Recurrent neural networks for Apple stock price prediction

In recent years, there has been a significant focus on exploring the application of neural network architectures for financial prediction. This present study investigates the utilization of a Long Short-Term Memory (LSTM) model trained on both quarterly fundamental data and daily historical stock pr...

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Main Author: Huang, Melville Bin
Other Authors: Wang Lipo
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/166942
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1669422023-07-07T17:42:09Z Recurrent neural networks for Apple stock price prediction Huang, Melville Bin Wang Lipo School of Electrical and Electronic Engineering ELPWang@ntu.edu.sg Engineering::Electrical and electronic engineering In recent years, there has been a significant focus on exploring the application of neural network architectures for financial prediction. This present study investigates the utilization of a Long Short-Term Memory (LSTM) model trained on both quarterly fundamental data and daily historical stock price data of Apple (AAPL). The study evaluates the accuracy of different LSTM model variations trained on 29 different fundamental indicators using the Mean Squared Error (MSE), Root Mean Square Error (RMSE), MAE (Mean Absolute Error) and Mean Absolute Percentage Error (MAPE) in predicting stock future stock prices. The results show that by selectively choosing the fundamental indicators for training the LSTM model based on fundamental analysis, it can achieve a higher accuracy in comparison to a LSTM model trained exclusively on historical price data. Bachelor of Engineering (Information Engineering and Media) 2023-05-19T11:51:04Z 2023-05-19T11:51:04Z 2023 Final Year Project (FYP) Huang, M. B. (2023). Recurrent neural networks for Apple stock price prediction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166942 https://hdl.handle.net/10356/166942 en A3280-221 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::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Huang, Melville Bin
Recurrent neural networks for Apple stock price prediction
description In recent years, there has been a significant focus on exploring the application of neural network architectures for financial prediction. This present study investigates the utilization of a Long Short-Term Memory (LSTM) model trained on both quarterly fundamental data and daily historical stock price data of Apple (AAPL). The study evaluates the accuracy of different LSTM model variations trained on 29 different fundamental indicators using the Mean Squared Error (MSE), Root Mean Square Error (RMSE), MAE (Mean Absolute Error) and Mean Absolute Percentage Error (MAPE) in predicting stock future stock prices. The results show that by selectively choosing the fundamental indicators for training the LSTM model based on fundamental analysis, it can achieve a higher accuracy in comparison to a LSTM model trained exclusively on historical price data.
author2 Wang Lipo
author_facet Wang Lipo
Huang, Melville Bin
format Final Year Project
author Huang, Melville Bin
author_sort Huang, Melville Bin
title Recurrent neural networks for Apple stock price prediction
title_short Recurrent neural networks for Apple stock price prediction
title_full Recurrent neural networks for Apple stock price prediction
title_fullStr Recurrent neural networks for Apple stock price prediction
title_full_unstemmed Recurrent neural networks for Apple stock price prediction
title_sort recurrent neural networks for apple stock price prediction
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/166942
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