Google search trends and stock markets: sentiment, attention or uncertainty?

Szczygielski, Jan Jakub; Charteris, Ailie; Bwanya, Princess Rutendo; Brzeszczyński, Janusz

Abstract

Keyword based measures purporting to reflect investor sentiment attention or uncertainty have been increasingly used to model stock market behaviour. We investigate and shed light on the narrative reflected by Google search trends (GST) by constructing a neutral and general stock market-related GST index. To do so we apply elastic net regression to select investor relevant search terms using a sample of 77 international stock markets. The index peaks around significant events that impacted global financial markets moves closely with established measures of market uncertainty and is predominantly correlated with uncertainty measures in differences implying that GST reflect an uncertainty narrative. Returns and volatility for developed emerging and frontier markets widely reflect changing Google search volumes and relationships conform to a prior expectations associated with uncertainty. Our index performs well relative to existing keyword-based uncertainty measures in its ability to approximate and predict systematic stock market drivers and factor dispersion underlying return volatility both in-sample and out-of-sample. Our study contributes to the understanding of the information reflected by GST their relationship with stock markets and points towards generalisability thus facilitating the development of further applications using search and return data.

Citation

SZCZYGIELSKI, J.J., CHARTERIS, A., BWANYA, P.R. and BRZESZCZYŃSKI, J. 2023. Google search trends and stock markets: sentiment, attention or uncertainty? International review of financial analysis [online], In Press, article 102549. Available from: https://doi.org/10.1016/j.irfa.2023.102549

• Journal Article Type: Article
• Acceptance Date: Jan 25, 2023
• Online Publication Date: Jan 31, 2023
• Deposit Date: Jan 31, 2023
• Publicly Available Date: Jan 31, 2023
• Journal: International review of financial analysis
• Print ISSN: 1057-5219
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Article Number: 102549
• DOI: https://doi.org/10.1016/j.irfa.2023.102549
• Keywords: Elastic net regression; Machine learning; Google search trends; Market uncertainty; Sentiment; Attention; Returns; Volatility
• Public URL: https://rgu-repository.worktribe.com/output/1871313