Systems and methods for multi-objective portfolio optimization
First Claim
1. A method for multi-objective portfolio optimization, the method comprising the steps of:
- generating an initial population of solutions of portfolio allocations using a combination of linear programming and sequential linear programming algorithms in a portfolio configuration space using a computing device, the portfolio configuration space having a plurality of dimensions;
generating a first interim efficient frontier in a portfolio performance space having at least three dimensions using a Pareto Sorting Evolutionary Algorithm (PSEA);
generating a second interim efficient frontier in the portfolio performance space using a Target Objectives Genetic Algorithm (TOGA);
concatenating the first interim efficient frontier with the second interim efficient frontier to create a third interim efficient frontier; and
passing the third interim efficient frontier through a dominance filter to generate a final efficient frontier for use in investment decisions.
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Abstract
The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: generating an initial population of solutions of portfolio allocations; performing a first multi-objective process, based on the initial population and the competing objectives, to generate a first interim efficient frontier; performing a second multi-objective process, based on the initial population and the competing objectives, to generate a second interim efficient frontier; and fusing the first interim efficient frontier with the second interim efficient frontier to create an augmented efficient frontier for use in investment decisioning.
161 Citations
39 Claims
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1. A method for multi-objective portfolio optimization, the method comprising the steps of:
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generating an initial population of solutions of portfolio allocations using a combination of linear programming and sequential linear programming algorithms in a portfolio configuration space using a computing device, the portfolio configuration space having a plurality of dimensions; generating a first interim efficient frontier in a portfolio performance space having at least three dimensions using a Pareto Sorting Evolutionary Algorithm (PSEA); generating a second interim efficient frontier in the portfolio performance space using a Target Objectives Genetic Algorithm (TOGA); concatenating the first interim efficient frontier with the second interim efficient frontier to create a third interim efficient frontier; and passing the third interim efficient frontier through a dominance filter to generate a final efficient frontier for use in investment decisions. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37)
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38. A system for multi-objective portfolio optimization, the system comprising:
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a processing machine; a physical computer readable medium storing a computer program in the processing machine, wherein the computer readable medium contains;
a population generation portion that generates an initial population of solutions of portfolio allocations using a combination of linear programming and sequential linear programming algorithms in a portfolio configuration space, the portfolio configuration space having a plurality of dimensions;a first processing portion that generates a first interim efficient frontier in a portfolio performance space having at least three dimensions using a Pareto Sorting Evolutionary Algorithm (PSEA); a second processing portion that generates a second interim efficient frontier in the portfolio performance space using a Target Objectives Genetic Algorithm (TOGA); a fusion portion that concatenates the first interim efficient frontier with the second interim efficient frontier to create a third interim efficient frontier; and a dominance filtering portion that generates a final efficient frontier from the third interim efficient frontier for use in investment decisions.
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39. A computer readable medium for multi-objective portfolio optimization, the computer readable medium comprising:
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a first portion that generates an initial population of solutions of portfolio allocations using a combination of linear programming and sequential linear programming algorithms in a portfolio configuration space, the portfolio configuration space having a plurality of dimensions; a second portion that generates a first interim efficient frontier in a portfolio performance space having at least three dimensions using a Pareto Sorting Evolutionary Algorithm (PSEA); a third portion that generates a second interim efficient frontier in the portfolio performance space using a Target Objectives Genetic Algorithm (TOGA); a fourth portion that concatenates the first interim efficient frontier with the second interim efficient frontier to create a third interim efficient frontier; and a fifth portion that generates a final efficient frontier from the third interim efficient frontier for use in investment decisions.
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Specification