Using Genetic Algorithm for Index Tracking–Evidence from Shanghai 50

Jian-he Liu, Xin Wu, Qing-Song Fang

Abstract


This paper uses historical data of Shanghai 50 Index as sample data, replaces traditional optimization methods with genetic algorithm, uses clustering analysis method to build tracking portfolio, and compares the empirical results with those of traditional optimization methods. The empirical results of Shanghai 50 index show that using genetic algorithm for index tracking can get better performance with lower volatility.

 

DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4801

 

 


Keywords


Index Tracking; Tracking Error; Genetic Algorithm; Clustering Analysis

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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