Fusion of Technical Indicators and Sentiment Analysis in a Hybrid Framework of Deep Learning Models for Stock Price Movement Prediction
Stock price movement prediction is challenging due to unpredictable fluctuations and the significant impact of market sentiment and news. Accurate prediction models can enhance investor decision-making and control over stock price movements. Creating a model for predicting high-accuracy stock price...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10806710/ |
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| author | Fatemeh Moodi Amir Jahangard Rafsanjani Sajjad Zarifzadeh Mohammad Ali Zare Chahooki |
| author_facet | Fatemeh Moodi Amir Jahangard Rafsanjani Sajjad Zarifzadeh Mohammad Ali Zare Chahooki |
| author_sort | Fatemeh Moodi |
| collection | DOAJ |
| description | Stock price movement prediction is challenging due to unpredictable fluctuations and the significant impact of market sentiment and news. Accurate prediction models can enhance investor decision-making and control over stock price movements. Creating a model for predicting high-accuracy stock price movements can improve investor control over stock prices. In this study, a wide range of technical indicators and various aspects of sentiment analysis in tweets were used to predict stock price movement. The impact of the maximum number of positive comments on Tesla stocks on price increases is investigated. Also, we proposed a method for adding sentiments to each tweet. Extracted advanced sentiment analysis features such as the number of positive comments, the number of negative comments, the average score of positive comments, the average score of negative comments, daily tweet volume, ratio of positive to negative tweets. Effect of time windows with variate size is investigate. A CNN-LSTM deep neural network is used to predict stock price movement and compared with LSTM and GRU models. According to the results, the proposed CNN-LSTM deep neural network has the best results to predict stock price movement over the 30-day interval. |
| format | Article |
| id | doaj-art-de9c49aa94de47c3a04a97b5aef8824b |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-de9c49aa94de47c3a04a97b5aef8824b2025-08-20T02:01:25ZengIEEEIEEE Access2169-35362024-01-011219569619570910.1109/ACCESS.2024.351955610806710Fusion of Technical Indicators and Sentiment Analysis in a Hybrid Framework of Deep Learning Models for Stock Price Movement PredictionFatemeh Moodi0Amir Jahangard Rafsanjani1https://orcid.org/0000-0003-2638-5722Sajjad Zarifzadeh2Mohammad Ali Zare Chahooki3Department of Computer Engineering, Yazd University, Yazd, IranDepartment of Computer Engineering, Yazd University, Yazd, IranDepartment of Computer Engineering, Yazd University, Yazd, IranDepartment of Computer Engineering, Yazd University, Yazd, IranStock price movement prediction is challenging due to unpredictable fluctuations and the significant impact of market sentiment and news. Accurate prediction models can enhance investor decision-making and control over stock price movements. Creating a model for predicting high-accuracy stock price movements can improve investor control over stock prices. In this study, a wide range of technical indicators and various aspects of sentiment analysis in tweets were used to predict stock price movement. The impact of the maximum number of positive comments on Tesla stocks on price increases is investigated. Also, we proposed a method for adding sentiments to each tweet. Extracted advanced sentiment analysis features such as the number of positive comments, the number of negative comments, the average score of positive comments, the average score of negative comments, daily tweet volume, ratio of positive to negative tweets. Effect of time windows with variate size is investigate. A CNN-LSTM deep neural network is used to predict stock price movement and compared with LSTM and GRU models. According to the results, the proposed CNN-LSTM deep neural network has the best results to predict stock price movement over the 30-day interval.https://ieeexplore.ieee.org/document/10806710/Long short-term memory (LSTM)convolutional neural network (CNN)gated recurrent unit (GRU)stock price movement predictionsentiment analysisTwitter |
| spellingShingle | Fatemeh Moodi Amir Jahangard Rafsanjani Sajjad Zarifzadeh Mohammad Ali Zare Chahooki Fusion of Technical Indicators and Sentiment Analysis in a Hybrid Framework of Deep Learning Models for Stock Price Movement Prediction IEEE Access Long short-term memory (LSTM) convolutional neural network (CNN) gated recurrent unit (GRU) stock price movement prediction sentiment analysis |
| title | Fusion of Technical Indicators and Sentiment Analysis in a Hybrid Framework of Deep Learning Models for Stock Price Movement Prediction |
| title_full | Fusion of Technical Indicators and Sentiment Analysis in a Hybrid Framework of Deep Learning Models for Stock Price Movement Prediction |
| title_fullStr | Fusion of Technical Indicators and Sentiment Analysis in a Hybrid Framework of Deep Learning Models for Stock Price Movement Prediction |
| title_full_unstemmed | Fusion of Technical Indicators and Sentiment Analysis in a Hybrid Framework of Deep Learning Models for Stock Price Movement Prediction |
| title_short | Fusion of Technical Indicators and Sentiment Analysis in a Hybrid Framework of Deep Learning Models for Stock Price Movement Prediction |
| title_sort | fusion of technical indicators and sentiment analysis in a hybrid framework of deep learning models for stock price movement prediction |
| topic | Long short-term memory (LSTM) convolutional neural network (CNN) gated recurrent unit (GRU) stock price movement prediction sentiment analysis |
| url | https://ieeexplore.ieee.org/document/10806710/ |
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