Deep neural networks for stock forecasting
Stock market is an important part of economy. How to effectively predict it to maximize the interests of investors has become a topic of concern to researchers. However, due to the uncertainty and nonstationarity of the stock series, forecasting stock price has become a big challenge. The emergen...
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sg-ntu-dr.10356-1554242023-07-04T17:43:11Z Deep neural networks for stock forecasting Gao, Kexin Lap-Pui Chau School of Electrical and Electronic Engineering elpchau@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Stock market is an important part of economy. How to effectively predict it to maximize the interests of investors has become a topic of concern to researchers. However, due to the uncertainty and nonstationarity of the stock series, forecasting stock price has become a big challenge. The emergence of neural network enables people to effectively train and predict the long-term correlated nonlinear time series. In this thesis, we use different neural networks to forecast the stock price. We observe the results of the prediction using some conventional neural networks such as Multilayer Perceptron, Recurrent Neural Network, Long Short-Term Memory, Gate Recurrent Unit and Temporal Convolutional Network. These networks all can achieve good performance. But Recurrent Neural Network some time is not stable because of the gradient vanishing phenomenon. Then we use two novel structures, Temporal Attention-Augmented Bilinear Network and a hybrid model named EWT-dpLSTM-PSO-ORELM framework, to do the forecasting. The Hybrid model get a better result than conventional ones. Finally, we propose a new structure named EWT-TCN-PSO-ORELM. The new model’s accuracy is good as the hybrid model but need less time to train and to obtain the prediction results. Keywords: Stock price forcasting, MLP, RNN, LSTM, GRU, TCN, TABL, EWT, PSO, ORELM. Master of Science (Signal Processing) 2022-02-24T00:43:13Z 2022-02-24T00:43:13Z 2021 Thesis-Master by Coursework Gao, K. (2021). Deep neural networks for stock forecasting. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/155424 https://hdl.handle.net/10356/155424 en application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering::Computer hardware, software and systems Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Gao, Kexin Deep neural networks for stock forecasting |
description |
Stock market is an important part of economy. How to effectively predict it
to maximize the interests of investors has become a topic of concern to researchers.
However, due to the uncertainty and nonstationarity of the stock series,
forecasting stock price has become a big challenge. The emergence of
neural network enables people to effectively train and predict the long-term correlated
nonlinear time series.
In this thesis, we use different neural networks to forecast the stock price. We
observe the results of the prediction using some conventional neural networks
such as Multilayer Perceptron, Recurrent Neural Network, Long Short-Term
Memory, Gate Recurrent Unit and Temporal Convolutional Network. These networks
all can achieve good performance. But Recurrent Neural Network some
time is not stable because of the gradient vanishing phenomenon.
Then we use two novel structures, Temporal Attention-Augmented Bilinear Network
and a hybrid model named EWT-dpLSTM-PSO-ORELM framework, to do
the forecasting. The Hybrid model get a better result than conventional ones.
Finally, we propose a new structure named EWT-TCN-PSO-ORELM. The new
model’s accuracy is good as the hybrid model but need less time to train and
to obtain the prediction results.
Keywords: Stock price forcasting, MLP, RNN, LSTM, GRU, TCN, TABL,
EWT, PSO, ORELM. |
author2 |
Lap-Pui Chau |
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Lap-Pui Chau Gao, Kexin |
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Thesis-Master by Coursework |
author |
Gao, Kexin |
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Gao, Kexin |
title |
Deep neural networks for stock forecasting |
title_short |
Deep neural networks for stock forecasting |
title_full |
Deep neural networks for stock forecasting |
title_fullStr |
Deep neural networks for stock forecasting |
title_full_unstemmed |
Deep neural networks for stock forecasting |
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deep neural networks for stock forecasting |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/155424 |
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1772826076805857280 |