A hybrid optimization approach to index tracking |
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Authors: | Rubén Ruiz-Torrubiano Alberto Suárez |
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Institution: | (1) Computer Science Department, Universidad Autónoma de Madrid, Calle Francisco Tomás y Valiente, 11, 28049 Madrid, Spain |
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Abstract: | Index tracking consists in reproducing the performance of a stock-market index by investing in a subset of the stocks included
in the index. A hybrid strategy that combines an evolutionary algorithm with quadratic programming is designed to solve this
NP-hard problem: Given a subset of assets, quadratic programming yields the optimal tracking portfolio that invests only in
the selected assets. The combinatorial problem of identifying the appropriate assets is solved by a genetic algorithm that
uses the output of the quadratic optimization as fitness function. This hybrid approach allows the identification of quasi-optimal
tracking portfolios at a reduced computational cost. |
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Keywords: | Data mining Financial modeling Asset management |
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