Financial data analysis application via multi-strategy text processing

TL;DR

This paper introduces a financial data analysis application that combines multi-strategy data mining, NLP, and knowledge graph technologies to analyze heterogeneous financial data, identify risks and opportunities, and extract market sentiment insights.

Contribution

It presents a novel multi-strategy data mining approach integrating textual and numerical data for financial analysis, along with plans for deep learning applications using NLP and knowledge graphs.

Findings

Market sentiment analysis towards companies and industries

News-level associations between companies identified

Effective extraction of entities, relations, and events from unstructured text

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.