References
- Altman, E. I., Iwanicz‐Drozdowska, M., Laitinen, E. K., and Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of Altman’s Z‐score model. Journal of International Financial Management & Accounting. 28(2): 131–171.
- Agarwal, R., and Gort, M. (2002). Firm and product life cycles and firm survival. American Economic Review. 92(2): 184–190.
- Arasti, Z. (2011). Gender differences in the causes of business failure. Journal of Global Entrepreneurship Research. 1(1): 95–106.
- Audretsch, D., and Mahmood, T. (1995). New firm survival: new results using a hazard function. The Review of Economics and Statistics. 77: 97–103.
- Aziz, M., and Dar, H. (2006). Predicting corporate bankruptcy: Where do we stand? Corporate Governance International Journal of Business in Society. 6: 18–33.
- Barboza, F., Kimura, H., and Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications. 83: 405–417.
- Bauer, J. and Agarwal, V. (2014). Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test. Journal of Banking & Finance. 40: 432–442.
- Bauman, S., Toomey, R. B., & Walker, J. L. (2013). Associations among bullying, cyberbullying, and suicide in high school students. Journal of adolescence, 36(2): 341–350.
- Brüderl, J., and Preisendörfer, P. (1998). Network support and success of newly founded businesses. Small Business Economics. 10: 213–225.
- Bunyaminu, A., and Issah, M. (2012). Predicting corporate failure of UK’s listed companies: Comparing multiple discriminant analysis and logistic regression. International Research Journal of Finance and Economics. 94: 6–22.
- Carter, R., and Auken, H.V. (2006). Small firm bankruptcy. Journal of Small Business Management. 44: 493–512.
- Ciampi, F., and Gordini, N. (2013). Small Enterprise Default Prediction Modeling through Artificial Neural Networks: An Empirical Analysis of Italian Small Enterprises. Journal of Small Business Management. 51(1): 23–45.
- Chaudhuri A., and De K. (2011), Fuzzy support vector machine for bankruptcy prediction, Applied Soft Computing. 11(2): 2472–2486.
- Chava, S. and Jarrow, R.A. (2004). Bankruptcy prediction with industry effects. Review of Finance, 8 (4): 537–569.
- Chen, T., and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM International conference on knowledge discovery and data mining. 785–794.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
- Conan, J., and Holder, M. (1979). Variables explicatives de performances et controle de gestion dans les P.M.I. [Explanatory Variables of Performance and Management Control in the SMEs]. Paris Dauphine University.
- Cultrera, L., and Brédart, X. (2016). Bankruptcy prediction: the case of Belgian SMEs. Review of Accounting and Finance. 15(1): 101–119.
- De Jong, R. M., & Woutersen, T. (2011). Dynamic time series binary choice. Econometric Theory, 673–702.
- El Kalak, I. and Hudson, R. (2016). The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model. International Review of Financial Analysis. 43: 135–145.
- Falck, O. (2007). Survival chances of new businesses: do regional conditions matter?. Applied Economics. 39(16): 2039–2048.
- Franco, M., and Haase, H. (2010). Failure factors in small and medium-sized enterprises: qualitative study from an attributional perspective. International Entrepreneurship and Management Journal. 6(4): 503–521.
- Friedman, J.H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics. 29 (5): 1189–1232.
- Gill, A., Biger, N., Pai, C., & Bhutani, S. (2009). The Determinants of Capital Structure in the Service Industry: Evidence from United States. The Open Business Journal, 2, 48–53.
- Gupta, J., Gregoriou, A. and Ebrahimi, T. (2017). Empirical Comparison of Hazard Models in Predicting SMEs Failure. Quantitative Finance. 18(3): 437–466.
- Gupta, J., Gregoriou, A., and Healy, J. (2015). Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?. Review of Quantitative Finance and Accounting. 45(4): 845–869.
- Harrell, F., Califf, R., Pryor, D., Lee, K. and Rosati R. (1982) Evaluating the yield of medical tests. Journal of the American Medical Association. 247: 2543–2546
- Huggins, R. Prokop, D. and Thompson, P. (2017). Entrepreneurship and the Determinants of Firm Survival within Regions: Human Capital, Growth Motivation and Locational Conditions. Entrepreneurship and Regional Development. 29(3–4): 357–389.
- Hutchinson, J., and Xavier, A. (2006). Comparing the impact of credit constraints on the growth of SMEs in a transition country with an established market economy. Small Business Economics. 27:169–179.
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York: Springer.
- Kim, H. S., and Sohn, S. Y. (2010). Support vector machines for default prediction of SMEs based on technology credit. European Journal of Operational Research. 201(3): 838–846.
- Kauppi, H., and Saikkonen, P. (2008). Predicting US recessions with dynamic binary response models. The Review of Economics and Statistics, 90(4): 777–791.
- Kotey, B., and Slade, P. (2005). Formal human resource management practices in small growing firms. Journal of Small Business Management, 43: 16–40.
- Lahmiri, S. (2016). Features selection, data mining and financial risk classification: A comparative study. Intelligent Systems in Accounting. Finance and Management. 23: 265–75.
- Lennox, C. (1999). Identifying failing companies: a re-evaluation of the logit, probit and DA approaches. Journal of economics and Business, 51(4): 347–364.
- Liu, J. (2004). Macroeconomic determinants of corporate failures: evidence from the UK. Applied Economics. 36(9): 939–945.
- Lopez, J.A., and Saidenberg, M.R. (2000). Evaluating credit risk models. Journal of Banking and Finance. 24(1): 151–165.
- Lundberg, S. M., Erion, G. G., and Lee, S.-I. (2018). Consistent individualized feature attribution for tree ensembles. arXiv preprint, arXiv:1802.03888.
- Lundberg, S. M., and Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 4765–4774.
- Maté-Sánchez-Val, M., López-Hernandez, F., and Fuentes, C. C. R. (2018). Geographical factors and business failure: An empirical study from the Madrid metropolitan area. Economic Modelling. 74: 275–283.
- Millán, J. M., Congregado, E., and Román, C. (2012). Determinants of self-employment survival in Europe. Small Business Economics. 38(2): 231–258.
- Mselmi, N., Lahiani, A., and Hamza, T. (2017). Financial distress prediction: The case of French small and medium-sized firms. International Review of Financial Analysis. 50: 67–80.
- Nakagawa, S. (2004). A farewell to Bonferroni: the problems of low statistical power and publication bias. Behavioral ecology, 15(6): 1044–1045.
- Oberschachtsiek, D. (2012). The experience of the founder and self-employment duration: a comparative advantage approach. Small Business Economics. 39(1): 1–17.
- Park, T., & Casella, G. (2008). The bayesian lasso. Journal of the American Statistical Association. 103(482): 681–686.
- Parker, S. C. (2004). The Economics of Self-Employment and Entrepreneurship, Cambridge University Press, Cambridge.
- Pretorius, M. (2009). Defining business decline, failure and turnaround: a content analysis. South African Journal of Entrepreneurship and Small Business Management. 2(1): 1–16
- Psillaki, M., Ioannis, E. T., and Margaritis, D. (2010). Evaluation of credit risk based on firm performance. European Journal of Operational Research. 201: 873–81.
- Ptak-Chmielewska, A. (2019). Predicting Micro-Enterprise Failures Using Data Mining Techniques. Journal of Risk and Financial Management. 12(30): 1–17.
- Ramalho, J.J., and Da Silva, J.V. (2009). A two-part fractional regression model for the financial leverage decisions of micro, small, medium and large firms. Journal of Quantitative Finance. 9: 621–636.
- Robb, A. M., and Watson, J. (2012). Gender differences in firm performance: Evidence from new ventures in the United States. Journal of Business Venturing. 27(5): 544–558.
- Ropega, J. (2011). The reasons and symptoms of failure in SME. International Advances in Economic Research. 17(4): 476–483.
- Renski, H. (2011). External economies of localization, urbanization and industrial diversity and new firm survival. Papers in Regional Science. 90(3): 473–503.
- Rissman, E. (2006). The self-employment duration of younger men over the business cycle. Economic perspectives, 30(3): 14–27.
- Salman, A. K., von Friedrichs, Y., and Shukur, G. (2011). The determinants of failure of small manufacturing firms: Assessing the macroeconomic factors. International Business Research. 4(3): 22–32.
- Schäfer, D., and Talavera, O. (2009). Small business survival and inheritance: evidence from Germany. Small Business Economics. 32(1): 95–109.
- Shumway, T. (2001). Forecasting bankruptcy more accurately: a simple hazard model. Journal of Business. 74:101–124.
- Sparck Jones, K. (2004). IDF term weighting and IR research lessons. Journal of Documentation. 60(6): 521–523.
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological). 58(1): 267–288.
- Van Praag, C. M. (2003). Business survival and success of young small business owners: an empirical analysis. Small Business Economics. 21: 1–17.
- Wei, Z., Miao, D., Chauchat, J. H., & Zhong, C. (2008). Feature selection on Chinese text classification using character n-grams. In International Conference on Rough Sets and Knowledge Technology (pp. 500-507). Springer, Berlin, Heidelberg.
- Williams, D. A. (2014). Resources and failure of SMEs: Another look. Journal of Developmental Entrepreneurship. 19(01): 1450007.
- Zhang, G., Hu, M. Y., Patuwo, B. E., and Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research. 116(1): 16–32.
- Zhang, W., Yoshida, T., and Tang, X. (2011). A comparative study of TF* IDF, LSI and multi-words for text classification. Expert Systems with Applications, 38(3): 2758–2765.
- Zoricák, M., Gnip, P., Drotár, P., and Gazda, V. (2019). Bankruptcy prediction for small-and medium-sized companies using severely imbalanced datasets. Economic Modelling. 84: 165–176.