CQF Alumni Story: Anqi Fu, Finance PhD Student
Read this alumni story in Chinese here.
I am a Finance PhD candidate focusing on applying machine learning to financial predictions. I started my career as a modelling engineer, and after that worked in fundamental investment analysis where I built considerable domain knowledge in equities. My previous studies and work experiences made me realize that I was passionate about applying my quantitative skill sets to the finance sector to help companies achieve better commercial outcomes. However, I was concerned that there were gaps in my knowledge that would prevent me from working in this area. To combat this, I decided to earn the Certificate in Quantitative Finance (CQF).
"The CQF opened more doors for me, especially in terms of machine learning and natural language processing."
Before I earned the CQF, I was a PhD student investigating market informativeness, liquidity, and analyst performance. Now, I am finishing one of my machine learning projects, which is a mortgage default prediction. The CQF opened more doors for me, especially in terms of machine learning and natural language processing.
I really enjoyed studying modules 4 and 5 of the program which focussed on machine learning. This is a very popular topic in the industry and is also very relevant to my current studies. In the future, I would like to start a project looking at the application of machine learning to portfolio optimization. The CQF also had a module on portfolio optimization – so you can see how there is a module for every practical skill you would need to work in the industry.
"Thanks to the CQF modules that focussed on Python and natural language processing, I can create a range of data sets myself and no longer need to, or will ever have to, rely on third party providers."
In the current world we live in there is a competition for data – the more complex, individual data that you have, the more competitive power you have commercially. Thanks to the CQF modules that focussed on Python and natural language processing, I can create a range of data sets myself and no longer need to, or will ever have to, rely on third party providers. This is a great asset to my career.
The CQF was quite challenging, especially in terms of the math and optional final exam for Distinction, but after finishing the program, I am more confident in math, programming, and the model assumptions and applications used in financial engineering. The program is well organized, and I would recommend it to other professionals like me.
I chose to earn the CQF alongside my full-time PhD studies. This was manageable thanks to the flexibility of the CQF program. The program does not need to take over your time, especially if you are organized. You can spend the time you need in your full-time role, and then use your remaining time to study for the CQF. The lectures are also delivered online and can be accessed on-demand at any time throughout the program. I think most professionals would be able to manage enrolling on the CQF and maintaining a full-time position at the same time.
Another benefit of the CQF program is the Lifelong Learning library. I tend to use it for 1-2 hours per week to check if there are any new trends in the finance or Fintech industries. The CQF program makes sure it stays up to date with all the cutting-edge industry trends, so I know this is a reliable way to make sure my knowledge is always kept up to date.