Deep Learning, Predictability, and Optimal Portfolio Returns

Optimal Portfolio with Nonparametric Learning

We consider the optimal investment-consumption problem with an unknown distribution for the investment returns. After taking the Bayesian approach, the problem is formulated as a Markov decision process in a nonparametric context. We discuss the existence of the optimal strategy and derive the closed form solution of the optimal portfolio. When the prior distribution is a Dirichlet process, the...

Robust Resampling Methods and Stock Returns Predictability

Predictability of Returns and Cash Flows ∗ Ralph

We review the literature on return and cash flow growth predictability form the perspective of the present-value identity. We focus predominantly on recent work. Our emphasis is on U.S. aggregate stock return predictability, but we also discuss evidence from other asset classes and countries. JEL classification: G10, G12, G14, G35.

Entropy and Predictability of Stock Market Returns¤

We examine the predictability of stock market returns by employing a new metric entropy measure of dependence with several desirable properties. We compare our results with a number of traditional measures. The metric entropy is capable of detecting nonlinear dependence within the returns series, and is also capable of detecting nonlinear\a±nity" between the returns and their predictions obtain...

Learning to Mix n-Step Returns: Generalizing lambda-Returns for Deep Reinforcement Learning

Reinforcement Learning (RL) can model complex behavior policies for goaldirected sequential decision making tasks. A hallmark of RL algorithms is Temporal Difference (TD) learning: value function for the current state is moved towards a bootstrapped target that is estimated using next state’s value function. λ-returns generalize beyond 1-step returns and strike a balance between Monte Carlo and...