Predicting Stock Market by Sentiment Analysis and Deep Learning
The stock market may be unpredictable; understanding when to purchase and sell can greatly assist businesses and individuals in maximizing profits and minimizing losses. Many companies have previously modified time-series analysis, a data mining technique, to forecast stock price movement. The idea...
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| Format: | Article |
| Language: | English |
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Wrocław University of Science and Technology
2024-01-01
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| Series: | Operations Research and Decisions |
| Online Access: | https://ord.pwr.edu.pl/assets/papers_archive/ord2024vol34no2_6.pdf |
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| author | Süreyya, Özögür Akyüz Pınar, Karadayı Ataş Aymane Benkhaldoun |
| author_facet | Süreyya, Özögür Akyüz Pınar, Karadayı Ataş Aymane Benkhaldoun |
| author_sort | Süreyya, Özögür Akyüz |
| collection | DOAJ |
| description | The stock market may be unpredictable; understanding when to purchase and sell can greatly assist businesses and individuals in maximizing profits and minimizing losses. Many companies have previously modified time-series analysis, a data mining technique, to forecast stock price movement. The idea of textual data mining has recently come up in debates about stock market forecasts. In this study, five of the largest firms' historical stock prices were used to train two deep learning models-long short-term memory (LSTM) and one-dimensional convolutional neural network (1D CNN), then the results of all the models were compared. To connect price value fluctuations with the general public, sentiment scores were offered in addition to stock price values by employing natural language processing techniques (TextBlob) to tweets. (original abstract) |
| format | Article |
| id | doaj-art-3ef89bcac6c646ffb7125f138a804775 |
| institution | DOAJ |
| issn | 2081-8858 2391-6060 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wrocław University of Science and Technology |
| record_format | Article |
| series | Operations Research and Decisions |
| spelling | doaj-art-3ef89bcac6c646ffb7125f138a8047752025-08-20T02:40:21ZengWrocław University of Science and TechnologyOperations Research and Decisions2081-88582391-60602024-01-01vol. 34no. 285107171694161Predicting Stock Market by Sentiment Analysis and Deep LearningSüreyya, Özögür Akyüz0Pınar, Karadayı Ataş1Aymane Benkhaldoun2Bahçesehir University, Istanbul, TurkeyBahçesehir University, Istanbul, TurkeyArel University, Istanbul, TurkeyThe stock market may be unpredictable; understanding when to purchase and sell can greatly assist businesses and individuals in maximizing profits and minimizing losses. Many companies have previously modified time-series analysis, a data mining technique, to forecast stock price movement. The idea of textual data mining has recently come up in debates about stock market forecasts. In this study, five of the largest firms' historical stock prices were used to train two deep learning models-long short-term memory (LSTM) and one-dimensional convolutional neural network (1D CNN), then the results of all the models were compared. To connect price value fluctuations with the general public, sentiment scores were offered in addition to stock price values by employing natural language processing techniques (TextBlob) to tweets. (original abstract)https://ord.pwr.edu.pl/assets/papers_archive/ord2024vol34no2_6.pdf |
| spellingShingle | Süreyya, Özögür Akyüz Pınar, Karadayı Ataş Aymane Benkhaldoun Predicting Stock Market by Sentiment Analysis and Deep Learning Operations Research and Decisions |
| title | Predicting Stock Market by Sentiment Analysis and Deep Learning |
| title_full | Predicting Stock Market by Sentiment Analysis and Deep Learning |
| title_fullStr | Predicting Stock Market by Sentiment Analysis and Deep Learning |
| title_full_unstemmed | Predicting Stock Market by Sentiment Analysis and Deep Learning |
| title_short | Predicting Stock Market by Sentiment Analysis and Deep Learning |
| title_sort | predicting stock market by sentiment analysis and deep learning |
| url | https://ord.pwr.edu.pl/assets/papers_archive/ord2024vol34no2_6.pdf |
| work_keys_str_mv | AT sureyyaozogurakyuz predictingstockmarketbysentimentanalysisanddeeplearning AT pınarkaradayıatas predictingstockmarketbysentimentanalysisanddeeplearning AT aymanebenkhaldoun predictingstockmarketbysentimentanalysisanddeeplearning |