AI 中文总结
研究通过整合拓扑数据分析、技术指标和新闻情绪得分，提出情绪调整投资组合优化框架，利用基于TDA的距离度量识别不同资产构建组合，采用动态再平衡和保留机制，实证显示其在回报、风险表现及市场不确定性期间均有优势。
AI 中文摘要
理解金融资产之间的相似性对于有效的投资组合多元化至关重要。本文提出了一种新颖的情绪调整投资组合优化框架，将拓扑数据分析（TDA）与技术指标以及从金融新闻中提取的基于FinBERT的情绪得分相结合。在凝聚聚类框架内采用基于TDA的距离度量来识别拓扑上不同的资产以构建投资组合。通过纳入情绪信息，该框架捕捉到仅技术指标无法反映的市场认知和投资者行为的快速变化。与传统方法不同，该方法通过拓扑摘要表征复杂非线性关系。采用动态滚动窗口再平衡策略并引入保留机制。实证分析表明该框架在回报和风险表现上优于传统方法及基准策略，在市场不确定性增加期间也表现出强大的稳健性。
英文摘要
Understanding similarity among financial assets is essential for effective portfolio diversification. This paper proposes a novel sentiment-adjusted portfolio optimization framework that integrates Topological Data Analysis (TDA) with technical indicators and FinBERT-based sentiment scores extracted from financial news. A TDA-based distance measure is employed within an agglomerative clustering framework to identify topologically dissimilar assets for portfolio construction. By incorporating sentiment information, the framework captures rapid changes in market perception and investor behavior that are not reflected by technical indicators alone. Unlike conventional correlation and Euclidean distance based approaches, the proposed method characterizes complex nonlinear relationships through topological summaries. To account for the transient nature of market sentiment, a dynamic rolling-window rebalancing strategy with frequent portfolio updates is adopted. A retention mechanism is further introduced to preserve high-quality assets across consecutive rebalancing windows, thereby reducing portfolio turnover and transaction costs. Extensive empirical analysis on S&P 500 constituents demonstrates that the proposed framework consistently outperforms correlation and Euclidean distance based methods, as well as benchmark strategies including Naïve, Index, and full-universe portfolios, in terms of returns and reward-risk performance. Furthermore, the framework exhibits strong robustness by delivering positive performance during periods of heightened market uncertainty, such as the U.S.-Israel-Iran conflict.
