Index tracking with controlled number of assets using a hybrid heuristic combining genetic algorithm and non-linear programming

Leonardo Riegel Sant’Anna (), Tiago Pascoal Filomena (), Pablo Cristini Guedes () and Denis Borenstein ()
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Leonardo Riegel Sant’Anna: Federal University of Rio Grande do Sul
Tiago Pascoal Filomena: Federal University of Rio Grande do Sul
Pablo Cristini Guedes: Federal University of Rio Grande do Sul
Denis Borenstein: Federal University of Rio Grande do Sul

Annals of Operations Research, 2017, vol. 258, issue 2, No 29, 849-867

Abstract: Abstract In this paper, we discuss the index tracking strategy using mathematical programming. First, we use a non-linear programming formulation for the index tracking problem, considering a limited number of assets. Since the problem is difficult to be solved in reasonable time by commercial mathematical packages, we apply a hybrid solution approach, combining mathematical programming and genetic algorithm. We show the efficiency of the proposed approach comparing the results with optimal solutions, with previous developed methods, and from real-world market indexes. The computational experiments focus on Ibovespa (the most important Brazilian market index), but we also present results for consolidated markets such as S&P 100 (USA), FTSE 100 (UK) and DAX (Germany). The proposed framework shows its ability to obtain very good results (gaps from the optimal solution smaller than 5 % in 8 min of CPU time) even for a highly volatile index from a developing country.

Keywords: Index tracking; Portfolio optimization; Genetic algorithm (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (13)

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DOI: 10.1007/s10479-016-2111-x

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