用最优传输对齐多语言新闻，提升股票预测准确性
Aligning Multilingual News for Stock Return Prediction
- 通过最优传输算法对齐中英文财经新闻语义
- 对齐后句子与股价变动相关性更强，策略收益提高10%
- 适合量化金融、跨语言信息融合方向研究者
新闻在多语言间快速传播，但翻译可能丢失细微语义。本文提出一种基于最优传输的方法，对齐多语言财经新闻中的句子，识别跨语言的语义相似内容。将该方法应用于2012-2024年间覆盖东京证券交易所约3500只股票的超14万对彭博英文与日文新闻。对齐后的句子更稀疏、可解释性更强，且语义相似度更高。基于对齐句构建的回报评分与实际股价变动相关性显著增强，基于此构建的多空交易策略相比全文本分析提升了10%的夏普比率。
原文摘要 · Abstract (English)
News spreads rapidly across languages and regions, but translations may lose subtle nuances. We propose a method to align sentences in multilingual news articles using optimal transport, identifying semantically similar content across languages. We apply this method to align more than 140,000 pairs of Bloomberg English and Japanese news articles covering around 3500 stocks in Tokyo exchange over 2012-2024. Aligned sentences are sparser, more interpretable, and exhibit higher semantic similarity. Return scores constructed from aligned sentences show stronger correlations with realized stock returns, and long-short trading strategies based on these alignments achieve 10\% higher Sharpe ratios than analyzing the full text sample.
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