DAYORO, DONATIEN (2024): Optimization of the Credit Portfolio and Methodology for Evaluating a Public Support Policy: The Case of the Support Fund for Large Ivorian Enterprises (FSGE). Published in: Optimization of the Credit Portfolio and Methodology for Evaluating a Public Support Policy: The Case of the Support Fund for Large Ivorian Enterprises (FSGE) , Vol. 39, : pp. 2-38.
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Abstract
The article employs a logistic regression model to predict defaults and optimize credit portfolios for enterprises receiving support, showcasing a rigorous methodological approach. It relies on empirical data to ensure the relevance of its findings and utilizes the Evidence-Based Policy Making (EBPM) method, incorporating propensity score matching techniques to correct for selection biases, thereby ensuring accurate evaluations. Additionally, the work adheres to international standards set by the INTOSAI Guide 9020, enhancing its academic credibility. Ultimately, the proposed solutions contribute to both financial theory and public management practices, illustrating the author's ability to harmonize theoretical frameworks with practical applications.
Item Type: | MPRA Paper |
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Original Title: | Optimization of the Credit Portfolio and Methodology for Evaluating a Public Support Policy: The Case of the Support Fund for Large Ivorian Enterprises (FSGE) |
English Title: | Optimization of the Credit Portfolio and Methodology for Evaluating a Public Support Policy: The Case of the Support Fund for Large Ivorian Enterprises (FSGE) |
Language: | English |
Keywords: | Evaluation of public policy INTOSAI standards Management of Covid-19 funds Credit risk Credit rating Logit econometric model |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C19 - Other G - Financial Economics > G2 - Financial Institutions and Services G - Financial Economics > G3 - Corporate Finance and Governance G - Financial Economics > G3 - Corporate Finance and Governance > G38 - Government Policy and Regulation H - Public Economics > H6 - National Budget, Deficit, and Debt > H63 - Debt ; Debt Management ; Sovereign Debt P - Economic Systems > P5 - Comparative Economic Systems > P50 - General |
Item ID: | 122408 |
Depositing User: | M DONATIEN DAYORO |
Date Deposited: | 16 Oct 2024 13:33 |
Last Modified: | 16 Oct 2024 13:33 |
References: | Guide 9020-Evaluation des politiques publiques 2019 INTOSAI Abdou, H. 2009a. Credit scoring models for Egyptian banks: neural nets and genetic programming versus conventional techniques, Ph.D. Thesis, The University of Plymouth, UK. Abdou, H. 2009b. An evaluation of alternative scoring models in private banking. Journal of Risk Finance 10 (1): 38-53. Abdou, H., Pointon, J., El Masry, A. 2008. Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Systems with Applications 35 (3): 1275-1292. Abdou, H., Pointon, J. 2009. Credit scoring and decision-making in Egyptian public sector banks. International Journal of Managerial Finance 5 (4): 391-406. Abdou, H. & Pointon, J. 2011 'Credit scoring, statistical techniques and evaluation criteria: a review of the literature', Intelligent Systems in Accounting, Finance & Management, 18 (2-3), pp. 59-88. Al Amari, A. 2002. The credit evaluation process and the role of credit scoring: A case study of Qatar. Ph.D. Thesis, University College Dublin. Altman, EI (1968): “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy”. Journal of finance, vol.23, n°4, 1968. Altman, E.I,. 2002 Credit Rating: Methodologies, Rationale and Default Risk, London Risk Books. Altman, EI, Haldeman, R. 1995. Corporate credit scoring models: Approaches and tests for successful implementation. Journal of Commercial Lending 77 (9): 10-22. Aziz, A., DC Emanuel and GH Lawson (1988) Bankruptcy prediction – An investigation of cash flow-based models. Journal of Management Studies 25 (5), 419-437 Becchetti, L. and J. Sierra (2003) Bankruptcy risk and productive efficiency in manufacturing firms. Journal of Banking and Finance 27 (11), 2099-2120. Ben-David, A., Frank, E. 2009. Accuracy of machine learning models versus “hand crafted” expert systems – a credit scoring case study. Expert Systems with Applications 36 (3/1): 5264-527. Beaver, W., 1966, “Financial Ratios as Predictors of Failures,” in Empirical Research in Accounting, selected studies, pp. 71-111. Beaver, W., 1968, “Alternative Accounting Measures as Predictors of Failure,” Accounting Review, January, pp. 46-53. Bouazzara, A., Riad, BAHA, & Bektache, F. (2020). Assessment of the risk of solvency failure of SMEs: an application of the logistic regression model. Dirassat Journal Economic Issue, 11(2), pp. 491-505. Crook, JN 1996. Credit scoring: An overview. Working paper series No. 96/13, British Association, Festival of Science. University of Birmingham, The University of Edinburgh. Diallo, B. (2006). A “credit scoring” model for an African microfinance institution: the case of Nyesigiso in Mali. Pre-and Post-Print documents halshs-00069163v1, HAL, CCSd/CNRS. Deakin, EB (1972): “A discriminant analysis of predictors of business failure”, Journal of accounting research, 1972, PP167-179. Edmister R. “an empirical test of financial ratios analysis for small business failure prediction” journal of financial and quantitative analysis March 1972. Gujarati, N.D. (2003) Basic Econometrics. Fourth Edition, McGraw-Hill, London. Gup, BE, Kolari, JW 2005. Commercial Banking: The management of risk. Alabama: John Wiley & Sons, Inc. Greene, W. 1998. Sample Selection in Credit-Scoring Models. Japan and the World Economy 10 (3): 299-316. Hand, DJ, Jacka, SD 1998. Statistics in Finance, Arnold Applications of Statistics: London. Haughwout, A., Peach, R., Tracy, J. 2008. Juvenile delinquent mortgages: bad credit or bad economy? Journal of Urban Economics 64 (2): 246-257. Hu, Y. 2008. Incorporating a non-additive decision making method into multi-layer neural networks and its application to financial distress analysis. Knowledge-Based Systems 21 (5): 383- 390 Lee, T., Chen, I. 2005. A Two-Stage Hybrid Credit Scoring Model Using Artificial Neural Networks and Multivariate Adaptive Regression Splines. Expert Systems with Applications 28 (4): 743-752. Lewis, EM 1992. An Introduction to Credit Scoring. California: Fair, Isaac & Co., Inc. Lucas, P. 2000a. Shedding light on credit scores, Credit Card Management (August) 78-80. Lussier, RN (1995), A non-financial business success versus failure prediction model for young firms. Journal of Small Business Management 33 (1), 8-20. Mester, LJ 1997. What's the point of credit scoring? Business Review (September) 3-16. Min, JH, Jeong, C. 2009. A binary classification method for bankruptcy prediction. Expert Systems with Applications 36(3): 5256-5263. Nzongang et Al (2010), measuring the financial and social efficiency of microfinance institutions in the MC² network in Cameroon. Ohlson, J. (1980) Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 18 (1), 109-131. Prakash, S. 1995. Mortgage lenders see credit scoring as key to hacking through red tape, American Banker (August) I. Sabato, G. (2008) Managing credit risk for retail low-default portfolios. Credit Risk: Models, Derivatives and Management, N. Wagner Ed., Chapman & Hall/CRC Financial Mathematics Series. Sustersic, M., Mramor, D., Zupan J. 2009. Consumer credit scoring models with limited data. Expert Systems with Applications 36 (3): 4736-4744. Taffler, RJ and H. Tisshaw (1977) Going, Going, Gone - Four Factors Which Predict. Accounting 88 (1083), 50-54. Tsai, C., Wu, J. 2008. Using neural networks ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications 34 (4): 2639-2649. American Speech-Language-Hearing Association. (2005). Evidence-based practice in communication disorders [Position statement]. Retrieved from http://www.asha.org/docs/html/PS2005-00221.html Bahr, D., & Rosenfeld-Johnson, S. (2010). Treatment of children with speech oral placement disorders (OPDS): A paradigm emerges. Communication Disorders Quarterly, 31, 131-138. Bandura, A. (2003). Auto-efficacité: Le sentiment d’efficacité personnelle (J. Lecomte, Trans.). Bruxelles, Belgique: de Boeck. (Ouvrage original publié en 1997) Brackenbury, T., Burroughs, E., & Hewitt, L. E. (2008). A qualitative examination of current guidelines for evidence-based practice in child language intervention. Language, Speech, and Hearing Services in Schools, 39, 78-88. Bernstein Ratner, N. (2006). Evidence-based practice: An examination of its ramifications for the practice of speech-language pathology. Language, Speech, and Hearing Services in Schools, 37, 257-267. Bernstein Ratner, N. (2011). Some pragmatic tips for dealing with clinical uncertainty. Language, Speech, and Hearing Services in Schools, 42, 77-80. Crosbie, S., Holm, A., & Dodd, B. (2005). Intervention for children with severe speech disorder: A comparison of two approaches. International Journal of Language and Communication Disorders, 40, 467-491. Crosbie, S., Pine, C., Holm, A., & Dodd, B. (2006). Treating Jarrod: A core vocabulary approach. Advances in Speech Language Pathology, 8, 316-321. Dodd, B. (2007). Evidence-based practice and speech-language pathology: Strengths, weaknesses, opportunities and threats. Folia Phoniatrica et Logopaedica, 59, 118-129. Dodd, B., & Bradford, A. (2000). A comparison of three therapy methods for children with different types of developmental phonological disorder. International Journal of Language and Communication Disorders, 35, 189-209. Dodd, B., Holm, A., Crosbie, S., & McIntosh, B. (2006). A core vocabulary approach for management of inconsistent speech disorder. Advances in Speech Language Pathology, 8, 220-230. Dollaghan, C. A. (2007). The handbook for evidence-based practice in communication disorders. Baltimore, MD: Brookes Publishing. Greenhalgh, T. (2010). How to read a paper: The basics of evidence-based medicine (4th ed.). Chichester, England: Wiley-Blackwell. Guo, R., Bain, B. A., & Willer, J. (2008). Results of an assessment of information needs among speech-language pathologists and audiologists in Idaho. Journal of the Medical Library Association, 96, 138-144. Guyatt, G., Rennie, D., Meade, M., & Cook, D. J. (2008). Users’ guide to the medical literature: A manual for evidence-based clinical practice (2nd ed.). New York, NY: McGraw Hill. Hayes, S. L., Savinelli, S., Roberts, E., & Caldito, G. (2007). Use of nonspeech oral motor treatment for functional articulation disorders. Early Childhood Services: An Interdisciplinary Journal of Effectiveness, 1, 261-281. Holm, A., & Crosbie, S. (2006). Introducing Jarrod: A child with a phonological impairment. Advances in Speech Language Pathology, 8, 164-175. Horn, S. D., Dejong, G., Smout, R. J., Gassaway, J., James, R., & Conroy, B. (2005). Stroke rehabilitation patients, practice, and outcomes: Is earlier and more aggressive therapy better? Archives of Physical Medicine and Rehabilitation, 86, S101-S114. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/122408 |