IDEAS home Printed from https://ideas.repec.org/a/spr/eurase/v4y2014i2p199-221.html
   My bibliography  Save this article

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

Author

Listed:
  • Alessandro Zeli

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. Copyright Eurasia Business and Economics Society 2014

Suggested Citation

  • Alessandro Zeli, 2014. "The financial distress indicators trend in Italy: an analysis of medium-size enterprises," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 4(2), pages 199-221, December.
    • Handle: RePEc:spr:eurase:v:4:y:2014:i:2:p:199-221
    DOI: 10.1007/s40822-014-0010-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s40822-014-0010-5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s40822-014-0010-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Giulio Bottazzi & Marco Grazzi & Angelo Secchi & Federico Tamagni, 2011. "Financial and economic determinants of firm default," Journal of Evolutionary Economics, Springer, vol. 21(3), pages 373-406, August.
    2. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    3. Marco Bisogno, 2012. "The Accessibility Of The Italian Bankruptcy Procedures: An Empirical Analysis," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 2(2), pages 1-24, December.
    4. Popov, Alexander & Udell, Gregory F., 2010. "Cross-border banking and the international transmission of financial distress during the crisis of 2007-2008," Working Paper Series 1203, European Central Bank.
    5. Becchetti, Leonardo & Sierra, Jaime, 2003. "Bankruptcy risk and productive efficiency in manufacturing firms," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2099-2120, November.
    6. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    7. Aaro Hazak & Kadri Männasoo, 2007. "Indicators of corporate default : an EU based empirical study," Bank of Estonia Working Papers 2007-10, Bank of Estonia, revised 04 Sep 2007.
    8. Maurice Peat, 2007. "Factors Affecting the Probability of Bankruptcy: A Managerial Decision Based Approach," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 303-324, September.
    9. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    10. Serrano Cinca, C. & Mar Molinero, C. & Gallizo Larraz, J.L., 2005. "Country and size effects in financial ratios: A European perspective," Global Finance Journal, Elsevier, vol. 16(1), pages 26-47, August.
    11. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    12. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    13. Afsaneh Assadian & Jon Ford, 1997. "Determinants of business failure: The role of firm size," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 21(1), pages 15-23, March.
    14. Appetiti, Sandro, 1984. "Identifying unsound firms in Italy : An attempt to use trend variables," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 269-279, June.
    15. Nurullah Gur, 2012. "Financial Constraints, Quality of Institutions and Firm Size: What Do Perceptions Tell Us?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 2(2), pages 17-36, December.
    16. Yujun Lian & Mohamad Sepehri & Maggie Foley, 2011. "Corporate Cash Holdings and Financial Crisis: An Empirical Study of Chinese Companies," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 1(2), pages 112-124, December.
    17. Alessandro Zeli & Paolo Mariani, 2009. "Productivity and profitability analysis of large Italian companies: 1998–2002," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 56(2), pages 175-188, June.
    18. Stijn Claessens, 2010. "The Financial Crisis," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 4(2), pages 177-196, May.
    19. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    20. Fulvio Coltorti, 2004. "Le medie imprese industriali italiane: nuovi aspetti economici e finanziari," ECONOMIA E POLITICA INDUSTRIALE, FrancoAngeli Editore, vol. 2004(121).
    21. Davies, S. & Rondi, L. & Sembenelli, A., 1998. "SEM and the Changing Structure of EU Manufacturing, 1987-1993," University of East Anglia Discussion Papers in Economics 9815, School of Economics, University of East Anglia, Norwich, UK..
    22. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    23. Stijn Claessens & Giovanni Dell’Ariccia & Deniz Igan & Luc Laeven, 2010. "Cross-country experiences and policy implications from the global financial crisis [From Great Depression to Great Credit Crisis: Similarities, differences and lessons]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 25(62), pages 267-293.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lucio Masserini & Matilde Bini & Alessandro Zeli, 2021. "A Longitudinal Analysis of Riskiness Indicators After the 2008 and 2011 Economic Crises: The Case of Italian Manufacturing," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 499-513, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    2. Van Laere, Elisabeth & Baesens, Bart, 2010. "The development of a simple and intuitive rating system under Solvency II," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 500-510, June.
    3. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    4. Şaban Çelik & Bora Aktan & Bruce Burton, 2022. "Firm dynamics and bankruptcy processes: A new theoretical model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 567-591, April.
    5. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    6. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    7. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
    8. Róbert Štefko & Jarmila Horváthová & Martina Mokrišová, 2020. "Bankruptcy Prediction with the Use of Data Envelopment Analysis: An Empirical Study of Slovak Businesses," JRFM, MDPI, vol. 13(9), pages 1-15, September.
    9. Becchetti, Leonardo & Sierra, Jaime, 2003. "Bankruptcy risk and productive efficiency in manufacturing firms," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2099-2120, November.
    10. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    11. Sebastian Klaudiusz Tomczak & Edward Radosiński, 2017. "The effectiveness of discriminant models based on the example of the manufacturing sector," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 27(3), pages 81-97.
    12. Michael Doumpos & Constantin Zopounidis, 1999. "A Multicriteria Discrimination Method for the Prediction of Financial Distress: The Case of Greece," Multinational Finance Journal, Multinational Finance Journal, vol. 3(2), pages 71-101, June.
    13. Ha Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," Working Papers hal-04133309, HAL.
    14. Akarsh Kainth & Ranik Raaen Wahlstrøm, 2021. "Do IFRS Promote Transparency? Evidence from the Bankruptcy Prediction of Privately Held Swedish and Norwegian Companies," JRFM, MDPI, vol. 14(3), pages 1-15, March.
    15. Ana Paula Matias Gama & Helena Susana Amaral Geraldes, 2012. "Credit risk assessment and the impact of the New Basel Capital Accord on small and medium‐sized enterprises," Management Research Review, Emerald Group Publishing Limited, vol. 35(8), pages 727-749, July.
    16. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    17. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    18. Fougère, D. & Golfier, C. & Horny, G. & Kremp, E., 2013. "What has been the impact of the 2008 crisis on firms’ default? (in French)," Working papers 463, Banque de France.
    19. Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
    20. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.

    More about this item

    Keywords

    Bankruptcy risk; Financial distress; Profitability; Multi-way data analysis; Financial ratio analysis; D21; M40; G32;
    All these keywords.

    JEL classification:

    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • M40 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:eurase:v:4:y:2014:i:2:p:199-221. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.