Predicting Stock Market Trends using Random Forest: A Comparative Analysis

For measuring the size of economies, stock price prediction is becoming a popular and engrossing research issue in the financial and academic sectors. For estimating and forecasting the amount of a stock exchange share, no substantial set of norms are followed. Although various evolutionary technologies, such as fundamental time, technical series analysis, and statistical aid in the forecast process exist, none of these methods have been shown to be a reliable and precise tool for the society to predict the share market or stock exchange scales. A comparison and contrast of several NSE and BSE-listed companies are carried out in this paper by using Random Forest Regression. The financial data generates new variables, such as open, high, low, and closing prices, as well as stock volume, which is used as inputs to the model. The models are good at predicting stock closing prices, as these two indicators have low values. The goal of this study is to use real historical data from the BSE and NSE indexes to suggest where a stock should be sold and bought in a given index.