ACS Applied Computer Science

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

Dias SATRIA
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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 Arima Box-Jenkins, 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. The limitation of this research was data type was only time series data. It limits our instrument to four statistical methode only.

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  • APA 7th style
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
  • Chicago style
Satria, Drias. "Predicting banking stock prices using RNN, LSTM, and GRU approach." Applied Computer Science 19, no. 1 (2023): 82-94. 
  • IEEE style
D. Satria, "Predicting banking stock prices using RNN, LSTM, and GRU approach," Applied Computer Science, vol. 19, no. 1, pp.82-94, 2023, doi: 10.35784/acs-2023-06.
  • Vancouver style
Satria D. Predicting banking stock prices using RNN, LSTM, and GRU approach. Applied Computer Science. Applied Computer Science. 2023;19(1):82-94.

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