Aligning Multilingual News for Stock Return Prediction
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 20 references that are in the map. First posted on arXiv 22 Oct 2025.
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6 of 20 references.
References · 6
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2018Computational Optimal Transportcited by 765
- 2021Word Alignment by Fine-tuning Embeddings on Parallel Corporacited by 186
- 2019Sparse Sequence-to-Sequence Modelscited by 167
- 2020Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transportcited by 20
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