A Practical Study of Ensemble Learning for Multi-Horizon Financial Forecasting

This thesis presents a practical study of ensemble learning techniques applied to the field of financial forecasting, conducted at Axyon AI, a fintech company which offers AI-based insights, asset signals and investment strategies. A central focus is on multi-horizon forecasting: integrating predictions from weak learners trained on different investment horizons in order to improve overall accuracy. The experiments were carried out on the Japan Target Market dataset, across 20-day and 60-day horizons. The dataset is based on the Morningstar Japan Target Market Exposure index, which measures the performance of large-cap and mid-cap stocks in Japan, and covers the top 85% of the market by capitalization. The tested Horizon Union methods demonstrated superior performance when compared to the 20-day single-horizon baseline. These results highlight the practical value of multi-horizon predictions in the context of AI-driven investment strategies.