A model for portfolio selection with order of expected returns
Computers and Operations Research
Self-Organizing Maps
Local Search Techniques for Constrained Portfolio SelectionProblems
Computational Economics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Risk curve and fuzzy portfolio selection
Computers & Mathematics with Applications
Principal Manifolds for Data Visualization and Dimension Reduction
Principal Manifolds for Data Visualization and Dimension Reduction
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
Using memetic algorithms to improve portfolio performance in static and dynamic trading scenarios
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Fuzzy mean-variance-skewness portfolio selection models by interval analysis
Computers & Mathematics with Applications
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We introduce a model called Asset Drivers Framework (ADF), which combines Dimensions Reduction Techniques (DRT) with a ranking procedure to find out assets to be inserted into a financial portfolio. The basic idea is that market securities can be described by a wider number of determinants, but only a few number of them can effectively characterize the assets to form well-balanced portfolios. The ADF manages this as a dimensions reduction problem, and extrapolates for each asset a reduced number of determinants as natural drivers of theirs. The procedure ends by assigning a score to the assets projected in such dimensionally reduced space, with a method of punishment/reward of the way the securities cluster into it. The beauty of the ADF scheme relies on a number of points: (i) it provides a platform to test various dimensions reduction techniques; (ii) looking at the performance, ADF makes possible to build portfolios whose returns are aligned to those of the traditional approach, but with lower variance, and hence lower risk.