In this paper, we use frequency of related phrases in site visit summary reports to denote the site visit content, and study whether site visit content reflecting institutional investors' market concerns can predict Chinese stock market return. We find that site visit content has greater forecasting power in Chinese stock market returns than other economic predictors after comparing out-of-sample R-2. The predictability is both statistically and economically significant. Additionally, our results also suggest that the particular information content has better forecasting power than general content in site visit summary reports.
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Huazhong Univ Sci & Technol, Sch Management, Wuhan, Peoples R ChinaHuazhong Univ Sci & Technol, Sch Management, Wuhan, Peoples R China
Wen, Zhuzhu
Gong, Xu
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Xiamen Univ, China Inst Studies Energy Policy, Sch Management, Collaborat Innovat Ctr Energy Econ & Energy Polic, Xiamen, Peoples R ChinaHuazhong Univ Sci & Technol, Sch Management, Wuhan, Peoples R China
Gong, Xu
Ma, Diandian
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Univ Auckland, Grad Sch Management, Auckland, New ZealandHuazhong Univ Sci & Technol, Sch Management, Wuhan, Peoples R China
Ma, Diandian
Xu, Yahua
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Cent Univ Finance & Econ, China Econ & Management Acad, 39 South Coll Rd, Beijing 100081, Peoples R ChinaHuazhong Univ Sci & Technol, Sch Management, Wuhan, Peoples R China
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Manchester Business Sch, Ctr Anal Investment Risk, Manchester M15 6PB, Lancs, England
Bocconi Univ, Dept Finance, I-20136 Milan, ItalyManchester Business Sch, Ctr Anal Investment Risk, Manchester M15 6PB, Lancs, England
Guidolin, Massimo
McMillan, David G.
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Univ Stirling, Accounting & Finance Div, Stirling Management Sch, Stirling FK9 4LA, ScotlandManchester Business Sch, Ctr Anal Investment Risk, Manchester M15 6PB, Lancs, England
McMillan, David G.
Wohar, Mark E.
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Univ Nebraska, Dept Econ, Omaha, NE 68182 USAManchester Business Sch, Ctr Anal Investment Risk, Manchester M15 6PB, Lancs, England