The Rise of Reddit: How Social Media Affects Retail Investors and Short-sellers’ Roles in Price Discovery

Hu, Danqi; Zhang, Valerie; Zhang, Xiaoyan

doi:10.2139/ssrn.3807655

Cited by 19 publications

(21 citation statements)

References 32 publications

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“…If we are to deploy crowd-sourced financial prediction and speculation systems at scale, it will be important to fully characterize the performance of these systems. This is especially given the growing importance of decentralized financial prediction and speculation including very recent events during which retail investors self-organized using social media and drove up asset and derivative prices [ 3 , 4 ]. However, crowd-sourced prediction systems and literature so far focus on measuring and optimizing for the accuracy of the predictions with little regard to the risk of these predictions even though measuring both accuracy and risk is standard in machine learning [ 8 , 9 , 10 ] and financial [ 14 , 15 , 16 ] forecasting applications.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Adjodah

Leng

Chong

et al. 2021

Entropy

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Adjodah

Leng

Chong

et al. 2021

Entropy

View full text Add to dashboard Cite

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“…Online users connect more and more via platforms that enable content sharing, either generic or around specific topics, and do this by means of text-only messages, or augmenting them with multimedia content such as pictures, audio or video. As such, these platforms have been used to understand user, group and organizationwide behaviours (Yang et al, 2021;Hu et al, 2021). In particular, Twitter, which is the main platform studied in this paper, has long been an important resource for understanding society at large (Weller et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

TweetNLP: Cutting-Edge Natural Language Processing for Social Media

Camacho-Collados¹,

Rezaee²,

Talayeh³

et al. 2022

Preprint

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In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media. TweetNLP supports a diverse set of NLP tasks, including generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. Taskspecific systems are powered by reasonablysized Transformer-based language models specialized on social media text (in particular, Twitter) which can be run without the need for dedicated hardware or cloud services. The main contributions of TweetNLP are: (1) an integrated Python library for a modern toolkit supporting social media analysis using our various task-specific models adapted to the social domain; (2) an interactive online demo for codeless experimentation using our models; and (3) a tutorial covering a wide variety of typical social media applications.

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“…Cryptocurrency (crypto) trading presents a new investment opportunity (Chuen et al, 2017) for maximizing profits. The rising ubiquity of speculative trading of cryptocurrencies over social media has lead to sentiment driven "bubbles" (Chohan, 2021;Hu et al, 2021). Such a bubble is characterized by rapid escalation of price in a short period of * Equal contribution.…”

Section: Introductionmentioning

confidence: 99%

Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models

Sawhney¹,

Agarwal²,

Mittal³

et al. 2022

Preprint

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The rapid spread of information over social media influences quantitative trading and investments. The growing popularity of speculative trading of highly volatile assets such as cryptocurrencies and meme stocks presents a fresh challenge in the financial realm. Investigating such "bubbles" -periods of sudden anomalous behavior of markets are critical in better understanding investor behavior and market dynamics. However, high volatility coupled with massive volumes of chaotic social media texts, especially for underexplored assets like cryptocoins pose a challenge to existing methods. Taking the first step towards NLP for cryptocoins, we present and publicly release CryptoBubbles, a novel multispan identification task for bubble detection, and a dataset of more than 400 cryptocoins from 9 exchanges over five years spanning over two million tweets. Further, we develop a set of sequence-to-sequence hyperbolic models suited to this multi-span identification task based on the power-law dynamics of cryptocurrencies and user behavior on social media. We further test the effectiveness of our models under zero-shot settings on a test set of Reddit posts pertaining to 29 "meme stocks", which see an increase in trade volume due to social media hype. Through quantitative, qualitative, and zero-shot analyses on Reddit and Twitter spanning cryptocoins and meme-stocks, we show the practical applicability of CryptoBubbles and hyperbolic models.

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The Rise of Reddit: How Social Media Affects Retail Investors and Short-sellers’ Roles in Price Discovery

Cited by 19 publications

References 32 publications

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

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

TweetNLP: Cutting-Edge Natural Language Processing for Social Media

Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models

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