View full metadata
THIS DOI METADATA WAS LAST UPDATED: 2025-01-13 16:02
Cite as
-
Full Title
FINANCIAL REPORTING
-
Publisher
FrancoAngeli
-
ISSN
2036-671X (Printed Journal)
2036-6779 (Online Journal)
-
Journal Issue Number
2
-
Journal Issue Designation
2
-
Journal Issue Date
2024
-
Full Title
Early Warning Systems for financial crises prediction in private companies: Evidence from the Italian context
-
By (author)
-
First Page
133
-
Last Page
161
-
Language of text
English
-
Publication Date
2024/12
-
Copyright
2024 FrancoAngeli srl
Main description
Purpose: This study compares models for predicting business financial crises, fo-cusing on which are most effective. In light of the new European Directive on business failure, it highlights a trade-off between predictive accuracy and timeli-ness in static models and offers an alternative approach. Design/methodology/approach: This study examines the Italian early warning system (EWS), testing static alert indicators' predictive ability on a large sample of private companies. It then proposes a dynamic version of the EWS. Findings: The results show a trade-off between predictive ability and timeliness for static models. In contrast, a dynamic system is more accurate in predicting cri-sis events, allowing managers to take corrective actions. Originality: The results highlight the limitations of static prediction models and emphasize the potential of a simple dynamic model that is specifically designed for small- and medium-sized entities (SMEs). Practical implications: This study proposes a dynamic model tailored for SMEs, which are particularly vulnerable to financial crises. This insight can help managers and policymakers balance accurate predictions with timely interventions, especial-ly in European countries implementing crisis prediction models.
Reference list of
10.3280/FR2024-002006
Unstructured Citation
Agarwal, V., & Taffler, R.J. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction
models. Journal of Banking & Finance, 32(8), 1541-1551.
https://doi.org/10.1016/j.jbankfin.2007.07.014
Unstructured Citation
Alonzo, T. A. (2002). Distribution-free ROC analysis using binary regression techniques. Biostatistics, 3(3), 421-432.
https://doi.org/10.1093/biostatistics/3.3.421
Unstructured Citation
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/10.1111/j.1540-6261.1968.tb00843.x
Unstructured Citation
Altman, E. I. (1993). Corporate financial distress and bankruptcy (2nd ed.). John Wiley & Sons.
Unstructured Citation
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
Unstructured Citation
Bauweraerts, J. (2016). Predicting Bankruptcy in Private Firms: Towards a Stepwise Regression Procedure. International Journal
of Financial Research, 7(2), 147-153.
https://doi.org/10.5430/ijfr.v7n2p147
Unstructured Citation
Bava, F., Cane, M., & di Trana, M. G. (2020). Can a quantitative approach be mitigated? Proposals for the application of the
“early warnings” required by the new Italian Insolvency Code. Financial Reporting, 2, 33-61.
https://doi.org/10.3280/FR2020-002002
Unstructured Citation
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111.
https://doi.org/10.2307/2490171
Unstructured Citation
Bussiere, M., & Fratzscher, M. (2006). Towards a new early warning system of financial crises. Journal of International Money
and Finance, 25(6), 953-973.
https://doi.org/10.1016/j.jimonfin.2006.07.007
Unstructured Citation
Caggiano, G., Calice, P., & Leonida, L. (2014). Early warning systems and systemic banking crises in low income countries:
A multinomial logit approach. Journal of Banking & Finance, 47, 258-269.
https://doi.org/10.1016/j.jbankfin.2014.07.002
Unstructured Citation
Caggiano, G., Calice, P., Leonida, L., & Kapetanios, G. (2016). Comparing logit-based early warning systems: Does the duration
of systemic banking crises matter?. Journal of Empirical Finance, 37, 104-116.
https://doi.org/10.1016/j.jempfin.2016.01.005
Unstructured Citation
Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63(6), 2899-2939.
https://doi.org/10.1111/j.1540-6261.2008.01416.x
Unstructured Citation
Canbas, S., Cabuk, A., & Kilic, S. B. (2005). Prediction of commercial bank failure via multivariate statistical analysis
of financial structures: The Turkish case. European Journal of Operational Research, 166(2), 528-546.
https://doi.org/10.1016/j.ejor.2004.03.023
Unstructured Citation
Candelon, B., Dumitrescu, E. I., & Hurlin, C. (2014). Currency crisis early warning systems: Why they should be dynamic. International
Journal of Forecasting, 30(4), 1016-1029.
https://doi.org/10.1016/j.ijforecast.2014.03.015
Unstructured Citation
Charalambakis, E. C., & Garrett, I. (2019). On corporate financial distress prediction: What can we learn from private firms
in a developing economy? Evidence from Greece. Review of Quantitative Finance and Accounting, 52, 467-491.
https://doi.org/10.1007/s11156-018-0716-7
Unstructured Citation
Charitou, A., Lambertides, N., & Trigeorgis, L. (2011). Distress risk, growth and earnings quality. Abacus, 47(2), 158-181.
https://doi.org/10.1111/j.1467-6281.2011.00337.x
Unstructured Citation
Chuang, C. L. (2013). Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction. Information
Sciences, 236, 174-185.
https://doi.org/10.1016/j.ins.2013.02.015
Unstructured Citation
Ciampi, F., Giannozzi, A., Marzi, G. & Altman, E.I. (2021). Rethinking SME default prediction: a systematic literature review
and future perspectives. Scientometrics, 126, 2141-2188.
https://doi.org/10.1007/s11192-020-03856-0
Unstructured Citation
De Meyst, K. J., Lowe, D. J., Peecher, M. E., Pickerd, J., & Reffett, A. (2020). Factors affecting the outcomes of legal claims
against auditors. Current Issues in Auditing, 15(2), 10-16.
https://doi.org/10.2308/CIIA-2020-023
Unstructured Citation
European Commission. (2023). 2022 SBA Fact Sheet. -- https://single-market-economy.ec.europa.eu/document/download/2bef0eda-2f75-497d-982e-c0d1cea57c0e_en?filename=Annual%20Report%20on%20European%20SMEs%202024.pdf.
Unstructured Citation
European Union. (2019). Directive (EU) 2019/1023 of the European Parliament and of the Council of 20 June 2019 on preventive restructuring frameworks, on discharge of debt and disqualifications, and on measures to increase the efficiency of procedures concerning restructuring, insolvency and discharge of debt, and amending Directive (EU) 2017/1132 (Directive on restructuring and insolvency). Official Journal of the European Union, L 172, 18-55. -- https://eur-lex.europa.eu/eli/dir/2019/1023/oj.
Unstructured Citation
Ge, W., & Whitmore, G. A. (2009). Binary response and logistic regression in recent accounting research publications: A methodological
note. Review of Quantitative Finance and Accounting, 34(1), 81-93.
https://doi.org/10.1007/s11156-009-0123-1
Unstructured Citation
Habib, A., Costa, M.D., Huang, H.J., Bhuiyan, M.B.U. & Sun, L. (2020). Determinants and consequences of financial distress:
review of the empirical literature. Accounting & Finance, 60, 1023-1075.
https://doi.org/10.1111/acfi.12400
Unstructured Citation
Hamdaoui, M. (2016). Are systemic banking crises in developed and developing countries predictable? Journal of Multinational
Financial Management, 37-38, 114-138.
https://doi.org/10.1016/j.mulfin.2016.09.002
Unstructured Citation
Hastie, T., Tibshirani, R., Friedman, J. (2001). The Elements of Statistical Learning. New York: Springer.
https://doi.org/10.1007/978-0-387-21606-5
Unstructured Citation
Hernández-Orallo, J. (2013). ROC curves for regression. Pattern Recognition, 46(12), 3395-3411.
https://doi.org/10.1016/j.patcog.2013.06.014
Unstructured Citation
Hillegeist, S.A., Keating, E.K., Cram, D.P., & Lundstedt, K.G. (2004). Assessing the Probability of Bankruptcy. Review of
Accounting Studies, 9(1), 5-34.
https://doi.org/10.1023/B:RAST.0000013627.90884.b7
Unstructured Citation
Italian Republic. (2019). Decreto legislativo 12 gennaio 2019, n. 14. Gazzetta Ufficiale della Repubblica Italiana -- https://www.gazzettaufficiale.it/eli/id/2019/02/14/19G00007/sg.
Unstructured Citation
Jemović, M., & Marinković, S. (2019). Determinants of financial crises – An early warning system based on panel logit regression.
International Journal of Finance and Economics, 26(1), 103-117.
https://doi.org/10.1002/ijfe.1779
Unstructured Citation
Jones, S. (2023). A literature survey of corporate failure prediction models. Journal of Accounting Literature, 45(2), 364-405.
https://doi.org/10.1108/JAL-08-2022-0086
Unstructured Citation
Kim, S. Y., & Upneja, A. (2014). Predicting restaurant financial distress using decision tree and AdaBoosted decision tree
models. Economic Modelling, 36, 354-362.
https://doi.org/10.1016/j.econmod.2013.10.005
Unstructured Citation
Laitinen, E. K. (1991). Financial ratios and different failure processes. Journal of Business Finance and Accounting, 18(5),
649-673.
https://doi.org/10.1111/j.1468-5957.1991.tb00231.x
Unstructured Citation
Li, H., & Sun, J. (2008). Ranking-order case-based reasoning for financial distress prediction. Knowledge-Based Systems, 21(8),
868-878.
https://doi.org/10.1016/j.knosys.2008.03.047
Unstructured Citation
Liang, D., Tsai, C. F., & Wu, H. T. (2015). The effect of feature selection on financial distress prediction. Knowledge-Based
Systems, 73, 289-297.
https://doi.org/10.1016/j.knosys.2014.10.010
Unstructured Citation
Lin, F., Liang, D., & Chen, E. (2011). Financial ratio selection for business crisis prediction. Expert Systems with Applications,
38(12), 15094-15102.
https://doi.org/10.1016/j.eswa.2011.05.035
Unstructured Citation
Lin, S.-M., Ansell, J., & Andreeva, G. (2012). Predicting default of a small business using different definitions of financial
distress. Journal of the Operational Research Society, 63(4), 539-548.
https://doi.org/10.1057/jors.2011.65
Unstructured Citation
Min, J. H., & Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function
parameters. Expert Systems with Applications, 28(4), 603-614.
https://doi.org/10.1016/j.eswa.2004.12.008
Unstructured Citation
Muñoz‐Izquierdo, N., Laitinen, E. K., Camacho‐Miñano, M., & Pascual‐Ezama, D. (2019). Does audit report information improve
financial distress prediction over Altman’s traditional Z‐Score model? Journal of International Financial Management & Accounting,
31(1), 65-97.
https://doi.org/10.1111/jifm.12110
Unstructured Citation
Nadarajah, S. (2009). The skew logistic distribution. AStA Advances in Statistical Analysis, 93(2), 187-203.
https://doi.org/10.1007/s10182-009-0105-6
Unstructured Citation
Platt, H.D. & Platt, M.B. (2006). Understanding differences between financial distress and bankruptcy. Review of Applied Economics, 2(2), 141-157.
Unstructured Citation
Putnam, R. D., Leonardi, R., & Nonetti, R. Y. (1993). Making democracy work: Civic traditions in modern Italy. Princeton,
NJ: Princeton University Press.
https://doi.org/10.2307/j.ctt7s8r7
Unstructured Citation
Schonlau, M. (2020). CROSSVALIDATE: Stata module to perform k-fold crossvalidation (Version S458860). Boston College Department of Economics. -- https://ideas.repec.org/c/boc/bocode/s458860.html.
Unstructured Citation
Serrano-Cinca, C., & Gutiérrez-Nieto, B. (2013). Partial least square discriminant analysis for bankruptcy prediction. Decision
Support Systems, 54(3), 1245-1255.
https://doi.org/10.1016/j.dss.2012.11.015
Unstructured Citation
Sun, J., Jia, M. Y., & Li, H. (2011). AdaBoost ensemble for financial distress prediction: An empirical comparison with data
from Chinese listed companies. Expert Systems with Applications, 38(8), 9305-9312.
https://doi.org/10.1016/j.eswa.2011.01.042
Unstructured Citation
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
Unstructured Citation
Tinoco, M.H., & Wilson, N. (2013). Financial Distress and Bankruptcy Prediction among Listed Companies Using Accounting, Market
and Macroeconomic Variables. International Review of Financial Analysis, 30, 394-419.
https://doi.org/10.1016/j.irfa.2013.02.013
Unstructured Citation
Tseng, F. M., & Lin, L. (2005). A quadratic interval logit model for forecasting bankruptcy. Omega, 33(1), 85-91.
https://doi.org/10.1016/j.omega.2004.04.002
Unstructured Citation
Wang, D., Song, X., Yin, W., & Yuan, J. (2015). Forecasting core business transformation risk using the optimal rough set
and the neural network. Journal of Forecasting, 34(6), 478-491.
https://doi.org/10.1002/for.2349