Deep Learning Approach to Analyze and Review of Stock Market Prediction

As accurate stock price predictions can result in substantial profits through wise decision-making, stock market trend prediction is regarded as a crucial undertaking that requires careful consideration. Because stock market data is often noisy, erratic, and unpredictable, predicting the stock market can be difficult for investors looking to make profitable investments. Existing methods for predicting stock market trends involve multiple approaches. In this article, numerous research publications that propose stock market prediction-based techniques—like long short-term memory (LSTM)—are thoroughly reviewed. Predictions differ in how the received papers are categorized. A comprehensive list and explanation of the research gaps and obstacles encountered by current methodologies aids future work by the researchers. A variety of datasets, software tools, performance evaluation metrics, prediction algorithms, and performance metrics are used to analyze the works. ANN and fuzzy-based algorithms are widely employed methods to achieve accurate stock market prediction. Despite extensive research, the existing method for predicting the stock market still has a number of limitations. The results of this survey point to the complexity of stock market prediction and the need for a variety of elements to be taken into account in order to make more accurate and efficient predictions about the future of the market.