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Financial market modeling integrating language information via deep learning

通过深度学习整合语言信息的金融市场建模

基本信息

批准号：
21J11781
负责人：
DU XIN
金额：
$ 1.09万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for JSPS Fellows
财政年份：
2021
资助国家：
日本
起止时间：
2021-04-28 至 2023-03-31
项目状态：
已结题

项目摘要

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."

近年来，金融市场面临重大挑战，如2018年比特币价格暴跌和2020年美国股市下跌。虽然以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通过整合新闻文章等自然语言数据来采取一种新颖的方法。深度学习技术被用于在一个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，并最终增强我们对金融市场的理解。在过去的一年中，研究人员提出了一个整合自然语言数据的股票投资组合优化通用模型。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。此外，研究人员还研究了股票矢量表示在描述一词多义等复杂现象时的局限性。为了解决这些问题，提出了一种新的表示方法，使用函数代替向量，并验证了语言数据。该方法将在金融市场上得到进一步验证，并已被接受发表在《神经信息处理系统进展2022》上。"