Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics

[EN] The aim of this study was to compare the performance of the well-known genetic algorithms and tabu search heuristics with the financial problem of the partial tracking of a stock market index. Although the weights of each stock in a tracking portfolio can be efficiently determined by means of quadratic programming, identifying the appropriate stocks to include in the portfolio is an NP-hard problem which can only be addressed by heuristics. Seven real-world indexes were used to compare the above techniques, and results were obtained for different tracking portfolio cardinalities. The results show that tabu search performs more efficiently with both real and artificial indexes. In general, the tracking portfolios obtained performed well in both in-sample and out-of-sample periods, so that these heuristics can be considered as appropriate solutions to the problem of tracking an index by means of a small subset of stocks. García García, F.; Guijarro, F.; Oliver-Muncharaz, J. (2018). Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics. Neural Computing and Applications. 30(8):2625-2641. https://doi.org/10.1007/s00521-017-2882-2 Aguilar-Rivera R, Valenzuela-Rendón M, Rodríguez-Ortiz JJ (2015) Genetic algorithms and Darwinian approaches in financial applications: a survey. Expert Syst Appl 42:7684–7697 Andriosopoulos K, Doumpos M, Papapostolou NC, Pouliasis PK (2013) Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms. Transp Res Part E Logist Transp Rev 52:16–34 Aouni B, Colapinto C, La Torre D (2013) A cardinality constrained stochastic goal programming model with satisfaction functions for venture capital investment decision making. Ann Oper Res 205:77–88 Barak S, Dahooie JH, Tichý T (2015) Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese candlestick. Expert Syst Appl 42:9221–9235 Beasley JE (1990) OR-Library: distributing test problems by electronic mail. J Oper Res Soc 41:1069–1072 Beasley JE, Meade N, Chang TJ (2003) An evolutionary heuristic for the index tracking problem. Eur J Oper Res 148:621–643 Berutich JM, López F, Luna F, Quintana D (2016) Robust technical trading strategies using GP for algorithmic portfolio selection. Expert Syst Appl 46:307–315 Canakgoz NA, Beasley JE (2008) Mixed-integer programming approaches for index tracking and enhanced indexation