TY - JOUR
T1 - Crowd characteristics and crowd wisdom
T2 - Evidence from an online investment community
AU - Hong, Hong
AU - Ye, Qiang
AU - Du, Qianzhou
AU - Wang, G. Alan
AU - Fan, Weiguo
N1 - The authors would like to thank the editor and reviewers for their helpful and constructive suggestions. This research was supported by the National Natural Science Foundation of China (Grant # 71532004, 71801063, 71850013, 71671154, 71531013, and 71572122) and the China Postdoctoral Science Foundation (Grant # 2018M640300).
PY - 2019
Y1 - 2019
N2 - Fueled by the explosive growth of Web 2.0 and social media, online investment communities have become a popular venue for individual investors to interact with each other. Investor opinions extracted from online investment communities capture “crowd wisdom” and have begun to play an important role in financial markets. Existing research confirms the importance of crowd wisdom in stock predictions, but fails to investigate factors influencing crowd performance (that is, crowd prediction accuracy). In order to help improve crowd performance, our research strives to investigate the impact of crowd characteristics on crowd performance. We conduct an empirical study using a large data set collected from a popular online investment community, StockTwits. Our findings show that experience diversity, participant independence, and network decentralization are all positively related to crowd performance. Furthermore, crowd size moderates the influence of crowd characteristics on crowd performance. From a theoretical perspective, our work enriches extant literature by empirically testing the relationship between crowd characteristics and crowd performance. From a practical perspective, our findings help investors better evaluate social sensors embedded in user-generated stock predictions, based upon which they can make better investment decisions.
AB - Fueled by the explosive growth of Web 2.0 and social media, online investment communities have become a popular venue for individual investors to interact with each other. Investor opinions extracted from online investment communities capture “crowd wisdom” and have begun to play an important role in financial markets. Existing research confirms the importance of crowd wisdom in stock predictions, but fails to investigate factors influencing crowd performance (that is, crowd prediction accuracy). In order to help improve crowd performance, our research strives to investigate the impact of crowd characteristics on crowd performance. We conduct an empirical study using a large data set collected from a popular online investment community, StockTwits. Our findings show that experience diversity, participant independence, and network decentralization are all positively related to crowd performance. Furthermore, crowd size moderates the influence of crowd characteristics on crowd performance. From a theoretical perspective, our work enriches extant literature by empirically testing the relationship between crowd characteristics and crowd performance. From a practical perspective, our findings help investors better evaluate social sensors embedded in user-generated stock predictions, based upon which they can make better investment decisions.
UR - https://www.scopus.com/pages/publications/85065991138
UR - https://www.scopus.com/pages/publications/85065991138#tab=citedBy
U2 - 10.1002/asi.24255
DO - 10.1002/asi.24255
M3 - Article
AN - SCOPUS:85065991138
SN - 2330-1635
VL - 71
SP - 423
EP - 435
JO - Journal of the Association for Information Science and Technology
JF - Journal of the Association for Information Science and Technology
IS - 4
ER -