I completed an undergraduate degree in Mechanical Engineering from the Indian Institute of Technology, IIT Kanpur, in 2006. At that point in time, a number of banks were coming on campus to recruit students and after graduation, I joined HSBC in Bangalore, India. My job involved looking at different ways to price certain financial instruments, helping the research team with analytics, and so forth. I was thinking about further education and in 2007 or 2008, a few people from the CQF program came to Bangalore. I attended the information session, and I became very interested in this opportunity because this sort of professional certificate was geared precisely towards the kinds of things I wanted to learn in a more rigorous way – the practicalities about pricing derivatives and coming up with analytics. My employer was happy to sponsor me, and I completed the CQF while still working full time for HSBC in India. I enjoyed the program very much and developed further skills in pricing complex derivatives and doing various kinds of statistical analysis.
In 2010, I moved to London and continued work there, developing analytics and transitioning into a role where I was talking with market participants as a sell side strategist. This work focused on generating trade ideas using fundamental and macro-economic analysis, so much of the quantitative stuff that I had done earlier was very useful in this effort. In 2020, I joined Brevan Howard and did strategy there for a year and now I'm a Portfolio Manager with another hedge fund, Symmetry.
When I look at the CQF course content online these days, I see that it has moved away from a concentration on pricing to encompass topics related to machine learning and systematic trading. That is exactly what I'm doing now in developing my own trading strategy, so I take advantage of the lifelong learning offerings through the lectures and videos that are applicable to my current work and interests. I think the CQF course curriculum has evolved well to keep pace with the times. Naturally, the pricing of derivatives is important, but from a practical standpoint, there is not much groundbreaking research going on in pricing right now. The exciting areas entail the use of those analytics to come up with trading strategies, either through machine learning techniques or data science applied to economics. Candidates who have the right mix of skills, including data handling, statistical analysis, and a thorough understanding of macroeconomics, tend to stand out vs their peers.
Many times, when I worked in a bank with quants, I could see that they were very good at understanding the nitty gritty details –knowing how a given mathematical approach should work, for example. But in the practical world, it is different; you have assumptions and limitations embedded into your modeling straightaway. It is important to understand those angles well before you jump in and start modeling the behavior or developing a simulation. I’d say that the CQF does a great job of training students on both aspects – understanding the theory and the practice and then encouraging students to take a deeper look at the world of quantitative finance to see how it all fits together. It takes a lot of time and work to cover so much ground, but it is well worth it in the long run.