Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions
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 ris...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/7/801 |
| _version_ | 1797528923007877120 |
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| author | Dhaval Adjodah Yan Leng Shi Kai Chong P. M. Krafft Esteban Moro Alex Pentland |
| author_facet | Dhaval Adjodah Yan Leng Shi Kai Chong P. M. Krafft Esteban Moro Alex Pentland |
| author_sort | Dhaval Adjodah |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:06:15Z |
| format | Article |
| id | doaj.art-1c86e49592e24e2f92d84193d59142cc |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T10:06:15Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-1c86e49592e24e2f92d84193d59142cc2023-11-22T01:29:16ZengMDPI AGEntropy1099-43002021-06-0123780110.3390/e23070801Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial PredictionsDhaval Adjodah0Yan Leng1Shi Kai Chong2P. M. Krafft3Esteban Moro4Alex Pentland5Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USAMcCombs School of Business, The University of Texas at Austin, Austin, TX 78712, USAMedia Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USAOxford Internet Institute, University of Oxford, Oxford OX1 2JD, UKDepartamento de Matemáticas & GISC, Universidad Carlos III de Madrid, 28911 Leganes, SpainMedia Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USAA 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.https://www.mdpi.com/1099-4300/23/7/801crowd-sourcingwisdom of the crowdsocial learningBayesian modelsrisk |
| spellingShingle | Dhaval Adjodah Yan Leng Shi Kai Chong P. M. Krafft Esteban Moro Alex Pentland Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions Entropy crowd-sourcing wisdom of the crowd social learning Bayesian models risk |
| title | Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions |
| title_full | Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions |
| title_fullStr | Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions |
| title_full_unstemmed | Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions |
| title_short | Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions |
| title_sort | accuracy risk trade off due to social learning in crowd sourced financial predictions |
| topic | crowd-sourcing wisdom of the crowd social learning Bayesian models risk |
| url | https://www.mdpi.com/1099-4300/23/7/801 |
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