Publication: Which characteristics predict the survival of insolvent firms? An SME reorganization prediction model
Loading...
Official URL
Full text at PDC
Publication Date
2015-04
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
International Council for Small Business
Citations
Exportar
Abstract
The negative impact of insolvency, especially in small and medium enterprises, informs the objective of this paper: to study the characteristics of bankrupt firms to achieve a preventive diagnosis for reorganization by means of artificial intelligence (AI) methodologies such as rough set and PART methods. The AI models obtained show not only the key variables to predict insolvency, but also their relations and the critical values. Using only five firm characteristics (sector, size, number of shareholdings, return on assets, and cash ratio), our model could reduce delays and costs, since it is able to predict which firms will undergo reorganization or liquidation before the legal procedure.
Description
UCM subjects
Keywords
Citation
Agarwal, V., and R. J. Taffler (2007). “Twentyfive Years of the Taffler Z-score Model: Does It Really Have Predictive Ability?,” Accounting and Business Research 37(4), 285–300.
Ahn, B. S., S. S. Cho, and C. Y. Kim (2000). “The Integrated Methodology Rough Set Theory and Artificial Neural Network for Business Failure Prediction,” Expert Systems with Applications 18, 65–74.
Alonso, M., and E. Furio (2010). “La economía española. Del crecimiento a la crisis pasando por la burbuja inmobiliaria.” http://ccec.revues.org/index3212.html#text [Accessed on January 3, 2011].
Altman, E. L. (1968). “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” The Journal of Finance 39(4), 1067–1089.
——— (2000). “Predicting Financial Distress of Companies: Revisiting the Z-score and ZETA Models.” Stern School of Business, New York University, 9–12. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.25
.1884&rep=rep1&type=pdf [Accessed on December 31, 2010].
Aziz, M. A., and H. A. Dar (2006). “Predicting Corporate Bankruptcy: Where We Stand?”, Corporate Governance 6(1), 18–33.
Balcaen, S., and H. Ooghe (2006). “35 Years of Studies on Business Failure: An Overview of the Classic Statistical Methodologies and Their Related Problems,” The British Accounting Review 38(1), 63–93.
Beaver, W. H. (1966). “Financial Ratios as Predictors of Failure,” Journal of Accounting Research 4, 71–111.
Beaver, W. H., M. McNichols, and J. W. Rhie (2005). “Have Financial Statements Become Less Informative? Evidence from the Ability of Financial Ratios to Predict Bankruptcy,” Review of Accounting Studies 10(1), 93–122.
Berg, D. (2005). “Bankruptcy Prediction by Generalized Additive Models.” http://www.math.uio.no/eprint/stat_report/2005/01-05.pdf [Accessed on December 20, 2010].
Beynon, M. J., and M. J. Peel (2001). “Variable Precision Rough Set Theory and Data Discrimination: An Application to CorporateFailure Prediction,” OMEGA: The International Journal of Management Science 29(6), 561–576.
Bhimani, A., M. A. Gulamhussen, and S. D. Lopes (2010). “Accounting and Non- Accounting Determinants of Default: An Analysis of Privately-Held Firms,” Journal of Accounting and Public Policy 29(6), 517–532.
Bris, A., I. Welch, and N. Zhu (2006). “The Costs of Bankruptcy: Chapter 7 Liquidation versus Chapter 11 Reorganization,” The Journal of Finance 61(3), 1253–1303.
Carter, R., and H. Van Auken (2006). “Small Firm Bankruptcy,” Journal of Small Business Management 44(4), 493–512.
Chava, S., and R. A. Jarrow (2004). “Bankruptcy Prediction with Industry Effects,” Review of Finance 8(4), 537–569.
Chen, J., L. Chollete, and R. Ray (2010). “Financial Distress and Idiosyncratic Volatility: An Empirical Investigation,” Journal of Financial Markets 13(2), 249–267.
Couwenberg, O., and A. de Jong (2008). “Costs and Recovery Rates in the Dutch Liquidation-Based Bankruptcy System,” European Journal of Law and Economics 26, 105–127.
Denning, K. C., S. P. Ferris, and R. M. Lawless (2001). “Serial Bankruptcy: Plan Infeasibility or Just Bad Luck?”, Applied Economics Letters 8, 105–109.
Diaz, Z., A. Sanchis, and M. J. Segovia (2009). “Analysis of Financial Instability by Means of Decision Trees and Lists,” in Emerging Topics in Macroeconomics. Ed. R. O. Bailly. New York: Editorial Nova Publishers, 303–327.
Dimitras, A. I., S. H. Zanakis, and C. Zopounidis (1996). “A Survey of Business Failures with an Emphasis on Prediction Methods and Industrial Applications,” European Journal of Operational Research 90, 487–513.
Dimitras, A. I., R. Slowinski, R. Susmaga, and C. Zopounidis (1999). “Business Failure Prediction Using Rough Sets,” European Journal of Operational Research 114, 263–280.
Easterbrook, F. H. (1990). “Is Corporate Bankruptcy Efficient?”, Journal of Financial Economics 27, 411–417.
Fich, E. M., and S. L. Slezak (2008). “Can Corporate Governance Save Distressed Firms from Bankruptcy? An Empirical Analysis,” Review of Quantitative Finance and Accounting 30, 225–251.
Frank, E., and I. H. Witten (1998). “Generate Accurate Rules Sets without Global Optimization,” in Proceedings of the Fifteenth International Conference on Machine Learning, Madison, Wisconsin. Ed. J. Shavlik. San Francisco: Morgan Kaufmann, 144–151.
Franks, J. R., and W. N. Torous (1989). “An Empirical Investigation of US Firms in Reorganization,” Journal of Finance 44, 747–769.
García Lara, J. M., B. García Osma, and E. Neophytou (2009). “Earnings Quality in Ex-Post Failed Firms,” Accounting and Business Research 39(2), 119–138.
Gilson, S. C. (1997). “Transactions Costs and Capital Structure Choice: Evidence from Financially Distressed Firms,” Journal of Finance 52, 161–196.
Greco, S., B. Matarazzo, and R. Slowinski (1998). “A New Rough Set Approach to Evaluation of Bankruptcy Risk,” in New
Operational Tools in the Management of Financial Risks. Ed. C. Zopounidis. Dordrecht, The Netherlands: Kluwert Academic Publishers, 121–136.
——— (2001). “Rough Sets Theory for Multicriteria Decision Analysis,” European Journal of Operational Research 129(1), 1–47.
Hensher, D. A., and S. Jones (2007). “Forecasting Corporate Bankruptcy: Optimizing the Performance of the Mixed Logit Model,” ABACUS 43(1), 241–264.
Hillegeist, S. A., E. K. Keating, D. P. Cram, and K. G. Lundstedt (2004). “Assessing the Probability of Bankruptcy,” Review of Accounting Studies 9, 5–34.
Jensen, M. C., and W. H. Meckling (1976). “Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure,” Journal of Financial Economics 3(4), 305–360.
Jones, S., and D. A. Hensher (2004). “Predicting Firm Financial Distress: A Mixed Logit Model,” The Accounting Review 79(4), 1011–1038.
Keasey, K., and P. McGuinness (1990). “The Failure of UK Industrial Firms for the Period 1976–1984, Logistic Analysis and Entropy Measures,” Journal of Business Finance and Accounting 17(1), 119–135.
Kumar, P. R., and V. Ravi (2007). “Bankruptcy Prediction in Banks and Firms Via Statistical and Intelligent Techniques—A Review,” European Journal of Operation Research 180, 1–28.
Laitinen, E. K. (1992). “Prediction of Failure of a Newly Founded Firm,” Journal of Business Venturing 7, 323–340.
——— (2011). “Assessing Viability of Finnish Reorganization and Bankruptcy Firms,” European Journal of Law and Economics 31, 167–198.
Laporta, R., F. López de Silanes, A. Shleifer, and R. W. Vishny (1998). “Law and Finance,” Journal of Political Economy 106(6), 1113–1155.
Lee, S. H., Y. Yamakawa, M. W. Peng, and J. B. Barney (2011). “How Do Bankruptcy Laws Affect Entrepreneurship Development around the World?”, Journal of Business Venturing 26, 505–520.
Lussier, R. N. (1995). “A Nonfinancial Business Success versus Failure Prediction Model for Young Firms,” Journal of Small Business Management 33(1), 8–20.
Lussier, R. N., and C. E. Halabi (2010). “A Three-Country Comparison of the Business Success versus Failure Prediction Model,” Journal of Small Business Management 48(3), 360–377.
Martin-López, S., C. García-Gutiérrez- Fernández, and G. Lejarriega-Perez-de-las- Vacas (2010). “The Financial Difficulties of the Labour Managed Firms Because of the Economic Crisis: The Creation of a Financial Organization,” Revista De Estudios Cooperativos 100, 11–42.
McKee, T. E. (2000). “Developing a Bankruptcy Prediction Model via Rough Sets Theory,” International Journal of Intelligent Systems in Accounting, Finance and Management 9, 159–173.
McKee, T. E., and T. Lensberg (2002). “Genetic Programming and Rough Sets: An Hybrid Approach to Bankruptcy Classification,” European Journal of Operational Research 118, 436–451.
National Statistics Institute of Spain (2010). http://www.ine.es/en/welcome_en.htm [Accessed October 2, 2013].
Nwogugu, M. (2007). “Decision-Making, Risk and Corporate Governance: A Critique of Methodological Issues in Bankruptcy/ Recovery Prediction Models,” Applied Mathematics and Computation 185, 178–196.
O’Leary, D. E. (1998). “Using Neural Networks to Predict Corporate Failure,” International Journal of Intelligent Systems in Accounting Finance and Management 7, 187–197.
Pawlak, Z. (1991). Rough Sets. Theoretical Aspects of Reasoning about Data. Dordrecht/Boston/London: Kluwert Academic
Publishers.
Registrars of Spain (2010). http://www.registroconcursal.es [Accessed September 30, 2013].
Sanchis, A., M. J. Segovia, J. A. Gil, A. Heras, and J. L. Vilar (2007). “Rough Sets and the Role of the Monetary Policy in Financial Stability (Macroeconomic Problem) and the Prediction of Insolvency in Insurance Sector (Microeconomic Problem),” European Journal of Operational Research 181, 1554–1573.
Shumway, T. (2001). “Forecasting Bankruptcy More Accurately: A Simple Hazard Model,” The Journal of Business 74(1), 101–124.
Slowinski, R., and C. Zopounidis (1995). “Application of the Rough Set Approach to Evaluation of Bankruptcy Risk,” International Journal of Intelligent Systems in Accounting, Finance and Management 4(1), 27–41.
Tsai, C. (2009). “Feature Selection in Bankruptcy Prediction,” Knowledge-Based Systems 22, 120–127.
Warner, J. B. (1977). “Bankruptcy Costs: Some Evidence,” Journal of Finance 32(2), 337–347.
Witten, I. H., and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. San Francisco: Morgan Kaufmann.
Wu, W. W. (2010). “Beyond Business Failure Prediction,” Expert Systems with Applications 37, 2371–2376.
Xu, M., and C. Zhan (2009). “Bankruptcy Prediction: the Case of Japanese Listed Companies,” Review of Accounting Studies 14(4), 534–558.
Xu, X., and Y. Wang (2009). “Financial Failure Prediction Using Efficiency as a Predictor,” Expert Systems with Applications 36, 366–373.