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Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100
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| Zeitschriftentitel: | Journal of Physics: Conference Series |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Journal of Physics: Conference Series, 1828, 2021, 1, S. 012111 |
| Format: | E-Article |
| Sprache: | Unbestimmt |
| veröffentlicht: |
IOP Publishing
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| Schlagwörter: |
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Yuen, Man-Chung Ng, Sin-Chun Leung, Man-Fai Yuen, Man-Chung Ng, Sin-Chun Leung, Man-Fai |
|---|---|
| author |
Yuen, Man-Chung Ng, Sin-Chun Leung, Man-Fai |
| spellingShingle |
Yuen, Man-Chung Ng, Sin-Chun Leung, Man-Fai Journal of Physics: Conference Series Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 General Physics and Astronomy |
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yuen, man-chung |
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Yuen, Man-Chung Ng, Sin-Chun Leung, Man-Fai 1742-6588 1742-6596 IOP Publishing General Physics and Astronomy http://dx.doi.org/10.1088/1742-6596/1828/1/012111 <jats:title>Abstract</jats:title> <jats:p>Passive management contributes a more stable return than an active management strategy over the long term. Index-tracking is one of the passive investment strategies that attempt to replicate market indexes to reproduce the performance. Sparse index-tracking considers a subset of market index stocks to minimize the difference between the market index and the replicated index. In this paper, two metaheuristics are applied to solve this problem. The sparse index-tracking problem formed by the objective function of the empirical tracking error with the penalty values that result in an NP-hard problem. The penalty value is used to restrict the numbers of the considered stocks. To show the performance of the metaheuristics, various penalty values are investigated, and they produce approximation solutions to the index-tracking problem. Among them, particle swarm optimization shows better or statistically similar performance to GA in solving the sparse index-tracking problem.</jats:p> Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 Journal of Physics: Conference Series |
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| title |
Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 |
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Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 |
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Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 |
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Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 |
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Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 |
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Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 |
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metaheuristics for sparse index-tracking problem: a case study on ftse 100 |
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General Physics and Astronomy |
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http://dx.doi.org/10.1088/1742-6596/1828/1/012111 |
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2021 |
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012111 |
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<jats:title>Abstract</jats:title>
<jats:p>Passive management contributes a more stable return than an active management strategy over the long term. Index-tracking is one of the passive investment strategies that attempt to replicate market indexes to reproduce the performance. Sparse index-tracking considers a subset of market index stocks to minimize the difference between the market index and the replicated index. In this paper, two metaheuristics are applied to solve this problem. The sparse index-tracking problem formed by the objective function of the empirical tracking error with the penalty values that result in an NP-hard problem. The penalty value is used to restrict the numbers of the considered stocks. To show the performance of the metaheuristics, various penalty values are investigated, and they produce approximation solutions to the index-tracking problem. Among them, particle swarm optimization shows better or statistically similar performance to GA in solving the sparse index-tracking problem.</jats:p> |
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| description | <jats:title>Abstract</jats:title> <jats:p>Passive management contributes a more stable return than an active management strategy over the long term. Index-tracking is one of the passive investment strategies that attempt to replicate market indexes to reproduce the performance. Sparse index-tracking considers a subset of market index stocks to minimize the difference between the market index and the replicated index. In this paper, two metaheuristics are applied to solve this problem. The sparse index-tracking problem formed by the objective function of the empirical tracking error with the penalty values that result in an NP-hard problem. The penalty value is used to restrict the numbers of the considered stocks. To show the performance of the metaheuristics, various penalty values are investigated, and they produce approximation solutions to the index-tracking problem. Among them, particle swarm optimization shows better or statistically similar performance to GA in solving the sparse index-tracking problem.</jats:p> |
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| spelling | Yuen, Man-Chung Ng, Sin-Chun Leung, Man-Fai 1742-6588 1742-6596 IOP Publishing General Physics and Astronomy http://dx.doi.org/10.1088/1742-6596/1828/1/012111 <jats:title>Abstract</jats:title> <jats:p>Passive management contributes a more stable return than an active management strategy over the long term. Index-tracking is one of the passive investment strategies that attempt to replicate market indexes to reproduce the performance. Sparse index-tracking considers a subset of market index stocks to minimize the difference between the market index and the replicated index. In this paper, two metaheuristics are applied to solve this problem. The sparse index-tracking problem formed by the objective function of the empirical tracking error with the penalty values that result in an NP-hard problem. The penalty value is used to restrict the numbers of the considered stocks. To show the performance of the metaheuristics, various penalty values are investigated, and they produce approximation solutions to the index-tracking problem. Among them, particle swarm optimization shows better or statistically similar performance to GA in solving the sparse index-tracking problem.</jats:p> Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 Journal of Physics: Conference Series |
| spellingShingle | Yuen, Man-Chung, Ng, Sin-Chun, Leung, Man-Fai, Journal of Physics: Conference Series, Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100, General Physics and Astronomy |
| title | Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 |
| title_full | Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 |
| title_fullStr | Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 |
| title_full_unstemmed | Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 |
| title_short | Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 |
| title_sort | metaheuristics for sparse index-tracking problem: a case study on ftse 100 |
| title_unstemmed | Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100 |
| topic | General Physics and Astronomy |
| url | http://dx.doi.org/10.1088/1742-6596/1828/1/012111 |