  The purpose of this study is to estimate the correlation structure between
multiple assets using financial text analysis. In recent years, as the
background of elevating inflation in the global economy and monetary policy
tightening by central banks, the correlation structure between assets,
especially interest rate sensitivity and inflation sensitivity, has changed
dramatically, increasing the impact on the performance of investors'
portfolios. Therefore, the importance of estimating a robust correlation
structure in portfolio management has increased. On the other hand, the
correlation coefficient using only the historical price data observed in the
financial market is accompanied by a certain degree of time lag, and also has
the aspect that prediction errors can occur due to the nonstationarity of
financial time series data, and that the interpretability from the viewpoint of
fundamentals is a little poor when a phase change occurs. In this study, we
performed natural language processing on news text and central bank text to
verify the prediction accuracy of future correlation coefficient changes. As a
result, it was suggested that this method is useful in comparison with the
prediction from ordinary time series data.
