Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Strub, Oliver; Baumann, Philipp (December 2015). Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming. Proceedings of the 2015 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1208-1212. Singapore: IEEE 10.1109/IEEM.2015.7385839

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Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.

Item Type:

Conference or Workshop Item (Paper)


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:

Strub, Oliver and Baumann, Philipp


600 Technology > 650 Management & public relations






Juliana Kathrin Moser-Zurbrügg

Date Deposited:

22 Feb 2016 12:00

Last Modified:

28 Jan 2020 13:48

Publisher DOI:



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