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
Full text not available from this repository.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) |
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Division/Institute: |
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, Baumann, Philipp |
Subjects: |
600 Technology > 650 Management & public relations |
Publisher: |
IEEE |
Language: |
English |
Submitter: |
Juliana Kathrin Moser-Zurbrügg |
Date Deposited: |
22 Feb 2016 12:00 |
Last Modified: |
05 Dec 2022 14:51 |
Publisher DOI: |
10.1109/IEEM.2015.7385839 |
URI: |
https://boris.unibe.ch/id/eprint/75710 |