We establish existence of Predictable Forward Performance Processes (PFPPs) in complete markets, which has been previously shown only in the binomial setting. Our market model can be a discrete-time or a continuous-time model, and the investment horizon can be finite or infinite. We show that...

The paper examines the problem of portfolio selection based on the forecasts of unknown quality in a mean-variance framework. Early work by Treynor and Black (1973) established a relationship between the correlation of forecasts, the number of independent securities available and the Sharpe...

We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model is calibrated at a quarterly frequency for ten European countries. We also use maximum-likelihood estimates and Bayesian estimates to account for parameter uncertainty. We find...

This paper examines the effect of biased expert opinions on asset allocations. Expert opinions, such as brokerage research and analyst views, are an essential component of the asset management sector and an important research topic. However, the effect of behavioral biases on expert opinions is...

We introduce a novel dynamic portfolio choice method, focusing on robust out-of-sample performance rather than on optimal in-sample performance. We therefore devise a strategy that rigorously tackles the problem of estimation error. The method involves defining a discrete set of single-period...

We propose a new asset-pricing framework in which all securities' signals are used to predict each individual return. While the literature focuses on each security's own- signal predictability, assuming an equal strength across securities, our framework is flexible and includes...

The inquiries to return predictability are traditionally limited to the first two moments, mean and volatility. Analogously, literature on portfolio selection also stems from a moment-based analysis with up to the fourth moment being considered. This paper develops a distribution-based framework...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance—in terms of SDF Sharpe ratio and average pricing errors—is improving in model parameterization (or “complexity”). Our results predict that the best...

We consider the problem of optimal dynamic trading in the presence of predictable returns and proportional transaction costs for an investor choosing among multiple assets. The value of each security equals the expected value of holding the asset plus the value of all options to trade. We...