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.

本文主要介绍了如何利用多种数据挖掘技术和深度学习应用于金融文本处理场景，并且展示了如何使用自然语言处理（NLP）和知识图谱（KG）技术来从异构数据中识别金融风险和机会。