A hybrid optimization approach to index tracking
A hybrid optimization approach to index tracking
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DOI：
10.1007/s10479-008-0404-4
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发表时间：
2009-02
影响因子：
4.8
通讯作者：
Rubén Ruiz-Torrubiano;A. Suárez
中科院分区：
文献类型：
--
作者：
Rubén Ruiz-Torrubiano;A. Suárez
指数跟踪包括通过投资指数中包含的股票子集来再现股票市场指数的表现。将进化算法与二次规划相结合的混合策略旨在解决这个 NP 难题：给定资产子集，二次规划产生仅投资于所选资产的最佳跟踪投资组合。识别适当资产的组合问题通过遗传算法解决，该算法使用二次优化的输出作为适应度函数。这种混合方法允许以降低的计算成本识别准最佳跟踪组合。
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.