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...

DeMiguel et al. (2009) report that naive diversification dominates mean-variance optimization in out-of-sample asset allocation tests. Our analysis suggests that this is largely due to their research design, which focuses on portfolios that are subject to high estimation risk and extreme...

We propose a comprehensive empirical strategy for optimizing the out-of-sample performance of sample mean-variance efficient portfolios. After constructing a sample objective function that accounts for the impact of estimation risk, specification errors, and transaction costs on portfolio...

In this paper, we document that an application of a moving average strategy of technical analysis to portfolios sorted by volatility generates investment timing portfolios that often outperform the buy-and-hold strategy substantially. For high volatility portfolios, the abnormal returns,...

We propose a new approach to imposing economic constraints on forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two...

Alpha-based performance evaluation may fail to capture correlated residuals due to model errors. This paper proposes using the Generalized Information Ratio (GIR) to measure performance under misspecified benchmarks. Motivated by the theoretical link between abnormal returns and residual...

A tree-structured linear and quantile regression framework is proposed for the analysis and modeling of equity market returns. The approach is based on the idea of a binary tree, where every terminal node parameterizes a local regression model for a specific partition of the data. A Bayesian...

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...

We develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike the previous approaches in the literature, we implement our constraints directly on the predictor, setting it to zero whenever its value falls below the variable's past 12-month high....