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

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.

The Paradox of Interaction: Communication Network Centralization, Shared Task Experience, and the Wisdom of Crowds in Online Crowdsourcing Communities

“…The first and potentially negative effect of crowd communication, social influence, is that as crowd members communicate and share their opinions, they may change their attitudes and predictions based on others' ideas (Burghardt et al, 2017;Koehler & Beauregard, 2006;Salganik et al, 2006;Salganik & Watts, 2009). As a result, crowd members provide less diverse predictions compared to when working independently, leading to herding and biases in their judgments (Hong et al, 2020;Lorenz et al, 2011;Muchnik et al, 2013;Salganik et al, 2006;Salganik & Watts, 2009;Toyokawa et al, 2018). Essentially, the negative effect of social influence is implied in the central idea of the WOC theory, which argues that crowds' wisdom is best achieved by assembling a large number of noncommunicating individuals and curbing social influence within the crowd (Page, 2007).…”

“…Shared task experience is a common pattern in online crowd communities and was found to impact crowd performance in platforms like Wikipedia (Ransbotham & Kane, 2011;Ren & Yan, 2017;Robert & Romero, 2016). Yet these communication patterns are generally understudied in the WOC literature (Hong et al, 2020), perhaps because research in this area tended to apply simulations or experiments instead of studying naturally occurring communities in field settings.…”

“…Further, centralized communication networks may impede collective learning as the information sharing among the less connected peripheral members is inefficient (Mason & Watts, 2012). Taken together, the tendency of network centralization among members of a task crowd can be harmful for the crowd’s performance as the structure facilitates the social influence of a few dominant members while hampering collective learning within the crowds (Hong et al, 2020).…”

“…Viewing shared task experiences of task crowds as indirect communication among members, prior research again offers competing predictions. Some WOC researchers would predict that the social influence effect may become more prominent as crowds gain shared task experience, generating detrimental effects on the WOC (Hong et al, 2020; Lamberson & Page, 2011; Page, 2007). If crowd members have constantly performed the same tasks, they probably have fewer opportunities to learn from different experiences and may possess lower cognitive diversity compared to crowds consisting of members with highly distinct task experiences (Hong et al, 2020; Robert & Romero, 2016).…”

“…Some WOC researchers would predict that the social influence effect may become more prominent as crowds gain shared task experience, generating detrimental effects on the WOC (Hong et al, 2020; Lamberson & Page, 2011; Page, 2007). If crowd members have constantly performed the same tasks, they probably have fewer opportunities to learn from different experiences and may possess lower cognitive diversity compared to crowds consisting of members with highly distinct task experiences (Hong et al, 2020; Robert & Romero, 2016). Early research on small offline groups also revealed that, as group members acquire more experience interacting and working together, cohesion among members is likely to increase (Festinger et al, 1950).…”