Data-Driven Merton's Strategies via Policy Randomization

Explore data-driven approaches to Merton's portfolio optimization strategies through policy randomization techniques in this quantitative finance seminar. Learn how modern machine learning methods can be applied to classical portfolio theory, examining the intersection of stochastic control and data science in financial decision-making. Discover how policy randomization can enhance traditional Merton strategies by incorporating empirical data and uncertainty quantification. Gain insights into the mathematical foundations underlying these approaches and their practical applications in portfolio management. Understand the theoretical framework connecting optimal control theory with contemporary data-driven methodologies for investment strategies.