STOCK MARKET PREDICTION BY USING MACHINE LEARNING TECHNIQUES

Abstract

Accurate prediction of stock market returns is a
very challenging task due to volatile and nonlinear nature of the financial stock markets. With
the introduction of artificial intelligence and
increased computational capabilities,
programmed methods of pre- diction 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 opera- tion. 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