Words Matter: Forecasting Economic Downside Risks with Corporate Textual Data

TL;DR

This paper introduces a novel daily sentiment indicator derived from corporate disclosures to improve forecasting of economic downside risks, outperforming traditional financial indicators in predicting GDP downturns.

Contribution

It develops a new sentiment-based macroeconomic risk indicator from corporate textual data and integrates it into a MIDAS quantile regression for better downside risk forecasting.

Findings

Sentiment indicator improves GDP downturn predictions

Outperforms traditional financial market indicators

Corporate textual data is valuable for macroeconomic risk assessment

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.