Optimization of Stock Predictions on Indonesia Stock Exchange: A New Hybrid Deep Learning Method

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This study presents a new method for predicting stock prices on the Indonesia Stock Exchange (IDX) using a hybrid deep learning model. The proposed model combines historical price data, consisting of open, high, low, and close values, with technical indicators such as Moving Average (MA), Simple Moving Average (SMA), and Exponential Moving Average (EMA). The proposed model offered improved accuracy and efficiency using distinct architectures of Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) to process the datasets. The results showed that the proposed hybrid model significantly outperformed traditional single-architecture models in terms of R2, achieving 0.98 for the BBCA stock, surpassing models using only RNN or GRU. In addition, comparable improvements were observed with additional equities, such as the PT. Bank Mandiri Tbk (BMRI) and PT. Bank Negara Indonesia Tbk (BBNI) stocks, achieving an R2 of 0.99, demonstrating the proficiency of the proposed model in capturing the complex dynamics of the stock market. The results demonstrated the significant potential of combining historical data and technical indicators into the modeling procedure to predict stock prices. This process can benefit investors and economic forecasters in the stock market. The results could be further expanded by classifying datasets and investigating different sets of models to improve the performance of financial forecasting.