论文标题
财务数据分析通过多策略文本处理应用
Financial data analysis application via multi-strategy text processing
论文作者
论文摘要
维持金融体系稳定对于经济发展至关重要，而早期识别风险和机遇至关重要。金融行业包含多种数据，例如财务报表，客户信息，股票交易数据，新闻等。大量的异质数据要求使用机器处理和理解的机器智能算法。本文主要关注有关中国A股公司的股票交易数据和新闻。我们提出了财务数据分析应用程序，即财务商搬运工，旨在使用多策略数据挖掘方法结合文本和数值数据。此外，我们在深度学习财务文本处理应用程序方案（NLP）和知识图（KG）技术方面介绍了我们的努力和计划。基于KG技术，可以从异质数据中确定风险和机会。 NLP技术可用于从非结构化文本中提取实体，关系和事件，并分析市场情绪。实验结果表明，市场对公司和行业的情感以及公司之间的新闻级关联。
Maintaining financial system stability is critical to economic development, and early identification of risks and opportunities is essential. The financial industry contains a wide variety of data, such as financial statements, customer information, stock trading data, news, etc. Massive heterogeneous data calls for intelligent algorithms for machines to process and understand. This paper mainly focuses on the stock trading data and news about China A-share companies. We present a financial data analysis application, Financial Quotient Porter, designed to combine textual and numerical data by using a multi-strategy data mining approach. Additionally, we present our efforts and plans in deep learning financial text processing application scenarios using natural language processing (NLP) and knowledge graph (KG) technologies. Based on KG technology, risks and opportunities can be identified from heterogeneous data. NLP technology can be used to extract entities, relations, and events from unstructured text, and analyze market sentiment. Experimental results show market sentiments towards a company and an industry, as well as news-level associations between companies.
