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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Bibliographic Details
Published in:2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) pp. 1208 - 1212
Main Authors: Strub, Oliver, Baumann, Philipp
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2015
Subjects:
Correlation
Cross validation
data mining
index tracking
Indexes
Investment
mixed-integer linear programming
Planning
Portfolios
Principal component analysis
Testing
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Summary: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.
DOI:10.1109/IEEM.2015.7385839