This paper considers how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of different vector autoregressive models, the investor is able, each period, to revise past predictive mistakes and learn about...

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 introduce a novel dynamic Bayesian model combination approach for predicting aggregate stock returns. Our method involves combining predictive densities in a data-adaptive fashion and simultaneously features (i) uncertainty about relevant predictor variables, (ii) parameter instability, (iii)...

We examine the incremental value of news-based data relative to the FRED-MD economic indicators for quantile predictions (now- and forecasts) of employment, output, inflation and consumer sentiment. Our results suggest that news data contain valuable information not captured by economic...

We propose a dynamic subset forecast combination approach to investigate local predictability of aggregate stock returns in high dimensions. Our method accommodates a vast number of heterogeneous predictive signals and is designed to pick up structural changes. Based on more than 12,000...

We propose a framework for combining portfolio rules while mitigating the impact of estimation error. Our main goal is to integrate heterogeneous rules that previously proposed combination methods are unable to accommodate, enabling researchers and investors to leverage established and ongoing...