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

German

Wir versuchen zu testen, ob die Verwendung eines Satztransformators eine Verbesserung gegenüber Techniken wie Latent Dirichlet Allocation (LDA) in der Kohärenz von themenbezogenen Begriffslisten zeigt, die aus unstrukturierten Textdaten generiert wurden. Um dies zu testen, wurde die gleiche Sammlung von unstrukturierten Textdaten aus 394.661 Artikeln und der gleiche nachgelagerte Finanzportfolio-Konstruktionspipeline mit der Textschicht unterschiedlich angewandt, einschließlich der Länge des Artikeltexts jedes verwendeten Modells und wie themenbezogene Begriffe rangiert wurden: Wir vergleichen LDA gegen einen gefrorenen Satztransformator mit k-me-Classifizierung. Wir fanden, dass die Satztransformationsbranche höhere Ergebnisse sowohl in Bezug auf Kohär

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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