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...

Is it theoretically legitimate for an investor to attempt to outperform the market index? Or is the investor simply deluding himself? The purpose of this paper is to apply the new Theory of Rational Beliefs (RB) to demonstrate the following result: Even when all agents have symmetric...

This paper considers a linear panel data model with time varying heterogeneity. Bayesian inference techniques organized around Markov chain Monte Carlo (MCMC) are applied to implement new estimators that combine smoothness priors on unobserved heterogeneity and priors on the factor structure of...

This article develops a Bayesian approach for estimating panel quantile regression with binary outcomes in the presence of correlated random effects. We construct a working likelihood using an asymmetric Laplace (AL) error distribution and combine it with suitable prior distributions to obtain...

The purpose of this study is to assess the diversification benefits resulting from international asset allocation. In this study, we examine Capital Asset Pricing Model (CAPM) in its international context (ICAPM) using the monthly equity returns for 26 countries (18 developed and 8 emerging...

We estimate conditional multifactor models over a large cross-section of stock returns matching 25 CAPM anomalies. Using conditioning information associated with different instruments improves the performance of the Hou, Xue, and Zhang (2015, HXZ) and Fama and French (2015, 2016, FF) models. The...

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...

Are excess returns predictable and if so, what does this mean for investors? Previous literature has tended toward two polar viewpoints: that predictability is useful only if the statistical evidence for it is incontrovertible, or that predictability should affect portfolio choice, even if the...

We evaluate predictive regressions that explicitly consider the time-variation of coefficients in a comprehensive Bayesian framework. For monthly returns of the S&P 500 index, we demonstrate statistical as well as economic evidence of out-of-sample predictability: relative to an investor using...