Vedi metadata

I METADATI DI QUESTO DOI SONO STATI AGGIORNATI IL: 2025-01-13 16:02

Dati del DOI

10.3280/FR2024-002006

Articolo

Cite as

Dati della rivista
Dati del fascicolo
Dati dell'articolo
Elenco citazioni del 10.3280/FR2024-002006

Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

Altman, E. I. (1993). Corporate financial distress and bankruptcy (2nd ed.). John Wiley & Sons.


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111.

https://doi.org/10.2307/2490171


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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.


Citazione non strutturata

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.


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

Hernández-Orallo, J. (2013). ROC curves for regression. Pattern Recognition, 46(12), 3395-3411.

https://doi.org/10.1016/j.patcog.2013.06.014


Citazione non strutturata

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


Citazione non strutturata

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.


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

Platt, H.D. & Platt, M.B. (2006). Understanding differences between financial distress and bankruptcy. Review of Applied Economics, 2(2), 141-157.


Citazione non strutturata

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


Citazione non strutturata

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.


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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


Citazione non strutturata

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