| 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 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. |