A Systematic Review of Recent Advances on Stock Markets Predictions Using Deep Learning Approach
Keywords:
Stock Market Prediction, Deep Learning (DL), Sentiment Analysis, Fake News Detection, Artificial Intelligence (AI)Abstract
The landscape of stock market prediction is undergoing a profound transformation driven by technological advancements and data-driven methodologies. Within this shifting paradigm, artificial intelligence (AI), particularly deep learning (DL), is emerging as a transformative tool to enhance predictive accuracy. This systematic literature review explores recent trends in the integration of AI, specifically DL, in stock market prediction, with a focus on the use of DL models for time series data analysis, sentiment data, and news data. The review aimed to investigate the effectiveness of DL-based models for stock market prediction using time series data, sentiment analysis, and news data, while also exploring their role in mitigating the impact of fake news and news sentiment. This paper systematically assessed 3,621 articles from 2019 to 2023, with rigorous selection criteria. The final dataset synthesis included 48 articles. The findings revealed that DL models have proven to be effective in predicting stock price based on time series data when used along with sentiment and news. Further, it is also evident that the use of DL models in mitigating the influence of fake news and news sentiment on market behavior contributes to enhanced predictive accuracy. The review concludes by identifying research gaps and outlining future directions to advance the field of stock market prediction. Future research endeavors in the stock market prediction field should prioritize the use of Transformer-based models for time series data, BERT models, and lightweight language models for sentiment analysis and fake news detection integration. The use of optimization algorithms in hyperparameter tuning can further improve prediction accuracy and reduce computational complexities. This review offers valuable insights not only for advanced stock prediction but also for financial market researchers, investors, and analysts in the context of advanced stock prediction
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