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. For my regular readers, I'm working on a post which tests the random walk hypothesis; it should be published soon.

1. CFA is hard for everybody; you are not special

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 an 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!

To be more specific, I found that the quantity of work that needed to be studied (and understood) challenging and the nature of the studying required unfamiliar and therefore difficult. As a computer scientist I am used to solving problems and writing code. I'm not used to sitting down in front of a pile of textbooks and reading, taking notes, and doing many small worked examples. The last time I studied that way (kind of) was in high-school. As such I found that I had to spend a lot of time first learning how to study before I could focus on what I needed to study. I'm not saying either method of study is better; I've learnt a lot doing the CFA Level I exam just as I did during my degrees.

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.

Almost immediately after completing my studies I found that the way I viewed my day to day work had evolved. I began seeing elements of corporate finance and aspects of financial reporting in what I did. This evolution has been incredibly helpful when "trouble-shooting" real world business problems because it lets me know what tools are already available to me to solve the problem. I am also guessing that this big picture view will become more valuable as I progress in my career and find myself doing less and less technical work and more and more managerial and strategic work. This point is discussed in more detail in section 3.

3. See the CFA as a long-term career investment

As I mentioned under the previous point, the CFA affords it's 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.

Another often unspoken long-term reason for pursuing the CFA charter is because it is a professional certification much like the ones an engineer, actuary, or accountant might obtain. Professional certifications are highly valued and respected by organizations and you will find that many executive board members hold such certifications. On a personal note I have decided to take a break from pursuing the CFA charter and focus instead on completing my Masters thesis. That having been said, I do see myself continuing the CFA in the future and I see myself finding the materials invaluable later on in life but for now I'm going to focus on becoming the best quant I can be.

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.

Free Educational Resources

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. QuantAtRisk - this blog covers, in detail, many interesting topics relevant to quantitative finance including optimization, risk management (especially tail risk), and programming concepts. Gestaltu - this blog is possibly the best source of thorough academic-level (yet accessible) research into different asset allocation strategies used by asset managers. QuantStrat TradeR - this blog is dedicated to identifying profitable quantitative trading strategies. The blog makes use of R which I recommend learning if you are serious about quantitative finance. Quantitative Trading - the blog companion to the book by the same name written by Ernest Chan, a well known quant who focuses on identifying profitable quantitative trading strategies. QuantsPortal - this relatively young blog is systematically covering some of the most common / original quantitative trading algorithms used by hedge funds across the world.

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