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

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，在预测其发展的同时确保安全。相反，公共和私人投资者热衷于能够预测一家公司在媒体上的表现。因此，预测“影响市场的新闻”（Chiang 2010）对公司利润和股票价格的影响对行业，投资者和企业都具有很大的及时性。此外，研究表明，新闻对特定公司的影响与相似或相关行业的公司相关（Ran et al. 2019），这意味着从结构性市场稳定性的角度研究新闻如何影响股票价格非常重要，因此定量金融研究。现有的预测方法采用基于内容的信息（来自新闻的内容）进行预测。最近的技术探索了新闻的网络结构，因为传播速度更快的新闻可能对公司的股票价格产生更大的影响。然而，这些技术很少在金融新闻影响预测中被结合在一起，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组合已经显示出其价值。我提出的解决方案是将文本的基于内容和基于上下文的表示合并起来，从自我注意模型家族中训练机器学习分类器，这些模型都显示出高准确性和高解释能力。经过培训后，我的解决方案在金融和商业领域的应用中具有巨大的潜力，可以帮助公司更好地预测股票价格，帮助投资者建立投资组合。此外，我提出的自我注意力模型也使我的模型比现有的预测技术更容易解释，因为自我注意力模型包含模型“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，被视为黑箱。因此，考虑到这种解释能力，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，从而积极制定财政、货币和金融政策。

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.