4.7 Article

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

期刊

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.irfa.2023.102549

关键词

Elastic net regression; Machine learning; Google search trends; Market uncertainty; Sentiment; Attention; Returns; Volatility

向作者/读者索取更多资源

This study investigates the narrative reflected by Google search trends (GST) and constructs a neutral and general stock market-related GST index. The index is found to peak around significant events that impact global financial markets and is closely correlated with established measures of market uncertainty. It performs well in approximating and predicting systematic stock market drivers and factor dispersion underlying return volatility both in-sample and out-of-sample. The study contributes to the understanding of the information reflected by GST and their relationship with stock markets and suggests the potential for further applications using internet search data.
Keyword-based measures purporting to reflect investor sentiment, attention or uncertainty have increasingly been 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 it 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 priori 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 internet search data.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据