Application of machine learning techniques for apple stock price prediction

Abstract

The objective of this publication is to compare the level of prediction of certain techniques such as time series and machine learning
in APPLE stock price forecasting, which It has the largest capitalization volume in the technology sector. The price history is between
January 1, 2019 and June 30, 2021, that it has been downloaded and it was used to measure the level of prediction having as a
target the future price of the following day, on the 15th and on the 30th, also, other type of textual information were tested in order
to measure their contribution to improving the prediction. The Root Mean Square Error (RMSE) and the Mean Absolute Percentage
Error (MAPE) adjustment indicators were used, considering the criterion that to the extent that a technique has a lower value, it will
be the best technique for each of the proposed scenarios. Regarding the results obtained in the application of the various techniques
for each scenario, it was found that the Machine Learning XGBOOST technique with parameters tuned by Cross Validation and the
Multiple Linear Regression model are the most useful for predicting the price of APPLE stock and textual information also improved
the prediction level of APPLE stock price