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Article

Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions

1
Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
2
McCombs School of Business, The University of Texas at Austin, Austin, TX 78712, USA
3
Oxford Internet Institute, University of Oxford, Oxford OX1 2JD, UK
4
Departamento de Matemáticas & GISC, Universidad Carlos III de Madrid, 28911 Leganes, Spain
*
Authors to whom correspondence should be addressed.
Academic Editor: Stanisław Drożdż
Entropy 2021, 23(7), 801; https://doi.org/10.3390/e23070801
Received: 25 May 2021 / Revised: 15 June 2021 / Accepted: 17 June 2021 / Published: 24 June 2021
(This article belongs to the Special Issue Swarms and Network Intelligence)
A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote. View Full-Text
Keywords: crowd-sourcing; wisdom of the crowd; social learning; Bayesian models; risk crowd-sourcing; wisdom of the crowd; social learning; Bayesian models; risk
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MDPI and ACS Style

Adjodah, D.; Leng, Y.; Chong, S.K.; Krafft, P.M.; Moro, E.; Pentland, A. Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions. Entropy 2021, 23, 801. https://doi.org/10.3390/e23070801

AMA Style

Adjodah D, Leng Y, Chong SK, Krafft PM, Moro E, Pentland A. Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions. Entropy. 2021; 23(7):801. https://doi.org/10.3390/e23070801

Chicago/Turabian Style

Adjodah, Dhaval, Yan Leng, Shi K. Chong, P. M. Krafft, Esteban Moro, and Alex Pentland. 2021. "Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions" Entropy 23, no. 7: 801. https://doi.org/10.3390/e23070801

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