Tracking and outperforming large stock-market indices

Gnägi, Mario; Strub, Oliver (2018). Tracking and outperforming large stock-market indices. Omega, 90, p. 101999. Elsevier 10.1016/

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Enhanced index-tracking funds aim to achieve a small target excess return over a given financial benchmark index with minimum additional risk relative to this index, i.e., a minimum tracking error. These funds are attractive to investors, especially when the index is large and thus well diversified. We consider the problem of determining a portfolio for an enhanced index-tracking fund that is benchmarked against a large stock-market index subject to real-life constraints that may be imposed by investors, stock exchanges, or investment guidelines. In the literature, various solution approaches have been proposed to enhanced index tracking that are based on different linear and quadratic tracking-error functions. However, it remains an open question which tracking-error function should be minimized to determine good enhanced index-tracking portfolios. Moreover, the existing approaches may neglect real-life constraints such as the minimum trading values imposed by stock exchanges or may not devise good feasible portfolios within a reasonable computational time when the index is large. To overcome these shortcomings, we propose novel mixed-integer linear and quadratic programming formulations and novel matheuristics. To address the open question, we minimize different tracking-error functions by applying the proposed matheuristics and exact solution approaches based on the proposed mixed-integer programming formulations in a computational experiment using a set of problem instances based on large stock-market indices with up to more than 9, 000 constituents. The results of our study suggest that minimizing the so-called tracking error variance, which is a quadratic function, is preferable to minimizing other tracking-error functions.

Item Type:

Journal Article (Original Article)


03 Faculty of Business, Economics and Social Sciences > Department of Business Management > Institute of Financial Management > Professorship for Quantitative Methods in Business Administration

UniBE Contributor:

Gnägi, Mario and Strub, Oliver


600 Technology > 650 Management & public relations








Juliana Kathrin Zurbrügg

Date Deposited:

15 Nov 2018 13:54

Last Modified:

11 Nov 2020 02:30

Publisher DOI:





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