A novel machine learning approach for portfolio optimization
Purpose: Selection of the best stocks for the portfolio as well as allocating the optimal amount of capital per stock in the portfolio are serious challenges in investing in the stock market. The use of machine learning capacities in the process of optimal capital allocation among portfolio assets h...
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| Format: | Article |
| Language: | fas |
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Ayandegan Institute of Higher Education, Tonekabon,
2023-09-01
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| Series: | تصمیم گیری و تحقیق در عملیات |
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| Online Access: | https://www.journal-dmor.ir/article_145630_60595b6b0514640b782cc64e685035fd.pdf |
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| _version_ | 1832577796138860544 |
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| author | Saman Haratizadeh Fatemeh Rezaee |
| author_facet | Saman Haratizadeh Fatemeh Rezaee |
| author_sort | Saman Haratizadeh |
| collection | DOAJ |
| description | Purpose: Selection of the best stocks for the portfolio as well as allocating the optimal amount of capital per stock in the portfolio are serious challenges in investing in the stock market. The use of machine learning capacities in the process of optimal capital allocation among portfolio assets has received less attention and usually, the same weight is assigned to portfolio stocks or traditional risk assessment methods are used to divide capital between portfolio stocks. The common disadvantage of these methods is that they all use simple and inflexible mechanisms to estimate the performance of a set. The purpose of this paper is to show for the first time, that machine learning can be used to create a more effective mechanism for estimating performance, which leads to a more efficient allocation of capital to portfolio stocks.Methodology: Our proposed framework, uses two predictive models based on machine learning. In the first step, stocks historical information is used in a return forecasting model, then based on the predicted returns, the appropriate stocks of the portfolio are selected. In the second step, a separate forecasting model predicts portfolio returns by taking into account both the forecasted returns in the first model and the expected risk of the stocks. At the end based on the predicted return of the numerous random portfolios, the appropriate weight for each asset is selected.Findings: Comparing the returns of adjusted portfolios with this model and adjusted portfolios with other portfolio optimization methods shows the superiority of the proposed model.Originality/Value: In this paper, by using machine learning models, the process of selecting the appropriate stock of the portfolio and allocating capital among the candidate stocks is done optimally. |
| format | Article |
| id | doaj-art-285ac75b5f07463f9353fb39d5a75826 |
| institution | Kabale University |
| issn | 2538-5097 2676-6159 |
| language | fas |
| publishDate | 2023-09-01 |
| publisher | Ayandegan Institute of Higher Education, Tonekabon, |
| record_format | Article |
| series | تصمیم گیری و تحقیق در عملیات |
| spelling | doaj-art-285ac75b5f07463f9353fb39d5a758262025-01-30T15:03:21ZfasAyandegan Institute of Higher Education, Tonekabon,تصمیم گیری و تحقیق در عملیات2538-50972676-61592023-09-018252753910.22105/dmor.2022.307005.1488145630A novel machine learning approach for portfolio optimizationSaman Haratizadeh0Fatemeh Rezaee1Department of Decision Sciences and Knowledge, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.Department of Decision Sciences and Knowledge, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.Purpose: Selection of the best stocks for the portfolio as well as allocating the optimal amount of capital per stock in the portfolio are serious challenges in investing in the stock market. The use of machine learning capacities in the process of optimal capital allocation among portfolio assets has received less attention and usually, the same weight is assigned to portfolio stocks or traditional risk assessment methods are used to divide capital between portfolio stocks. The common disadvantage of these methods is that they all use simple and inflexible mechanisms to estimate the performance of a set. The purpose of this paper is to show for the first time, that machine learning can be used to create a more effective mechanism for estimating performance, which leads to a more efficient allocation of capital to portfolio stocks.Methodology: Our proposed framework, uses two predictive models based on machine learning. In the first step, stocks historical information is used in a return forecasting model, then based on the predicted returns, the appropriate stocks of the portfolio are selected. In the second step, a separate forecasting model predicts portfolio returns by taking into account both the forecasted returns in the first model and the expected risk of the stocks. At the end based on the predicted return of the numerous random portfolios, the appropriate weight for each asset is selected.Findings: Comparing the returns of adjusted portfolios with this model and adjusted portfolios with other portfolio optimization methods shows the superiority of the proposed model.Originality/Value: In this paper, by using machine learning models, the process of selecting the appropriate stock of the portfolio and allocating capital among the candidate stocks is done optimally.https://www.journal-dmor.ir/article_145630_60595b6b0514640b782cc64e685035fd.pdfportfolio selectionportfolio optimizationdeep learningmachine learning |
| spellingShingle | Saman Haratizadeh Fatemeh Rezaee A novel machine learning approach for portfolio optimization تصمیم گیری و تحقیق در عملیات portfolio selection portfolio optimization deep learning machine learning |
| title | A novel machine learning approach for portfolio optimization |
| title_full | A novel machine learning approach for portfolio optimization |
| title_fullStr | A novel machine learning approach for portfolio optimization |
| title_full_unstemmed | A novel machine learning approach for portfolio optimization |
| title_short | A novel machine learning approach for portfolio optimization |
| title_sort | novel machine learning approach for portfolio optimization |
| topic | portfolio selection portfolio optimization deep learning machine learning |
| url | https://www.journal-dmor.ir/article_145630_60595b6b0514640b782cc64e685035fd.pdf |
| work_keys_str_mv | AT samanharatizadeh anovelmachinelearningapproachforportfoliooptimization AT fatemehrezaee anovelmachinelearningapproachforportfoliooptimization AT samanharatizadeh novelmachinelearningapproachforportfoliooptimization AT fatemehrezaee novelmachinelearningapproachforportfoliooptimization |