Evolving type-2 neural fuzzy inference system with embedded deep learning in dynamic portfolio rebalancing

• 主要作者: Dinh Khoat Hoang Anh
• 其他作者: Quek Hiok Chai
• 格式: Final Year Project
• 語言: English
• 出版: Nanyang Technological University 2021
• 主題: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
• 在線閱讀: https://hdl.handle.net/10356/148085

This paper examines the benefits of integrating neuro-fuzzy system and deep learning architecture for making predictions in a noisy environment with dynamically changing data, and its feasibility in financial market applications. Previous research has been carried out to implement deep neural networks to extract features for the fuzzy system [1], but this combination does not make the learning model entirely interpretable. To benefit both from the interpretability property of the fuzzy system and the performance of deep learning, the proposed Evolving Type-2 Neural Fuzzy Inference System with Embedded Deep Learning model (eT2FIS-EDL) employs an embedded deep learning component parallel with the rule generation. This proposed method resolves the black-box nature of deep learning architecture while maintaining computation efficiency during the inference process. The eT2FIS-EDL is implemented in two variations, one with convolutional neural network (CNN), and the other with long short-term memory (LSTM). Type-2 fuzzy model is chosen in order to increase the model’s tolerance with noisy data. Learning mechanism features including the modification, merger and deletion of rules are implemented to help maintain the interpretability of the model, as well as to ensure its adaptation to shifts and drifts in data. The eT2FIS-EDL demonstrated its effectiveness through the prediction tasks on a nonlinear system dataset, as well as on real-life stock and exchange-traded fund (ETF) datasets. The model’s usefulness is further illustrated through the implementation of dynamic portfolio rebalancing strategy. This strategy combines reinforcement learning with eT2FIS-EDL to rebalance the portfolio with the considerations of market trends, risks, and returns. Both CNN and LSTM variants are integrated into the learning model to reduce the information lag. The experimental results are highly encouraging as the proposed rebalancing strategy outperforms the other four existing portfolio management strategies, namely buy and hold, equal weight (periodic) rebalancing, inverse variance rebalancing and max Sharpe ratio rebalancing.