Bottom Up vs Top Down: What Does Firm 10-K Tell Us?

63 Pages Posted: 4 Apr 2024 Last revised: 30 Apr 2024

See all articles by Landon Ross

Landon Ross

U.S. Securities and Exchange Commission

Jim Horn

Washington University in St. Louis - John M. Olin Business School

Mert Pilanci

Stanford University

KaiHong Luo

Hong Kong University of Science & Technology (HKUST) - Department of Finance

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

Date Written: January 31, 2024

Abstract

In contrast to the recent increasing focus on large languages model, we propose a bottom-up approach that exploits the individual predictive power of each word. Our word dictionary is constructed by using a data-driven approach, and it is these selected words that are used to build the predictive model with lasso regularized regressions and large panels of word counts. We find that our approach effectively estimates the cross-section of stocks' expected returns, so that a factor that summarizes the information generates economically and statistically significant returns, and these returns are largely unexplained by standard factor models. However, an inspection of the factor dictionary indicates the element contains many words with possible risk-related interpretations, such as currency, oil, research, and restructuring, which increase a stock's expected return, while the words acquisition, completed, derivatives, and quality decrease the expected return.

Keywords: Text Analysis, Asset Pricing, Word Dictionary, Word Count

JEL Classification: C23, C53, G11, G14, G17

Suggested Citation

Ross, Landon and Horn, Jim and Pilanci, Mert and Luo, KaiHong and Zhou, Guofu, Bottom Up vs Top Down: What Does Firm 10-K Tell Us? (January 31, 2024). Available at SSRN: https://ssrn.com/abstract=4747519 or http://dx.doi.org/10.2139/ssrn.4747519

Landon Ross (Contact Author)

U.S. Securities and Exchange Commission ( email )

Jim Horn

Washington University in St. Louis - John M. Olin Business School ( email )

Mert Pilanci

Stanford University ( email )

Stanford, CA 94305
United States

KaiHong Luo

Hong Kong University of Science & Technology (HKUST) - Department of Finance ( email )

Clear Water Bay, Kowloon
Hong Kong

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

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