Optimization of venture portfolio based on LSTM and dynamic programming
A rational investor always pursues a portfolio with the greatest possible return and the least possible risk. Therefore, a core issue of investment decision analysis is how to make an optimal investment choice in the market with fuzzy information and realize the balance between maximizing the return...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-01-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.2023275?viewType=HTML |
| _version_ | 1811176461354139648 |
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| author | Jiuchao Ban Yiran Wang Bingjie Liu Hongjun Li |
| author_facet | Jiuchao Ban Yiran Wang Bingjie Liu Hongjun Li |
| author_sort | Jiuchao Ban |
| collection | DOAJ |
| description | A rational investor always pursues a portfolio with the greatest possible return and the least possible risk. Therefore, a core issue of investment decision analysis is how to make an optimal investment choice in the market with fuzzy information and realize the balance between maximizing the return on assets and minimizing the risk. In order to find optimal investment portfolios of financial assets with high volatility, such as gold and Bitcoin, a mathematical model for formulating investment strategies based on the long short-term memory time series and the dynamic programming model combined with the greedy algorithm has been proposed in this paper. The model provides the optimal daily strategy for the five-year trading period so that it can achieve the maximum expected return every day under the condition of a certain investment amount and a certain risk. In addition, a reasonable risk measure based on historical increases is established while considering the weights brought by different investment preferences. The empirical analysis results show that the optimal total assets and initial capital obtained by the model change in the same proportion, and the model is relatively stable and has strong adaptability to the initial capital. Therefore, the proposed model has practical reference value and research significance for investors and promotes a better combination of computer technology and financial investment decision. |
| first_indexed | 2024-04-10T19:52:59Z |
| format | Article |
| id | doaj.art-8fdad854121c438680d138d447df0608 |
| institution | Directory Open Access Journal |
| issn | 2473-6988 |
| language | English |
| last_indexed | 2024-04-10T19:52:59Z |
| publishDate | 2023-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj.art-8fdad854121c438680d138d447df06082023-01-28T01:34:52ZengAIMS PressAIMS Mathematics2473-69882023-01-01835462548310.3934/math.2023275Optimization of venture portfolio based on LSTM and dynamic programmingJiuchao Ban0Yiran Wang 1Bingjie Liu2Hongjun Li 3College of Science, Beijing Forestry University, Beijing 100083, ChinaCollege of Science, Beijing Forestry University, Beijing 100083, ChinaCollege of Science, Beijing Forestry University, Beijing 100083, ChinaCollege of Science, Beijing Forestry University, Beijing 100083, ChinaA rational investor always pursues a portfolio with the greatest possible return and the least possible risk. Therefore, a core issue of investment decision analysis is how to make an optimal investment choice in the market with fuzzy information and realize the balance between maximizing the return on assets and minimizing the risk. In order to find optimal investment portfolios of financial assets with high volatility, such as gold and Bitcoin, a mathematical model for formulating investment strategies based on the long short-term memory time series and the dynamic programming model combined with the greedy algorithm has been proposed in this paper. The model provides the optimal daily strategy for the five-year trading period so that it can achieve the maximum expected return every day under the condition of a certain investment amount and a certain risk. In addition, a reasonable risk measure based on historical increases is established while considering the weights brought by different investment preferences. The empirical analysis results show that the optimal total assets and initial capital obtained by the model change in the same proportion, and the model is relatively stable and has strong adaptability to the initial capital. Therefore, the proposed model has practical reference value and research significance for investors and promotes a better combination of computer technology and financial investment decision.https://www.aimspress.com/article/doi/10.3934/math.2023275?viewType=HTMLportfoliolstm time seriesgreedy algorithmdynamic programming |
| spellingShingle | Jiuchao Ban Yiran Wang Bingjie Liu Hongjun Li Optimization of venture portfolio based on LSTM and dynamic programming AIMS Mathematics portfolio lstm time series greedy algorithm dynamic programming |
| title | Optimization of venture portfolio based on LSTM and dynamic programming |
| title_full | Optimization of venture portfolio based on LSTM and dynamic programming |
| title_fullStr | Optimization of venture portfolio based on LSTM and dynamic programming |
| title_full_unstemmed | Optimization of venture portfolio based on LSTM and dynamic programming |
| title_short | Optimization of venture portfolio based on LSTM and dynamic programming |
| title_sort | optimization of venture portfolio based on lstm and dynamic programming |
| topic | portfolio lstm time series greedy algorithm dynamic programming |
| url | https://www.aimspress.com/article/doi/10.3934/math.2023275?viewType=HTML |
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