Introduction

Economic uncertainty—the degree of ambiguity about future growth and inflation—affects asset prices, Fed expectations, and corporate investment. Unlike confidence indices (which are surveys), topic volatility in financial news offers a continuous, high-frequency measure of uncertainty. Machine learning can track topic diversity, sentiment fluctuations, and discussion frequency to construct economic uncertainty indices predicting market volatility and economic outcomes.

Topic Modeling and Volatility Extraction

Use Latent Dirichlet Allocation (LDA) or neural topic models to identify topics in economics and finance news: Fed policy, trade wars, recession, inflation, earnings, etc. For each topic, measure daily sentiment volatility (high volatility indicates uncertainty). Aggregate topic volatility into economic uncertainty index. Higher uncertainty predicts higher stock market volatility, credit spreads, and economic downturns.

Conclusion

Topic volatility-based uncertainty measures offer real-time signals of economic ambiguity, improving nowcasts and risk assessment.