Predicting short-term financial distress - An empirical comparison between Logistic Regression and Tree-based models applied to Italian companies

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dc.contributor.advisor Pizzi, Claudio it_IT
dc.contributor.author Zanotto, Jessica <1996> it_IT
dc.date.accessioned 2021-04-05 it_IT
dc.date.accessioned 2021-07-21T07:45:35Z
dc.date.issued 2021-05-11 it_IT
dc.identifier.uri http://hdl.handle.net/10579/18841
dc.description.abstract Corporate Financial Distress Prediction (FDP) has been a major concern for companies in the last years. Therefore, it has been deemed necessary to implement some techniques for predicting whether or not a firm will incur into financial distress on the basis of available financial data, through mathematical, statistical, or artificial intelligence-based models. This dissertation is aimed at comparing the outcome of a specific set of machine learning models, namely tree-based methods, with the performance of a benchmark technique to predict corporate failure, namely logistic regression, because of its widespread use in the literature. it_IT
dc.language.iso en it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Jessica Zanotto, 2021 it_IT
dc.title Predicting short-term financial distress - An empirical comparison between Logistic Regression and Tree-based models applied to Italian companies it_IT
dc.title.alternative Predicting short-term financial distress - An empirical comparison between Logistic Regression and Tree-based models applied to Italian companies it_IT
dc.type Master's Degree Thesis it_IT
dc.degree.name Economia e finanza it_IT
dc.degree.level Laurea magistrale it_IT
dc.degree.grantor Dipartimento di Economia it_IT
dc.description.academicyear 2019-2020, sessione straordinaria LM it_IT
dc.rights.accessrights closedAccess it_IT
dc.thesis.matricno 855976 it_IT
dc.subject.miur SECS-P/09 FINANZA AZIENDALE it_IT
dc.description.note it_IT
dc.degree.discipline it_IT
dc.contributor.co-advisor it_IT
dc.date.embargoend 10000-01-01
dc.provenance.upload Jessica Zanotto (855976@stud.unive.it), 2021-04-05 it_IT
dc.provenance.plagiarycheck Claudio Pizzi (pizzic@unive.it), 2021-04-26 it_IT


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