Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models
CryptoBubbles: A dataset and hyperbolic models for detecting cryptocurrency bubbles using social media data.
What it examines
The paper introduces CryptoBubbles, a novel multi-span identification task for detecting cryptocurrency bubbles using a dataset of over 400 cryptocoins and two million tweets. It aims to address the challenges of high volatility and chaotic social media texts in forecasting speculative risks.
What it concludes
The research highlights the practical applicability of CryptoBubbles and MBHN in forecasting speculative financial risks. Potential applications include quantitative trading and risk management. Future research could explore enhancing model interpretability and applying the approach to other domains like healthcare.
Evidence objects
The research highlights the practical applicability of CryptoBubbles and MBHN in forecasting speculative financial risks. Potential applications include quantitative trading and risk management. Future research could explore enhancing model interpretability and applying the approach to other domains like healthcare.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: 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… ▽ More 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 multi-span 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. △ Less
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