Widened Learning of Index Tracking Portfolios
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Index investing has an advantage over active investment strategies, because less frequent trading results in lower expenses, yielding higher long-term returns. Index tracking is a popular investment strategy that attempts to find a portfolio replicating the performance of a collection of investment vehicles. This paper considers index tracking from the perspective of solution space exploration. Three search space heuristics in combination with three portfolio tracking error methods are compared in order to select a tracking portfolio with returns that mimic a benchmark index. Experimental results conducted on real-world datasets show that Widening, a metaheuristic using diverse parallel search paths, finds superior solutions than those found by the reference heuristics. Presented here are the first results using Widening on time-series data.
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GAVRIUSHINA, Iuliia, Oliver R. SAMPSON, Michael R. BERTHOLD, Winfried POHLMEIER, Christian BORGELT, 2019. Widened Learning of Index Tracking Portfolios. 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). Boca Raton, FL, USA, 16. Dez. 2019 - 19. Dez. 2019. In: 18th IEEE International Conference On Machine Learning And Applications (ICMLA). Piscataway: IEEE, 2019, pp. 1800-1805. ISBN 978-1-72814-551-8. Available under: doi: 10.1109/ICMLA.2019.00291BibTex
@inproceedings{Gavriushina2019-12Widen-50111, year={2019}, doi={10.1109/ICMLA.2019.00291}, title={Widened Learning of Index Tracking Portfolios}, isbn={978-1-72814-551-8}, publisher={IEEE}, address={Piscataway}, booktitle={18th IEEE International Conference On Machine Learning And Applications (ICMLA)}, pages={1800--1805}, author={Gavriushina, Iuliia and Sampson, Oliver R. and Berthold, Michael R. and Pohlmeier, Winfried and Borgelt, Christian} }
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