We propose a novel Bayesian model combination approach where the combination weights depend on the past forecasting performance of the individual models entering the combination through a utility-based objective function. We use this approach in the context of stock return predictability and...

We propose a novel Bayesian model combination approach where the combination weights depend on the past forecasting performance of the individual models entering the combination through a utility-based objective function. We use this approach in the context of stock return predictability and...

We propose a novel Bayesian model combination approach where the combination weights depend on the past forecasting performance of the individual models entering the combina- tion through a utility-based objective function. We use this approach in the context of stock return predictability and...

The intuitiveness and practicability of mean-variance portfolios largely depends on the accuracy of moment estimates, which are subject to large estimation errors and conditional on time. We propose a model accounting for factor dynamics in a Bayesian setting, in which the impact of estimation...

We introduce a simulation-free method to model and forecast multiple asset returns and employ it to investigate the optimal ensemble of features to include when jointly predicting monthly stock and bond excess returns. Our approach builds on the Bayesian Dynamic Linear Models of West and...

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with exogenous predictors. Unlike existing Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms, our approach is not based on a structural form representation of the...

Determining the optimal mix of assets in the context of a portfolio construction involves “smart” forecasts of asset returns as well as good estimates of the asset return variances and covariances. Typically, sample moments are used as best estimates of the population moments. Several...

We analyze the introduction of a diversification constraint into the portfolio optimization program. We show that such a constraint is equivalent to an unconstrained portfolio optimization program with a change of the sample covariance matrix by another matrix obtained as the sum of the sample...

The formulation of the Black-Litterman model as a Bayesian mixed estimation approach allows for computing the posterior expected returns taking into account the views of investor on future returns. When the views turn out to be wrong, the resulting portfolio may lead to losses. Sometimes, it may...

Securities selection attempts to distinguish prospective winners from losers conditional on beliefs and available information. This article surveys relevant academic research on this subject, including work about the combining of forecasts (Bates and Granger 1969), the Black-Litterman model...