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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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166942 |
| _version_ | 1811695272988770304 |
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| author | Huang, Melville Bin |
| author2 | Wang Lipo |
| author_facet | Wang Lipo Huang, Melville Bin |
| author_sort | Huang, Melville Bin |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T07:20:50Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166942 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:20:50Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Engineering::Electrical and electronic engineering Huang, Melville Bin Recurrent neural networks for Apple stock price prediction |
| title | 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_short | Recurrent neural networks for Apple stock price prediction |
| title_sort | recurrent neural networks for apple stock price prediction |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/166942 |
| work_keys_str_mv | AT huangmelvillebin recurrentneuralnetworksforapplestockpriceprediction |