用金融领域NLP+可解释模型从新闻情绪中挖掘宏观经济收益信号。
Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study
- 用FinBERT处理全球新闻，构建包含情绪均值、离散度和事件影响的日度情绪指数。
- XGBoost模型在外汇和债券上实现超50%年化收益，夏普比率最高达5.87。
- 通过SHAP解释发现情绪离散度和事件冲击是关键预测因子，适合量化交易研究者。
本研究提出一种可解释机器学习框架，从全球新闻情绪中提取宏观经济阿尔法。利用金融领域预训练的FinBERT模型处理GDELT项目提供的全球新闻数据，构建包含情绪均值、离散度和事件影响的日度情绪指数。这些指数输入XGBoost分类器，用于预测欧元/美元、美元/日元及10年期美国国债期货（ZN）次日收益率，并与逻辑回归对比。采用5折扩展窗口交叉验证进行严格的样本外回测（2017年至2025年4月），结果显示XGBoost策略表现优异：欧元/美元夏普比率达5.87，美元/日元和国债分别为4.65，外汇类资产复合年化增长率超50%，债券类达22%。SHAP分析确认情绪离散度和文章影响是核心预测特征。研究证明，将领域特定NLP与可解释机器学习结合，可提供强大且可解释的宏观经济阿尔法来源。
原文摘要 · Abstract (English)
This study introduces an interpretable machine learning (ML) framework to extract macroeconomic alpha from global news sentiment. We process the Global Database of Events, Language, and Tone (GDELT) Project's worldwide news feed using FinBERT -- a Bidirectional Encoder Representations from Transformers (BERT) based model pretrained on finance-specific language -- to construct daily sentiment indices incorporating mean tone, dispersion, and event impact. These indices drive an XGBoost classifier, benchmarked against logistic regression, to predict next-day returns for EUR/USD, USD/JPY, and 10-year U.S. Treasury futures (ZN). Rigorous out-of-sample (OOS) backtesting (5-fold expanding-window cross-validation, OOS period: c. 2017-April 2025) demonstrates exceptional, cost-adjusted performance for the XGBoost strategy: Sharpe ratios achieve 5.87 (EUR/USD), 4.65 (USD/JPY), and 4.65 (Treasuries), with respective compound annual growth rates (CAGRs) exceeding 50% in Foreign Exchange (FX) and 22% in bonds. Shapley Additive Explanations (SHAP) affirm that sentiment dispersion and article impact are key predictive features. Our findings establish that integrating domain-specific Natural Language Processing (NLP) with interpretable ML offers a potent and explainable source of macro alpha.
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