There is a large and growing literature on spillovers but no study that systematically evaluates the importance of spillovers for portfolio management. This paper provides such an analysis and demonstrates that spillovers are fully embedded in estimates of expected returns, variances, and...

We examine machine learning and factor-based portfolio optimization. We find that factors based on autoencoder neural networks exhibit a weaker relationship with commonly used characteristic-sorted portfolios than popular dimensionality reduction techniques. Machine learning methods also lead to...

We introduce diversified risk parity embedded with various reward-risk measures and more generic allocation rules for portfolio construction. We empirically test advanced reward-risk parity strategies and compare their performance with an equally-weighted risk portfolio in various asset...

We present a semiparametric portfolio optimization method in which portfolio weights are parameterized as a non-linear function of firm characteristics. This approach generalizes the linear parametric portfolio policy of Brandt et al. (2009) and can be applied to high-dimensional problems at a...

The empirical finding that market movements in stock prices may be correlated with the order flow of other stocks has led to the notion of "cross-impact" and has prompted the development of multivariate models of market impact. These models are parametrized by a matrix of impact coefficients...

We introduce a flexible utility-based empirical approach to directly determine asset allocation decisions between risky and risk-free assets. This is in contrast to the commonly used two-step approach where least squares optimal statistical equity premium predictions are first constructed to...

Graphical models have shown remarkable performance in uncovering the conditional dependence structure across a set of given variables. In this paper, we introduce two new graphical modelling approaches—called Gslope and Tslope—to the portfolio selection literature for directly estimating the...

Technical indicators are widely used by market participants to identify trends in asset prices and in trading volumes. However, it is unclear how to reconcile this approach with a portfolio selection policy that guide investment decisions in many assets at the same time. We bridge the gap...

Stock and oil relationship is usually time-varying and depends on the current economic conditions. In this study, we propose a new Dynamic Stochastic Mixed data frequency sampling (DSM) copula model, that decomposes the stock-oil relationship into a short-run dynamic stochastic component and a...