Self-attention context and content embeddings for better and more explainable prediction of news effects on stockmarkets

自注意力上下文和内容嵌入可以更好、更可解释地预测新闻对股市的影响

• 批准号: 2597209
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2597209
• 关键词: Self; attention; context; content; embeddings

Confidence from the general public and from the investor market is a feature companies constantly attempt to build, secure while predicting its' evolution. Conversely, public and private investors are keen on being able to forecast how well a company will do in relation to what is said about it in the press. Thus, forecasting the impact of "news which moves the market" (Chiang 2010) on a company's bottom line and stock price is of great timeliness to industry, investors and businesses alike. Moreover, research has shown that news effect on a given company correlates to companies in similar or interlinked industries (Ran et al. 2019), this implies that studying how news impact stock prices is of great importance from the standpoint of structural market stability, and thus of quantitative financial research. Existing forecasting methods to forecast employ content-based information (from the content of the news piece). More recent techniques explore the network structure of news pieces as news which spread faster may have more impact on a company's stock price. However, these techniques are very rarely put together in news impact prediction for finance, despite the fact that in closely related news-mining fields such as disinformation detection, this combination of techniques from both natural language processing and graph theory has shown its' worth. My proposed solution would be to merge content-based and context-based representations of a text to train a Machine Learning classifier from the family of self-attention models which have both shown their high accuracy and high degree of explain ability. Once trained, my solution has great potential in application across finance and business in order to empower companies to better predict their stock price and investors to build their portfolios. Moreover, the self-attention model I propose also allows my model to be more easily interpretable than existing forecasting techniques as self-attention models contain a representation of what information in the input data the model "looks at" to make its' prediction. This is far from a token feature, it is key to be able to back up forecasts based on news sentiment. Indeed, without it, forecasts of stock changes based on news article sentiments may be resisted, being perceived as a black box. Thus, given this explain ability, my solution would also have useful applications in governance and central banking institutions to help foresee and explain potential market downturns to proactively shape fiscal, monetary and financial policy.

来自公众和投资者市场的信心是公司不断尝试建立的一个特征，在预测其演变的同时确保其安全。相反，公共和私人投资者热衷于能够根据媒体对一家公司的评价来预测该公司的表现。因此，预测“影响市场的新闻”（Chiang 2010）对公司底线和股价的影响，对行业、投资者和企业都具有很强的时效性。此外，研究表明，新闻对特定公司的影响与类似或相互关联行业的公司相关（Ran et al. 2019），这意味着从市场结构稳定的角度研究新闻如何影响股价，从而进行定量金融研究非常重要。现有的预测方法采用基于内容的信息（来自新闻片段的内容）。最近的技术探索新闻片段的网络结构，因为传播更快的新闻可能对公司的股票价格有更大的影响。然而，这些技术很少被放在金融新闻影响预测中，尽管在密切相关的新闻挖掘领域，如虚假信息检测，这种自然语言处理和图论技术的结合已经显示出其价值。我提出的解决方案是合并基于内容和基于上下文的文本表示，以训练来自自关注模型家族的机器学习分类器，这些模型都显示出高准确性和高度的解释能力。经过培训后，我的解决方案在金融和商业领域具有很大的应用潜力，可以让公司更好地预测他们的股票价格，让投资者更好地建立他们的投资组合。此外，我提出的自注意模型也使我的模型比现有的预测技术更容易解释，因为自注意模型包含了模型“查看”输入数据中的信息以进行“预测”的表示。这远不是一个象征性的功能，关键是能够支持基于新闻情绪的预测。事实上，如果没有它，基于新闻文章情绪的股票变化预测可能会受到抵制，被视为一个黑盒子。因此，鉴于这种解释能力，我的解决方案也将在治理和中央银行机构中得到有用的应用，以帮助预测和解释潜在的市场低迷，从而主动制定财政、货币和金融政策。