Energy Sector Stock Price Forecasting with Time Series Clustering Approach
Peramalan Harga Saham Sektor Energi dengan Pendekatan Penggerombolan Data Deret Waktu
DOI:
https://doi.org/10.29244/ijsa.v8i2p132-142Keywords:
ARIMA , autocorrelation-based, dynamic time warping, stock prices, time series clusteringAbstract
Stock investment promises higher returns but carries high risks because unpredictable price fluctuations. Energy sector shows potential due to its highest sectoral index growth in 2022. However, this doesn’t indicate that stock price increases occur evenly among all issuers. Therefore, it’s necessary to analyze clustering of issuers based on similarity of their stock price movements and used for forecasting stock prices at cluster level. This study aims to evaluate performance of clustering energy sector issuers using autocorrelation-based distance and dynamic time warping(DTW), and to forecast stock prices at cluster level. The data used consists weekly closing stock prices. The clustering used hierarchical average linkage method. Stock price forecast for each cluster used ARIMA model and its performance was evaluated using rolling-cross validation. The results showed that DTW distance had the best clustering performance. Energy sector issuers were grouped into four clusters with strong cluster category, indicated by silhouette coefficient >0.71. ARIMA models for each cluster produced MAPE values between 10-20%, categorizing them as good forecasting models. Clusters A and D were recommended for investors because have highest potential for capital gain based on forecasted stock prices. That clusters also consisted of companies with strong fundamentals and dividend policies.
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