Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions
Name
entropy-23-00801-v2.pdf
Description
Published version
Size
1.33 MB
Format
Adobe PDF
Checksum (MD5)
39cdd6d5f7460569f2a681dac7d0063c
Author(s)
Adjodah, Dhaval
Leng, Yan
Chong, Shi Kai
Krafft, PM
Moro, Esteban
Pentland, Alex
Date Issued
2021
Journal
Entropy
Publisher
MDPI AG
Citation
Adjodah, Dhaval, Leng, Yan, Chong, Shi Kai, Krafft, PM, Moro, Esteban et al. 2021. "Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions." Entropy, 23 (7).
Version
Final published version
Abstract
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.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/E23070801
