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This study evaluates the effectiveness of various machine learning models, specifically RNN, LSTM, and GRU, for predicting stock prices of four major banks in Indonesia from 2013 to 2022. The findings indicate that while the ARIMA Box-Jenkins model is unsuitable for this purpose, the GRU model outperforms the others in terms of prediction accuracy. The research is limited to time series data and does not explore other potential modeling approaches.

Author

nisaramadhani1993

Language

EN