Dynamic Network Model of Investment Control for Quadratic Risk Function
Automation and Remote Control
Automation and Remote Control
The Class of Polyhedral Coherent Risk Measures
Cybernetics and Systems Analysis
Polyhedral coherent risk measures and investment portfolio optimization
Cybernetics and Systems Analysis
On LP Solvable Models for Portfolio Selection
Informatica
Models and Simulations for Portfolio Rebalancing
Computational Economics
The optimal statistical median of a convex set of arrays
Journal of Global Optimization
Stock portfolio selection using mathematical programming: an educational approach
MCBE'09 Proceedings of the 10th WSEAS international conference on Mathematics and computers in business and economics
A portfolio model based on the minimax risk and return factors
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Symbiotic multi-swarm PSO for portfolio optimization
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Nadir compromise programming: A model for optimization of multi-objective portfolio problem
Expert Systems with Applications: An International Journal
Computational asset allocation using one-sided and two-sided variability measures
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Risk-neutral valuation with infinitely many trading dates
Mathematical and Computer Modelling: An International Journal
Risk-sensitive planning support for forest enterprises: The YAFO model
Computers and Electronics in Agriculture
Journal of Global Optimization
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A new principle for choosing portfolios based on historical returns data is introduced; the optimal portfolio based on this principle is the solution to a simple linear programming problem. This principle uses minimum return rather than variance as a measure of risk. In particular, the portfolio is chosen that minimizes the maximum loss over all past observation periods, for a given level of return. This objective function avoids the logical problems of a quadratic (nonmonotone) utility function implied by mean-variance portfolio selection rules. The resulting minimax portfolios are diversified; for normal returns data, the portfolios are nearly equivalent to those chosen by a mean-variance rule. Framing the portfolio selection process as a linear optimization problem also makes it feasible to constrain certain decision variables to be integer, or 0-脗 1, valued; this feature facilitates the use of more complex decision-making models, including models with fixed transaction charges and models with Boolean-type constraints on allocations.