The Prediction of Closing Prices of Company Stocks Using Random Forests and Artificial Neural Networks

Abstract

Tracking accurate movement of stock market is a difficult job for investors in recent times due to the nonlinear behavior and volatility of the stocks, which gets affected because of different reasons. Now, advancement in technology, introduction of artificial intelligence, improved computation power and programming methods have enabled researchers to forecast the market more efficiently. This paper is an attempt to create new variables with the help of artificial neural network (ANN) and random forests (RF) algorithm, to be used to forecast closing price of the next day. For this, open, high, closing and low price are used to create new variables, which can be treated as inputs to the model. The indicators used in this model are RMSE, MBE and MAPE and their low values are indicative of the fact that the proposed machine learning methods are suitable for the prediction of stock prices of the companies under consideration.