Investor sentiment: a retail trader activity approach

Purpose This study creates a measure of investor sentiment directly from retail trader activity to identify misvaluation and to examine the link between sentiment and subsequent returns. Design/methodology/approach Using investor reports from a large discount brokerage that include measures of activity such as net buying, net new accounts and net new assets, this study creates a measure of retail trader sentiment using principal components. This study examines the relation between sentiment and returns through conditional mean and regression analyses. Findings Retail sentiment activity coincides with aggregate Google Trends search data and firms with the greatest sensitivity to retail sentiment tend to be small, young and volatile. Periods of high retail sentiment precede poor subsequent market returns. Cross-sectional results detail the strongest impact on subsequent returns within difficult to value or difficult to arbitrage firms. Originality/value This study links a rich measure of retail trader activity to subsequent market and cross-sectional returns. These results deepen our understanding of noise trader risk and aggregate investor sentiment.

Automated summary

This study creates a measure of investor sentiment based on retail trader activity and examines its link with subsequent returns. The study finds that retail sentiment activity correlates with Google Trends search data and that companies with high sensitivity to retail sentiment tend to be small, young, and volatile. High retail sentiment periods precede poor market returns.