Dynamic portfolio insurance strategy: a robust machine learning approach
In this paper, we propose a robust genetic programming (RGP) model for a dynamic strategy of stock portfolio insurance. With portfolio insurance strategy, we divide the money in a risky asset and a risk-free asset. Our applied strategy is based on a constant proportion portfolio insurance strategy....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-10-01
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| Series: | Journal of Information and Telecommunication |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24751839.2018.1431447 |
| _version_ | 1828465856714113024 |
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| author | Siamak Dehghanpour Akbar Esfahanipour |
| author_facet | Siamak Dehghanpour Akbar Esfahanipour |
| author_sort | Siamak Dehghanpour |
| collection | DOAJ |
| description | In this paper, we propose a robust genetic programming (RGP) model for a dynamic strategy of stock portfolio insurance. With portfolio insurance strategy, we divide the money in a risky asset and a risk-free asset. Our applied strategy is based on a constant proportion portfolio insurance strategy. For determining the amount for investing in the risky asset, a critical parameter is a constant risk multiplier that is calculated in our proposed model using RGP to reflect market dynamics. Our model includes four main steps: (1) Selecting the best stocks for constructing a portfolio using a density-based clustering strategy. (2) Enhancing the robustness of our proposed model with an application of the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for forecasting the future prices of the selected stocks. The findings show that using ANFIS, instead of a regular multi-layer artificial neural network improves the prediction accuracy and our model’s robustness. (3) Implementing the RGP model for calculating the risk multiplier. Risk variables are used to generate equation trees for calculating the risk multiplier. (4) Determining the optimal portfolio weights of the assets using the well-known Markowitz portfolio optimization model. Experimental results show that our proposed strategy outperforms our previous model. |
| first_indexed | 2024-12-11T03:40:11Z |
| format | Article |
| id | doaj.art-1db794702aee40f699f52cd5a6518ae5 |
| institution | Directory Open Access Journal |
| issn | 2475-1839 2475-1847 |
| language | English |
| last_indexed | 2024-12-11T03:40:11Z |
| publishDate | 2018-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Information and Telecommunication |
| spelling | doaj.art-1db794702aee40f699f52cd5a6518ae52022-12-22T01:22:09ZengTaylor & Francis GroupJournal of Information and Telecommunication2475-18392475-18472018-10-012439241010.1080/24751839.2018.14314471431447Dynamic portfolio insurance strategy: a robust machine learning approachSiamak Dehghanpour0Akbar Esfahanipour1Amirkabir University of TechnologyAmirkabir University of TechnologyIn this paper, we propose a robust genetic programming (RGP) model for a dynamic strategy of stock portfolio insurance. With portfolio insurance strategy, we divide the money in a risky asset and a risk-free asset. Our applied strategy is based on a constant proportion portfolio insurance strategy. For determining the amount for investing in the risky asset, a critical parameter is a constant risk multiplier that is calculated in our proposed model using RGP to reflect market dynamics. Our model includes four main steps: (1) Selecting the best stocks for constructing a portfolio using a density-based clustering strategy. (2) Enhancing the robustness of our proposed model with an application of the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for forecasting the future prices of the selected stocks. The findings show that using ANFIS, instead of a regular multi-layer artificial neural network improves the prediction accuracy and our model’s robustness. (3) Implementing the RGP model for calculating the risk multiplier. Risk variables are used to generate equation trees for calculating the risk multiplier. (4) Determining the optimal portfolio weights of the assets using the well-known Markowitz portfolio optimization model. Experimental results show that our proposed strategy outperforms our previous model.http://dx.doi.org/10.1080/24751839.2018.1431447Robust genetic programming (RGP)portfolio insurance strategymachine learningportfolio optimization modelconstant proportion portfolio insurance (CPPI) |
| spellingShingle | Siamak Dehghanpour Akbar Esfahanipour Dynamic portfolio insurance strategy: a robust machine learning approach Journal of Information and Telecommunication Robust genetic programming (RGP) portfolio insurance strategy machine learning portfolio optimization model constant proportion portfolio insurance (CPPI) |
| title | Dynamic portfolio insurance strategy: a robust machine learning approach |
| title_full | Dynamic portfolio insurance strategy: a robust machine learning approach |
| title_fullStr | Dynamic portfolio insurance strategy: a robust machine learning approach |
| title_full_unstemmed | Dynamic portfolio insurance strategy: a robust machine learning approach |
| title_short | Dynamic portfolio insurance strategy: a robust machine learning approach |
| title_sort | dynamic portfolio insurance strategy a robust machine learning approach |
| topic | Robust genetic programming (RGP) portfolio insurance strategy machine learning portfolio optimization model constant proportion portfolio insurance (CPPI) |
| url | http://dx.doi.org/10.1080/24751839.2018.1431447 |
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