Understanding the variations in trading price (volatility), and its response
to exogenous information, is a well-researched topic in finance. In this study,
we focus on finding stable and accurate volatility predictors for a relatively
new asset class of cryptocurrencies, in particular Bitcoin, using deep learning
representations of public social media data obtained from Twitter. For our
experiments, we extracted semantic information and user statistics from over 30
million Bitcoin-related tweets, in conjunction with 15-minute frequency price
data over a horizon of 144 days. Using this data, we built several deep
learning architectures that utilized different combinations of the gathered
information. For each model, we conducted ablation studies to assess the
influence of different components and feature sets over the prediction
accuracy. We found statistical evidences for the hypotheses that: (i) temporal
convolutional networks perform significantly better than both classical
autoregressive models and other deep learning-based architectures in the
literature, and (ii) tweet author meta-information, even detached from the
tweet itself, is a better predictor of volatility than the semantic content and
tweet volume statistics. We demonstrate how different information sets gathered
from social media can be utilized in different architectures and how they
affect the prediction results. As an additional contribution, we make our
dataset public for future research.

本研究旨在利用公共社交媒体数据（特别是 Twitter）的深度学习表示来寻找一种新资产类别 —— 加密货币（尤其是比特币）的波动率稳定且准确的波动率预测方法。研究利用超过 3000 万条比特币相关推文的语义信息和用户统计信息，并结合 144 天内每 15 分钟的价格数据来构建了多种深度学习构架。实证结果显示，时间卷积网络在预测精度上比经典自回归模型和其它深度学习构架表现更优，同时发现推文作者元信息是比推文的语义内容和推文体积更好的波动率预测因子。