معرفی
Yoshihiro Kitamura is a Professor at Waseda University's Faculty of Social Sciences, School of Social Sciences, specializing in international finance and market microstructure. He has held academic positions at Waseda University since 2005 and previously taught at University of Toyama from 2006-2011. His academic journey began with undergraduate studies at Waseda University's Faculty of Humanities and Social Sciences in 1998, followed by graduate studies at Waseda University's Graduate School, Division of Economics in 2002.
- Professor, Waseda University (2013-Present)
- Associate Professor, Waseda University (2011-2013)
- Associate Professor, University of Toyama (2008-2011)
- Lecturer, University of Toyama (2006-2008)
- Research Assistant, Waseda University (2005-2006)
- Research Fellow, Japan Society for the Promotion of Science (2003-2005)
Dr. Kitamura's research focuses on the application of machine learning to finance, market microstructure, foreign exchange markets, and international finance. His work bridges theoretical finance with empirical analysis, particularly examining how market participants process information and how that affects price formation in foreign exchange markets. He has developed innovative methodologies for measuring market efficiency, assessing foreign exchange intervention effectiveness, and understanding the role of informed trading in price discovery processes.
His most recent research explores the application of cutting-edge AI techniques including generative adversarial networks (GANs) and large language models to foreign exchange rate prediction, building on his earlier work with LSTM networks and traditional econometric approaches. This represents a clear evolution in his research from theoretical models of exchange rate regimes to sophisticated machine learning applications for understanding market dynamics.
Dr. Kitamura has received significant research funding from the Japan Society for the Promotion of Science, including current projects on price discovery processes using generative adversarial networks (2023-2028) and previous projects on deep learning applications in finance theory evaluation (2019-2023). His work demonstrates a consistent focus on understanding how information flows through financial markets and affects price formation, with increasing emphasis on computational methods in recent years.
He actively contributes to the academic community through teaching courses in International Economics and International Finance at both graduate and undergraduate levels, and serves as a member of the Japan Society of Finance and Japanese Economic Association. His research has been published in top finance and economics journals including Journal of International Money and Finance, International Review of Financial Analysis, and Journal of International Financial Markets, Institutions & Money.
