Published online by Cambridge University Press: 05 May 2022
Using detailed data on company visits by Chinese mutual funds, we provide direct evidence of mutual fund information acquisition activities and the consequent informational advantages mutual funds establish in local firms. Mutual funds are more likely to visit local and nearby firms both in and outside of their portfolios, but the ease of travel between fund and firm locations can substantially alleviate geographic distance constraints. Company visits by mutual funds are strongly associated with both fund trading activities and fund trading performance. Our results show that geographic constraints and costly information acquisition amplify information asymmetry in financial markets.
This article combines two separate working papers, previously circulated as “The Geography of Information Acquisition” by Honghui Chen, Yuanyu Qu, Tao Shen, and Qinghai Wang and “Costly Information Acquisition and Investment Decisions: Quasi-Experimental Evidence” by David X. Xu. We appreciate helpful comments and constructive suggestions from Aydogan Alti, Stephen Brown (discussant), Tao Chen, Jennifer Conrad (the editor), Luis Goncalves-Pinto (discussant), Kewei Hou, Jennifer Huang, Sheng Huang, Travis Johnson, Ron Kaniel, Stephen Karolyi (discussant), Chun Kuang, Lin Peng, Alberto Rossi, Tianyue Ruan, Clemens Sialm, Sheridan Titman, Shane Underwood (the referee), Xue Wang, Michael Weber (discussant), John Wei, Jingyun Yang, Qi (Jacky) Zhang, and seminar participants at the 2019 SFS Cavalcade North America, 2019 Australasian Finance & Banking Conference, 2020 Midwest Finance Association Annual Meeting, Tsinghua Finance Workshop 2019, Modern Risk Society 2019, Economics and Management Advanced Forum at the Wuhan University of Technology 2019, and University of International Business and Economics 2019. Qu thanks the National Natural Science Foundation of China (grant ID 72003025) for financial support. Shen thanks the Tsinghua University Initiative Scientific Research Program (grant ID 2019THZWJC17) and National Natural Science Foundation of China (grant ID 71603147) for financial support. All errors are our own.