Understanding the variations in trading price (volatility), and its response to external information is a well-studied topic in finance. In this study, we focus on volatility predictions for a relatively new asset class of cryptocurrencies (in particular, Bitcoin) using deep learning representations of public social media data from Twitter. For the field work, we extracted semantic information and user interaction statistics from over 30 million Bitcoin-related tweets, in conjunction with 15-minute intraday price data over a 144-day horizon. Using this data, we built several deep learning architectures that utilized a combination of the gathered information. For all architectures, we conducted ablation studies to assess the influence of each component and feature set in our model. We found statistical evidences for the hypotheses that: (i) temporal convolutional networks perform significantly better than both autoregressive and other deep learning-based models in the literature, and (ii) the tweet author meta-information, even detached from the tweet itself, is a better predictor than the semantic content and tweet volume statistics.
翻译:了解贸易价格(波动)的变化,以及其对外部信息的反应,是金融领域一个研究周密的专题。在本研究中,我们利用从Twitter上对公共社交媒体数据进行深层次的学习介绍,侧重于对相对新的加密资产类别(特别是Bitcoin)的波动预测。关于实地工作,我们从3 000多万个与Bitcoin有关的推文中提取了语义信息和用户互动统计数据,同时在144天的视野中收集了15分钟的日间价格数据。我们利用这一数据,建立了几个利用所收集信息组合的深层次学习结构。对于所有结构,我们进行了相关研究,以评估我们模型中设定的每个组成部分和特征的影响。我们找到了有关假设的统计证据:(一) 时间革命网络比文献中的自动递进化和其他深层次学习模型都好得多,以及(二) 推特作者的元信息,甚至脱离了推文本身,比语义内容和推文量统计要好得多。