Bottom Up vs Top Down: What Does Firm 10-K Tell Us?
63 Pages Posted: 4 Apr 2024 Last revised: 16 Nov 2024
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Bottom Up vs Top Down: What Does Firm 10-K Tell Us?
Bottom Up vs Top Down: What Does Firm 10-K Tell Us?
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: Suggested Citation