A decision-focused learning framework for goal-based investing
研究了决策聚焦学习在基于目标的投资中的优势，提出新的决策质量指标，证明其比传统预测聚焦学习更能提升投资组合与个人目标的匹配度。
This paper explores the application of Decision-focused learning (DFL) within the framework of Goal-Based Investing (GBI), highlighting its advantages over traditional Prediction-focused Learning (PFL) methods. A key contribution of this research is the introduction of a novel decision quality metric tailored for goal programming, prioritizing investor-specific goals in multi-objective optimization. Our findings demonstrate that DFL enhances decision quality and portfolio feasibility, particularly under uncertain market conditions, outperforming PFL in aligning investment strategies with prioritized financial goals. By integrating decision-making directly into the learning process, DFL enables more accurate parameter predictions, resulting in improved portfolio performance. This study underscores DFL's practical effectiveness in navigating market complexities and its potential as a powerful tool for optimizing portfolios in alignment with long-term investment objectives.