مقاله انگلیسی دقت-ریسک معاوضه به دلیل یادگیری اجتماعی در پیش بینی های مالی جمعی
ترجمه نشده

مقاله انگلیسی دقت-ریسک معاوضه به دلیل یادگیری اجتماعی در پیش بینی های مالی جمعی

عنوان فارسی مقاله: دقت-ریسک معاوضه به دلیل یادگیری اجتماعی در پیش بینی های مالی جمعی
عنوان انگلیسی مقاله: Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions
مجله/کنفرانس: آنتروپی - Entropy
رشته های تحصیلی مرتبط: مدیریت، اقتصاد
گرایش های تحصیلی مرتبط: مدیریت مالی، اقتصاد مالی، مهندسی مالی و ریسک
کلمات کلیدی فارسی: جمع سپاری، خرد جمعی، یادگیری اجتماعی، مدل های بیزی، خطر
کلمات کلیدی انگلیسی: crowd-sourcing - wisdom of the crowd - social learning - Bayesian models - risk
شناسه دیجیتال (DOI): https://doi.org/10.3390/e23070801
دانشگاه: Media Lab, Massachusetts Institute of Technology, Cambridge, USA
صفحات مقاله انگلیسی: 18
ناشر: ام دی پی آی - MDPI
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2021
ایمپکت فاکتور: 2.524 در سال 2020
شاخص H_index: 74 در سال 2020
شاخص SJR: 0.468 در سال 2020
شناسه ISSN: 1099-4300
شاخص Quartile (چارک): Q2 در سال 2020
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
آیا این مقاله فرضیه دارد: ندارد
کد محصول: E15881
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract
Introduction
Related Work
Materials and Methods
Results
Discussion
References

بخشی از مقاله (انگلیسی)

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.
Introduction
Distributed financial platforms are on the rise, ranging from Decentralized Autonomous Organizations [1], crowd-sourced prediction systems [2] to the very recent events during which retail investors self-organized using social media and drove up asset and derivative prices [3,4]. In this work, we investigate how financial agents process information from one another and predict-individually and collectively—the future prices of real assets. Specifically, we are interested in understanding the computational models they use to update their beliefs after information exposure and how different social vs. non-social belief update strategies lead to trade-offs in prediction performance. Here, we expand the typical definition of performance for collective prediction to include the concept of risk. Typically, the prediction performance of collectives and swarms is measured mostly by the accuracy of the group over collections of tasks [5–7]. However, it has been shown theoretically [8,9] and observed in a variety of applications [10,11] that there is a fundamental trade-off between prediction accuracy (average error) and prediction risk (variance of error).