SME’s crisis and warning tools - A verification test in the manufactoring sector

Author/s Mauro Paoloni, Massimiliano Celli
Publishing Year 2018 Issue 2018/2 Language Italian
Pages 22 P. 85-106 File size 294 KB
DOI 10.3280/MACO2018-002005
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Score-based models play an important role for predicting the failure of non-listed SME’s within a few years before bankruptcy, and the the reason of their success is in the fact that they are easy to understand and use easily obtainable data from both balance sheets and statements. This paper ascertains if two of such warning tools, one originally developed for U.S. firms (Z’-Score) while the other one is an adaptation of the latter to the peculiarities of the socio-economic Italian enviroment (BCS), are effectively reliable in measuring the default potential of industrial businesses in Italy. First, we have analyzed the theoretical and practical characteristics of the two models and we highlight some of its potential shortcomings. Second, we have examined a sample of 200 industrial SME’s, of which 100 companies have been subjected in the period 2005-2015 in bankruptcy proceedings because of a default, whereas the remaining 100 companies, which have been selected based on same core business, year of data collection and a comparable size of total assets, did not go bankruptcy. The models were tested by verifying, yesterday for today, whether the future health status of the businesses in the sample, and the impending business failures specifically, could have been predicted up to three years earlier and with reasonable precision using the examined models. We found that the models still work effectively and perform well in predicting failures of Italian SME’s, provided that some critical points illustrated in this study are taken into account.

Keywords: Z’-Score, Sme, Businesses crises, Financial ratios, Warning tools.

  1. Joy M.O., Tollefson J.O. (1975), On the financial applications of discriminant analysis, Journal of Financial and Quantitative Analysis, 10, 4, pp. 723-739. DOI: 10.2307/2330267
  2. Agarwal W., Taffler R.J. (2007), 25 years of the Taffler Z-score model: does it really have predictive ability?, Accounting and Business Research, 37, 4, pp. 285-300. DOI: 10.1080/00014788.2007.9663313
  3. Alareeni B., Branson J. (2013), Predicting listed companies’ failure in Jordan using Altman models: a case study, International Journal of Business and Management, 8, 1, pp. 113-126.
  4. Alberici A. (1975), Analisi dei bilanci e previsione delle insolvenze, Milano, Isedi.
  5. Altman E.I. (1968), Financial ratios. Discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance, 23, 4, pp. 589-609.
  6. Altman E.I. (1970), Ratio analysis and the prediction of firm failure. A reply, The Journal of Finance, 25, 11, pp. 1169-1172. DOI: 10.2307/2325591
  7. Altman E.I., Haldeman R., Narayanan P. (1977), Zeta analysis: a new model to identify bankruptcy risk of corporations, Journal of Banking & Finance, 1, 1, pp. 29-54. DOI: 10.1016/0378-4266(77)90017-6
  8. Altman E.I. (1993), Corporate financial distress and bankruptcy, Second Edition, New York, Wiley&Sons,.
  9. Altman E.I., Hotchkiss E. (2006), Corporate financial distress and bankruptcy: predict and avoid bankruptcy, New York, Wiley&Sons.
  10. Altman E.I., Sabato G. (2007), Modelling credit risk for SMEs: evidence from the U.S. market, Abacus, 43, 7, pp. 332-357.
  11. Altman E.I., Danovi A., Falini A. (2013), Z-score models’ application to Italian companies subject to extraordinary administration, Journal of Applied Finance, 1, pp. 24-37.
  12. Altman E.I. (2013), Predicting financial distress of companies: revisiting the Z-score and Zeta models, in Bell A.R., Brooks C., Prokopzuk M., Handbook of research methods and applications in empirical finance, Colos, UK, Elgar Publishing.
  13. Balcaen S., Ooghe H. (2016), 35 years of studies on business failure: an overview of the classical statistical methodologies and their related problems, The British Accounting Review, 38, 1, pp. 63-93.
  14. Balwind J., Glezen G. (1992), Bankruptcy prediction using quarterly financial statement data, Journal of Accounting, Auditng & Finance, 3, 7, pp. 269-285. DOI: 10.1177/0148558X9200700301
  15. Bastia P. (1996), Pianificazione e controllo dei risanamenti aziendali, Giappichelli, Torino.
  16. Becchetti L., Sierra J. (2003), Bankruptcy risk and productive efficiency in manufacturing firms, Journal of Banking and Finance, 27, 3, pp. 2099-2120. DOI: 10.1016/S0378-4266(02)00319-9
  17. Begley J., Ming J., Watts S. (1996), Bankruptcy classifications errors in the 1980s: an empirical analysis of Altman’s and Ohlson’s models, Review of Accounting Studies, 1, 4, pp. 267-284.
  18. Bellovary J.L., Giacomino D.E., Akers M.D. (2007), A review of bankruptcy prediction studies: 1930 to present, Journal of Financial Education, 33, 4, pp. 1-43.
  19. Beretta S., Bozzolan S. (2013), Il governo della performance dei processi di business: dai Kei Performance Indicator ai Key Risk Indicator, Management Control, Special Issue 2, pp. 9-37. DOI: 10.3280/MACO2013-002002
  20. Bottani P., Cipriani L., Serao F. (2004), Analisi del rischio d’insolvenza di una PMI tramite l’utilizzo del modello Z-Score, Amministrazione e Finanza, 19, 1, pp. 50-57.
  21. Celli M. (2015), Can Z-Score model predict listed companies’failures in Italy? An empirical test, International Journal of Business and Management, 10, 3, pp. 57-69.
  22. Chiucchi M.S. (2014), Il gap tra teoria e prassi nel Management Accounting: il contributo della field-based research, Management Control, 3, pp. 5-9. DOI: 10.3280/MACO2014-003001
  23. Danovi A., Quagli A. (2012), Crisi aziendali e processi di risanamento, Ipsoa, Milano.
  24. Deakin E. (1972), A discriminant analysis of predictors of business failure, Journal of Accounting Research, 10, 3, pp. 167-179. DOI: 10.2307/2490225
  25. Flagg J., Giroux G., Wiggins C. (1991), Predicting corporate bankruptcy using failing firms, Review of Financial Economics, 1, 4, pp. 67-78.
  26. Galeotti M., Garzella S. (2013), Governo strategico dell’azienda, Giappichelli, Torino.
  27. Gerantonis N., Vergos K., Christopoulos A.G. (2009), Can Altman Z-score models predict business failures in Greece?, Research Journal of International Studies, 12, 10, pp. 21-28.
  28. Gilbert L., Menon K., Schwarts K. (1990), Predicting bankruptcy for firms in financial dis-tress, Journal of Business Finance and Accounting, 17, 1, pp. 161-171.
  29. Grice J., Ingram R. (2001), Tests of generalizability of Altman’s bankruptcy prediction model, Journal of Business Research, 54, 1, pp. 53-61. DOI: 10.1016/S0148-2963(00)00126-
  30. Grice J., Dugan M. (2001), The limitations of bankruptcy prediction models. Some cautions for the researcher, Review of Quantitative Finance and Accounting, 17, 2, pp.151-166. DOI: 10.1023/A:101797360478
  31. Haber J.R. (2005), Assessing how bankruptcy prediction models are evaluated, Journal of Business and Economic Research, 3, 1, pp. 23-34.
  32. Hamza T., Lahiani A., Mselmi N. (2017), Financial distress prediction: the case of French Sme, International Review of Financial Analysis, 50, 3, pp. 67-80.
  33. Li J.F. (2012), Prediction of corporate bankruptcy from 2008 through 2011, Journal of Ac-counting and Finance, 12, 1, pp. 31-41.
  34. Llobet-Dalmases J., Plana D., Fito M.A. (2017), Accounting ratio-based prediction: an analysis of the relationship between indicators and accounting manipulation, European Accounting and Management Review, 3, 2, pp. 11-27.
  35. Lucianetti L., Battista V. (2015), La manipolazione dei valori di bilancio: pressione del management e tratti personali nell’attività del controller, Management Control, 1, pp. 101-132. DOI: 10.3280/MACO2015-001005
  36. Luerti A. (1992), La previsione dello stato di insolvenza delle imprese. Il modello AL/93 di credit scoring, Etas, Milano.
  37. Madonna S., Cestari G. (2015), The accuracy of bankruptcy prediction models: a comparative analysis of multivariate discriminant models in the Italian context, European Scientific Journal, 11, 34, pp. 106-133.
  38. Madonna S., Cestari G., Callegari F. (2016), Does the development context affect bankruptcy prediction models’general accuracy? A comparative analysis of four multivariate discriminant models in the Italian context, European Scientific Journal, 12, 10, pp. 445-469.
  39. Marchi L., Greco G. (2016), Percorsi di integrazione tra auditing e controllo di gestione, Management Control, 3, pp. 5-8. DOI: 10.3280/MACO2016-003001
  40. McGurr P., DeVaney S. (1998), Predicting business failure of retail firms. An analysis using mixed industry models, Journal of Business Research, 43, 4, pp. 169-176. DOI: 10.1080/095939698342779
  41. Megaravalli A.V., Sampagnaro G. (2018), Predicting the growth of high-growth sme’s: evidence from family business firms, Journal of Family Business Management, 8, 1.
  42. Mossman E., Bell G.G., Swartz L.M., Turtle H. (1998), An empirical comparison of bankruptcy models, The Financial Review, 33, 2, pp. 35-54.
  43. Ooghe H., Joos P., De Bourdeaudhuij C. (1994), Financial distress models in Belgium: the results of a decade of empirical research, The International Journal of Accounting, 30, 3, pp. 245-274.
  44. Paoloni M. (2003), La crisi della piccola impresa tra liquidazione e risanamento, Giappichelli, Torino.
  45. Platt H., Platt M. (1990), Development of a class of stable predictive variables: the case of bankruptcy predictions, Journal of Business Finance & Accounting, 17, 1, pp. 31-51.
  46. Scott J. (1991), The probability of bankruptcy: a comparison of empirical predictions and theoretical models, Journal of Banking and Finance, 5, 3, pp. 317-344. DOI: 10.1016/0378-4266(81)90029-
  47. Szego G., Varetto F. (1999), Il rischio creditizio. Misura e controllo, Torino, Utet.
  48. Teodori C. (1989), Modelli di previsione nell’analisi economico-aziendale, Torino, Giappichelli.
  49. Tian S., Yu Y. (2017), Financial ratios and bankruptcy predictions: an international evidence, International Review of Economics and Finance, 51, 9, pp. 510-526.

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Mauro Paoloni, Massimiliano Celli, Crisi delle PMI e strumenti di warning. Un test di verifica nel settore manifatturiero in "MANAGEMENT CONTROL" 2/2018, pp 85-106, DOI: 10.3280/MACO2018-002005