Prof. Jong-Min Kim

Division of Science and Mathematics
University of Minnesota-Morris, Morris

Short Bio: Prof. Dr. Jong-Min Kim received his Ph.D. from Oklahoma State University in Statistics. Jong-Min Kim is Editor in Chief of Measurement: Interdisciplinary Research and Perspective (Q1, Scopus, emerging SCI and Taylor & Francis Publisher). He is member of various International Statistical associations. His research areas include Survey Sampling, Functional Data Analysis, Big Data Analytics, and Text Mining for Patent Data and Spatial Statistics.

Keynote Topic: Forecasting Consumer Price Index with FOMC Sentimental Index

The Federal Open Market Committee (FOMC) is a component of the Federal Reserve System responsible for overseeing open market operations. The FOMC meets roughly 8 or more times per year to assess the economy of the United States. After each meeting, the FOMC releases a statement to the press outlining its assessment of the U.S. economy and its monetary policy stance. The sentiment of these statements may have an influence on the U.S. economy and financial markets. Using sentiment and correlational analyses, this research examines how the sentiment of these statements affects the U.S. economy and financial markets by analyzing how FOMC statement sentiment is correlated with the Consumer Price Index (CPI), the National Financial Conditions Index (NFCI), and the Adjusted National Financial Conditions Index

(ANFCI). We find evidence to suggest that there is a moderate negative correlation between an FOMC statement’s sentiment, and the United States City Average CPI value associated with the month before and the month after the statement’s release. We also find that there is no evidence to suggest there exists a correlation between an FOMC statement’s sentiment and the NFCI value associated with the week before or the week after the statement’s release. However, we do find evidence to suggest that there is a moderate negative correlation between an FOMC statement’s sentiment and the ANFCI value associated with the week before and the week after the statement’s release. We also found that out of the three models we tested (linear regression, Vine copula regression, and Gaussian copula regression), the Gaussian copula regression model performs the best when forecasting the CPI and the ANFCI. Additionally, we find that when forecasting CPI values, the models that include FOMC statement sentiment are more accurate than the models that exclude FOMC statement sentiment.