Automatic Summarization of Analyst Reports Based on Causal Relationship Text-Mined from News Reports

Abstract

In this paper, we proposed a new approach taking causal relationship into consideration with text mining for analyst report and news in automatic summarization. This approach can be used for reducing work load to read analyst report for institutional investors and gathering important economic information for investment decision for investment analysts. First, we analyzed the validity of the method in extracting causal relationship which can be evaluated from the textual data of Nomura Securities Co., Ltd. As a result, the using method could extract basis information of analyst's opinion from analyst report in higher precision, and we could confirm the style of analyst in expression of opinion and basis.