- -

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

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

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

Mostrar el registro completo del ítem

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/s11628-012-0173-7

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/62639

Ficheros en el ítem

Metadatos del ítem

Título: Monitoring credit risk in the social economy sector by means of a binary goal programming model
Autor: García García, Fernando Guijarro Martínez, Francisco Moya Clemente, Ismael
Entidad UPV: Universitat Politècnica de València. Departamento de Economía y Ciencias Sociales - Departament d'Economia i Ciències Socials
Fecha difusión:
Resumen:
Monitoring 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 ...[+]
Palabras clave: Credit risk , Cooperative firms , Financial information , Financial service institutions
Derechos de uso: Reserva de todos los derechos
Fuente:
Service Business. (issn: 1862-8516 ) (eissn: 1862-8508 )
DOI: 10.1007/s11628-012-0173-7
Editorial:
Springer Verlag (Germany)
Versión del editor: http://dx.doi.org/10.1007/s11628-012-0173-7
Descripción: The final publication is available at Springer via http://dx.doi.org/10.1007/s11628-012-0173-7
Tipo: Artículo

References

Alfares H, Duffuaa S (2009) Assigning cardinal weights in multi-criteria decision making based on ordinal rankings. J Multicriteria Decis Anal 15:125–133

Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Financ 23:589–609

Altman EI, Hadelman RG, Narayanan P (1977) Zeta analysis: a new model to identify bankruptcy risk of corporations. J Bank Financ 1:29–54 [+]
Alfares H, Duffuaa S (2009) Assigning cardinal weights in multi-criteria decision making based on ordinal rankings. J Multicriteria Decis Anal 15:125–133

Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Financ 23:589–609

Altman EI, Hadelman RG, Narayanan P (1977) Zeta analysis: a new model to identify bankruptcy risk of corporations. J Bank Financ 1:29–54

Andenmatten 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, Lausanne

Beaver WH (1966) Financial ratios as predictors of failure. J Account Res 4:71–111

Boritz JE, Kennedey DB (1995) Effectiveness of neural network types for prediction of business failure. Expert Syst Appl 9:503–512

Bottomley P, Doyle J, Green R (2000) Testing the reliability of weight elicitation methods: direct rating versus point allocation. J Mark Res 37:508–513

Casey M, McGee V, Stinkey C (1986) Discriminating between reorganized and liquidated firms in bankruptcy. Account Rev 61:249–262

Cruz S, Gonzalez T, Perez C (2010) Marketing capabilities, stakeholders’ satisfaction, and performance. Serv Bus 4:209–223

Díaz M, Marcuello C (2010) Impacto económico de las cooperativas. La generación de empleo en las sociedades cooperativas y su relación con el PIB. CIRIEC 67:23–44

Dimitras AI, Zopounidis C, Hurson C (1995) A multicriteria decision aid method for the assessment of business failure risk. Found Comput Decis Sci 20:99–112

Dimitras AI, Slowinski R, Susmaga R, Zopounidis C (1999) Business failure prediction using rough sets. Eur J Oper Res 114:263–280

Elmer PJ, Borowski DM (1988) An expert system approach to financial analysis: the case of S&L bankruptcy. Financ Manage 17:66–76

Frydman H, Altman EI, Kao DL (1985) Introducing recursive partitioning for financial classification: the case of financial distress. J Financ 40:269–291

García F, Guijarro F, Moya I (2008) La valoración de empresas agroalimentarias: una extensión de los modelos factoriales. Rev Estud Agro-Soc 217:155–181

Gupta MC, Huefner RJ (1972) A cluster analysis study of financial ratios and industry characteristics. J Account Res 10:77–95

Jensen RE (1971) A cluster analysis study of financial performance of selected firms. Account Rev 16:35–56

Juliá J (2011) Social economy: a responsible people-oriented economy. Serv Bus 5:173–175

Keasey 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 18:85–94

Li H, Adeli H, Sun J, Han JG (2011) Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction. Comput Oper Res 38:409–419

Luoma M, Laitinen EK (1991) Survival analysis as a tool for firm failure prediction. Omega-Int J Manage S 19:673–678

March I, Yagüe RM (2009) Desempeño en empresas de economía social. Un modelo para su medición. CIRIEC 64:105–131

Martin D (1977) Early warning of bank failure: a logit regression approach. J Bank Financ 1:249–276

Mateos A, Marín M, Marí S, Seguí E (2011) Los modelos de predicción del fracaso empresarial y su aplicabilidad en cooperativas agrarias. CIRIEC 70:179–208

McKee T (2000) Developing a bankruptcy prediction model via rough sets theory. Int J Intell Syst Account Finan Manage 9:159–173

Messier WF, Hansen JV (1988) Inducing rules for expert system development: an example using default and bankruptcy data. Manage Sci 34:1403–1415

Ohlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Account Res 18:109–131

Peel MJ (1987) Timeliness of private firm reports predicting corporate failure. Invest Anal J 83:23–27

Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York

Scapens RW, Ryan RJ, Flecher L (1981) Explaining corporate failure: a catastrophe theory approach. J Bus Finan Account 8:1–26

Skogsvik R (1990) Current cost accounting ratios as predictors of business failures: the Swedish case. J Bus Finan Account 17:137–160

Slowinski R, Zopounidis C (1995) Application of the rough set approach to evaluation of bankruptcy risk. Int J Intell Syst Account Finan Manage 4:24–41

Vranas AS (1992) The significance of financial characteristics in predicting business failure: an analysis in the Greek context. Found Comput Decis Sci 17:257–275

Westgaard S, Wijst N (2001) Default probabilities in a corporate bank portfolio: a logistic model approach. Eur J Oper Res 135:338–349

Wilson RL, Sharda R (1994) Bankruptcy prediction using neuronal networks. Decis Support Syst 11:545–557

Zavgren CV (1985) Assessing the vulnerability to failure of American industrial firms. A logistic analysis. J Bus Financ Account 12:19–45

Zmijewski M (1984) Methodological issues related to the estimation of financial distress prediction models. Studies on Current Econometric Issues in Accounting Research. J Account Res 22:59–86

Zopounidis C, Doumpos M (2002) Multicriteria classification and sorting methods: a literature review. Eur J Oper Res 138:229–246

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem