Cluster Level Time Series Forecasting on Indonesian Banking Stock Prices Using the Gated Recurrent Unit Method
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Abstract
In recent years, there has been a significant increase in the number of Single Investor Identification registrations in the Indonesian capital market, as reported by the Financial Services Authority. Many investors favor stocks for their potential for high returns and liquidity. However, stock investments come with high risks due to their fluctuating prices, which are influenced by multiple factors. With 47 listed banking companies in the Indonesia Stock Exchange, clustering can help identify investor patterns. Forecasting stock prices is essential for anticipating future fluctuations. The large number of issuers and the tendency of stock prices to fluctuate increase the potential for outliers, requiring an appropriate clustering method. A study using the k-medoid method and dynamic time warping distance revealed 41 banking companies clustered into 5 clusters with a silhouette coefficient of 0.524. The Gated Recurrent Unit modeling, based on prototypes from the formed clusters, showed an excellent forecasting performance with root mean squared error and mean absolute percentage error ranging from 1-10%. The forecast for the next 8 weeks indicated varying price increases for each cluster. The first and third clusters are recommended for investors looking to maximize capital gains, due to their price increases and diverse cluster member characteristics. Additionally, investors should consider dividends provided by certain banking companies in their investment decision-making process.
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