Complete analysis of bankruptcy syndrome using grey systems theory

Grey Systems

Volumn 1, Issue 1, 2011, Pages 19-32

  Scarlat, Emil ^a   Delcea, Camelia ^a  

^a BUCHAREST UNIVERSITY OF ECONOMIC STUDIES   (Romania)

Author keywords

Bankruptcy; Organizational analysis; Systems theory; Value analysis

Indexed keywords

EID: 80051653814     PISSN: 20439377     EISSN: 20439385     Source Type: Journal    
DOI: 10.1108/20439371111106704     Document Type: Article

References (29)

1. Altman, E.I. (1968), “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy”, Journal of Finance, Vol. 23 No. 4, pp. 589‐609.
2. Analoui, M., Sharifi, M. and Rezvani, M.H. (2010), “Probabilistic proximity‐aware resource location in peer‐to‐peer networks using resource replication”, Journal of Computers & Control, Vol. V No. 4, pp. 418‐31.
3. Beaver, W.H. (1966), “Financial ratios as predictors of failure”, Journal of Accounting Research, Vol. 4, pp. 71‐111.
4. Bodea, C.N. and Dascălu, M.I. (2009), “Modeling research project risks with fuzzy maps”, Journal of Applied Quantitative Methods, Vol. 4 No. 1.
5. Chen, H.‐J., Huang, S.Y. and Lin, C.‐S. (2009), “Alternative diagnosis of corporate bankruptcy: a neuro fuzzy approach”, Expert Systems with Applications, Vol. 36, pp. 7710‐20.
6. Chen, L.‐J., Chen, C.‐C. and Lee, W.‐R. (2008), “Strategic capabilities, innovation intensity, and performance of service firms”, Journal of Service Science and Management, Vol. 1 No. 2, pp. 111‐22.
7. Collins, R.A. and Green, R.D. (1982), “Statistical methods for bankruptcy forecasting”, Journal of Economics and Business, Vol. 34 No. 4, pp. 349‐54.
8. Deakin, E. (1974), “A discriminant analysis of predictors of business failure”, Journal of Accounting Research, Vol. 10, pp. 167‐79.
9. Delcea, C. and Scarlat, E. (2010), “Finding companies' bankruptcy causes using a hybrid grey‐fuzzy model”, Economic Computation and Economic Cybernetics Studies and Research, Vol. 2, pp. 77‐94.
10. Diosteanu, A., Cotfas, L., Smeureanu, A. and Dumitrescu, S. (2010), “Multi‐agents and GIS framework for collaborative supply chain management applications”, 9th Roedunet International Conference (RoEduNet), IEEE, Sibui, pp. 157‐62.
11. Dodgson, M. and Rothwell, R. (1995), The Handbook of Industrial Innovation, Edward Elgar, London.
12. Gessner, G., Kamakura, W.A., Malhortra, N.K. and Zmijewski, M.E. (1988), “Estimating models with binary dependent variables: some theoretical and empirical observations”, Journal of Business Research, Vol. 16, pp. 49‐65.
13. Harrell, F.E. Jr and Lee, K.L. (1985), A Comparison of the Discrimination of Discriminant Analysis and Logistic Regression under Multivariate Normality, Biostatistics: Statistics in Biomedical Public Health and Environmental Sciences, North Holland, Amsterdam.
14. Harris, L. and Ogbonna, E. (2001), “Strategic human resource management, market orientation and organizational performance”, Journal of Business Research, Vol. 51 No. 2, pp. 157‐66.
15. Hitt, M.A., Bierman, L., Shimizu, K. and Kochhar, R. (2001), “Direct and moderating effects of human capital on strategy and performance in professional service firms: a resource‐based perspective”, Academy of Management Journal, Vol. 44 No. 1, pp. 13‐28.
16. Koch, M.J. and McGrath, R.G. (1996), “Improving labor productivity: human resource management policies do matter”, Strategic Management Journal, Vol. 17 No. 5, pp. 35‐354.
17. Lee, S.H., Phan, P.H. and Chan, E. (2005), “The impact of HR configuration on firm performance in Singapore: a resource‐based explanation”, International Journal of Human Resource Management, Vol. 16 No. 9, pp. 1740‐58.
18. Liu, S. and Lin, Y. (2005), Grey Information. Theory and Practical Applications, Springer, London.
19. Maleyeff, J. (2009), “Analysis of service processes characteristics across a range of enterprises”, Journal of Service Science and Management, Vol. 2, pp. 29‐35.
20. Mărăcine, V. and Delcea, C. (2009), “How we can diagnose the firm's diseases using grey systems theory”, Economic Computation and Economic Cybernetics Studies and Research, Vol. 3, pp. 39‐55.
21. Miller, T. and Berger, D. (2001), Totally Integrated Enterprises – A Framework and Methodology for Business and Technology Improvement, Raytheon Professional Services, St Lucie Press, Falls Church, VA.
22. Myer, P.A. and Pifer, H.W. (1970), “Prediction of bank failure”, Journal of Finance, Vol. 4, pp. 853‐68.
23. Ohlson, J.A. (1980), “Financial ratios and the probabilistic prediction of bankruptcy”, Journal of Accounting Research, Vol. 18, pp. 109‐31.
24. Scarlat, E., Mărăcine, V. and Delcea, C. (2009), “A grey‐fuzzy model for setting the firms failure reasons”, IEEE International Conference on Grey Systems and Intelligent Services, Nanjing.
25. Scarlat, E., Mărăcine, V. and Delcea, C. (2010), “Genetic‐fuzzy‐grey algorithms: a hybrid model for establishing companies' failure reasons”, paper presented at IEEE SMC Conference, Istanbul, Turkey.
26. Vigier, H.P. and Terceno, A. (2008), “A model for prediction of ‘diseases’ of firms by means of fuzzy relations”, Fuzzy Sets and Systems, Vol. 159, pp. 2299‐316.
27. Xu, X. and Wang, Y. (2009), “Financial failure prediction using efficiency as a predictor”, Experts Systems with Applications, Vol. 36, pp. 388‐93.
28. Yoshida, Z. (2010), Nonlinear Science – The Challenge of Complex Systems, Springer, Berlin.
29. Zmijewski, M.E. (1984), “Methodological issues related to the estimation of financial distress prediction models”, Journal of Accounting Research, Vol. 22, pp. 59‐82.