Financial data analysis application via multi-strategy text processing
This addresses the need for early risk and opportunity identification in the financial industry, particularly for China A-share companies, but appears incremental as it applies existing NLP and KG methods to financial data.
The paper tackled the problem of analyzing financial data by developing Financial Quotient Porter, an application that combines textual and numerical data using multi-strategy data mining, NLP, and knowledge graph technologies to identify risks and opportunities, with experimental results showing market sentiments and news-level associations between companies.
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.