Working Paper: NBER ID: w24430
Authors: Charles W. Calomiris; Harry Mamaysky
Abstract: We develop a classification methodology for the context and content of news articles to predict risk and return in stock markets in 51 developed and emerging economies. A parsimonious summary of news, including topic-specific sentiment, frequency, and unusualness (entropy) of word flow, predicts future country-level returns, volatilities, and drawdowns. Economic and statistical significance are high and larger for year-ahead than monthly predictions. The effect of news measures on market outcomes differs by country type and over time. News stories about emerging markets contain more incremental information. Out-of-sample testing confirms the economic value of our approach for forecasting country-level market outcomes.
Keywords: news; risk; returns; stock markets; textual analysis
JEL Codes: G12; G14; G15
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| frequency, sentiment, unusualness (C46) | future country-level returns (O57) |
| frequency, sentiment, unusualness (C46) | future volatilities (G17) |
| frequency, sentiment, unusualness (C46) | future drawdowns (G19) |
| positive sentiment (G41) | higher returns (G12) |
| negative sentiment (G41) | increased risk (D81) |
| news sentiment (G14) | market outcomes (P42) |
| news impact varies by country type (F69) | market outcomes (P42) |
| emerging market news (G15) | incremental information (D89) |
| methodology (B41) | incremental predictive value (C52) |
| context of news (P17) | relationship between news sentiment and market outcomes (G14) |
| nature of news (G14) | returns and risk (G12) |
| developed vs emerging markets (O53) | sensitivity to news content (G14) |