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DeepTrust：评估社交媒体对股票极端波动的证据 | Stratmill

文章 arXiv papers · 作者: Pok Wah Chan
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总结

DeepTrust是一个用于查找和评估可能解释股票价格极端变动信息的框架。其工作流程包括三个部分：使用机器学习从历史价格中检测异常变动；使用能适应搜索条件的查询从Twitter检索相关非结构化信息；以及评估检索内容的可靠性。评估会考虑推文特征、机器生成语言的迹象、论证结构、主观性和情感倾向，从而筛选出数量精简且可能可信的帖子。

该框架使用2021年4月自行标注的Twitter和Facebook股票异常事件进行评估。据报告，最优设置在两种案例中的F0.5分数和精确率均优于基准分类器。作者还分别考察了检索模块和可靠性评估模块，并指出限制其表现的因素。说明中提供的证据仅涉及两个标注异常事件，因此不能证明该框架对其他事件、资产或时期有效。该方法可用于信息筛选；可信的社交媒体帖子本身并不能证明价格变动的原因。

核心观点

  • DeepTrust结合价格异常检测、Twitter信息检索和信息可靠性评估。
  • 系统使用动态查询条件，检索与异常价格变动相关的社交媒体内容。
  • 可靠性评估依据推文特征、机器生成语言迹象、论证结构、主观性和情感倾向。
  • 据报告，在两个标注股票异常事件上的评估中，该方法的F0.5分数和精确率优于基准分类器。
  • 所述评估规模较小，限制了对泛化能力的判断；检索到的帖子也不能证明因果关系。

标签

股票机器学习市场情绪事件驱动
全文
# DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies


# DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies









Extreme pricing anomalies may occur unexpectedly without a trivial cause, and equity traders typically experience a meticulous process to source disparate information and analyze its reliability before integrating it into the trusted knowledge base. We introduce DeepTrust, a reliable financial knowledge retrieval framework on Twitter to explain extreme price moves at speed, while ensuring data veracity using state-of-the-art NLP techniques. Our proposed framework consists of three modules, specialized for anomaly detection, information retrieval and reliability assessment. The workflow starts with identifying anomalous asset price changes using machine learning models trained with historical pricing data, and retrieving correlated unstructured data from Twitter using enhanced queries with dynamic search conditions. DeepTrust extrapolates information reliability from tweet features, traces of generative language model, argumentation structure, subjectivity and sentiment signals, and refine a concise collection of credible tweets for market insights. The framework is evaluated on two self-annotated financial anomalies, i.e., Twitter and Facebook stock price on 29 and 30 April 2021. The optimal setup outperforms the baseline classifier by 7.75% and 15.77% on F0.5-scores, and 10.55% and 18.88% on precision, respectively, proving its capability in screening unreliable information precisely. At the same time, information retrieval and reliability assessment modules are analyzed individually on their effectiveness and causes of limitations, with identified subjective and objective factors that influence the performance. As a collaborative project with Refinitiv, this framework paves a promising path towards building a scalable commercial solution that assists traders to reach investment decisions on pricing anomalies with authenticated knowledge from social media platforms in real-time.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

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