This article shows how sparse solutions can be generated in parametric portfolio selection methods. Sparse mean-variance optimization procedures can be applied after the translation of parametric weight estimates into implied mean return estimates. The results of our empirical analysis suggest...

We propose a new approach that allows for incorporating qualitative views, such as ordering information, into estimates of future asset returns within the Black-Litterman model. We develop a mathematical framework and numerical computation methods for this setting. We find importance sampling to...

Prefetching is a simple and general method for single-chain parallelisation of the Metropolis-Hastings algorithm based on the idea of evaluating the posterior in parallel and ahead of time. Improved Metropolis-Hastings prefetching algorithms are presented and evaluated. It is shown how to use...

We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model is calibrated at a quarterly frequency for ten European countries. We also use maximum-likelihood estimates and Bayesian estimates to account for parameter uncertainty. We find...

Retailers face the important but challenging task of optimizing their product assortments. The challenge is to find, for every category in every store, the assortment that maximizes (expected) category profit. Adding to the complexity of this 0-1 knapsack problem, retailers should also consider...

The purpose of this study is to incorporate some of the influential findings in the forecasting literature in an integrated framework to examine whether a real-time optimizing investor can benefit from the stock market by allocating assets based on a predictive model that only uses industry...

Covariance appears throughout investment management, e.g., in risk reporting and control, portfolio construction, risk parity, smart beta, algorithmic trading, and hedging. It is usually represented via multi-factor model. The form’s fewer parameters and structure—comovement through...

In this paper we explore the use of Genetic Algorithms (GA) to calibrate seasonal BVAR models. In this way, the mechanistic use of seasonal adjustment procedures is avoided, since seasonality becomes a structural, basic and explicit part of the BVAR model. At the same time, the use of GA allows...

It is evident that linear programming model remains the most potent mathematical tool for the efficient allocation of scarce operational resources of an organization. Whilst projecting the graphical method as the easiest solution approach to linear programming where only two constraining factors...

I/B/E/S removes 6% of one-quarter-ahead earnings forecasts from the calculation of the consensus forecast. This study examines managers' role in these removals. We show optimistic forecasts are removed more often than pessimistic forecasts, after controlling for removal policies that I/B/E/S...