Deep learning in stock market forecasting

Regression in machine learning is a task of predicting continuous dependent output based on multiple independent inputs. One of the best machine learning metohd is called deep learning. Deep learning is able to achieve high performance derived from high complexity of machine learning model which...

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Main Author: Wijaya, Michael
Other Authors: Ponnuthurai Nagaratnam Suganthan
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/157860
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1578602023-07-07T19:03:05Z Deep learning in stock market forecasting Wijaya, Michael Ponnuthurai Nagaratnam Suganthan School of Electrical and Electronic Engineering EPNSugan@ntu.edu.sg Engineering::Computer science and engineering Regression in machine learning is a task of predicting continuous dependent output based on multiple independent inputs. One of the best machine learning metohd is called deep learning. Deep learning is able to achieve high performance derived from high complexity of machine learning model which is essential to accurately forecast stock market price. However, this comes with cost of high computational power and high tendency of overfitting. In other words, having more parameters in the model can easily improve the performance by solving the underfitting problem, but the model is more likely exposed to overfitting which leads the model unably to reach the best expected result. Hence, one of possible solution is to randomize and freeze some of the parameters, reducing model complexity which can possibly enhance the performance. Therefore, this project experiments on multiple deep learning models as well as randomized deep learning models: Simple Recurrent Neural Networks (RNN), Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN), Random Vector Functional Link (RVFL), Ensemble Deep Random Vector Functional Link (edRVFL), Echo State Networks (ESN), Temporal Convolutional Networks (TCN). We test it on historical datasets of stock market value from 5 different companies. The result shows randomized model generally works better than non-randomized model. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-24T02:53:43Z 2022-05-24T02:53:43Z 2022 Final Year Project (FYP) Wijaya, M. (2022). Deep learning in stock market forecasting. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157860 https://hdl.handle.net/10356/157860 en A1103-211 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
spellingShingle Engineering::Computer science and engineering
Wijaya, Michael
Deep learning in stock market forecasting
description Regression in machine learning is a task of predicting continuous dependent output based on multiple independent inputs. One of the best machine learning metohd is called deep learning. Deep learning is able to achieve high performance derived from high complexity of machine learning model which is essential to accurately forecast stock market price. However, this comes with cost of high computational power and high tendency of overfitting. In other words, having more parameters in the model can easily improve the performance by solving the underfitting problem, but the model is more likely exposed to overfitting which leads the model unably to reach the best expected result. Hence, one of possible solution is to randomize and freeze some of the parameters, reducing model complexity which can possibly enhance the performance. Therefore, this project experiments on multiple deep learning models as well as randomized deep learning models: Simple Recurrent Neural Networks (RNN), Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN), Random Vector Functional Link (RVFL), Ensemble Deep Random Vector Functional Link (edRVFL), Echo State Networks (ESN), Temporal Convolutional Networks (TCN). We test it on historical datasets of stock market value from 5 different companies. The result shows randomized model generally works better than non-randomized model.
author2 Ponnuthurai Nagaratnam Suganthan
author_facet Ponnuthurai Nagaratnam Suganthan
Wijaya, Michael
format Final Year Project
author Wijaya, Michael
author_sort Wijaya, Michael
title Deep learning in stock market forecasting
title_short Deep learning in stock market forecasting
title_full Deep learning in stock market forecasting
title_fullStr Deep learning in stock market forecasting
title_full_unstemmed Deep learning in stock market forecasting
title_sort deep learning in stock market forecasting
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/157860
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