Learning optimal portfolios with intrinsic rewards
A profitable stock trading strategy is crucial for financial institutions. However, it is difficult to find a successful trading strategy in the complex and dynamic financial market. A wise choice of an appropriate risk measure in trading problems is crucial to evaluate the investment performance as...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/156941 |
| _version_ | 1826116934250266624 |
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| author | Guan, Zihang |
| author2 | Pun Chi Seng |
| author_facet | Pun Chi Seng Guan, Zihang |
| author_sort | Guan, Zihang |
| collection | NTU |
| description | A profitable stock trading strategy is crucial for financial institutions. However, it is difficult to find a successful trading strategy in the complex and dynamic financial market. A wise choice of an appropriate risk measure in trading problems is crucial to evaluate the investment performance as well as to guide the RL trading agent to profit. In this dissertation, we are motivated to study the efficacy of learning optimal portfolios with intrinsic rewards. The main contributions of this dissertation include formally deriving the algorithm to incorporate the optimal intrinsic reward on Advantage Actor-Critic (A2C) RL algorithm and first applying the A2C algorithm with optimal intrinsic reward in finance environment. |
| first_indexed | 2024-10-01T04:19:32Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/156941 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:19:32Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1569412023-02-28T23:13:37Z Learning optimal portfolios with intrinsic rewards Guan, Zihang Pun Chi Seng School of Physical and Mathematical Sciences Nixie Sapphira Lesmana cspun@ntu.edu.sg Science::Mathematics::Statistics A profitable stock trading strategy is crucial for financial institutions. However, it is difficult to find a successful trading strategy in the complex and dynamic financial market. A wise choice of an appropriate risk measure in trading problems is crucial to evaluate the investment performance as well as to guide the RL trading agent to profit. In this dissertation, we are motivated to study the efficacy of learning optimal portfolios with intrinsic rewards. The main contributions of this dissertation include formally deriving the algorithm to incorporate the optimal intrinsic reward on Advantage Actor-Critic (A2C) RL algorithm and first applying the A2C algorithm with optimal intrinsic reward in finance environment. Bachelor of Science in Mathematical Sciences 2022-04-29T05:30:18Z 2022-04-29T05:30:18Z 2022 Final Year Project (FYP) Guan, Z. (2022). Learning optimal portfolios with intrinsic rewards. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156941 https://hdl.handle.net/10356/156941 en MATH/21/040 application/pdf Nanyang Technological University |
| spellingShingle | Science::Mathematics::Statistics Guan, Zihang Learning optimal portfolios with intrinsic rewards |
| title | Learning optimal portfolios with intrinsic rewards |
| title_full | Learning optimal portfolios with intrinsic rewards |
| title_fullStr | Learning optimal portfolios with intrinsic rewards |
| title_full_unstemmed | Learning optimal portfolios with intrinsic rewards |
| title_short | Learning optimal portfolios with intrinsic rewards |
| title_sort | learning optimal portfolios with intrinsic rewards |
| topic | Science::Mathematics::Statistics |
| url | https://hdl.handle.net/10356/156941 |
| work_keys_str_mv | AT guanzihang learningoptimalportfolioswithintrinsicrewards |