Modeling index tracking portfolio based on stochastic dominance for stock selection

We propose a three-step method using the stochastic dominance (SD) approach on stock filtering to determine the number and candidate stocks in a portfolio. We empirically prove that our model can be used to efficiently construct a partial tracking portfolio and replicate the return of the index. Fir...

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Published inThe Engineering economist Vol. 67; no. 3; pp. 172 - 194
Main Authors Wu, Liangchuan, Wang, Yuju, Wu, Liang-Hong
Format Journal Article
LanguageEnglish
Published Norcross Taylor & Francis 03.07.2022
Taylor & Francis Inc
Subjects
Algorithms
Artificial intelligence
Constituents
Tracking
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Abstract We propose a three-step method using the stochastic dominance (SD) approach on stock filtering to determine the number and candidate stocks in a portfolio. We empirically prove that our model can be used to efficiently construct a partial tracking portfolio and replicate the return of the index. First, the low standard deviation feature is found in the proposed portfolio using SD for the risk avoider. Second, our model generates constituents for a portfolio and fills the gap in the index tracking strategy. Third, the portfolios chosen from the SD-based model outperform the FTSE index and traditional index trackers' returns. Artificial intelligence algorithms of weighting constituents can be examined in future research.
AbstractList We propose a three-step method using the stochastic dominance (SD) approach on stock filtering to determine the number and candidate stocks in a portfolio. We empirically prove that our model can be used to efficiently construct a partial tracking portfolio and replicate the return of the index. First, the low standard deviation feature is found in the proposed portfolio using SD for the risk avoider. Second, our model generates constituents for a portfolio and fills the gap in the index tracking strategy. Third, the portfolios chosen from the SD-based model outperform the FTSE index and traditional index trackers’ returns. Artificial intelligence algorithms of weighting constituents can be examined in future research.
Author Wu, Liangchuan
Wu, Liang-Hong
Wang, Yuju
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Snippet We propose a three-step method using the stochastic dominance (SD) approach on stock filtering to determine the number and candidate stocks in a portfolio. We...
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SubjectTerms Algorithms
Artificial intelligence
Constituents
Tracking
Title Modeling index tracking portfolio based on stochastic dominance for stock selection
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