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.

翻译：本研究旨在通过金融文本分析来估计多资产间的相关性结构。近年来，随着全球经济通胀抬升及央行货币政策收紧的背景，资产间的相关性结构——特别是利率敏感性与通胀敏感性——已发生显著变化，从而加剧了对投资者投资组合绩效的影响。因此，在投资组合管理中估计稳健的相关性结构的重要性日益凸显。另一方面，仅使用金融市场观测到的历史价格数据计算相关系数存在一定时滞，且由于金融时间序列数据的非平稳性可能导致预测误差；此外，当市场发生相位变化时，从基本面角度出发的可解释性也较为有限。本研究通过对新闻文本与央行文本进行自然语言处理，验证了其对未来相关系数变化的预测精度。结果表明，与基于常规时间序列数据的预测相比，该方法具有显著优势。