Index tracking using data-mining techniques and mixed-binary linear programming
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 s...
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| Veröffentlicht in: | 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) S. 1208 - 1212 |
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| Sprache: | Englisch |
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01.12.2015
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Baumann, Philipp Strub, Oliver |
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| Snippet | Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that... |
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| SubjectTerms | Correlation Cross validation data mining index tracking Indexes Investment mixed-integer linear programming Planning Portfolios Principal component analysis Testing |
| Title | Index tracking using data-mining techniques and mixed-binary linear programming |
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