I was always very interested in math, and I always wanted to pursue a career where I could combine math and finance. I wasn't aware of the concept of a quant back then, and when I was introduced to quant finance, I was immediately fascinated. I chose to participate in an experimental program in undergrad, and so I majored in math and finance in college. That's how everything began.
I learned about the CQF program when I was doing an internship at a Chinese hedge fund, at the beginning of the private equity era in China. I was a junior college student, and I was debating whether I should pursue a master’s in financial engineering or participate in a statistical program, which would give me more capability in handling real-world data science. When I was introduced to CQF in 2015, I saw an opportunity to learn what I could in an MFE program and at the same time spend my grad school years doing statistical research and focusing more on big data analysis tools. That's why I chose the CQF program. It has helped me bridge the gap between the two.
All the math modules were very interesting, especially the stochastic calculus modules, but the most useful was the sixth module. I chose to learn more about portfolio construction and optimization. I already had experience learning about performance theories in college, but the knowledge was quite theoretical and superficial for actual portfolio management. The CQF helped me because I learned the theories and then projects helped me understand the execution. This knowledge eventually helped me do more factor analysis and risk management on portfolio research, which has been very helpful.
