Newest papers

From Word Counts to Context: Topic Models for Asset Pricing

Kevin Foley, Jonathan Hartadi, Shivesh Prakash, Swapnil Vatsal · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.34169 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

报道可能揭示系统性风险,但其背景是否增强了系统性风险因素的构建仍然不清楚。我们寻求测试使用句子转换器是否代表了对文本分配(LDA)等技术的改进,在由非结构化文本数据生成的主题术语列表的一致性方面。 为了测试这一点,由394,661条组成的非结构化文本数据的相同集合和相同的下游财务组合构建管道是否被应用于文本层的差异,包括每个模型所使用的文章文本的长度以及主题术语的排名:我们将LDA与被冻结的句子转换器与kme分类进行比较。 我们发现句子转换器分支部在一致性(由NPMI衡量)以及财务表现(由Sharpe确认

Machine translation (Meta M2M100); the English original follows.

Abstract

News may reveal systematic risk, but whether its context enhances the construction of systematic risk factors is still unclear. We seek to test whether utilizing a sentence transformer represents an improvement over techniques such as Latent Dirichlet Allocation (LDA) in the coherence of topic term lists generated from unstructured text data. To test this, the same collection of unstructured text data comprising of 394,661 articles and the same downstream financial portfolio construction pipeline were applied with the text layer differing, including the length of article text each model used and how topic terms were ranked: we benchmark LDA against a frozen sentence transformer with k-means clustering. We find that the sentence transformer branch had higher observed scores both in terms of coherence (measured by NPMI) as well as financial performance (measured by Sharpe), although the available tests do not establish outperformance. Further exploratory specifications such as utilizing spherical clustering and multi-horizon exposures had an observed excess-return Sharpe of 1.03 for the combined model. We believe that there is some promise in applying context-aware techniques on unstructured news text, but stricter tests using only information available at each date and broader datasets may be required to enhance the confidence in the observed performance.

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Topics: Latent Dirichlet allocation, Topic model, Computer science, Portfolio, Sentence, Transformer

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