Price Prediction of S&P 500 Using Machine Learning

Abstract

Forecasting trends in stock indices is considered a difficult task in financial time series forecasting. Accurate forecasts of stock price trends can generate profits for investors. Due to the complexity of stock market data, developing effective forecasting models is very challenging. We are trying to predict stock prices for the next few days. This will become the basis for knowing the right time to invest or exit positions and generate profits.
With the introduction of artificial intelligence and the increase in computing capacity, programmed forecasting methods have proven to be more effective at predicting stock prices. In this work, we used supervised machine learning algorithms such as linear regression model, SVR, XGBoost, and random forest.
Thus, these models are evaluated using standard strategic indicators such as the EMR. A low value of the indicator shows that the models are effective in predicting stock prices.