Relevance of the legal form of companies for the bankruptcy prediction
PDF

Keywords

Bankruptcy prediction models
Legal form
Predictive capacity

How to Cite

Poli, S., Giuliani, M., & Baccarini, L. (2023). Relevance of the legal form of companies for the bankruptcy prediction. Piccola Impresa Small Business, (2). https://doi.org/10.14596/pisb.3787
Received 2023-04-25
Accepted 2023-09-30
Published 2023-12-28

Abstract

Purpose. This study aimed to understand whether a general bankruptcy prediction model for small Italian companies having any legal forms has a different predictive capacity than specific bankruptcy prediction models for those having specific legal forms. On the one hand, it focuses on cooperative companies, and on the other, joint-stock and limited-liability companies.

Design/methodology/approach. A general bankruptcy prediction model and two specific bankruptcy prediction models (one for cooperative companies and one for joint-stock and limited-liability companies) were constructed and compared regarding predictive capacity.

Findings. The overall accuracy levels of the general and specific models were the same, but the percentage of companies correctly predicted to be in crisis out of the total number of companies effectively in crisis (sensitivity) of the latter (in particular, referring to joint-stock and limited-liability companies) was higher than that of the former. Considering the high economic and social costs that can derive from the predictive errors of companies in crisis, specific models should be preferred to the general model.

Practical and social implications. This study offers to those who may be interested in evaluating the financial health of a company (stakeholders, such as banks, suppliers, customers, etc., as well as the management and control bodies of the company) bankruptcy prediction models having a high predictive capacity differentiated according to its legal form.

Originality of the study. No previous study has verified whether a general bankruptcy prediction model for companies having any legal forms has a different predictive capacity than specific bankruptcy prediction models for companies having specific legal forms. At the same time, in the Italian context, no previous study has proposed a bankruptcy prediction model for cooperative companies.

https://doi.org/10.14596/pisb.3787
PDF

References

Alaka, H.A., Oyedele, L.O., Owolabi, H.A., Kumar, V., Ajayi, S.O., Akinade, O.O., & Bilal, M. (2018). A systematic review of bankruptcy prediction mod-els: towards a framework for tool selection. Expert Systems with Applica-tions, 94, 164-184. https://doi.org/10.1016/j.eswa.2017.10.040

Altman, E.I. (1968), Financial ratios discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.2307/2978933

Altman, E.I., Haldeman, R.G., & Narayanan, P. (1977). ZETATM analysis A new model to identify bankruptcy risk of corporations. Journal of Banking & Fi-nance, 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6

Altman, E.I, & Sametz, A.W. (Eds.) (1977); Financial crises: institutions and mar-kets in a fragile environment. New York, John Wiley & Sons.

Altman, E. I., & Sabato, G. (2007). Modelling credit risk for SMEs: Evidence from the US market. Abacus, 43(3), 332-357. https://doi.org/10.1111/j.1467-6281.2007.00234.x

Altman, E. I., Iwanicz‐Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Fi-nancial distress prediction in an international context: A review and empirical analysis of Altman's Z‐score model. Journal of International Financial Man-agement & Accounting, 28(2), 131-171. https://doi.org/10.1111/jifm.12053

Amendola, A., Restaino, M., & Sensini, L. (2011). Dynamic statistical models for bankruptcy prediction of Italian firms. Paper presented at the 4th Annual Eu-roMed Conference of the EuroMed Academy of Business, Elounda, Crete, Greece, 20-21 October (pp. 97-109).

Amendola, A., Restaino, M., & Sensini, L. (2013). Corporate financial distress and bankruptcy: A comparative analysis in France, Italy and Spain. Paper presented at the 6th Annual Conference of the EuroMed Academy of Business, Estoril, Lisbon, Portugal, 23-24 September (pp. 107-120).

Amendola, A., Restaino, M., & Sensini, L. (2015). An analysis of the determi-nants of financial distress in Italy: A competing risks approach. International Review of Economics & Finance, 37, 33-41. https://doi.org/10.1016/j.iref.2014.10.012

Arnis, N.I., Chytis, E.T., & Kolias, G.D. (2018). Bankruptcy prediction and homo-geneity of firm samples: the case of Greece. Journal of Accounting and Taxa-tion, 10(9), 110-125. https://doi.org/10.5897/JAT2018.0321

Aziz, A., Emanuel, D.C., & Lawson, G.H. (1988). Bankruptcy prediction – an in-vestigation of cash flow based models. Journal of Management Studies, 25(5), 419-437. https://doi.org/10.1111/j.1467-6486.1988.tb00708.x

Balcaen, S., & Ooghe, H. (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. https://doi.org/10.1016/j.bar.2005.09.001

Baldissera, A. (2019). Italian small-sized enterprise and procedures of warning crisis. Piccola Impresa/Small business, (2), 9-34. https://doi.org/10.14596/pisb.319

Barontini, R. (2000). La valutazione del rischio di credito: i modelli di previsione delle insolvenze. Il Mulino, Bologna.

Bellovary, J.L., Giacomino, D.E., & Akers, M.D. (2007). A review of bankruptcy prediction studies: 1930 to present. Journal of Financial Education, 33, 1-42.

Berg, D. (2007). Bankruptcy prediction by generalized additive models. Applied Stochastic Models in Business and Industry, 23(2), 129-143. https://doi.org/10.1002/asmb.658

Beynon, M.J., & Peel, M.J. (2001). Variable precision rough set theory and data discretisation: an application to corporate failure prediction. Omega, 29(6), 561-576. https://doi.org/10.1016/S0305-0483(01)00045-7

Branciari, S., Giuliani, M., & Poli, S. (2022). L’impatto del settore economico sull’efficacia dei modelli di previsione dell’insolvenza: il caso delle imprese italiane. In Dell'Atti, V., Muserra, A. L., Marasca, S., & Lombardi, R. (Eds.), Dalla crisi allo sviluppo sostenibile. Principi e soluzioni nella prospettiva eco-nomico-aziendale (pp. 58-83). Franco Angeli, Milano.

Brockman P., & Turtle, H.J. (2003). A barrier option framework for corporate se-curity valuation. Journal of Financial Economics, 67(3), 511-529. https://doi.org/10.1016/S0304-405X(02)00260-X

Camacho‐Miñano, M. D. M., Segovia‐Vargas, M. J., & Pascual‐Ezama, D. (2015). Which characteristics predict the survival of insolvent firms? An SME reorganization prediction model. Journal of Small Business Management, 53(2), 340-354. https://doi.org/10.1111/jsbm.12076

Cesaroni, F. M., & Sentuti, A. (2016). Strategie ambidestre e crisi economica: le peculiarità della piccola impresa. Piccola Impresa/Small business, (1), 54-77. https://doi.org/10.14596/pisb.224

Charitou, J.A., Neophutou, E., & Charalambous, C. (2004). Predicting corporate failure: empirical evidence for the UK. European Accounting Review, 13(3), 465-497. https://doi.org/10.1080/0963818042000216811

Chava, S., & Jarrow, R.A. (2004). Bankruptcy prediction with industry effects. Review of Finance, 8(4), 537-569. https://doi.org/10.1093/rof/8.4.537

Chen, N., Ribeiro, B., & Chen, A. (2016). Financial credit risk assessment: a re-cent review. Artificial Intelligence Review, 45(1), 1-23. https://doi.org/10.1007/s10462-015-9434-x

Ciampi, F. (2015). Corporate governance characteristics and default prediction modeling for small enterprises: an empirical analysis of Italian firms. Journal of Business Research, 68(5), 1012-1025. https://doi.org/10.1016/j.jbusres.2014.10.003

Comuzzi, E. (1995). L’analisi degli squilibri finanziari d’impresa. Giappichelli, Tori-no.

Cruz, E. D., & Sabado, J. R. F. (2022). Credit risk and performance evaluation of cooperatives in region xi using data envelopment analyses (DEA). European Journal of Economic and Financial Research, 6(1). https://doi.org/10.46827/ejefr.v6i1.1268

Daubie, M., & Meskens, N. (2002). Business failure prediction: a review and analysis of the literature. In Zopunidis C. (Ed.), New trends in banking man-agement: contributions to management science (pp. 71-86). Physica, Heidel-berg.

Dietrich, J., Arcelus, F. J., & Srinivasan, G. (2005). Predicting financial failure: some evidence from new brunswick agricultural co‐ops. Annals of Public and Cooperative Economics, 76(2), 179-194. https://doi.org/10.1111/j.1370-4788.2005.00275.x

Dirickx, Y., & Van Landeghem, G. (1994). Statistical failure prevision problems. Tijdschrift voor economie en management, 39(4), 429-462.

do Prado, J.W., de Castro Alcântara, V., de Melo Carvalho, F., Vieira, K.C., Ma-chado, L.K.C., & Tonelli, D.F. (2016). Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014). Scientometrics, 106(3), 1007-1029. https://doi.org/10.1007/s11192-015-1829-6

Du Jardin, P. (2009). Bankruptcy prediction models: how to choose the most relevant variables? Bankers, Markets & Investors, 98, 39-46.

Du Jardin, P. (2017). Dynamics of firm financial evolution and bankruptcy pre-diction. Expert Systems with Applications, 75, 25-43. https://doi.org/10.1016/j.eswa.2017.01.016

Fagerland, M.W., Lydersen, S., & Laake, P. (2013). The McNemar test for binary matched-pairs data: mid-p and asymptotic are better than exact conditional. BMC medical research methodology, 13, 1-8. https://doi.org/10.1186/1471-2288-13-91

Foreman, R.D. (2002). A logistic analysis of bankruptcy within the US local tele-communications industry. Journal of Economics and Business, 6(1), 1-32. https://doi.org/10.1016/S0148-6195(02)00133-9

Gemar, G., Soler, I. P., & Guzman-Parra, V. F. (2019). Predicting bankruptcy in resort hotels: a survival analysis. International Journal of Contemporary Hospi-tality Management, 31(4), 1546-1566. https://doi.org/10.1108/IJCHM-10-2017-0640

Giacosa, E., & Mazzoleni, A. (2018). I modelli di previsione dell’insolvenza azien-dale: efficacia predittiva, limiti e prospettive di utilizzo. Giappichelli, Torino.

Giriūniene, G., Giriūnas, L., Morkunas, M., & Brucaite, L. (2019). A comparison on leading methodologies for bankruptcy prediction: The case of the con-struction sector in Lithuania. Economies, 7(3), 82. https://doi.org/10.3390/economies7030082

Herman, S. (2017). Industry specifics of joint-stock companies in Poland and their bankruptcy prediction. In Proceedings of the 11th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena, 9-12 May (pp. 93-102). Zakopane, Poland.

Horta, I. M., & Camanho, A. S. (2013). Company failure prediction in the con-struction industry. Expert Systems with Applications, 40(16), 6253-6257. https://doi.org/10.1016/j.eswa.2013.05.045

Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic re-gression (Vol. 398). John Wiley & Sons.

Jackson, R., & Wood, A. (2013). The performance of insolvency prediction and credit risk models in the UK: a comparative study. The British Accounting Re-view, 45(3), 183-202. https://doi.org/10.1016/j.bar.2013.06.009

Jones, S., Johnstone, D., & Wilson, R. (2015). An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings chang-es. Journal of Banking & Finance, 56, 72-85. https://doi.org/10.1016/j.bankfin.2015.02.006

Kahya, E., & Theodossiou, P. (1999). Predicting corporate financial distress: a time-series CUSUM methodology. Review of Quantitative Finance and Ac-counting, 13(4), 323-345. https://doi.org/10.1023/A:1008326706404

Keasey, K., & Watson, R. (1987). Non‐financial symptoms and the prediction of small company failure: a test of Argenti's hypotheses. Journal of Business Finance & Accounting, 14(3), 335-354. https://doi.org/10.1111/j.1468-5957.1987.tb00099.x

Keasey, K., & McGuinness, P. (1990). The failure of UK industrial firms for the period 1976–1984, logistic analysis and entropy measures. Journal of Busi-ness Finance & Accounting, 17(1), 119-135. https://doi.org/10.1111/j.1468-5957.1990.tb00553.x

Korol, T. (2013). Early warning models against bankruptcy risk for Central Euro-pean and Latin American enterprises. Economic Modelling, Vol. 31, 22-30. https://doi.org/10.1016/j.econmod.2012.11.017

Kou, G., Xu, Y., Peng, Y., Shen, F., Chen, Y., Chang, K., & Kou, S. (2021). Bankruptcy prediction for SMEs using transactional data and two-stage mul-tiobjective feature selection. Decision Support Systems, 140, 113429. https://doi.org/10.1016/j.dss.2020.113429

Laitinen, E.K., & Laitinen, T. (1998). Cash management behavior and failure pre-diction. Journal of Business Finance and Accounting, 25(7-8), 893-919. https://doi.org/10.1111/1468-5957.00218

Lin, W.Y., Hu, Y.H., & Tsai, C.F. (2011). Machine learning in financial crisis pre-diction: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 421-436. https://doi.org/10.1109/TSMCC.2011.2170420

Lohmann, C., & Ohliger, T. (2017). Nonlinear relationships and their effect on the bankruptcy prediction. Schmalenbach Business Review, 18(3), 261-287. https://doi.org/10.1007/s41464-017-0034-y

Marques, A.I., García, V., & Sánchez, J.S. (2013). A literature review on the ap-plication of evolutionary computing to credit scoring. Journal of the Opera-tional Research Society, 64(9), 1384-1399. https://doi.org/10.1057/jors.2012.145

Mateos Ronco, A. M., & López Mas, Á. (2011). Developing a business failure prediction model for cooperatives: Results of an empirical study in Spain. Af-rican Journal of Business Management, 5(26), 10565-10576. https://doi.org/10.5897/AJBM11.1415

McGurr, P.T., & DeVaney, S.A. (1998). Predicting business failure of retail firms: an analysis using mixed industry models. Journal of Business Research, 43(3), 169-176. https://doi.org/10.1016/S0148-2963(97)00222-1

Neophytou, E., Charitou, A., & Charalambous, C. (2001). Predicting corporate failure: empirical evidence for the UK. School of Management, University of Southampton, Southampton. https://doi.org/10.1080/0963818042000216811

Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395

Pal, R., Kupka, K., Aneja, A. P., & Militky, J. (2016). Business health characteri-zation: A hybrid regression and support vector machine analysis. Expert Sys-tems with Applications, 49, 48-59. https://doi.org/10.1016/j.eswa.2015.11.027

Palazzi, F., Sgrò, F., & Ciambotti, M. (2018). Business crisis during the global economic recession: focus on Italian SMEs. Piccola Impresa/Small business, (3), 39-58. https://doi.org/10.14596/pisb.294

Papík, M., & Papíková, L. (2023). Impacts of crisis on SME bankruptcy predic-tion models’ performance. Expert Systems with Applications, 214, 119072. https://doi.org/10.1016/j.eswa.2022.119072

Pierri, F., & Caroni, C. (2017). Bankruptcy prediction by survival models based on current and lagged values of time-varying financial data. Communications in Statistics: Case Studies Data Analysis and Applications, 3(3-4), 62-70. https://doi.org/10.1080/23737484.2018.1431816

Platt, H.D., & Platt, M.B. (1990). Development of a class of stable predictive variables: the case of bankruptcy prediction. Journal of Business Finance & Accounting, 17(1), 31-51. https://doi.org/10.1111/j.1468-5957.1990.tb00548.x

Poli, S. (2020). I modelli di previsione della crisi d’impresa: la prospettiva esterna mediante i bilanci in forma abbreviata. Giappichelli, Torino.

Ptak-Chmielewska, A. (2019). Predicting micro-enterprise failures using data min-ing techniques. Journal of Risk and Financial Management, 12(1), 30. https://doi.org/10.3390/jrfm12010030

Ptak-Chmielewska, A., & Matuszyk, A. (2018). The importance of financial and non-financial ratios in SMEs bankruptcy prediction. Bank i kredyt, 49(1), 45-62.

Ptak-Chmielewska, A., & Matuszyk, A. (2020). Application of the random survival forests method in the bankruptcy prediction for small and medium enterprise. Argum. Econ, 44, 127-142. https://doi.org/10.15611/aoe.2020.1.06

Ravi, V., & Pramodh, C. (2008). Threshold accepting trained principal component neural network and feature subset selection: Application to bankruptcy pre-diction in banks. Applied Soft Computing, 8(4), 1539-1548. https://doi.org/10.1016/j.asoc.2007.12.003

Salchenberger, L.M., Cinar, E.M., & Lash, N.A. (1992). Neural networks: a new tool for predicting thrift failures. Decision Sciences, 23(4), 899-916. https://doi.org/10.1111/j.1540-5915.1992.tb00425.x

Šlefendorfas, G. (2016). Bankruptcy prediction model for private limited compa-nies of Lithuania. Ekonomika, 95(1), 134-152. https://doi.org/10.15388/Ekon.2016.1.9910.

Sun, J., Li, H., Huang, Q.H., & He, K.Y. (2014). Predicting financial distress and corporate failure: a review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41-56. https://doi.org/10.1016/j.knosys.2013.12.006

Teodori C. (2022), Analisi di bilancio: lettura e interpretazione, IV edizione aggior-nata e ampliata. Giappichelli, Torino.

Theodossiou, P.T. (1991). Alternative models for assessing the financial condi-tion of business in Greece. Journal of Business Finance and Accounting, 18(5), 697-720. https://doi.org/10.1111/j.1468-5957.1991.tb00233.x

Trajman, A., & Luiz, R. R. (2008). McNemar χ2 test revisited: comparing sensitivi-ty and specificity of diagnostic examinations. Scandinavian journal of clinical and laboratory investigation, 68(1), 77-80. https://doi.org/10.1080/00365510701666031

Varetto, F. (1999). Metodi di previsione delle insolvenze: un’analisi comparata. In Szegö, G., & Varetto, F. (Eds.), Il rischio creditizio: misura e controllo. Utet, Torino.

Veganzones, D., & Severin, E. (2021). Corporate failure prediction models in the twenty-first century: a review. European Business Review, 33(2), 204-226. https://doi.org/10.1108/EBR-12-2018-0209

Verikas, A., Kalsyte, Z., Bacauskiene, M., & Gelzinis, A. (2010). Hybrid and en-semble-based soft computing techniques in bankruptcy prediction: a survey. Soft Computing, 14(9), 995-1010. https://doi.org/10.1007/s00500-009-0490-5

Ward, T.J. (1994). An empirical study of the incremental predictive ability of Beaver’s naıve operating flow measure using four-state-ordinal models of fi-nancial distress. Journal of Business Finance and Accounting, 21(4), 547-561. https://doi.org/10.1111/j.1468-5957.1994.tb00335.x

Ward, T.J., & Foster, B.P. (1997). A note on selecting a response measure for financial distress. Journal of Business Finance & Accounting, 24(6), 869-879. https://doi.org/10.1111/1468-5957.00138

Westgaard, S., & Wijst, N. (2001). Default probabilities in a corporate bank port-folio: a logistic model approach. European Journal of Operational Research, 135(2), 338-349. https://doi.org/10.1016/S0377-2217(01)00045-5

Zavgren, C.V. (1985). Assessing the vulnerability to failure of American industrial firms: a logistic analysis. Journal of Banking and Finance, 12(1), 19-45. https://doi.org/10.1111/j.1468-5957.1985.tb00077.x

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2023 Simone Poli, Marco Giuliani, Luca Baccarini