Stock Market Prediction Using Deep Reinforcement Learning
Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has introduced advanced predictive algorithms, reshap...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied System Innovation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-5577/6/6/106 |
| _version_ | 1797382130042404864 |
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| author | Alamir Labib Awad Saleh Mesbah Elkaffas Mohammed Waleed Fakhr |
| author_facet | Alamir Labib Awad Saleh Mesbah Elkaffas Mohammed Waleed Fakhr |
| author_sort | Alamir Labib Awad |
| collection | DOAJ |
| description | Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has introduced advanced predictive algorithms, reshaping investment strategies. Essential to this transformation is the profound reliance on historical data analysis, driving the automation of decisions, particularly in individual stock contexts. Recent strides in deep reinforcement learning algorithms have emerged as a focal point for researchers, offering promising avenues in stock market predictions. In contrast to prevailing models rooted in artificial neural network (ANN) and long short-term memory (LSTM) algorithms, this study introduces a pioneering approach. By integrating ANN, LSTM, and natural language processing (NLP) techniques with the deep Q network (DQN), this research crafts a novel architecture tailored specifically for stock market prediction. At its core, this innovative framework harnesses the wealth of historical stock data, with a keen focus on gold stocks. Augmented by the insightful analysis of social media data, including platforms such as S&P, Yahoo, NASDAQ, and various gold market-related channels, this study gains depth and comprehensiveness. The predictive prowess of the developed model is exemplified in its ability to forecast the opening stock value for the subsequent day, a feat validated across exhaustive datasets. Through rigorous comparative analysis against benchmark algorithms, the research spotlights the unparalleled accuracy and efficacy of the proposed combined algorithmic architecture. This study not only presents a compelling demonstration of predictive analytics but also engages in critical analysis, illuminating the intricate dynamics of the stock market. Ultimately, this research contributes valuable insights and sets new horizons in the realm of stock market predictions. |
| first_indexed | 2024-03-08T21:01:03Z |
| format | Article |
| id | doaj.art-d673f35e4a884b9d98b4017ef7fdf969 |
| institution | Directory Open Access Journal |
| issn | 2571-5577 |
| language | English |
| last_indexed | 2024-03-08T21:01:03Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj.art-d673f35e4a884b9d98b4017ef7fdf9692023-12-22T13:52:33ZengMDPI AGApplied System Innovation2571-55772023-11-016610610.3390/asi6060106Stock Market Prediction Using Deep Reinforcement LearningAlamir Labib Awad0Saleh Mesbah Elkaffas1Mohammed Waleed Fakhr2College of Computing and IT, Arab Academy for Science, Technology and Maritime Transport, Alexandria 5517220, EgyptCollege of Computing and IT, Arab Academy for Science, Technology and Maritime Transport, Alexandria 5517220, EgyptCollege of Computing and IT, Arab Academy for Science, Technology and Maritime Transport, Alexandria 5517220, EgyptStock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has introduced advanced predictive algorithms, reshaping investment strategies. Essential to this transformation is the profound reliance on historical data analysis, driving the automation of decisions, particularly in individual stock contexts. Recent strides in deep reinforcement learning algorithms have emerged as a focal point for researchers, offering promising avenues in stock market predictions. In contrast to prevailing models rooted in artificial neural network (ANN) and long short-term memory (LSTM) algorithms, this study introduces a pioneering approach. By integrating ANN, LSTM, and natural language processing (NLP) techniques with the deep Q network (DQN), this research crafts a novel architecture tailored specifically for stock market prediction. At its core, this innovative framework harnesses the wealth of historical stock data, with a keen focus on gold stocks. Augmented by the insightful analysis of social media data, including platforms such as S&P, Yahoo, NASDAQ, and various gold market-related channels, this study gains depth and comprehensiveness. The predictive prowess of the developed model is exemplified in its ability to forecast the opening stock value for the subsequent day, a feat validated across exhaustive datasets. Through rigorous comparative analysis against benchmark algorithms, the research spotlights the unparalleled accuracy and efficacy of the proposed combined algorithmic architecture. This study not only presents a compelling demonstration of predictive analytics but also engages in critical analysis, illuminating the intricate dynamics of the stock market. Ultimately, this research contributes valuable insights and sets new horizons in the realm of stock market predictions.https://www.mdpi.com/2571-5577/6/6/106stock trading marketsdeep reinforcement learningDRLneural networksstock predictionvariational mode decomposition |
| spellingShingle | Alamir Labib Awad Saleh Mesbah Elkaffas Mohammed Waleed Fakhr Stock Market Prediction Using Deep Reinforcement Learning Applied System Innovation stock trading markets deep reinforcement learning DRL neural networks stock prediction variational mode decomposition |
| title | Stock Market Prediction Using Deep Reinforcement Learning |
| title_full | Stock Market Prediction Using Deep Reinforcement Learning |
| title_fullStr | Stock Market Prediction Using Deep Reinforcement Learning |
| title_full_unstemmed | Stock Market Prediction Using Deep Reinforcement Learning |
| title_short | Stock Market Prediction Using Deep Reinforcement Learning |
| title_sort | stock market prediction using deep reinforcement learning |
| topic | stock trading markets deep reinforcement learning DRL neural networks stock prediction variational mode decomposition |
| url | https://www.mdpi.com/2571-5577/6/6/106 |
| work_keys_str_mv | AT alamirlabibawad stockmarketpredictionusingdeepreinforcementlearning AT salehmesbahelkaffas stockmarketpredictionusingdeepreinforcementlearning AT mohammedwaleedfakhr stockmarketpredictionusingdeepreinforcementlearning |