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The stock market, known for its unpredictable nature and complex workings, has been the focus of significant study. Conventional models frequently fail to fully capture the complex and diverse nature of stock market behavior. With the increasing complexity of financial markets, there is a rising demand for advanced strategies that can offer investors precise predictions and essential insight. There are several studies in this regard, from which we chose 24 modern studies from the year 2018 to the year 2023 in various ways to come out with results that are the focus of a new start in the prediction. This review collected different machine learning methods and deep learning techniques, including deep neural networks, which were then used to build powerful prediction models. Integrating behavioral finance theory with quantitative indicators and financial news provides a unique perspective on stock market dynamics. Also, The Efficient Market Hypothesis (EMH) highlights the significant impact of future information, such as news, on the valuation of stocks. Combining deep learning techniques in forecasting and examining the stock market represents an essential stage in financial research, it offers a more intricate comprehension of the intricacies of stock market dynamics. However, obstacles still need to be overcome, and it is essential to use prudence when applying reported accuracies to actual trading situations. The convergence of behavioral finance, efficient market hypothesis, and deep learning present opportunities for more investigation, offering a more comprehensive method of forecasting stock market trends
Alkhfajee et al. (Mon,) studied this question.