There has been considerable research into dynamic global tactical asset allocation (GTAA) strategies driven by simple measures of Valuation and Momentum applied to a baseline balanced portfolio of equities and fixed income (see Blitz and van Vliet 2008, Wang and Kochard 2011, Gnedenko and Yelnik...

The longest bull market in US stock market history is over. Uncertainty over the public health and economic impact of the coronavirus pandemic will keep markets extremely volatile, making it likely we'll touch a wide range of price levels in the months ahead. Amidst such uncertainty, it's a...

This paper studies hedge fund return predictability in a multivariate setting. Our research design and analysis is motivated by the empirical observations that a specific forecasting model that is going to perform well is not known ex-ante and that modelling time varying return...

Given expected returns and return covariances, portfolio weights are known in closed form in a mean-variance framework. The real difficulty is in estimating these parameters. Using recent advances in Bayesian techniques, we show how investors can incorporate any prior information for optimal...

Extending previous work on hedge fund pricing, this paper introduces the idea of modelling the conditional quantiles of hedge fund returns using a set of risk factors. Quantile regression analysis provides a way of understanding how the relationship between hedge fund returns and risk factors...

In this paper, we analyze the usefulness of technical analysis, specifically the widely used moving average trading rule, from an asset allocation perspective. We show that when stock returns are predictable, technical analysis adds value to commonly used allocation rules that invest fixed...

Bayesian Model Averaging (BMA) has recently been discussed in the financial literature as an effective way to account for model uncertainty. In this paper we compare BMA to a new model uncertainty framework introduced by Yang (2004), called Aggregate Forecasting Through Exponential Reweighting,...

We present and estimate a Bayesian Hierarchical model of mutual fund returns. In our model, a fund's alpha reflects not only that fund's return history, but also information from other fund returns. Because parameters are estimated simultaneously for all funds, we can identify common residual...

A concise look in three unrelated, yet complex environments reveals a strong human dependence on the recognition heuristic. Its marketability to the mind as a good decision making tool (over other complex approaches), is shown to be almost innate and ultimately successful. The three...

This paper analyzes the performance of portfolio strategies that invest in no-load, open-end U.S. domestic equity mutual funds, incorporating predictability in (i) manager skills, (ii) fund risk-loadings, and (iii) benchmark returns. Predictability in manager skills is found to be the dominant...