In Search of Attention


  • ZHI DA,



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    • Da is with University of Notre Dame, Engelberg is with the University of California at San Diego, and Gao is with University of Notre Dame. We thank Nick Barberis; Robert Battalio; Andriy Bodnaruk; Zhiwu Chen; Jennifer Conrad; Shane Corwin; Mark Greenblatt; Campbell Harvey (the editor); David Hirshleifer; Kewei Hou; Byoung-Hyoun Hwang; Ryan Israelsen; Ravi Jagannathan; Robert Jennings; Gabriele Lepori; Dong Lou; Tim Loughran; Ernst Schaumburg; Paul Schultz; Mark Seasholes; Ann Sherman; Sophie Shive; Avanidhar Subrahmanyam; Paul Tetlock; Heather Tookes; Annette Vissing-Jorgensen; Mitch Warachka; Yu Yuan; an anonymous associate editor; two anonymous referees; and seminar participants at AQR Capital Management, HEC Montreal, Purdue University, Singapore Management University, University of California at Irvine, University of North Carolina at Chapel Hill, University of Georgia, University of Hong Kong, University of Oklahoma, University of Notre Dame, Fifth Yale Behavioral Science Conference, the 2009 NBER Market Microstructure meeting, Macquarie Global Quant Conference, 2009 Chicago Quantitative Aliance Academic Competition, 2010 American Finance Association, 2010 Crowell Memorial Prize Paper Competition, and Center of Policy and Economic Research (CEPR) European Summer Symposia for helpful comments and discussions. We thank Frank Russell and Company for providing us with the historical Russell 3000 index membership data, Dow Jones & Company for providing us with the news data, Market System Incorporated (MSI) for providing us with the Dash-5 data, and IPO SCOOP for providing us with the IPO rating data. We are grateful to Robert Battalio, Hyunyoung Choi, Amy Davison, Ann Sherman, and Paul Tetlock for their assistance with some of the data used in this study. Xian Cai, Mei Zhao, Jianfeng Zhu, and Mendoza IT Group provided superb resesarch assistance. We are responsible for remaining errors.


We propose a new and direct measure of investor attention using search frequency in Google (Search Volume Index (SVI)). In a sample of Russell 3000 stocks from 2004 to 2008, we find that SVI (1) is correlated with but different from existing proxies of investor attention; (2) captures investor attention in a more timely fashion and (3) likely measures the attention of retail investors. An increase in SVI predicts higher stock prices in the next 2 weeks and an eventual price reversal within the year. It also contributes to the large first-day return and long-run underperformance of IPO stocks.