Words Matter: Forecasting Economic Downside Risks with Corporate Textual Data

Cansu Isler

Papers from arXiv.org

Abstract: Accurate forecasting of downside risks to economic growth is critically important for policymakers and financial institutions, particularly in the wake of recent economic crises. This paper extends the Growth-at-Risk (GaR) approach by introducing a novel daily sentiment indicator derived from textual analysis of mandatory corporate disclosures (SEC 10-K and 10-Q reports) to forecast downside risks to economic growth. Using the Loughran--McDonald dictionary and a word-count methodology, I compute firm-level tone growth as the year-over-year difference between positive and negative sentiment expressed in corporate filings. These firm-specific sentiment metrics are aggregated into a weekly tone index, weighted by firms' market capitalizations to capture broader, economy-wide sentiment dynamics. Integrated into a mixed-data sampling (MIDAS) quantile regression framework, this sentiment-based indicator enhances the prediction of GDP growth downturns, outperforming traditional financial market indicators such as the National Financial Conditions Index (NFCI). The findings underscore corporate textual data as a powerful and timely resource for macroeconomic risk assessment and informed policymaking.

Date: 2025-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-rmg
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