Stock market trading is an activity in which investors need fast and accurate information to make effective decisions to increase their profit. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict because the stock market can easily fluctuates to different styles in overtime period. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. Over the past decades, the Deep Learning (DL) algorithms has been developed to predict the stock market performances. DL models can assists the investors to predict the future movements in stock market, increases their profit rate, takes right decision with earlier time response, minimise their risk in both investment and management to achieve better performance in their securities investment. In this paper, the background of stock market detection techniques is studied to encourage further research in this field. First, the review is planned to investigate the various DL algorithms for stock market prediction system. Next, the merits and demerits of every framework are analyzed based on its performance. Finally, potential improvements are suggested to realize greater efficiency in predicting the stock market.
Stock Market Trading, Investors, Decision-Making Deep Learning, Accurate Prediction.
R Gayathri , Dr S Devi Suganya "A Systematic Analysis of Stock Prediction Models using Artificial Intelligence Approaches" Iconic Research And Engineering Journals Volume 7 Issue 4 2023 Page 256-269
R Gayathri , Dr S Devi Suganya "A Systematic Analysis of Stock Prediction Models using Artificial Intelligence Approaches" Iconic Research And Engineering Journals, 7(4)