Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional...

This paper considers mean-variance optimization under uncertainty, specifically when one desires a sparsified set of optimal portfolio weights. From the standpoint of a Bayesian investor, our approach produces a small portfolio from many potential assets while acknowledging uncertainty in asset...

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...

This paper considers passive fund selection from an individual investor's perspective. The growth of the passive fund market over the past decade is staggering. Individual investors who wish to buy these funds for their retirement and brokerage accounts have many options and are faced with a...

Academia faces a challenge, as activism replaces inquiry as the primary goal of the university. In particular, the growing dominance of activist academics, who explicitly reject objective inquiry, has started and will continue to constrain what information researchers can report and what ideas...

We propose a generalization of the rational expectations framework to allow for multiplicative sunspot shocks and temporarily unstable paths. Then, we provide an econometric strategy to estimate this generalized model on the data. Our approach yields drifting parameters and stochastic...