress prediction models, Journal of Accounting Research, 22, (Studies on Current Econometric Issues in Accounting Research), pp.59–86.