An improvement of stochastic gradient descent approach for mean-variance portfolio optimization problem
In this paper, the current variant technique of the stochastic gradient descent (SGD) approach, namely, the adaptive moment estimation (Adam) approach, is improved by adding the standard error in the updating rule. ,e aim is to fasten the convergence rate of the Adam algorithm. ,is improvement is...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/2327/1/J12286_58525d433e35f3854a4226ebd4fc4e38.pdf |
| _version_ | 1825709704296267776 |
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| author | S. W. Su, Stephanie Kek, Sie Long |
| author_facet | S. W. Su, Stephanie Kek, Sie Long |
| author_sort | S. W. Su, Stephanie |
| collection | UTHM |
| description | In this paper, the current variant technique of the stochastic gradient descent (SGD) approach, namely, the adaptive moment
estimation (Adam) approach, is improved by adding the standard error in the updating rule. ,e aim is to fasten the convergence
rate of the Adam algorithm. ,is improvement is termed as Adam with standard error (AdamSE) algorithm. On the other hand,
the mean-variance portfolio optimization model is formulated from the historical data of the rate of return of the S&P 500 stock,
10-year Treasury bond, and money market. ,e application of SGD, Adam, adaptive moment estimation with maximum
(AdaMax), Nesterov-accelerated adaptive moment estimation (Nadam), AMSGrad, and AdamSE algorithms to solve the meanvariance
portfolio optimization problem is further investigated. During the calculation procedure, the iterative solution converges
to the optimal portfolio solution. It is noticed that the AdamSE algorithm has the smallest iteration number. ,e results show that
the rate of convergence of the Adam algorithm is significantly enhanced by using the AdamSE algorithm. In conclusion, the
efficiency of the improved Adam algorithm using the standard error has been expressed. Furthermore, the applicability of SGD,
Adam, AdaMax, Nadam, AMSGrad, and AdamSE algorithms in solving the mean-variance portfolio optimization problem
is validated. |
| first_indexed | 2024-03-05T21:42:38Z |
| format | Article |
| id | uthm.eprints-2327 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:42:38Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | uthm.eprints-23272021-10-20T01:42:59Z http://eprints.uthm.edu.my/2327/ An improvement of stochastic gradient descent approach for mean-variance portfolio optimization problem S. W. Su, Stephanie Kek, Sie Long QA76 Computer software In this paper, the current variant technique of the stochastic gradient descent (SGD) approach, namely, the adaptive moment estimation (Adam) approach, is improved by adding the standard error in the updating rule. ,e aim is to fasten the convergence rate of the Adam algorithm. ,is improvement is termed as Adam with standard error (AdamSE) algorithm. On the other hand, the mean-variance portfolio optimization model is formulated from the historical data of the rate of return of the S&P 500 stock, 10-year Treasury bond, and money market. ,e application of SGD, Adam, adaptive moment estimation with maximum (AdaMax), Nesterov-accelerated adaptive moment estimation (Nadam), AMSGrad, and AdamSE algorithms to solve the meanvariance portfolio optimization problem is further investigated. During the calculation procedure, the iterative solution converges to the optimal portfolio solution. It is noticed that the AdamSE algorithm has the smallest iteration number. ,e results show that the rate of convergence of the Adam algorithm is significantly enhanced by using the AdamSE algorithm. In conclusion, the efficiency of the improved Adam algorithm using the standard error has been expressed. Furthermore, the applicability of SGD, Adam, AdaMax, Nadam, AMSGrad, and AdamSE algorithms in solving the mean-variance portfolio optimization problem is validated. 2021 Article PeerReviewed text en http://eprints.uthm.edu.my/2327/1/J12286_58525d433e35f3854a4226ebd4fc4e38.pdf S. W. Su, Stephanie and Kek, Sie Long (2021) An improvement of stochastic gradient descent approach for mean-variance portfolio optimization problem. Journal of Mathematics. pp. 1-10. https://doi.org/10.1155/2021/8892636 |
| spellingShingle | QA76 Computer software S. W. Su, Stephanie Kek, Sie Long An improvement of stochastic gradient descent approach for mean-variance portfolio optimization problem |
| title | An improvement of stochastic gradient descent approach for
mean-variance portfolio optimization problem |
| title_full | An improvement of stochastic gradient descent approach for
mean-variance portfolio optimization problem |
| title_fullStr | An improvement of stochastic gradient descent approach for
mean-variance portfolio optimization problem |
| title_full_unstemmed | An improvement of stochastic gradient descent approach for
mean-variance portfolio optimization problem |
| title_short | An improvement of stochastic gradient descent approach for
mean-variance portfolio optimization problem |
| title_sort | improvement of stochastic gradient descent approach for mean variance portfolio optimization problem |
| topic | QA76 Computer software |
| url | http://eprints.uthm.edu.my/2327/1/J12286_58525d433e35f3854a4226ebd4fc4e38.pdf |
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