Financial market modeling integrating language information via deep learning

近年来，金融市场面临着重大挑战，如2018年比特币价格暴跌和2020年美国股市下跌。虽然之前的研究主要集中在分析价格数据上，但这位研究人员试图采用一种新颖的方法，将自然语言数据（如新闻文章）纳入其中。深度学习技术被用于在单一计算框架内处理价格和语言数据，在这两个复杂的社会系统之间建立联系，最终增强我们对金融市场的理解。在过去的一年中，研究人员提出了一种整合自然语言数据的广义股票投资组合优化模型。该模型用从新闻文章中获得的向量表示股票，并从这些文章中识别出股票之间的极端风险相关性，有效地分散了风险。这项工作被接受发表在“基于知识的系统”上。此外，研究人员还研究了股票向量表示在描述多义等复杂现象时的局限性。为了解决这些限制，提出了一种新的用函数代替向量的表示方法，并在语言数据上进行了验证。该方法将在金融市场上进一步验证，并已被接受发表在“神经信息处理系统进展2022”上。

In recent years, the financial markets have faced significant challenges such as the 2018 bitcoin price crashes and the 2020 US stock market declines. While previous research has largely focused on analyzing price data, this researcher sought to take a novel approach by incorporating natural language data such as news articles. Deep learning techniques were employed to process both price and language data within a single computational framework, establishing a connection between these two complex social systems and ultimately enhancing our understanding of financial markets.In the past year, the researcher proposed a generalized model for stock portfolio optimization that integrates natural language data. This model represented a stock with a vector obtained from news articles and identified extreme risk correlations between stocks from these articles, effectively diversifying the risks. This work was accepted for publication in "Knowledge-Based Systems."Additionally, the researcher investigated the limitations of vector representations of stocks in describing complex phenomena like polysemy. To address these limitations, a new representation method using functions instead of vectors was proposed and validated on language data. The method will be further validated on financial markets and has been accepted for publication in "Advances in Neural Information Processing Systems 2022."