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Journal of Financial Markets Vol. 79 2026

Bottom up vs. top down: What does firm 10-K tell us?

Landon J. Ross1,2,3; Jim Horn4,5,6; Mert Pilanci7; Kaihong Luo8; Guofu Zhou4

1 Tulane University · 2 United States Securities and Exchange Commission · 3 Tula University · 4 Washington University in St. Louis · 5 Professional Solutions (United States) · 6 Saint Louis University · 7 Stanford University · 8 Hong Kong University of Science and Technology

open access

Abstract

While financial textual analysis increasingly relies on complex machine learning, we propose a simpler, data-driven alternative. Using elastic net regressions on a massive panel of 10-K n-grams, we construct a specialized dictionary that weights phrases by their marginal predictive power. This bottom-up methodology effectively forecasts expected stock returns, with a spread portfolio generating significant average returns. Our approach outperforms prominent financial dictionaries, off-the-shelf large language models, and machine learning algorithms. These results demonstrate the value of identifying financial meaning from the bottom up, highlighting the need for domain- specific models trained on relevant financial contexts.

DOI
10.1016/j.finmar.2026.101070
Volume
79
Pages
101070
Language
en
Sources
crossref openalex

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