Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models
Accurately predicting stock price trends is of critical importance in the financial sector, enabling both individuals and enterprises to make informed and profitable decisions. In recent years, researchers have employed a variety’ of techniques to forecast stock market trends, yet the challenge of i...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
|
| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04008.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825206552112398336 |
|---|---|
| author | Wang Hao |
| author_facet | Wang Hao |
| author_sort | Wang Hao |
| collection | DOAJ |
| description | Accurately predicting stock price trends is of critical importance in the financial sector, enabling both individuals and enterprises to make informed and profitable decisions. In recent years, researchers have employed a variety’ of techniques to forecast stock market trends, yet the challenge of improving accuracy remains. This research introduces an innovative approach to predicting stock prices, employing two sophisticated models: Long Short-Tenn Memory (LSTM) and Bidirectional Long Short-Tenn Memory (Bi-LSTM) networks. Through rigorous analysis, the research demonstrates that, with proper hypeiparameter tuning. LSTM models are capable of making highly accurate predictions of future stock trends, a capability’ that is also exhibited by Bi-LSTM models. The study’ evaluates the models by’ measuring the Root Mean Square Error (RMSE) while varying key factors. Publicly available stock market information. such as the highest and lowest prices, and opening and closing prices, is utilized for evaluating model effectiveness. The results indicate that the Bi-LSTM model is superior to the LSTM model in terms of RMSE. making it a more effective methodology for stock price forecasting and aiding in strategic decision-making. |
| format | Article |
| id | doaj-art-33e8c94eb4ba4bb9ab01d73234d08257 |
| 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-33e8c94eb4ba4bb9ab01d73234d082572025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700400810.1051/itmconf/20257004008itmconf_dai2024_04008Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM ModelsWang Hao0School of Information, Beijing Wuzi UniversityAccurately predicting stock price trends is of critical importance in the financial sector, enabling both individuals and enterprises to make informed and profitable decisions. In recent years, researchers have employed a variety’ of techniques to forecast stock market trends, yet the challenge of improving accuracy remains. This research introduces an innovative approach to predicting stock prices, employing two sophisticated models: Long Short-Tenn Memory (LSTM) and Bidirectional Long Short-Tenn Memory (Bi-LSTM) networks. Through rigorous analysis, the research demonstrates that, with proper hypeiparameter tuning. LSTM models are capable of making highly accurate predictions of future stock trends, a capability’ that is also exhibited by Bi-LSTM models. The study’ evaluates the models by’ measuring the Root Mean Square Error (RMSE) while varying key factors. Publicly available stock market information. such as the highest and lowest prices, and opening and closing prices, is utilized for evaluating model effectiveness. The results indicate that the Bi-LSTM model is superior to the LSTM model in terms of RMSE. making it a more effective methodology for stock price forecasting and aiding in strategic decision-making.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04008.pdf |
| spellingShingle | Wang Hao Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models ITM Web of Conferences |
| title | Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models |
| title_full | Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models |
| title_fullStr | Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models |
| title_full_unstemmed | Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models |
| title_short | Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models |
| title_sort | enhancing stock price forecasting accuracy using lstm and bi lstm models |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04008.pdf |
| work_keys_str_mv | AT wanghao enhancingstockpriceforecastingaccuracyusinglstmandbilstmmodels |