Hybrid deep neural network and deep reinforcement learning for algorithmic finance
Deep learning is a recent breakthrough in the field of machine learning that has greatly improved predictive and modelling capabilities. While there are many significant achievements using deep learning in fields such as natural language processing and recognition problems, the application of d...
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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/10356/157234 |
| _version_ | 1826124275016269824 |
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| author | Ooi, Min Hui |
| author2 | Quek Hiok Chai |
| author_facet | Quek Hiok Chai Ooi, Min Hui |
| author_sort | Ooi, Min Hui |
| collection | NTU |
| description | Deep learning is a recent breakthrough in the field of machine learning that has greatly
improved predictive and modelling capabilities. While there are many significant achievements
using deep learning in fields such as natural language processing and recognition problems, the
application of deep learning in finance is still heavily being researched.
Traditional prediction models utilise deep neural networks, but face difficulty achieving high
levels of accuracy when solving complex problems. Additionally, such models lack
interpretability which could prevent informed decision making using these models. This paper
proposes a hybrid fuzzy deep neural network architecture. The proposed architecture
consistently obtains high accuracy levels despite complex problem definitions and datasets.
Furthermore, by embedding fuzzy logic, the model enables meaningful interpretations and
insights surrounding the derivation of predictions through use of fuzzy rules.
The proposed architecture was applied to the complex stock price prediction problem and
maintained the high levels of accuracy, while increasing interpretability. The predicted stock
prices were used in calculations of technical indicators such as the MACD to generate a better
analysis of market trends and enable better informed trading decisions.
Using deep learning as a method to solve complex problem often comes with error-prone and
arduous development and debugging. This paper proposes a deep reinforcement learning (DRL)
architecture that delivers good performance when dealing with complex problems.
Furthermore, the proposed architecture is easily extendable to other complex problems, due to
ability to change and adapt to environments.
The proposed DRL architecture was applied to portfolio management. Different portfolio
constraints were added to the environment, and the trade-offs between each portfolio decision
and constraint under different market conditions were observed. An investor can use these
results to weigh trade-offs and make more informed decisions in portfolio management. |
| first_indexed | 2024-10-01T06:17:59Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157234 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:17:59Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1572342022-05-11T06:47:53Z Hybrid deep neural network and deep reinforcement learning for algorithmic finance Ooi, Min Hui Quek Hiok Chai School of Computer Science and Engineering ASHCQUEK@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Deep learning is a recent breakthrough in the field of machine learning that has greatly improved predictive and modelling capabilities. While there are many significant achievements using deep learning in fields such as natural language processing and recognition problems, the application of deep learning in finance is still heavily being researched. Traditional prediction models utilise deep neural networks, but face difficulty achieving high levels of accuracy when solving complex problems. Additionally, such models lack interpretability which could prevent informed decision making using these models. This paper proposes a hybrid fuzzy deep neural network architecture. The proposed architecture consistently obtains high accuracy levels despite complex problem definitions and datasets. Furthermore, by embedding fuzzy logic, the model enables meaningful interpretations and insights surrounding the derivation of predictions through use of fuzzy rules. The proposed architecture was applied to the complex stock price prediction problem and maintained the high levels of accuracy, while increasing interpretability. The predicted stock prices were used in calculations of technical indicators such as the MACD to generate a better analysis of market trends and enable better informed trading decisions. Using deep learning as a method to solve complex problem often comes with error-prone and arduous development and debugging. This paper proposes a deep reinforcement learning (DRL) architecture that delivers good performance when dealing with complex problems. Furthermore, the proposed architecture is easily extendable to other complex problems, due to ability to change and adapt to environments. The proposed DRL architecture was applied to portfolio management. Different portfolio constraints were added to the environment, and the trade-offs between each portfolio decision and constraint under different market conditions were observed. An investor can use these results to weigh trade-offs and make more informed decisions in portfolio management. Bachelor of Engineering (Computer Science) 2022-05-11T06:47:53Z 2022-05-11T06:47:53Z 2022 Final Year Project (FYP) Ooi, M. H. (2022). Hybrid deep neural network and deep reinforcement learning for algorithmic finance. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157234 https://hdl.handle.net/10356/157234 en SCSE21-0438 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Ooi, Min Hui Hybrid deep neural network and deep reinforcement learning for algorithmic finance |
| title | Hybrid deep neural network and deep reinforcement learning for algorithmic finance |
| title_full | Hybrid deep neural network and deep reinforcement learning for algorithmic finance |
| title_fullStr | Hybrid deep neural network and deep reinforcement learning for algorithmic finance |
| title_full_unstemmed | Hybrid deep neural network and deep reinforcement learning for algorithmic finance |
| title_short | Hybrid deep neural network and deep reinforcement learning for algorithmic finance |
| title_sort | hybrid deep neural network and deep reinforcement learning for algorithmic finance |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/157234 |
| work_keys_str_mv | AT ooiminhui hybriddeepneuralnetworkanddeepreinforcementlearningforalgorithmicfinance |