Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157234
Title: Hybrid deep neural network and deep reinforcement learning for algorithmic finance
Authors: Ooi, Min Hui
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: 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
Project: SCSE21-0438 
Abstract: 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.
URI: https://hdl.handle.net/10356/157234
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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