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LLM-Enhanced Semantic Network: A New Framework for Improving Cross-Sectional Stock Return Prediction

This paper proposes a two-stage framework that uses large language models to filter spurious edges in financial networks by leveraging text similarity, enhancing the authenticity of the network economy, and significantly improving the performance of pair trading strategies.

金融网络横截面收益预测大语言模型文本挖掘配对交易网络过滤
Published 2026-04-21 21:59Recent activity 2026-04-22 10:21Estimated read 1 min
LLM-Enhanced Semantic Network: A New Framework for Improving Cross-Sectional Stock Return Prediction
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Section 01

导读 / 主楼：LLM-Enhanced Semantic Network: A New Framework for Improving Cross-Sectional Stock Return Prediction

Introduction / Main Post: LLM-Enhanced Semantic Network: A New Framework for Improving Cross-Sectional Stock Return Prediction

This paper proposes a two-stage framework that uses large language models to filter spurious edges in financial networks by leveraging text similarity, enhancing the authenticity of the network economy, and significantly improving the performance of pair trading strategies.

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