PREDICTING BANKING STOCK PRICES USING RNN, LSTM, AND GRU APPROACH

Dias Satria

doi:10.35784/acs-2023-06

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Published: Mar 31, 2023

DOI: https://doi.org/10.35784/acs-2023-06

DOI

https://doi.org/10.35784/acs-2023-06

Authors

Dias Satria

dias.satria@ub.ac.id

Universitas Brawijaya

https://orcid.org/0000-0002-4068-6807

Abstract

In recent years, the implementation of machine learning applications started to apply in other possible fields, such as economics, especially investment. But, many methods and modeling are used without knowing the most suitable one for predicting particular data. This study aims to find the most suitable model for predicting stock prices using statistical learning with RNN, LSTM, and GRU deep learning methods using stock price data for 4 (four) major banks in Indonesia, namely BRI, BNI, BCA, and Mandiri, from 2013 to 2022. The result showed that the ARIMA Box-Jenkins modeling is unsuitable for predicting BRI, BNI, BCA, and Bank Mandiri stock prices. In comparison, GRU presented the best performance in the case of predicting the stock prices of BRI, BNI, BCA, and Bank Mandiri.

Keywords:

GRU, Indonesia Stock Price Prediction, Machine Learning

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Satria, D. (2023). PREDICTING BANKING STOCK PRICES USING RNN, LSTM, AND GRU APPROACH. Applied Computer Science, 19(1), 82–94. https://doi.org/10.35784/acs-2023-06

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