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The financial distress indicators trend in Italy: an analysis of medium-size enterprises

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Abstract

This study analyses financial distress as evidenced by financial ratios calculated for Italian medium-sized enterprises from 1989 to 2007, the study is carried out by means of a Dynamic Factorial Analysis (DFA). The data for the Italian firms come from about 240,000 surveys’ records and financial statements over the period. Four basic areas identified as being economically significant in affecting the financial distress include: the leverage index, the measure of the efficiency, the measure of performance, and the measure of liquidity. The financial distress trend has been analysed through a “synthetic” indicator summarizing the information represented by financial ratios by means of a dynamic factorial analysis. The results highlight a slight increase in financial distress over the period and different behaviours of the financial distress indicator among the Italian industries. In particular specific tendencies are detected for capital intensive sectors and for export sectors.

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Notes

  1. During the most part of the period under investigation the Italian currency with legal tender status was the Italian Lira whose exchange rate with the Euro was later fixed at 1€ per 1936.27 Lira.

  2. Here we can define two other DFA approaches. The first one, related to structure X(JT,I), is called Dual- here units and variables play a different roles with respect to the Direct approach. The other, related to the structure X(IJ,T), is called Tridual because of its duality with respect to the other approaches (Coppi and D’Urso 2001).

  3. Data have been elaborated by means of the XLISP-STAT software conceived and accomplished by I. Corazziari.

References

  • Acar, E., & Yener, B. (2009). Unsupervised multiway data analysis: a literature survey. IEEE Transactions on Knowledge and Data Engineering, 21(1), 6–20.

    Article  Google Scholar 

  • Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.

    Article  Google Scholar 

  • Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETA™ Analysis. A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1, 29–54.

    Article  Google Scholar 

  • Altman, E., Marco, G., & Varetto, G. (1994). Corporate distress diagnosis: comparison using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance, 18, 505–529.

    Article  Google Scholar 

  • Appetiti, A. (1984). Identifying unsound firms in Italy: an attempt to use trend variables. Journal of Banking & Finance, 8(2), 269–279.

    Article  Google Scholar 

  • Assadian, A., & Ford, J. M. (1997). Determinants of business failure: the role of firm size. Journal of Economics and Finance, 21(1), 15–23.

    Article  Google Scholar 

  • Beaver, W. (1966). Financial ratios as predictor of failure. Journal of Accounting Research, 5, 71–111.

    Article  Google Scholar 

  • Beaver, W. (1968). Alternative accounting measures as predictors of failure. The Accounting Review, 43.

  • Becchetti, L., & Sierra, J. (2003). Bankruptcy risk and productive efficiency in manufacturing firms. Journal of Banking & Finance, 27(11), 2099–2120.

    Article  Google Scholar 

  • Bellovary, J. L., Giacomino, D. E., & Akers, M. D. (2007). A review of bankruptcy prediction studies: 130 to present. Journal of Financial Education, 33, 1–42.

    Google Scholar 

  • Bisogno, M. (2012). The Accessibility of the Italian bankruptcy procedures: an Empirical analysis. Eurasian Business Review, 2(2), 1–24.

  • Blanco, J., & Coppi R. (1999). Su alcuni sviluppi metodologici dell’analisi fattoriale dinamica (AFD). Dipartimento di Statistica. Probabilità e Statistiche Applicate. Serie A—Ricerche. n.13. Università degli studi di Roma ‘La Sapienza’. Roma.

  • Bottazzi, G., Grazzi, M., Secchi, A., & Tamagni, F. (2011). Financial and economic determinants of firm default. Journal of Evolutionary Economics, 21, 373–406.

    Article  Google Scholar 

  • Carlsson, B. (2008). Industrial dynamics: a review of the literature 1990–1999 paper presented at the 25th DRUID conference. Copenhagen. June 17–20.

  • Cinca Serrano, C., Molinero Mar, C., & Larraz Gallizo, J.L. (2001). Country and Size Effects in Financial Ratios: A European Prospective Discussion Paper in Accounting and Finance. University of Southampton. n. AF01-3.

  • Cinca Serrano, C., Molinero Mar, C., & Larraz Gallizo, J.L. (2002). A multivariate study of the economy of the European Union via financial statements analysis Journal of the Royal Statistical Society. Series D. The Statistician. 51. Part 3. pp. 335–354.

  • Claessens, S., Dell’Ariccia, G., Igan, D., & Laeven, L. (2010). Cross-country experiences and policy implications from the global financial crisis. Economic Policy, 25(62), 267–293.

    Article  Google Scholar 

  • Coltorti, F. (2004). Le medie imprese industriali italiane: nuovi aspetti economici e finanziari. Economia Politica e Industriale, 121(1), 2–25.

    Google Scholar 

  • Coppi, R. (1989). Utilità nell’approccio matematico-statistico all’economia: precisazioni metodologiche ed indicazioni di sviluppo Quaderni del Dipartimento di Statistica n. 5. Università degli Studi ‘La Sapienza’. Roma.

  • Coppi, R., & Bolasco, S. (1989). Multiway data analysis. Amsterdam: North Holland.

    Google Scholar 

  • Coppi, R., & D’Urso, P. (2001) The “Dual” and “Tridual” Dynamic Factor Analysis. In: Proceedings of the 53rd Session of International Statistical Institute. Seoul.

  • Coppi R., & Di Ciaccio A. (1994). Multiway data analysis: software and application. Special issue of Computational statistics and data analysis, vol. 18, North Holland. Amsterdam.

  • Coppi, R., & Zannella, F. (1978) L’analisi fattoriale di una serie temporale multipla relativa allo stesso insieme di unità statistiche. In: Atti della XXIX Riunione scientifica della SIS. Bologna.

  • Corazziari, I. (1999). Dynamic factor analysis. In M. Vichi & O. Opitz (Eds.), Classification and data analysis. Theory and application (pp. 171–178). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Davies, S., Rondi, L., & Sembenelli, A. (2001). S.E.M and the changing structure of EU manufacturing 1987–1993. Industrial and Corporate Change, 10(1), 37–75.

    Article  Google Scholar 

  • Di Clemente, A. (2008). Rischio d’insolvenza e ciclo economico: un’analisi di macro stress-testing per le imprese non finanziarie italiane In Proceedings of the 49th Annual scientific conference of Società Italiana degli Economisti—Perugia University—24th October.

  • Duffie, D., Saita, L., & Wanga, K. (2007). Multi-period corporate default prediction with stochastic covariates. Journal of Financial Economics, 83(3), 636–665.

    Article  Google Scholar 

  • Eurostat (2003). Manual for Structural Business Statistics Luxemburg.

  • Fantozzi, D., & Marotta, M. (2006) Un’analisi fattoriale dinamica dei percorsi di sviluppo delle regioni italiane In: Atti del A.I.S.Re. XXVII Conferenza scientifica annuale. Pisa. 12–14 October.

  • Federici, A., & Mazzitelli, A. (2005). Dynamic Factor Analysis with Stata 2nd Italian Stata Users Group meeting Milano 10th October.

  • Frydman, H., Altman, E. I., & Kao, D. L. (1985). Introducing recursive partitioning for financial classification: the case of financial distress. Journal of Finance, 40(1), 269–291.

    Article  Google Scholar 

  • Gifi, F. (1990). Nonlinear multivariate analysis J. New York: Wiley.

    Google Scholar 

  • Grazzi, M., Sanzo, R., Secchi, A., & Zeli A. (2009). ISTAT—MICRO3. A new integrated system of business micro-data 1989–04. Documenti Istat. n.11.

  • Greco, S., Matarazzo, B., & Slowinski, R. (1998). A new rough set approach to evaluation of bankruptcy risk. In C. Zopounidis (Ed.), Operational tools in the management of financial risks (pp. 121–136). New York: Springer.

    Chapter  Google Scholar 

  • Gur, N. (2012). Financial constraints, quality of institutions and firm size: what do perceptions tell us? Eurasian Economic Review, 2(2), 17–36.

  • Hol, S. (2006). The influence of Business cycle on bankruptcy probability Statistic Norway Discussion Paper n. 466.

  • Istat (2007). Conti economici delle imprese—Anno 2003. Collana Informazioni n. 8.

  • Laviola, S., & Trapanese, M. (1997). Previsione delle insolvenze delle imprese e qualità del credito bancario. Un’analisi statistica. Temi di discussione del Servizio Studi. Banca d’Italia (318).

  • Lian, Y., Sepehri, M., & Foley, M. (2011). Corporate cash holdings and financial crisis: an empirical study of Chinese companies. Eurasian Business Review, 1(2), 112–124.

  • Mannaso, K., & Hazak, A. (2007). Indicators of Corporate Default—An EU Based Empirical study Bank of Estonia Working Paper Series n. 10.

  • Mckee, T. E. (2000). Developing a bankruptcy prediction model via rough sets theory. International Journal of Intelligent Systems in Accounting Finance & Management, 9(3):159–173(15).

  • Peat, M. (2003). Factor Affecting the Probability of Bankruptcy. University of Technology Sidney—School of Finance and Economics Working Paper n. 130.

  • Popov, A., & Udell, G. F. (2010). Cross-Border Banking and the International Transmission of Financial Distress During the Crisis of 2007–2008 European Central Bank Working paper n. 1203.

  • Zeli, A. (1995). Il rapporto redditività-livello di capitalizzazione. Un’indagine sui bilanci delle banche italiane. Credito Popolare, 4, 461–489.

    Google Scholar 

  • Zeli, A., & Mariani, P. (2009). Productivity and profitability analysis of large Italian companies: 1998–2002. International Review of Economics, 56, 175–188.

    Article  Google Scholar 

Download references

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  1. Istituto Nazionale di Statistica (Istat), Rome, Italy

    Alessandro Zeli

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  1. Alessandro Zeli
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Correspondence to Alessandro Zeli.

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This paper represents the views of the author and does not necessarily reflect the opinions of the affiliating institution.

Alessandro Zeli grateful to the anonymous referees whose many suggestions contributed to enhance the quality of this paper. I am also grateful to Prof. Giulio Bottazzi and Prof. Mario Pianta for reading the paper and granting useful comments. I remain responsible for any errors or shortcomings.

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Zeli, A. The financial distress indicators trend in Italy: an analysis of medium-size enterprises. Eurasian Econ Rev 4, 199–221 (2014). https://doi.org/10.1007/s40822-014-0010-5

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  • DOI: https://doi.org/10.1007/s40822-014-0010-5

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