Open Access

Bankruptcy Prediction: A Survey on Evolution, Critiques, and Solutions


Cite

Adnan, M.; Dar, A. H. (2006). Predicting corporate bankruptcy: where we stand? Corporate Governance 6(1): 18–33.10.1108/14720700610649436Search in Google Scholar

Agarwal, V.; Taffler, R. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking and Finance 32: 1541–1555.Search in Google Scholar

Alam, P.; Booth, D.; Lee, K.; Thordarson, T. (2000). The use of fuzzy clustering algorithm and self-organizing neural networks for identifying potentially failing banks: an experimental study. Expert Systems with Applications 18: 185–199.Search in Google Scholar

Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance 23(4): 589–609.Search in Google Scholar

Altman, E. I.; Sabato, G. (2007). Modelling credit risk for SMEs. Evidence from US Market. A Journal of Accounting, Finance and Business Studies. 43(3): 332–357.Search in Google Scholar

Altman, E. I.; Haldeman, R.; Narayanan, P. (1977). ZETA analysis, a new model for bankruptcy classification. Journal of Banking and Finance 1(1): 29–54.Search in Google Scholar

Altman, E. I.; Wilson, N.; Sabato, G. (2007). The value of non-financial information in SME risk management. The Journal of Credit Risk, 6(2): 1–40.Search in Google Scholar

Balcaen, S.; Ooghe, H. (2004). 35 years of studies on business failure: an overview of the classical statistical methodologies and their related problems. Working paper. Ghent University, June, 248: 1–62.Search in Google Scholar

Beaver, W. H.; McNichols, M. F.; Rhie, Jung-Wu (2005). Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy, Review of Accounting Studies 10: 95–122.10.1007/s11142-004-6341-9Search in Google Scholar

Bellovary, J.; Giacomino, D.; Akers, M. (2007). A review of bankruptcy prediction studies: 1930–present. Journal of Finance Education 33: 1–42.Search in Google Scholar

Blanco, A.; Irimia, A.; Dolores, M. O. (2012). The prediction of bankruptcy of small firms in the UK using logistic regression. Anolisis Financiero 118: 32–40.Search in Google Scholar

Bottazzi, G.; Grazzi, M.; Secchi, A.; Tamagni, F. (2010). Financial and economic determinants of firm default. FINNIV Discussion Paper. pp. 1–29.Search in Google Scholar

Camerer, C.; Lovallo, D. (1999). Overconfidence and excess entry: An experimental approach. The American Economic Review 89(1): 306–318.Search in Google Scholar

Chen, M. Y. (2011). Bankruptcy prediction in firms with statistical and intelligent techniques and comparison of evolutionary computation approaches. Comput. Math. Appl. 62: 4514–4524.Search in Google Scholar

Constand, L. R.; Yazdipour, R. (2011). Firm failure prediction models: a critique and a review of recent developments, advances in entrepreneurial finance: with applications from behavioral finance and economics. Springer Science and Business Media. pp. 185–204.Search in Google Scholar

Chudson, W. (1945). The pattern of corporate financial structure. New York: National Bureau of Economic Research.Search in Google Scholar

Dewaelheyns, N.; van Hulle, C. (2008). Legal reform and aggregate small and micro business bankruptcy rates: evidence from the 1997 Belgian bankruptcy code. Small Business Economics 3: 409–424.Search in Google Scholar

Dimitras, A.; Zanakis, S.; Zopounidis, C. (1996). A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research 90: 487–513.Search in Google Scholar

Dimitras, A. I.; Slowinski, R. S.; Zoupounidis, C. (1999). Business failure prediction using rough sets. European Journal of Operation Research 114(2): 263–280.Search in Google Scholar

Doumpos M.; Zopoudinis C. (1999). A multicriteria discrimination method for the prediction of financial distress: the case of Greece. Multinational Finance Journal 3(2): 71–101.Search in Google Scholar

Dwyer, M. (1992). A comparison of statistical techniques and artificial neural network models in corporate bankruptcy prediction. Ph.D. dissertation. University of Wisconsin-Madison.Search in Google Scholar

Fitzpatrick, P. (1932). A comparison of the ratios of successful industrial enterprises with those of failed companies. Washington: The Accountants’ Publishing Company.Search in Google Scholar

Fulmer, J.; Moon, J.; Gavin, T.; Erwin, M. (1984). A bankruptcy classification model for small firms. Journal of Commercial Bank Lending July: 25–37.Search in Google Scholar

García-Posada, M.; Mora-Sanguinetti, J. S. (2014). Are there alternatives to bankruptcy? A study of small business distress in Spain. SERIEs 5: 287–332.Search in Google Scholar

Gupta, J.; Gregoriou, A.; Healy, J. (2014). Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter? Review of Quantitative Finance and Accountings, Springer Science+Business Media. pp. 1–25.Search in Google Scholar

Hernandez, M. T.; Wilson, N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomics variables. International Review of Financial Analysis 30: 394–419.Search in Google Scholar

De Andrés, J.; Lorca, P.; Javier de Cos Juez, F.; Sánchez-Lasheras, F. (2011). Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS). Expert System with Applications 38: 1866–1875.Search in Google Scholar

Jones, F. (1987). Current techniques in bankruptcy prediction. Journal of Accounting Literature 6: 131–164.Search in Google Scholar

Kahneman, D.; Lovallo, D. (1993). Timid choices and bold forecasts. A cognitive perspective on risk taking. Management Science 39(1): 17–31.Search in Google Scholar

Kirkos, E. (2015). Assessing methodologies for intelligent bankruptcy prediction. Artificial Intelligence Review 43: 83–123.Search in Google Scholar

Koro, T.; Korodi, A. (2011). An evaluation of effectiveness of Fuzzy logic model in predicting the business bankruptcy. Romanian Journal of Economic Forecasting 3: 92–107.Search in Google Scholar

Laitinen, E. K. (2011). Assessing viability of Finnish reorganization and bankruptcy firms. European Journal of Law and Economics 31: 167–198.Search in Google Scholar

Lee, K. C.; I. Han; Y. Kwon (1996). Hybrid neural network models for bankruptcy predictions. Decision Support Systems 18(1): 63–72.Search in Google Scholar

Lee, K.; Booth, D.; Alam, P. (2005). A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms. Expert System Application 29: 1–16.Search in Google Scholar

Lee, S.; Choi, W. S. (2013). A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis. IExpert System with Applications 40: 2941–2946.Search in Google Scholar

Lo, A. W. (1986). Logit versus discriminant analysis. A specification test and application to corporate bankruptcies. Journal of Econometrics 31: 151–178.Search in Google Scholar

Martikainen, T.; Perttunen, J.; Yli-Ollie, P.; Gunasekaran, A. (1995). Financial ratio distribution irregularities: implication for ratio classification. European Journal of Operational Research 80: 34–44.Search in Google Scholar

Martin, D. (1977). Early warning of bank failures: A logit regression approach. Journal of Banking and Finance 1: 249–276.Search in Google Scholar

Martin-del-Brio, B.; Serrano-Cinca, C. (1995). Self-organizing neural networks: The financial state of Spanish companies. Neural Network in the Capital Markets. In: Refenes (ed.), Chichester: Wiley, pp. 341–357.Search in Google Scholar

Messier, Jr., W.; Hansen, J. (1988). Inducing rules for expert system development: an example using default and bankruptcy data. Management Science 34(12): 1403–1415.Search in Google Scholar

Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 18(1): 109–131.Search in Google Scholar

Pantalone, C.; Platt, M. (1987). Predicting commercial bank failure since deregulation. New England Economic Review, Federal Reserve Bank of Boston July–August, pp. 37–47.Search in Google Scholar

Perez, M. (2006). Artificial neural network and bankruptcy forecasting: a state of the art. Neural Comput. Appl. 15: 154–163.Search in Google Scholar

Premachandra, I. M.; Bhbra, G. S.; Sueyoshi, T. (2009). DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique. European Journal of Operational Research 193: 412–424.Search in Google Scholar

Ravi, K. P.; Ravi, V. (2007). Bankruptcy prediction in firms and banks via statistical and intelligent techniques – a review. European Journal of Operation Research 180: 1–28.Search in Google Scholar

Rose, P.; Kolari, J. (1985). Early bankruptcy prediction using neural networks: A new tool for predicting bank failures. Decision Sciences 23: 899–916.Search in Google Scholar

Sands, E.; Springate, G.; Var, T. (1983). Predicting business failures: a Canadian approach. CGA Magazine 17: 24–27.Search in Google Scholar

Santomero, A.; Vinso, J. (1977). Estimating the probability of failure for commercial banks and the banking system. Journal of Banking and Finance 1(2): 185–205.Search in Google Scholar

Smith, R.; Winakor, A. (1935). Changes in financial structure of unsuccessful industrial corporations. Bureau of Business Research, Bulletin no. 51. Urbana: University of Illinois Press.Search in Google Scholar

Trabelsi, S.; He, R.; He, L.; Kusy, M. (2014). A comparison of Bayesian, Hazard and mixed logit model of bankruptcy prediction. Computing Management Science January: 1–17.Search in Google Scholar

Vareto, F. (1998). Genetic algorithms applications in the analysis of insolvency risk. Journal of Banking and Finance 22: 1421–1439.Search in Google Scholar

Verikas, A.; Kalsyte, Z.; Bacauskiene, M.; Gelzinis, A. (2010). Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey. Soft Computing 14: 995–1010.Search in Google Scholar

Virág, M.; Kristóf, T.; Fiáth, A.; Varsányi J. (2013). Pénzügyi elemzés, csődelőrejelzés, válságkezelés. Budapest: Kossuth Kiadó.Search in Google Scholar

Virág, M.; Kristof, T. (2005). Az első hazai csődmodell újraszámítása neurális hálók segítségével. Közgazdasági Szemle LII(February): 144–162.Search in Google Scholar

Xu, M.; Zhang, C. (2009). Bankruptcy prediction: the case of Japanese listed companies. Review of Accounting Studies 14: 534–558.Search in Google Scholar

Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research 24(Supplement): 59–82.Search in Google Scholar

eISSN:
2360-0047
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Business and Economics, Political Economics, Economic Theory, Systems and Structures