Stock-bond Yield Correlation Analysis based on Natural Language Processing

Abstract

U.S. Treasury yield rates are the most important reference for global asset pricing and usually affect the stock market. Therefore, research on the correlation between China's core asset valuation and Treasury yield rates is becoming more and more important. The current statistical measurement methods have shortcomings such as the short period of market variables, low frequency, and inability to observe indicators of different countries in real-time. News, as information that reflects the public's attention and cognition, directly affects investors' stock trading behavior in the short term and has timeliness. We construct Correlation Strength by News (CSN) index for the first time to measure the correlation strength between treasury yield rates and the stock market from the perspective of media attention. The proposed method effectively solves the problem of the traditional method, such as the lack of data update timeliness and forecasting effectiveness. The capability of the index as an alternative variable of the correlation degree between the treasury yield rates and the stock market is verified.