Do recession fears help predict stock market volatility? International evidence

We develop a direct measure of recession fears using global Google search activity (Recession Fear Index, RFI) and demonstrate its strong predictive power for stock market volatility across 11 industrialized countries, both in and out of sample. This predictability delivers sizable utility benefits to investors and survives after controlling for common economic uncertainty factors and fundamentals. A decomposition analysis shows that the information content of the RFI is concentrated in its globally shared component, U.S. search activity, and finance-related search domains. Moreover, our results suggest that RFI’s predictability primarily stems from its contemporaneous reflection of economic conditions and its spillover effects on investor sentiment. • Using global Google search data, we develop a measure of Recession Fear Index (RFI). • RFI significantly predicts stock market volatility across 11 industrialized countries. • The information value of RFI varies across search domains and geographical sources. • Including RFI in the volatility forecasting framework improves investors’ utility.

Authors

Institutions

Publication Details

Journal
Journal of International Financial Markets Institutions and Money
Published
2026-04-01
DOI
https://doi.org/10.1016/j.intfin.2026.102331
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Do recession fears help predict stock market volatility? International evidence

Shaofeng Zhang, Mingtao Zhou, Xiaozhou Zhou, Yong Ma
Journal of International Financial Markets Institutions and Money
Market Dynamics and Volatility
article

Do recession fears help predict stock market volatility? International evidence

Shaofeng Zhang, Mingtao Zhou, Xiaozhou Zhou, Yong Ma
article en

Abstract

We develop a direct measure of recession fears using global Google search activity (Recession Fear Index, RFI) and demonstrate its strong predictive power for stock market volatility across 11 industrialized countries, both in and out of sample. This predictability delivers sizable utility benefits to investors and survives after controlling for common economic uncertainty factors and fundamentals. A decomposition analysis shows that the information content of the RFI is concentrated in its globally shared component, U.S. search activity, and finance-related search domains. Moreover, our results suggest that RFI’s predictability primarily stems from its contemporaneous reflection of economic conditions and its spillover effects on investor sentiment. • Using global Google search data, we develop a measure of Recession Fear Index (RFI). • RFI significantly predicts stock market volatility across 11 industrialized countries. • The information value of RFI varies across search domains and geographical sources. • Including RFI in the volatility forecasting framework improves investors’ utility.

Journal of International Financial Markets Institutions and MoneyVol. 109
Université du Québec à Montréal (CA), Hunan University of Finance and Economics (CN), Changsha University of Science and Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 27%
Market Dynamics and Volatility
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.