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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Ruiz-Torrubiano, R., Suárez, A. A hybrid optimization approach to index tracking. Ann Oper Res 166, 57–71 (2009). https://doi.org/10.1007/s10479-008-0404-4
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DOI: https://doi.org/10.1007/s10479-008-0404-4