Small Enterprise Default Prediction Modeling through Artificial Neural Networks: An Empirical Analysis of Italian Small Enterprises

References

• Altman, E. I. (1968). “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” The Journal of Finance 23(4), 589–609.
   Web of Science ®Google Scholar
• Altman, E. I. (1993). Corporate Financial Distress and Bankruptcy, 2nd ed. New York: Wiley.
   Google Scholar
• Altman, E. I. (2004). Corporate Credit Scoring Insolvency Risk Models in a Benign Credit and Basel II Environment. New York: New York University Working Paper.
   Google Scholar
• Altman, E. I., B. Brady, A. Resti, and A. Sironi (2005). “The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications,” The Journal of Business 78(6), 2203–2227.
   Web of Science ®Google Scholar
• Altman, E. I., G. Marco, and F. Varetto (1994). “Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks (the Italian Experience),” Journal of Banking and Finance 18(3), 505–529.
   Web of Science ®Google Scholar
• Altman, E. I., and G. Sabato (2005). “Effects of the New Basel Capital Accord on Bank Capital Requirements for SMEs,” Journal of Financial Services Research 28(1–3), 15–42.
   Google Scholar
• Altman, E. I., and G. Sabato (2007). “Modeling Credit Risk for SMEs: Evidence from the US Market,” Abacus 43(2), 332–357.
   Google Scholar
• Altman, E. I., and A. Saunders (1996). “Credit Risk Measurement: Development over the Last 20 Years,” Working Paper Series, New York: New York University Working Paper, 1–38.
   Google Scholar
• Altman, E. I., and A. Saunders (2001). “An Analysis and Critique of the BIS Proposal on Capital Adequacy and Ratings,” Journal of Banking and Finance 25(1), 25–46.
   Web of Science ®Google Scholar
• Barnes, P. (1982). “Methodological Implications of Non‐Normality Distributed Financial Ratios,” Journal of Business Finance and Accounting 9(1), 51–62.
   Google Scholar
• Basel Committee on Banking Supervision (2006). “International Convergence of Capital Measurement and Capital Standards: A Revised Framework,” Bank for International Settlements, Basel, Switzerland.
   Google Scholar
• Beaver, W. (1966). “Financial Ratios as Predictors of Failure,” Empirical Research in Accounting: Selected Studies, Supplement to Journal of Accounting Research 4, 71–111.
   Web of Science ®Google Scholar
• Beaver, W. (1968). “Alternative Accounting Measures as Predictors of Failure,” The Accounting Review 43(1), 113–122.
   Web of Science ®Google Scholar
• Behr, P., and A. Güttler (2007). “Credit Risk Assessment and Relationship Lending: An Empirical Analysis of German Small and Medium‐Sized Enterprises,” Journal of Small Business Management 45(2), 194–213.
   Web of Science ®Google Scholar
• Berger, A. N. (2006). “Potential Competitive Effects of Basel II on Banks in SME Credit Markets in the United States,” Journal of Financial Services Research 29(1), 5–36.
   Google Scholar
• Berger, A. N., and S. W. Frame (2007). “Small Business Credit Scoring and Credit Availability,” Journal of Small Business Management 45(1), 5–22.
   Web of Science ®Google Scholar
• Berger, A. N., and G. F. Udell (2002). “Small Business Credit Availability and Relationship Lending. The Importance of Bank Organizational Structure,” Economic Journal 112(477), F32–F53.
   Web of Science ®Google Scholar
• Berger, A. N., and G. F. Udell (2006). “A More Complete Conceptual Framework about SME Finance,” Journal of Banking and Finance 30(11), 2945–2966.
   Web of Science ®Google Scholar
• Blum, M. P. (1974). “Failing Company Discriminant Analysis,” Journal of Accounting Research 12(1), 1–25.
   Web of Science ®Google Scholar
• Boritz, E. J., and D. B. Kennedy (1995). “Effectiveness of Neural Network Types for Prediction of Business Failure,” Expert System with Application 9(4), 503–512.
   Web of Science ®Google Scholar
• Boritz, E. J., D. B. Kennedy, and A. de Miranda e albuquerque (1995). “Predicting Corporate Failure Using a Neural Network Approach,” International Journal of Intelligent Systems in Accounting, Finance and Management 4(2), 95–111.
   Google Scholar
• Burke, I. G., and D. Jarrat (2004). “The Influence of Information and Advice on Competitive Strategy Definition in Small and Medium Sized Enterprises,” Qualitative Market Research 7(2), 126–138.
   Google Scholar
• Carter, R., and H. Van auken (2006). “Small Firm Bankruptcy,” Journal of Small Business Management 44(4), 493–512.
   Web of Science ®Google Scholar
• Ciampi, F. (1994). “Squilibri di assetto finanziario nelle P.M.I. Finanziamenti e contributi della Comunità Europea [Small Enterprise Financial Imbalances. European Community Funding],” Studie Informazioni, Supplement to Issue 3, 1–202.
   Google Scholar
• Ciampi, F., and N. Gordini (2008). “Using Economic–Financial Ratios for Small Enterprise Default Prediction Modeling: An Empirical Analysis,” 2008 Oxford Business & Economics Conference Proceedings, Association for Business and Economics Research (ABER), 1–21.
   Google Scholar
• Ciampi, F., and N. Gordini (2009). “Default Prediction Modeling for Small Enterprises: Evidence from Small Manufacturing Firms in Northern and Central Italy,” Oxford Journal 8(1), 13–29.
   Google Scholar
• Coats, P. K., and F. F. Fant (1993). “Recognizing Financial Distress Patterns Using a Neural Network Tool,” Financial Management 22(3), 142–156.
   Web of Science ®Google Scholar
• Crouhy, M., D. Galai, and R. Mark (2001). “Prototype Risk Rating System,” Journal of Banking and Finance 25(1), 47–95.
   Google Scholar
• Cybenko, G. (1989). “Approximation by Superpositions of a Sigmoidal Function,” Mathematical Control Signals Systems 2(4), 303–314.
   Google Scholar
• Deakin, E. B. (1972). “A Discriminant Analysis of Predictors of Business Failure,” Journal of Accounting Research 10(1), 167–180.
   Web of Science ®Google Scholar
• Demyanyk, Y., and I. Hasan (2010). “Financial Crises and Bank Failure: A Review of Prediction Methods,” Omega 38, 315–324.
   Web of Science ®Google Scholar
• Edmister, R. O. (1972). “An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction,” Journal of Financial and Quantitative Analysis 7(2), 1477–1493.
   Web of Science ®Google Scholar
• Fanning, K. M., and K. O. Cogger (1994). “A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction,” International Journal of Intelligent Systems in Accounting, Finance and Management 3(4), 241–252.
   Google Scholar
• Fletcher, D., and E. Gross (1993). “Forecasting with Neural Networks: An Application Using Bankruptcy Data,” Information and Management 24(3), 159–167.
   Web of Science ®Google Scholar
• Härdle, W., Y.‐J. Lee, D. Schäfer, and Y.‐R. Yeh (2009). “Variable Selection and Oversampling in the Use of Smooth Vector Machines for Predicting the Default Risk of Companies,” Journal of Forecasting 58, 512–534.
   Google Scholar
• Hornik, K. (1991). “Approximation Capabilities of Multilayer Feed Forward Networks,” Neural Networks 4(2), 251–257.
   Web of Science ®Google Scholar
• Jo, H., I. Han, and H. Lee (1997). “Bankruptcy Prediction Using Case Based Reasoning, Neural Networks, and Discriminant Analysis,” Expert System with Application 13(2), 97–108.
   Web of Science ®Google Scholar
• Karels, G. V., and A. J. Prakash (1987). “Multivariate Normality and Forecasting of Business Bankruptcy,” Journal of Business Finance and Accounting 14(4), 573–593.
   Google Scholar
• Lacher, R. C., P. K. Coats, S. C. Sharma, and L. F. Fant (1995). “A Neural Network Tool for Classifying the Financial Health of a Firm,” European Journal of Operation Research 85(1), 53–65.
   Web of Science ®Google Scholar
• Lippmann, R. P. (1987). “An Introduction to Computing with Neural Nets,” IEEE, ASSP Magazine 4(2), 4–22.
   Google Scholar
• Maki, K., and T. Pukkinen (2000). “Barriers to Growth and Employment in Finnish Small Enterprises,” Paper presented at the ICSB World Conference2000, Brisbane, Australia, June.
   Google Scholar
• Martin, D. (1977). “Early Warning of Bank Failure: A Logit Regression Approach,” Journal of Banking and Finance 1(3), 249–276.
   Google Scholar
• Mcculloch, W. S., and W. A. Pitts (1943). “Logical Calculus of Ideas Immanent in Nervous Activity,” Bulletin of Mathematical Biophysic 5, 115–133.
   Google Scholar
• Mcleay, S., and A. Omar (2000). “The Sensitivity of Prediction Models to the Non‐Normality of Bounded an Unbounded Financial Ratios,” British Accounting Review 32(2), 213–230.
   Google Scholar
• Montgomery, D. C., and E. A. Peck (1992). Introduction to Linear Regression Analysis. New York: John Wiley & Sons.
   Google Scholar
• Morrison, A., J. Breen, and S. Ali (2003). “Small Business Growth: Intention, Ability, and Opportunity,” Journal of Small Business Management 41(4), 417–425.
   Web of Science ®Google Scholar
• Ohlson, J. A. (1980). “Financial Ratios and the Probabilistic Prediction of Bankruptcy,” Journal of Accounting Research 18(1), 109–131.
   Web of Science ®Google Scholar
• Patuwo, E. B., M. Y. Hu, and M. S. Hung (1993). “Two‐Group Classification Using Neural Networks,” Decision Science 24(4), 825–845.
   Google Scholar
• Pompe, P. P. M., and J. Bilderbeek (2005). “The Prediction of Bankruptcy of Small‐ and Medium‐Sized Industrial Firms,” Journal of Business Venturing 20, 847–868.
   Web of Science ®Google Scholar
• Rumelhart, D. E., G. E. Hinton, and R. J. Williams (1986). “Learning Representations by Back‐Propagating Errors,” Nature 323, 533–536.
   Web of Science ®Google Scholar
• Salchenberger, L. M., M. E. Cinar, and N. A. Lash (1992). “Neural Networks: A New Tool for Predicting Thrift Failures,” Decision Sciences 23(4), 899–916.
   Web of Science ®Google Scholar
• Saurina, J., and C. Trucharte (2004). “The Impact of Basel II on Lending to Small‐ and‐Medium‐Sized Firms: A Regulatory Policy Assessment Based on Spanish Credit Register Data,” Journal of Finance Services 26, 121–144.
   Google Scholar
• Schwaiger, W. S. A. (2002). “Basel II: Quantitative Impact Studies on Austrian Small and Medium‐Sized Enterprises,” Technical University of Vienna.
   Google Scholar
• Sharda, R., and R. L. Wilson (1996). “Neural Network Experiments in Business‐Failure Forecasting: Predictive Performance Measurement Issues,” International Journal of Computational Intelligence and Organizations 1(2), 107–117.
   Google Scholar
• Tam, K. Y. (1991). “Neural Network Models and the Prediction of Bank Bankruptcy,” OMEGA: The International Journal of Management Science 19(5), 429–445.
   Web of Science ®Google Scholar
• Tam, K. Y., and M. Y. Kiang (1992). “Predicting Bank Failure: A Neural Network Approach,” Decision Science 23, 926–947.
   Google Scholar
• Vallini, C., F. Ciampi, and N. Gordini (2009). “Using Artificial Neural Networks Analysis for Small Enterprise Default Prediction Modeling: Statistical Evidence from Italian Firms,” 2009 Oxford Business & Economics Conference Proceedings, Association for Business and Economics Research (ABER), 1–26.
   Google Scholar
• Vallini, C., F. Ciampi, N. Gordini, and M. Benvenuti (2008). “Can Credit Scoring Models Effectively Predict Small Enterprise Default? Statistical Evidence from Italian Firms,” Proceedings of the 8th Global Conference on Business & Economics, Association for Business and Economics Research (ABER), 1–23.
   Google Scholar
• Vallini, C., F. Ciampi, N. Gordini, and M. Benvenuti (2009). “Are Credit Scoring Models Able to Predict Small Enterprise Default? Statistical Evidence from Italian Firms,” International Journal of Business & Economics 8(1), 3–18.
   Google Scholar
• Wilson, R. L., and R. Sharda (1994). “Bankruptcy Prediction Using Neural Networks,” Decision Support System 11(5), 545–557.
   Web of Science ®Google Scholar
• Zhang, G. P., M. Y. Hu, E. B. Patuwo, and D. C. Indro (1999). “Artificial Neural Networks in Bankruptcy Prediction: General Framework and Cross‐Validation Analysis,” European Journal of Operational Research 116(1), 16–32.
   Web of Science ®Google Scholar