Interconnected Stocks Examination for Predicting the Next Day's High on the Indonesian Stock Exchange

Abstract

We observed in many WhatsApp/Telegram Indonesian stock market groups, but we didn't find any stock prediction method that utilizes interconnectivity between stocks. In this paper, we examined the interconnected stock dynamics in the IDX and used it to predict the next day's high. We employed a novel method called "Connected Stocks + Rolling Window Method" which uses both the temporal dynamics of the stock market and the interconnectedness of IDX's stocks. We explored the characteristics of the interconnected stocks by implementing three machine learning algorithms - K-nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) - and found valuable insight. The experiment showed that several factors including a balanced threshold model and increased stock input size helped the performance of a model, while several factors including window size, additional features added, and using specific sectors as training data did not help the model's performance. The result also showed that several stocks like ANTM and ERAA show signs of interconnectedness and are influenceable while some like KLBF are hard to influence and show no sign of interconnectedness based on their results. This research contributes to a deeper understanding of stock market dynamics on the IDX, especially the characteristics of interconnected stocks on the IDX.