This paper incorporates Bayesian estimation and optimization into portfolio selection framework, particularly for high-dimensional portfolio in which the number of assets is larger than the number of observations. We leverage a constrained 𝓁1 minimization approach, called linear programming...

One of the main challenges investors have to face is model uncertainty. Typically, the dynamic of the assets is modeled using two parameters: the drift vector and the covariance matrix, which are both uncertain. Since the variance/covariance parameter is assumed to be estimated with a certain...

The dissertation consists of three chapters, each focusing on a different application of learning in financial markets. The first chapter addresses survivorship bias in the global equity markets. Survivorship bias refers to people’s tendency of focusing on successful individuals in inferring...