4 research outputs found

    Monitoring credit risk in the social economy sector by means of a binary goal programming model

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    The final publication is available at Springer via http://dx.doi.org/10.1007/s11628-012-0173-7Monitoring the credit risk of firms in the social economy sector presents a considerable challenge, since it is difficult to calculate ratings with traditional methods such as logit or discriminant analysis, due to the relatively small number of firms in the sector and the low default rate among cooperatives. This paper intro- duces a goal programming model to overcome such constraints and to successfully manage credit risk using economic and financial information, as well as expert advice. After introducing the model, its application to a set of Spanish cooperative societies is described.García García, F.; Guijarro Martínez, F.; Moya Clemente, I. (2013). Monitoring credit risk in the social economy sector by means of a binary goal programming model. Service Business. 7(3):483-495. doi:10.1007/s11628-012-0173-7S48349573Alfares H, Duffuaa S (2009) Assigning cardinal weights in multi-criteria decision making based on ordinal rankings. J Multicriteria Decis Anal 15:125–133Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Financ 23:589–609Altman EI, Hadelman RG, Narayanan P (1977) Zeta analysis: a new model to identify bankruptcy risk of corporations. J Bank Financ 1:29–54Andenmatten A (1995) Evaluation du risque de défaillance des emetteurs d’obligations: Une approche par l’aide multicritère á la décision. Presses Polytechniques et Univertitaires Romandes, LausanneBeaver WH (1966) Financial ratios as predictors of failure. J Account Res 4:71–111Boritz JE, Kennedey DB (1995) Effectiveness of neural network types for prediction of business failure. Expert Syst Appl 9:503–512Bottomley P, Doyle J, Green R (2000) Testing the reliability of weight elicitation methods: direct rating versus point allocation. J Mark Res 37:508–513Casey M, McGee V, Stinkey C (1986) Discriminating between reorganized and liquidated firms in bankruptcy. Account Rev 61:249–262Cruz S, Gonzalez T, Perez C (2010) Marketing capabilities, stakeholders’ satisfaction, and performance. Serv Bus 4:209–223Díaz M, Marcuello C (2010) Impacto económico de las cooperativas. La generación de empleo en las sociedades cooperativas y su relación con el PIB. CIRIEC 67:23–44Dimitras AI, Zopounidis C, Hurson C (1995) A multicriteria decision aid method for the assessment of business failure risk. Found Comput Decis Sci 20:99–112Dimitras AI, Slowinski R, Susmaga R, Zopounidis C (1999) Business failure prediction using rough sets. Eur J Oper Res 114:263–280Elmer PJ, Borowski DM (1988) An expert system approach to financial analysis: the case of S&L bankruptcy. Financ Manage 17:66–76Frydman H, Altman EI, Kao DL (1985) Introducing recursive partitioning for financial classification: the case of financial distress. J Financ 40:269–291García F, Guijarro F, Moya I (2008) La valoración de empresas agroalimentarias: una extensión de los modelos factoriales. Rev Estud Agro-Soc 217:155–181Gupta MC, Huefner RJ (1972) A cluster analysis study of financial ratios and industry characteristics. J Account Res 10:77–95Jensen RE (1971) A cluster analysis study of financial performance of selected firms. Account Rev 16:35–56Juliá J (2011) Social economy: a responsible people-oriented economy. Serv Bus 5:173–175Keasey K, Mcguinnes P, Short H (1990) Multilogit approach to predicting corporate failure: further analysis and the issue of signal consistency. Omega-Int J Manage S 18:85–94Li H, Adeli H, Sun J, Han JG (2011) Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction. Comput Oper Res 38:409–419Luoma M, Laitinen EK (1991) Survival analysis as a tool for firm failure prediction. Omega-Int J Manage S 19:673–678March I, Yagüe RM (2009) Desempeño en empresas de economía social. Un modelo para su medición. CIRIEC 64:105–131Martin D (1977) Early warning of bank failure: a logit regression approach. J Bank Financ 1:249–276Mateos A, Marín M, Marí S, Seguí E (2011) Los modelos de predicción del fracaso empresarial y su aplicabilidad en cooperativas agrarias. CIRIEC 70:179–208McKee T (2000) Developing a bankruptcy prediction model via rough sets theory. Int J Intell Syst Account Finan Manage 9:159–173Messier WF, Hansen JV (1988) Inducing rules for expert system development: an example using default and bankruptcy data. Manage Sci 34:1403–1415Ohlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Account Res 18:109–131Peel MJ (1987) Timeliness of private firm reports predicting corporate failure. Invest Anal J 83:23–27Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New YorkScapens RW, Ryan RJ, Flecher L (1981) Explaining corporate failure: a catastrophe theory approach. J Bus Finan Account 8:1–26Skogsvik R (1990) Current cost accounting ratios as predictors of business failures: the Swedish case. J Bus Finan Account 17:137–160Slowinski R, Zopounidis C (1995) Application of the rough set approach to evaluation of bankruptcy risk. Int J Intell Syst Account Finan Manage 4:24–41Vranas AS (1992) The significance of financial characteristics in predicting business failure: an analysis in the Greek context. Found Comput Decis Sci 17:257–275Westgaard S, Wijst N (2001) Default probabilities in a corporate bank portfolio: a logistic model approach. Eur J Oper Res 135:338–349Wilson RL, Sharda R (1994) Bankruptcy prediction using neuronal networks. Decis Support Syst 11:545–557Zavgren CV (1985) Assessing the vulnerability to failure of American industrial firms. A logistic analysis. J Bus Financ Account 12:19–45Zmijewski M (1984) Methodological issues related to the estimation of financial distress prediction models. Studies on Current Econometric Issues in Accounting Research. J Account Res 22:59–86Zopounidis C, Doumpos M (2002) Multicriteria classification and sorting methods: a literature review. Eur J Oper Res 138:229–24
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