Comparative Analysis of various Machine Learning Algorithms for Stock Price Prediction

Stock market is volatile in nature which subjects to great amount of risk. Manual analysis and prediction of such volatile nature is a tedious task and results to less reliability. Stock market is the backbone of finance and economy which has inspired many researchers over a period to build better predictive models. These predictions need analysis of data over a long period of time. Machine learning models such as Artificial Neural Network (ANN), Auto Regressive Integrated Moving Average (ARIMA), Linear Regression (LR), Random Forest (RF) have been explored in this survey paper. This survey paper aims to find an optimal model which can be used for short term stock price prediction. Comparison of results from multiple algorithms reveals an algorithm that will help traders to maximize their profits. Our study shows that time series analysis using ARIMA gives more accurate results than other models for short term stock price prediction.