My journey began with my undergraduate degree, where I studied math, operational research, statistics, and economics. I was always interested in math, in particular the applied side of things, but I didn't have much exposure to finance. I secured an internship with Bloomberg in a data-related role, where I was working with ETF and mutual fund data, and was later successful in getting an offer to return as a full-time employee. After a few years, I met people in more quantitative roles within the firm, and I realized this was something I was interested in.
I knew I wanted to pivot into a more quantitative role, but I also knew I needed more quant finance knowledge for credibility. I was exploring the various options for qualifications and the CQF was the best match. It had the right blend of theory and, more importantly, practical application. The CQF has greatly impacted my career trajectory, as it gave me firm ground to stand on when I was applying for my current role and it really helped me through the interview process. It allowed me to answer any technical or theoretical questions. It also gave me some great talking points, in particular with regards to my final project, which was on Black-Litterman-based portfolio optimization, as this directly related to my current role within the indexing space.
From a theoretical perspective, the portfolio construction and optimization content from the CQF has been very helpful to my role, especially when it comes to working on optimized indices or volatility. You can draw on strategies using your learnings about optimal portfolio construction. Also knowing how to implement everything in practice, not just the theoretical side of things, was very helpful. For example, when you're running back tests for optimized multi-asset strategies, knowing the Python implementation was useful, because it allows you to build these strategies to advocate weights to satisfy mean variance constraints. That was one of the things I picked up from the CQF program.
