Z’-score and logit models for default prediction: an empirical analysis on post-Covid M&A transactions
Martini, Alessandro (A.A. 2024/2025) Z’-score and logit models for default prediction: an empirical analysis on post-Covid M&A transactions. Tesi di Laurea in Risk management, Luiss Guido Carli, relatore Daniele Penza, pp. 61. [Master's Degree Thesis]
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Abstract/Index
Literature review. Introduction to corporate default prediction. Altman’s Z’-score model: structure and use. Recent literature: evolutions and limitations of the Z’-score. Logistic regression in default prediction. Applications of Z’-score and logit in M&A contexts. Data and methodology. Data sources and sample selection. Description of variables. Computation of Z’-score. Specification of the logit model. Comparison metrics and validation techniques. Empirical results. Quantitative overview of the sample. Sectoral breakdown. Case studies/outliers. Results and interpretation of the logit model. Comparative evaluation and discussion. Robustness and model validation.
References
Bibliografia: p. 59.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree program in Corporate Finance, English language (LM-77) |
| Chair: | Risk management |
| Thesis Supervisor: | Penza, Daniele |
| Thesis Co-Supervisor: | Morelli, Giacomo |
| Academic Year: | 2024/2025 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 31 Mar 2026 13:28 |
| Last Modified: | 31 Mar 2026 13:28 |
| URI: | https://tesi.luiss.it/id/eprint/45346 |
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