Tatsuyoshi Okimoto - Appointed in AY2022

My specialty is econometric finance, macroeconometrics, and energy economics. I research the development and application of methods to analyze data and clarify its implications for financial markets, the macroeconomy, commodity markets, and more.

In the modern era, where big data has become relatively easy to obtain and the new field of data science has emerged, this type of research can be considered a part of data science. Within this field, my analysis focuses primarily on economics and finance.

The foundation of my research is statistics and econometrics, with the application of a method called time-series analysis to economic and financial data forming the core of my work. I first encountered time-series analysis in a seminar during my third year of university. I joined an econometrics seminar, and as it happened, the theme for that year was time-series analysis. I became fascinated by the interesting theory of time-series analysis and its wide range of applications, and since then, I have continued to explore econometric analysis methods with a focus on time-series analysis. Now, I am also dedicated to pioneering the newly emerged field of data science.

As for specific research themes, I analyze the interdependence of financial markets and consider its application to international portfolio diversification, and I quantitatively evaluate the effects of major countries' monetary policies on international financial markets. Recently, I have been focusing on the expanding field of ESG investing, and an important research theme for me is examining the relationship between corporate ESG ratings and market valuations, as well as the performance of ESG investments.

A major appeal of data science is the ability to tackle such a wide range of issues by identifying the truth contained in data through econometric analysis and then using the insights gained to help solve social problems or apply them to business. However, correctly identifying the truth from data is not easy and is often a complex challenge. I believe that devising ways to make the data "speak the truth" through a process of trial and error with methods capable of addressing the problem is also one of the great pleasures of data science.

Correctly identifying the truth within data is not easy, and the results of data analysis always involve uncertainty. For this reason, I conduct my research by verifying methods to obtain results that are closer to the truth, accurately assessing the degree of uncertainty in the estimation results, and then applying the insights gained to real-world problems. If you ever come across interesting data, I encourage you to think about what you can learn from it, how certain those findings are, and how you can apply the results to improve your own life and society. If you are interested in data analysis and its real-world applications in the fields of economics and finance, I think you would find it fascinating to study econometrics and econometric finance at the undergraduate or graduate level.