AI-driven stock market prediction
The accuracy of deep learning techniques used for prediction has always been deemed superior as compared to regression techniques. In this report, deep learning techniques such as Long Short-Term Memory, Recurrent Neural Network, Multi-Layer Perceptron and Gated Recurrent Unit will be used in...
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sg-ntu-dr.10356-1576482023-07-07T18:59:38Z AI-driven stock market prediction Chong, Noel Zhenjie Alex Chichung Kot School of Electrical and Electronic Engineering EACKOT@ntu.edu.sg Engineering::Electrical and electronic engineering The accuracy of deep learning techniques used for prediction has always been deemed superior as compared to regression techniques. In this report, deep learning techniques such as Long Short-Term Memory, Recurrent Neural Network, Multi-Layer Perceptron and Gated Recurrent Unit will be used in a comparison with regression techniques such as Gradient Boosting Regressor and Support Vector Regressor to forecast the Straits Times Index (STI). The data sourced will also be non-linear and will be used as inputs into the algorithms to generate the results. The results will be compared using Fundamental Analysis and Technical Analysis. This experiment shows that the results from deep learning techniques does not generally mean that it is more accurate as compared to regression techniques. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-21T12:02:56Z 2022-05-21T12:02:56Z 2022 Final Year Project (FYP) Chong, N. Z. (2022). AI-driven stock market prediction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157648 https://hdl.handle.net/10356/157648 en P3052-202 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Chong, Noel Zhenjie AI-driven stock market prediction |
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The accuracy of deep learning techniques used for prediction has always been deemed superior
as compared to regression techniques. In this report, deep learning techniques such as Long
Short-Term Memory, Recurrent Neural Network, Multi-Layer Perceptron and Gated Recurrent
Unit will be used in a comparison with regression techniques such as Gradient Boosting
Regressor and Support Vector Regressor to forecast the Straits Times Index (STI). The data
sourced will also be non-linear and will be used as inputs into the algorithms to generate the
results. The results will be compared using Fundamental Analysis and Technical Analysis. This
experiment shows that the results from deep learning techniques does not generally mean that
it is more accurate as compared to regression techniques. |
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Alex Chichung Kot |
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Alex Chichung Kot Chong, Noel Zhenjie |
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Final Year Project |
author |
Chong, Noel Zhenjie |
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Chong, Noel Zhenjie |
title |
AI-driven stock market prediction |
title_short |
AI-driven stock market prediction |
title_full |
AI-driven stock market prediction |
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AI-driven stock market prediction |
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AI-driven stock market prediction |
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ai-driven stock market prediction |
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Nanyang Technological University |
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2022 |
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https://hdl.handle.net/10356/157648 |
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1772826258780979200 |