Application of Machine Learning to Fundamental Analysis of SecuritiesPanteleev, Aleksandr (Avtor) Marinšek, Denis (Mentor) Možina, Martin (Komentor) Benjamin Grahamvalue investingmachine learningfundamental analysisfair valuesurvivorship biasfinancial accountingtime series smoothingThis inquiry tests whether a classic, fundamentals-based valuation philosophy can be systematically validated in today's data-rich environment. My approach centers on estimating a security's intrinsic worth using linear and non-liner models and maintaining a buffer for error, a contrast to methods that prioritize price momentum. I constructed and historically simulated statistical models on two decades of corporate data from 2003 to 2023 to derive these rational value estimates. The process incorporated a novel signal processing technique and was rigorously designed to avoid the analytical illusion of survivorship bias. The findings from the simulation were unambiguous: portfolios of the most favorably priced assets consistently generated returns superior to a broad market proxy. Crucially, simpler, interpretable models performed on par with their complex counterparts. The work thus validates these enduring principles for a modern context, while noting the growing challenge of finding a unique edge in automated markets.20252025-10-10 12:55:00Magistrsko delo/naloga174951VisID: 62686COBISS_ID: 253453315sl