A Survey of Stock Market Prediction - Based on Machine Learning Techniques

Stock market Prediction has been a topic of attention for numerous researchers since its beginning. Often traditional statistical methods get conflict to grab the complex, non-linear patterns in stock market data. Due to various factors predicting stock prices is challenging, including a company’s financial performance, present assets, politics, general economic conditions, politics, unanticipated occurrence, global situation, etc. The fluctuating behavior of the inventory place market makes it difficult to expect stock prices, as they constantly change. This paper comprehensively reviews machine and deep learning techniques used to predict the stock market and highlights the proper techniques for predicting inventory cost. This paper also covers the performance metrics for measuring the performance of prediction models, research gaps, and issues found in previous work.