Forecasting Stock Prices with Artificial Intelligence
The purpose of this study is to investigate the closing prices of stocks in Artificial intelligence. The objective is to enhance the accuracy of future stock price Prediction to support investment or trading decisions. The models used in this paper include Simple Recurrent Neural Network (RNN), Long...
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02023.pdf |
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| _version_ | 1825206585267322880 |
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| author | Zhao Danxuan |
| author_facet | Zhao Danxuan |
| author_sort | Zhao Danxuan |
| collection | DOAJ |
| description | The purpose of this study is to investigate the closing prices of stocks in Artificial intelligence. The objective is to enhance the accuracy of future stock price Prediction to support investment or trading decisions. The models used in this paper include Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM with peephole connectivity, and Gated Recurrent Unit (GRU). To conduct the study, Wal-Mart stock data is utilized to accurately predict future stock prices. The results show that the MSE for the SimpleRNN test is higher, indicating weaker generalization. The MSE of the basic LSTM test is lower than that of the RNN, indicating stronger generalization. The validated and tested MSEs of LSTM with peephole connectivity are higher than the basic LSTM and GRU. GRU performs as well as the basic LSTM but has the lowest LSTM training MSE. This stock prediction task requires the GRU model, which is the most suitable choice based on the training time. |
| format | Article |
| id | doaj-art-ad6f7d81568040ac9ea1d26effc8f6b5 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-ad6f7d81568040ac9ea1d26effc8f6b52025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700202310.1051/itmconf/20257002023itmconf_dai2024_02023Forecasting Stock Prices with Artificial IntelligenceZhao Danxuan0Course of Professional Study (CPS), Northeastern UniversityThe purpose of this study is to investigate the closing prices of stocks in Artificial intelligence. The objective is to enhance the accuracy of future stock price Prediction to support investment or trading decisions. The models used in this paper include Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM with peephole connectivity, and Gated Recurrent Unit (GRU). To conduct the study, Wal-Mart stock data is utilized to accurately predict future stock prices. The results show that the MSE for the SimpleRNN test is higher, indicating weaker generalization. The MSE of the basic LSTM test is lower than that of the RNN, indicating stronger generalization. The validated and tested MSEs of LSTM with peephole connectivity are higher than the basic LSTM and GRU. GRU performs as well as the basic LSTM but has the lowest LSTM training MSE. This stock prediction task requires the GRU model, which is the most suitable choice based on the training time.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02023.pdf |
| spellingShingle | Zhao Danxuan Forecasting Stock Prices with Artificial Intelligence ITM Web of Conferences |
| title | Forecasting Stock Prices with Artificial Intelligence |
| title_full | Forecasting Stock Prices with Artificial Intelligence |
| title_fullStr | Forecasting Stock Prices with Artificial Intelligence |
| title_full_unstemmed | Forecasting Stock Prices with Artificial Intelligence |
| title_short | Forecasting Stock Prices with Artificial Intelligence |
| title_sort | forecasting stock prices with artificial intelligence |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02023.pdf |
| work_keys_str_mv | AT zhaodanxuan forecastingstockpriceswithartificialintelligence |