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James Huang ’22

Master’s Degree in Asset Management

Quant Researcher, AQR Capital Management

James Huang

Prior to Yale SOM, I was an investment banking analyst at Morgan Stanley in Asia, and then one of the directors of the corporate finance department for a startup. I picked up basic quantitative research skills, and I followed the U.S. market into the early mornings for three years, developing a real passion for investing and systematic trading. With the support of my now-fiancée, I committed to learning more and to developing the skill set for a career in the field: I chose Yale SOM’s program because of David Swensen’s and Toby Moskowitz’s vision and their dedication to training future asset managers. This program is also unique in the way it puts a heavy emphasis on the fiduciary responsibilities of practitioners, which really appealed to me.

Greater China Club members outdoors at Evans Hall
two people on Audubon Street
four people hiking on East Rock
Outside at Yale SOM
From left: At the Greater China Club’s Mid-Autumn Festival; with a friend on Audubon Street, a few blocks from Evans Hall; hiking up East Rock during a mixer with Yale Law School; and outdoors at Evans Hall

I feel lucky to be part of the first group of students in this program. I was fortunate to have truly great faculty mentors, in addition to the incredible Career Development Office staff, who guided and supported me through the recruiting process. Investment management is not an easy career track. It requires great preparation and resilience to navigate not just the challenging course load in our program, but also a recruitment cycle that mirrors the ups and downs of the market. Seeing the talented, strong-willed individuals at Yale SOM rise to the challenge has been inspiring.


Investment management is a very dynamic field. The use of existing models is being refined through the types of new techniques that we’re learning in the program. For example, after covering the leading quant methodologies—factor models, portfolio optimizations, and performance simulations and evaluations—I got to dive deeper into statistical techniques for bootstrapping, maximum likelihood estimation, machine learning, and other large data set techniques. This type of STEM training gives managers more robust back-test strategies to evaluate risk exposures.