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

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 netw...

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Main Author: Dinh Khoat Hoang Anh
Other Authors: Quek Hiok Chai
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
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Online Access:https://hdl.handle.net/10356/148085
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1480852021-04-22T13:15:18Z Evolving type-2 neural fuzzy inference system with embedded deep learning in dynamic portfolio rebalancing Dinh Khoat Hoang Anh Quek Hiok Chai School of Computer Science and Engineering ASHCQUEK@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence 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. Bachelor of Engineering (Computer Science) 2021-04-22T13:15:18Z 2021-04-22T13:15:18Z 2021 Final Year Project (FYP) Dinh Khoat Hoang Anh (2021). Evolving type-2 neural fuzzy inference system with embedded deep learning in dynamic portfolio rebalancing. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148085 https://hdl.handle.net/10356/148085 en SCSE20-0217 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Dinh Khoat Hoang Anh
Evolving type-2 neural fuzzy inference system with embedded deep learning in dynamic portfolio rebalancing
description 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.
author2 Quek Hiok Chai
author_facet Quek Hiok Chai
Dinh Khoat Hoang Anh
format Final Year Project
author Dinh Khoat Hoang Anh
author_sort Dinh Khoat Hoang Anh
title Evolving type-2 neural fuzzy inference system with embedded deep learning in dynamic portfolio rebalancing
title_short Evolving type-2 neural fuzzy inference system with embedded deep learning in dynamic portfolio rebalancing
title_full Evolving type-2 neural fuzzy inference system with embedded deep learning in dynamic portfolio rebalancing
title_fullStr Evolving type-2 neural fuzzy inference system with embedded deep learning in dynamic portfolio rebalancing
title_full_unstemmed Evolving type-2 neural fuzzy inference system with embedded deep learning in dynamic portfolio rebalancing
title_sort evolving type-2 neural fuzzy inference system with embedded deep learning in dynamic portfolio rebalancing
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148085
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