Stock Market Prediction using Linear Regression

The stock market allows companies to interact and transact with interested buyers based on publicly listed shares. The process of buying and selling stocks is identical to purchasing goods or services. The essence of stock market trading is to buy a stock at a lower value and anticipating for future price increase to be sold at a higher price. However, accurate prediction of outcome is definitely not assured. There are several ways to forecast how stocks perform in the future based on its momentum, people's interest, as well as health and economic challenges such as pandemics or war. There are numerous published research articles on stock values prediction using machine learning, but no general structure or mechanism made available which appropriately identifies predictors to accurately forecast the prices in the future. Hence, this study presents a simple comparison of errors in using linear regression to Artificial Neural Network (ANN) and Random Forest (RF). Additionally, this paper presents the possible predictors to accurately forecast the value of the closing stock price.