An Integrative Analysis of Texual Data and Time-Series Market Data

Abstract

In this study, we proposed a new text-mining methods for long-term market analysis. Using our method, we analysed monthly price data of Japanese government bond market. First we extracted feature vectors from monthly reports of Bank of Japan. Then, trends of the JGB market were estimated by regression analysis using the feature vectors. As a result, determination coefficients were over 75%, and market trends were explained well by the information that was extracted from textual data. Finally, we compared the predictive power of textual data with that of numerical data. As a result, Our text mining method had prediction power superior to the numerical data analysis.