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CDAR

Leading research at the intersection of financial economics and data science.

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ARTICLES

CURRENT EVENTS

SEM217: Emmanouil Platanakis, University of Bath: When Bayes-Stein Meets Machine Learning: A Generalized Approach for Portfolio Optimization

Tuesday, October 3rd @ 11:00-12:30 PM via Zoom

The Bayes-Stein model is widely used to tackle parameter uncertainty in the classical Markowitz mean-variance portfolio optimization framework. In practice, however, it suffers from estimation errors and often fails to outperform the naive 1/N asset allocation rule. To address this, we develop a generalized counterpart that leverages machine learning (ML) techniques to estimate some core model parameters.