论文标题
深层信任：可靠的财务知识检索框架，用于解释极端定价异常
DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies
论文作者
论文摘要
极端定价异常可能会出乎意料地发生，而没有微不足道的原因，而权益交易者通常会经历细致的过程，以获取不同的信息并分析其可靠性，然后再将其集成到可信的知识库中。我们在Twitter上介绍了一个可靠的财务知识检索框架，以速度解释极端的价格转移，同时使用最先进的NLP技术来确保数据真实性。我们提出的框架由三个模块组成，专门用于异常检测，信息检索和可靠性评估。工作流程首先使用使用历史定价数据训练的机器学习模型来识别异常的资产价格变化，并使用具有动态搜索条件的增强查询从Twitter中检索相关的非结构化数据。深层信任将信息可靠性从推文功能，生成语言模型的痕迹，论证结构，主观性和情感信号的痕迹中推断出来，并完善了针对市场见解的可靠推文的简明收集。该框架对两个自注重的财务异常进行了评估，即2021年4月29日和30日的Twitter和Facebook股价。最佳设置优于基线分类器在F0.5得分上的基线分类器的优于基线分类器7.75％和15.77％，并在精确的能力中分别在精确的情况下进行了10.55％和10.55％和18.88％的信息。同时，信息检索和可靠性评估模块分别分析其有效性和限制原因，并确定了影响性能的主观和客观因素。作为与修订的合作项目，该框架为建立可扩展的商业解决方案铺平了有希望的途径，该解决方案可帮助交易者实时从社交媒体平台上实现认证的知识来定价异常。
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.
