Suppose a fund manager uses predictors in changing portfolio allocations over time. How does predictability translate into portfolio decisions? To answer this question we derive a new model within the Bayesian framework, where managers are assumed to modulate the systematic risk in part by...

We use a holdings-based attribution model to disaggregate the benchmark-adjusted returns to U.S. equity mutual funds into components that reflect persistent segment tilts, the timing of segment returns, and stock selection relative to their benchmarks. We find that large-cap funds add value by...

In this work, we apply cutting edge machine learning algorithms to one of the oldest challenges in finance: Predicting returns. For the sake of simplicity, we focus on predicting the direction (e.g. either up or down) of several liquid ETFs and do not attempt to predict the magnitude of price...

A substantial amount is incurred in ETF transaction costs each year. This paper examines the performance of a vector autoregressive (VAR) model and other naïve models to time trades in 1,350 ETFs over the 2011 to 2017 period. We find varied spread savings for large and retail ETF traders by...