In the financial portfolio selection (FPS) problem, the investor usually considers several
conflicting objectives such as return, risk, and liquidity. The values of these objectives are …

The year 2015 marks the 30th birthday of DC (Difference of Convex functions) programming
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …

We consider a basic model of multi-period trading, which can be used to evaluate the
performance of a trading strategy. We describe a framework for single-period optimization …

This paper introduces a new approach to constructing optimal mean-variance portfolios. The
approach relies on a novel unconstrained regression representation of the mean-variance …

The poor out‐of‐sample performance of mean–variance portfolio model is mainly caused by
estimation errors in the covariance matrix and the mean return, especially the mean return …

Parameter uncertainty and estimation errors often cause the presence of unstable asset
weights and the poor performance of portfolio model. In addition, in the real world, most …

Financial engineering and electrical engineering are seemingly different areas that share
strong underlying connections. Both areas rely on statistical analysis and modeling of …

Machine learning (ML) is progressively reshaping the fields of quantitative finance and
algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers …

In this article, model predictive control is used to dynamically optimize an investment
portfolio and control drawdowns. The control is based on multi-period forecasts of the mean …

We introduce a financial portfolio optimization framework that allows to automatically select
the relevant assets and estimate their weights by relying on a sorted ℓ 1-Norm penalization …