We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and...

We provide results on the existence and uniqueness of equilibrium in dynamically incomplete financial markets in discrete time. Our framework allows for heterogeneous agents, unspanned random endowments and convex trading constraints. In the special case where all agents have preferences of the...

We consider a full equilibrium model in continuous time comprising a finite number of agents and tradable securities.We show that, if the agents' endowments are spanned by the securities and if the agents have entropic utilities, an equilibrium exists and the agents' optimal trading strategies...

We provide results on the existence and uniqueness of equilibrium in dynamically incomplete financial markets in discrete time. Our framework allows for heterogeneous agents, unspanned random endowments and convex trading constraints. In the special case where all agents have preferences of the...

We consider a full equilibrium model in continuous time comprising a finite number of agents and tradable securities.We show that, if the agents' endowments are spanned by the securities and if the agents have entropic utilities, an equilibrium exists and the agents' optimal trading strategies...

We propose a general discrete-time framework for deriving equilibrium prices of financial securities. It allows for heterogeneous agents, unspanned random endowments and convex trading constraints. We give a dual characterization of equilibria and provide general results on their existence and...