Other Articles by Authors

Francesco Ciampi

Authors and WSEAS

Francesco Ciampi

WSEAS Transactions on Business and Economics

Print ISSN: 1109-9526
E-ISSN: 2224-2899

Volume 14, 2017

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Volume 14, 2017

The Potential of Top Management Characteristics for Small Enterprise Default Prediction Modelling

AUTHORS: Francesco Ciampi

Download as PDF

ABSTRACT: The aim of this study is to verify the potential of top management characteristics for small enterprise (SE) default prediction modelling. Logistic regression was applied to a sample of 423 Italian SEs, as defined in the Base Capital Accords (firms with a turnover below 5 million Euro) in order to develop a SE default prediction model based on both financial ratios and SE top management characteristics. The predictive power of this model was then compared to that of a second model whose predictive variables were exclusively represented by balance sheet financial ratios. The main findings are: i) managerial characteristics significantly improve the SE default prediction accuracy rates; ii) the smaller is a firm the higher is the increase in prediction accuracy that can be obtained by using managerial characteristics as default predictors; iii) SEs belonging to different size groups need to be treated with different prediction models; iv) SE management’s over-confidence in its ability to control the outcome of all events, especially external events, reduces a firm’s capacity to survive.

KEYWORDS: Bankruptcy, Credit rating, Default prediction modelling, Financial ratios, Managerial characteristics, Small enterprise, Top management


[1] Aaron, A., Nainggolan, Y. A., Trinugroho, I., Corporate Failure Prediction Model in Indonesia: Revisiting the Z-Scores, Discriminant Analysis, Logistic Regression and Artificial Neural Network, Journal for Global Business Advancement, 10(2), 2017, pp. 187- 209.

[2] Altman, E. I., Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy, The Journal of Finance, 23(4), 1968, pp. 589-609.

[3] Altman, E. I., Corporate Financial Distress and Bankruptcy (2nd ed.), Wiley, 1993.

[4] Altman, E. I., Corporate Credit Scoring Insolvency Risk Models in a Benign Credit and Basel II Environment, New York University, 2004.

[5] Altman, E.I., Brady, B., Resti, A., Sironi, A., The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications, The Journal of Business, 78(6), 2005, pp. 2203- 2228.

[6] Altman, E. I., Haldeman, R. G., Narayanan, P., Zeta-analysis. A New Model to Identify Bankruptcy Risk of Corporations, Journal of Banking and Finance, 1(1), 1977, pp. 29-54.

[7] Altman, E.I., Sabato, G., Effects of the New Basel Capital Accord on Bank Capital Requirements for SMEs, Journal of Financial Services Research, 28(1-3), 2005, pp. 15-42.

[8] Altman, E. I., Sabato, G., Modelling Credit Risk for SMEs: Evidence from the US Market, Abacus, 43(2), 2007, pp. 332-357.

[9] Altman, E. I., Sabato, G., Wilson, N., The value of Non-Financial Information in Small and Medium-Sized Enterprise Risk Management, The Journal of Credit Risk, 6(2), 2010, pp. 1-33.

[10] Altman, E. I., Saunders, A., Credit Risk Measurement: Development over the Last 20 Years, New York University, 1996.

[11] Argenti, J., Corporate Collapse: The Causes and Symptoms, McGraw‐Hill , 1976.

[12] Back, B., Laitinen, T., Sere, K., Neural networks and genetic algorithms for bankruptcy predictions, Expert System with Applications, 11(4), 1996, pp. 407-413.

[13] Barkham, R., Gudgin, G., Hart, M., Hanvey, E., The determinants of Small Firm Growth - An Interregional Study in the UK: 1986-90, Jessica Kingsley, 1996.

[14] Beaver, W., Financial Ratios Predictors of Failure, Journal of Accounting Research, Supplement to Volume 4, 1966, pp. 71-111.

[15] Beaver, W., Alternative Accounting Measures as Predictors of Failure, The Accounting Review, 43(1), 1968, pp. 113-122.

[16] Behr, P., Güttler, A., Credit risk Assessment and Relationship Lending: An Empirical Analysis of German Small and Medium-Sized Enterprises, Journal of Small Business Management, 45(2), 2007, pp. 194-213.

[17] Berger, A. N., Frame, S. W., Small Business Credit Scoring and Credit Availability, Journal of Small Business Management, 45(1), 2007, pp. 5-22.

[18] Blum, M., Failing Company Discriminant Analysis, Journal of Accounting Research, 12(1), 1974, pp. 1-25.

[19] Bruno, A., Leidecker, J., Harder, J., Why Firms Fail, Business Horizons, 30(2), 1987, pp. 50- 58.

[20] Burke, I. G., Jarrat, D., The Influence of Information and Advice on Competitive Strategy Definition in Small and Medium Sized Enterprises, Qualitative Market Research, 7(2), 2004, pp. 126-138.

[21] Carter, R., Van Auken, H., Small Firm Bankruptcy, Journal of Small Business Management, 44(4), 2006, pp. 493-512.

[22] Ciampi, F., The Knowledge Creation Potential of Management Consulting, IOS Press, 2008.

[23] Ciampi, F., Corporate Governance Characteristics and Default Prediction Modelling for Small Enterprises. An Empirical Analysis of Italian Firms, Journal of Business Research, 68 (5), 2015, pp. 1012-1025.

[24] Ciampi, F., Gordini, N., Small Enterprise Default Prediction Modelling through Artificial Neural Networks: An Empirical Analysis of Italian Small Enterprises, Journal of Small Business Management, 51(1), 2013, pp. 23-45.

[25] Cooper, A., Gascon, J., Woo, C., A ResourceBased Prediction of New Venture Survival and Growth, in Proceedings of the Academy of Management, 113-119. Academy of Management, 1991.

[26] Crouhy, M., Galai, D., Mark, R., Prototype Risk Rating System, Journal of Banking and Finance, 25(1), 2001, pp. 47-95.

[27] Daily, C. M., Dalton, D. R., Bankruptcy and Corporate Governance: The Impact of Board Composition and Structure, Academy of Management Journal, 37(6), 1994, pp. 1603- 1617.

[28] Daily, C. M., Dalton, D. R., Corporate Governance and the Bankrupt Firm: An empirical Assessment, Strategic Management Journal, 15(8), 1994, pp. 643-654.

[29] D’Aveni, R., The Aftermath of Organizational Decline: A Longitudinal Study of the Strategic and Managerial Characteristics of Declining Firms, Academy of Management Journal, 32(3), 1989, pp. 577-605.

[30] Deakin, E. B., A Discriminant Analysis of Predictors of Business Failure, Journal of Accounting Research, 10(1), 1972, pp. 167- 179.

[31] Edmister, R. O., An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction, Journal of Financial and Quantitative Analysis, 7(2), 1972, pp. 1477- 1493.

[32] Etemadi, H., Rostamy, A. A. A., Dehkordi, H. F., A Genetic Programming Model for Bankruptcy Prediction: Empirical Evidence from Iran, Expert System with Applications, 36(2), 2009, pp. 3199-3207.

[33] Evans D. S., Leighton, L., Some Empirical Aspects of Entrepreneurship, American Economic Review, 79(3), 1989, 519-535.

[34] Figini, S., Savona, R., Vezzoli, M., Corporate Default Prediction Model Averaging: A Normative Linear Pooling Approach, Intelligent Systems in Accounting, Finance and Management, 23(1-2), 2016, pp. 6-20.

[35] Flahvin, A., Why Small Businesses Fail, Australian Accountant, 55(9), 1985, pp. 56-72.

[36] Fletcher, D., Gross, E., Forecasting with Neural Networks: An Application Using Bankruptcy Data, Information and Management, 24(3), 1993, pp. 159-167.

[37] Fulmer, J. G. Jr., Moon, J. E., Gavin, T. A., Erwin, M. J., A Bankruptcy Classification Model for Small Firms, The Journal of Commercial Bank Lending, 66(11), 1984, 25- 37.

[38] Gaskill, L. R., Van Auken, H.E., Manning, R.A., A Factor Analytic Study of the Perceived Causes of Small Business Failure, Journal of Small Business Management, 31(4), 1993, pp. 18-31.

[39] Grice, J. S., Ingram, R. W., Tests of the Generalizability of Altman's Bankruptcy Prediction Model, Journal of Business Research, 54(1), 2001, pp. 53-61.

[40] Gupta, V., Analysis of Default Risk for Listed Companies in India: A Comparison of Two Prediction Models, International Journal of Business and Management, 9(9), 2014, pp. 223-234.

[41] Hambrick, D. C., D'Aveni, R. A., Top Team Deterioration as Part of the Downward Spiral of Large Corporate Bankruptcies, Management Science, 38(10), 1992, pp. 1445-1466.

[42] Haswell, S., Holmes, S., Estimating the Small Business Failure Rate: A Reappraisal, Journal of Small Business Management, 27(3), pp. 1989, 68-74.

[43] Hisrich, R. D., Brush, C., Characteristics of the Minority Entrepreneur, Journal of Small Business Management, 24(1), 1986, pp. 1-8.

[44] Hoad, W., Rosco, P., Management Factors Contributing to the Success or Failure of New Small Manufactures, University of Michigan Press, 1964.

[45] Huijuan, L., Default Prediction Model for SME’s: Evidence from UK Market Using Financial Ratios, International Journal of Business and Management, 10 (2), 2015, pp. 81-106.

[46] Karels, G. V., Prakash, A. J., Multivariate Normality and Forecasting of Business Bankruptcy, Journal of Business Finance & Accounting, 14(4), 1987, pp. 573-593.

[47] Kennedy, C., Thinking of Opening Your Own Business? Be Prepared!, Business Horizons, 33(5), 1985, pp. 38-42.

[48] Koh, H. C., Testing Hypotheses of Entrepreneurial Characteristics: A Study of Hong Kong MBA Students, Journal of Managerial Psychology, 11(3), 1996, pp. 12- 25.

[49] Lacher, R. C., Coats, P. K., Sharma, S. C., Fant, L. F., A Neural Network Tool for Classifying the Financial Health of a Firm, European Journal of Operation Research, 85(1), 1995, pp. 53-65.

[50] Larson, C. M., Clute, R. C., The Failure Syndrome, American Journal of Small Business, 4(2), 1979, pp. 35-43.

[51] Lauzen, L., Small Business Failures Are Controllable, Corporate Accounting, 3(3), 1985, pp. 34-38.

[52] Lussier, R. N., A Nonfinancial Business Success Versus Failure Prediction Model for Young Firms, Journal of Small Business Management, 33(1), 1995, pp. 8-20.

[53] Mallette, P., Fowler, K. L., Effects of Board Composition and Stock Ownership on the Adoption of Poison Pills, Academy of Management Journal, 35(5), 1992, pp. 1010- 1035.

[54] Martin, D., Early Warning of Bank Failure: A Logit Regression Approach, Journal of Banking and Finance, 1(3), 1977, pp. 249-276.

[55] Mitton, D. G., The Complete Entrepreneur, Entrepreneurship: Theory and Practice, 13(3), 1989, pp. 9-19.

[56] Morrison, A., Breen J., Ali S., Small Business Growth: Intention, Ability, and Opportunity, Journal of Small Business Management, 41(4), 2003, pp. 417-425.

[57] Ohlson, J., Financial Ratios and the Probabilistic Prediction of Bankruptcy, Journal of Accounting Research, 18(1), 1980, pp. 109- 131.

[58] Pindado, J., Rodrigues, L., De la Torre, C., Estimating Financial Distress Likelihood, Journal of Business Research, 61(9), 2008, pp. 995-1003.

[59] Pompe, P. M., Bilderbeek, J., The Prediction of Bankruptcy of Small-and Medium-Sized Industrial Firms, Journal of Business Venturing, 20(6), 2005, pp. 847-868.

[60] Saurina, J., Trucharte, C., 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(2), 2004, pp. 121-144.

[61] Skogsvik, K, Skogsvik, S., On the Choice Based Sample Bias in Probabilistic Bankruptcy Prediction, Investment Management and Financial Innovation, 10(1), 2013, pp. 29-37.

[62] Soares, J. O., Pina, J.P., Ribeiro, M. S., Catalào-Lopes, M., Quantitative vs. Qualitative Criteria for Credit Risk Assessment, Frontiers in Finance and Economics, 8(1), 2011, pp. 69- 87.

[63] Tam, K. Y., Neural Network Models and the Prediction of Bank Bankruptcy, OMEGA: The International Journal of Management Science, 19(5), 1991, pp. 429-445.

[64] Tam, K. Y., Kiang, M. Y., Managerial Applications of Neural Networks: The Case of Bank Failure Predictions, Management Science, 38(7), 1992, pp. 926-947.

[65] Traczynski, J., Firm Default Prediction: A Bayesian Model-Averaging Approach, Journal of Financial and Quantitative Analysis, 52(3), 2017, 1211-1245.

[66] Von Stein, J. H., Ziegler, W., The Prognosis and Surveillance of Risks from Commercial Credit Borrowers, Journal of Banking and Finance, 8(2), 1984, pp. 249-268.

[67] Weitzel, W., Jonsson, E., Decline in Organizations: A Literature Integration and Extension, Administrative Science Quarterly, 34(1), 1989, pp. 91-109.

[68] Wilson, R. L., Sharda, R., Bankruptcy Prediction Using Neural Networks, Decision Support System, 11(5), 1994, pp. 545-557.

[69] Zenzerović, R., Business Financial Problem Prediction-Croatian Experience, Ekonomska Istraživanja, 22(4), 2009, pp. 1-16.

[70] Zhang, G. P., Hu, M. J., Patuwo E. B., Indro D. C., Artificial Neural Networks in Bankruptcy Prediction: General Framework and CrossValidation Analysis, European Journal of Operational Research, 116(1), 1999, pp. 16-32.

WSEAS Transactions on Business and Economics, ISSN / E-ISSN: 1109-9526 / 2224-2899, Volume 14, 2017, Art. #41, pp. 397-408

Copyright © 2017 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0