Financial data analysis application via multi-strategy text processing
- URL: http://arxiv.org/abs/2204.11394v1
- Date: Mon, 25 Apr 2022 01:56:36 GMT
- Title: Financial data analysis application via multi-strategy text processing
- Authors: Hongyin Zhu
- Abstract summary: This paper mainly focuses on the stock trading data and news about China A-share companies.
We present our efforts and plans in deep learning financial text processing application scenarios using natural language processing (NLP) and knowledge graph (KG) technologies.
- Score: 0.2741266294612776
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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