Having just written and, thankfully, passed the CFA Level I exam I wanted to take this opportunity to share my experience writing the CFA Level I exam given that I come from an unconventional academic background and work in the industry as a quantitative analyst. I also want to share some helpful online resources with would-be CFA Level I candidates who might find the quantitative methods section of the exam particularly challenging.
During my undergraduate and honours degrees I studied Computer Science. During my studies I majored in a challenging application area of Computer Science called Machine Learning which is more widely known as Artificial Intelligence. Shortly after graduating I found myself working as a quantitative analyst. Quants (and machine learning researchers) are typically assumed to be intelligent and many possess and above-average IQ … but here’s the thing: that didn’t help me pass the exam and if you are in the same boat that I was in, it most probably won’t help you either. CFA is hard for everybody so my advice to my past self would have simply been,
Leave your ego at the door. You need to put in 300 hours or more to pass, just like everybody else. Remember Fight Club – you are not special!
2. For specialists, it will help you gain perspective
The saying that “when you are a hammer, everything looks like a nail”, can easily be applied to both quantitative finance and computer science … “when you are a quant (computer scientist), everything looks like maths (code)“. The nicest thing about CFA Level I for me was that it exposed me to many different schools of thought and ways of thinking. This has helped me gain perspective on the different areas of financial services. For those unfamiliar with the curriculum, here are the subjects covered from CFA Level I to Level III: Ethical and Professional Standards, Quantitative Methods, Economics, Financial Reporting and Analysis, Corporate Finance, Portfolio Management, Equity Investments, Fixed Income, Derivatives, and Alternative Investments. Talk about breadth!
When you are a quant (computer scientist), everything looks like maths (code) … but that doesn’t mean it is. The CFA helps you gain perspective into finance as a whole.
3. See the CFA as a long-term career investment
As I mentioned under the previous point, the CFA affords its candidates a much bigger picture of finance as a whole. For specialists like myself, this view is most probably going to become more and more valuable as I progress through my career. This is because unfortunately at some point everybody is promoted beyond the technical work and into managerial roles which come with more strategic responsibilities. That having been said, for specialists studying for the CFA is almost certainly not going to be as enjoyable as building stochastic models and using neural networks to approximate credit risk, which is why taking a long-term view is essential.
For specialists, studying CFA is not going to be as fun as building stochastic models (for example), which is a why a long-term career view is essential.
4. A list of useful quantitative methods resources
If you are a CFA candidate and you are either struggling with the quantitative methods section, or you find quantitative methods interesting and want to learn more about how they are applied in practice, you may find the following collection of online resources and articles valuable and (hopefully) interesting.
- Khan Academy – most of the quant work in CFA Level I is basic probability theory and statistics. The Khan Academy online resources explain these concepts better than anybody else (in my opinion).
- Coursera – whilst it may seem like overkill to do a whole Coursera course to help with the CFA, for candidates really interested in the application of quantitative methods these courses are useful:
- Financial Engineering I and Financial Engineering II
- Computational Investing I
- Asset Pricing
- Game Theory I and Game Theory II
- Computational Finance and Econometrics
- Mathematical Methods for Quantitative Finance
Quantitative Finance Blogs
- Quantocracy – this website aggregates hundreds of different quant blogs from around the world, including this one, and offers a fantastic source of new quant strategies every day.
- QuantStart – as the name suggests, this blog is all about helping people get started with a career in quantitative finance. It covers many of the softer aspects of being a quant as well.
- TuringFinance – sorry for the shameless plug; this blog covers more of the computational aspects of quantitative finance including machine learning, optimization theory, and algorithmic trading.
Recommended Reading List
Paul Wilmott Introduces Quantitative Finance
This is an accessible introductory textbook which introduces the more classical side of quantitative finance for university students. It condenses the material presented in his other works, Derivatives and Paul Wilmott on Quantitative Finance.
Paul Wilmott on Quantitative Finance 3 Volume Set
This three part series goes into considerably more depth into classical quantitative finance topics including Mathematical foundations, derivatives theory, risk and return, exotic contracts and path dependency, fixed income modelling, derivatives modelling, credit risk, numerical methods, programming, and other advanced topics.
Options, Futures, and Other Derivatives
John C Hull
In the industry, this is referred to the bible of derivatives pricing. It covers everything from the mathematical foundations and theory of derivatives all the way through to exotic path dependent derivatives. It also includes software for derivatives pricing.