Forecasting Financial Assets Using Recursive Clustering Approach

ABSTRACT

Machine learning‐based forecasting has been gaining attention for its flexibility and adaptability. Given the dynamics of temporal data, clustering can be an effective approach for improving forecasting accuracy. While clustering for classification typically considers all observations in the data to effectively divide the data into clusters, clustering for temporal data forecasting may differ. Instead of focusing on dividing the entire data, identifying a single cluster close to the future value may yield better forecasting accuracy. A method to determine the single cluster by dividing a cluster obtained from previous clustering is adopted in this study, thereby improving the forecasting accuracy of clustering methods. Various financial assets are used to validate this approach across different time intervals. The empirical validation demonstrates the improved forecasting accuracy of the method, suggesting its broad applicability in other forecasting contexts.