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Selected Methods of optimized Sampling for Index Tracking – Evidence from German Stocks

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  • Frieder Meyer-Bullerdiek

Abstract

The aim of this study is to verify the tracking quality of four different optimization approaches used for approximate replication (sampling) of a stock index. These approaches include relative optimization, optimization according to Markowitz, the use of regression methods and linear optimization. To test the tracking qualities of these strategies, an empirical analysis of portfolios of 10 stocks included in the German stock index DAX is used to determine the in-sample and out-of-sample results. In addition, a portfolio composition based on market capitalization and an equally weighted portfolio are considered. The analysis shows that the in-sample results are quite similar for all index tracking methods used in this study. Considering the out-of-sample results, it can be stated that all four index tracking methods lead to a portfolio that initially shows a high degree of similarity to the benchmark. However, it is surprising that the equally weighted portfolio leads to the best overall results. Therefore, the analysis presented here gives the impression that the uncomplicated equal weighting is preferable to the more sophisticated index tracking methods considered in this study. JEL classification number: G11.

Suggested Citation

  • Frieder Meyer-Bullerdiek, 2022. "Selected Methods of optimized Sampling for Index Tracking – Evidence from German Stocks," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(6), pages 1-8.
    • Handle: RePEc:spt:apfiba:v:12:y:2022:i:6:f:12_6_8
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    References listed on IDEAS

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    1. Andrea Scozzari & Fabio Tardella & Sandra Paterlini & Thiemo Krink, 2013. "Exact and heuristic approaches for the index tracking problem with UCITS constraints," Annals of Operations Research, Springer, vol. 205(1), pages 235-250, May.
    2. Leonardo Riegel Sant’Anna & Tiago Pascoal Filomena & Pablo Cristini Guedes & Denis Borenstein, 2017. "Index tracking with controlled number of assets using a hybrid heuristic combining genetic algorithm and non-linear programming," Annals of Operations Research, Springer, vol. 258(2), pages 849-867, November.
    3. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    4. Wu, Dexiang & Kwon, Roy H. & Costa, Giorgio, 2017. "A constrained cluster-based approach for tracking the S&P 500 index," International Journal of Production Economics, Elsevier, vol. 193(C), pages 222-243.
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    More about this item

    Keywords

    Index tracking; Sampling; Optimization; Tracking error; Residual risk.;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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