An ontology based framework for mining dependence relationships between news and financial instruments

Before news is input into financial trading algorithms/models, it needs human judgements for exploring the market implications of news content, distinguishing significance extent of news, and finding out the impact of polar type of each kind of news on certain financial instrument trading activity. But Dawes and Faust (1989) reported that people usually rely on clinical judgements, especially it is hard for them to distinguish valid decision variables from invalid ones in decision making. Thus, in order to alleviate this problem and provide more objective decision making support about news in financial market, an ontology based framework is proposed, for investigating the actuarial dependence relationships between news and financial instruments trading activities as well as identifying more valid news for trading decision making. This framework is expected to help people in financial market how to consider weight for each kind of news when inputted in trading algorithms/models of certain financial instruments.

Research highlights► The main contribution is to provide an ontology based data mining framework. Specifically, an ontology is first proposed for processing news, and financial data. Second, a reasoning system is designed to integrate the background knowledge. With this framework, dependence extent between news and financial trading is known.