Ovallesco, Francesca Ludovica (A.A. 2023/2024) The ESG equation in M&A: using a deep learning model to decode capital costs. Tesi di Laurea in Advanced corporate finance, Luiss Guido Carli, relatore Raffaele Oriani, pp. 117. [Master's Degree Thesis]

Full text for this thesis not available from the repository.

Abstract/Index

Foundations of M&A and cost of capital. M&A as a growth strategy in corporate finance. Fundamentals of the cost of capital: definitions and drivers. Traditional approaches to valuing M&A deals. The role of risk, uncertainty, and market conditions in M&A valuation. ESG considerations in corporate finance. From CSR to ESG: evolution of sustainable finance concepts. Stakeholder theory and triple bottom line principles in modern finance. ESG criteria and their measurement challenges in financial markets. ESG’s influence on firm risk profiles, investor preferences, and cost of capital. Data and methodology. Deep learning in finance: state of the art. Research design and hypotheses. Sample selection and pre-processing steps. Neural networks and deep learning architectures for cost of capital prediction. Empirical results and discussion. Descriptive statistics of M&A sample and ESG scores. Predictive model performance: accuracy and error analysis. Comparison with conventional regression techniques. Addressing methodological limitations and ensuring robustness.

References

Bibliografia: pp. 108-117.

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:	Santella, Rosella
Academic Year:	2023/2024
Session:	Extraordinary
Deposited by:	Alessandro Perfetti
Date Deposited:	09 Jul 2025 13:49
Last Modified:	09 Jul 2025 13:49
URI:	https://tesi.luiss.it/id/eprint/42842

Downloads

Repository Staff Only

View Item