mlsanityData to 14 Oct 2026

Financial data analysis application via multi-strategy text processing

arXiv 2204.11394 ↗First posted 25 Apr 2022cs.CLcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 25 of its 87 references that are in the map. First posted on arXiv 25 Apr 2022.

Builds on

25 of 87 references.

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5 papers.

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