Abstract
Artificial intelligence and social media analytics have played a critical role in avoiding the errors investors make when making investment decisions. There is a need to rely on data, and this study addresses the various ways that data can be obtained, analyzed, and utilized in making essential investment decisions. The study utilizes a qualitative research model that is guided by research questions. Secondary sources of data are used to identify the impact of social media analytics and artificial intelligence at large in the field of behavioral finance. The crowd-based method and the swarm method are based on the stock market because researchers in the field of Collective Intelligence show that groups can outperform individuals when making decisions, predictions, and forecasts. The most common method for harnessing a group's intelligence treats the population as a “crowd” of independent agents that provide input in isolation in the form of polls, surveys, and market transactions. While such crowd-based methods can be effective, they are markedly different from how natural systems harness group intelligence. The present study compares the predictive ability of crowds and swarms when tapping the intelligence of human groups. More specifically, the present study tasked a of 469 football fans and a swarm of 29 football fans in a challenge to predict 20 Prop Bets during the 2016 Super Bowl. Although 16 times larger in size, results revealed that the was significantly less accurate (at 47% correct) than the swarm (at 68% correct). Further, the swarm outperformed 98% of the individuals in the full study. These results suggest that swarming, with closed-loop feedback, is potentially a more effective method for tapping groups' insights than traditional polling. Researchers in Collective Intelligence has shown that groups can outperform individuals when making decisions, predictions, and forecasts. The most common method for harnessing the intelligence of a group threats the population as a crowd of independent agents that provide input in isolation in the form of polls, surveys, and action. While such crowd-based methods can be effective, they are markedly different from how natural systems harness group intelligence. The present study compares the predictive ability of crowds and swarms when tapping the intelligence of human groups. More specifically, the present study tasked a of 469 football fans and a swarm of 29 football fans in a challenge to predict 20 Prop Bets during the 2016 Super Bowl. Although 16 times larger in size, results revealed that the was significantly less accurate (at 47% correct) than the swarm (at 68% correct). Further, the swarm outperformed 98% of the individuals in the full study. These results suggest that swarming, with closed-loop feedback, is potentially a more effective method for tapping groups' insights than traditional polling.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
