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Crowd characteristics and crowd wisdom: Evidence from an online investment community

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  • Hong Hong
  • Qiang Ye
  • Qianzhou Du
  • G. Alan Wang
  • Weiguo Fan

Abstract

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.

Suggested Citation

  • Hong Hong & Qiang Ye & Qianzhou Du & G. Alan Wang & Weiguo Fan, 2020. "Crowd characteristics and crowd wisdom: Evidence from an online investment community," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(4), pages 423-435, April.
    • Handle: RePEc:bla:jinfst:v:71:y:2020:i:4:p:423-435
    DOI: 10.1002/asi.24255
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    Cited by:

    1. Ashraf Labib & Salem Chakhar & Lorraine Hope & John Shimell & Mark Malinowski, 2022. "Analysis of noise and bias errors in intelligence information systems," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(12), pages 1755-1775, December.
    2. Jiangnan Qiu & Min Zuo & Jingxian Wang & Chengjie Cai, 2021. "Knowledge order in an online knowledge community: Group heterogeneity and two paths mediated by group interaction," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 1075-1091, August.
    3. Guiyang Zhang, 2021. "Employee co-invention network dynamics and firm exploratory innovation: the moderation of employee co-invention network centralization and knowledge-employee network equilibrium," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7811-7836, September.

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