Visualizing, Comparing and Forecasting Stock Market Prediction

Abstract

Stock market forecasting is becoming increasingly popular among academics. It is also an important topic in finance. Stock market forecasting and analysis assist investors in making educated judgements. To estimate the stock price, many prediction approaches such as technical analysis, fundamental analysis, time series analysis, and statistical analysis are all utilized, but none of these are proven to be efficient prediction methods. Stock prediction is considered to be incredibly difficult on account of the volatile nature of the stock market. The stock market works in cycles, what comes down goes up and vice versa. It is, therefore, considered unpredictable and uncontrollable. However, Machine learning is very effective in the field of stock market prediction as it contains a huge pool of data to be used. In the course of this paper, we aim to compare different Machine Learning algorithms used to predict stock prices and attempt to forecast stock prices using various prediction tools.