Application of machine learning techniques for apple stock price prediction
DOI:
https://doi.org/10.15381/risi.v15i1.23737Keywords:
Time series, machine learning, prediction, stock price, stock exchangeAbstract
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.
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Copyright (c) 2022 Roberto Evaristo Broncano
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