Algorithmic peer selection: machine learning-driven comparable analysis for private companies valuation
Moreschini, Michele (A.A. 2024/2025) Algorithmic peer selection: machine learning-driven comparable analysis for private companies valuation. Tesi di Laurea in Advanced corporate finance, Luiss Guido Carli, relatore Raffaele Oriani, pp. 74. [Master's Degree Thesis]
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Abstract/Index
Literature review & context. Traditional peer selection and valuation multiples. Machine learning approaches to CCA. Methodology. Research design and objectives. Data and variables. Clustering methods and model selection. Target assignment and peer extraction. Valuation procedure. Validation and statistical testing. Extension: Beta & WACC. Empirical results. Extensions: Beta and WACC.
References
Bibliografia: pp. 67-70.
| 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: | Advanced corporate finance |
| Thesis Supervisor: | Oriani, Raffaele |
| Thesis Co-Supervisor: | Vulpiani, Marco |
| Academic Year: | 2024/2025 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 25 Mar 2026 14:46 |
| Last Modified: | 25 Mar 2026 14:46 |
| URI: | https://tesi.luiss.it/id/eprint/45235 |
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