Stock Closing Price Prediction using Machine Learning Techniques

TLDR

Stock market return prediction is challenging due to volatility and nonlinearity, but AI and advanced computing have improved predictive efficiency. The study applies Artificial Neural Networks and Random Forests to forecast next‑day closing prices for five companies across diverse sectors. Models use Open, High, Low, Close data to generate new variables as inputs, and performance is assessed with RMSE and MAPE. Low RMSE and MAPE values indicate the models effectively predict stock closing prices.

Abstract

Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. In this work, Artificial Neural Network and Random Forest techniques have been utilized for predicting the next day closing price for five companies belonging to different sectors of operation. The financial data: Open, High, Low and Close prices of stock are used for creating new variables which are used as inputs to the model. The models are evaluated using standard strategic indicators: RMSE and MAPE. The low values of these two indicators show that the models are efficient in predicting stock closing price.