I have worked on developing quantitative models in finance for more than twenty years. I am what is referred to as a quant. People who work as quants typically have advanced degrees in a STEM discipline and expertise in developing and testing mathematical models. It is common for quants to enter finance after they have gotten PhDs in physics, engineering, mathematics or other applied sciences.
The most common roles for quants are at big financial institutions and hedge funds, engaged in trying to create trading strategies that will outperform some benchmark. At these types of firms, quants also work on risk managment. Increasingly, there are quant jobs in developing financial technology (fintech). Interesting fintech applications include robo advisors, automated loan qualification, enhanced credit scoring, and many other types of tools.
For people who are good at mathematical analysis but do not want to pursue an academic or traditional research career, there is a lot to recommend in a quant role. The pay is good and there are plenty of interesting problems and mountains of data. What I particularly enjoy is that there is so much open territory than has not been explored. It is important to know, however, that just because you are good at math does not mean that you will thrive as a quant.
Here are a few thoughts for STEM people who are considering moving to finance.
Its not enough to be smart
There are plenty of smart, well-educated people in the quant world. Being mathematically skillful just gets you in the door. Solving financial problems using mathematical methods requires that you bring considerable creativity to your work, in addition to being dexterous with mathematical methods.
Quite often, simpler quantitative strategies beat complexity. The key is to figure out what works rather than focusing on the most sophisticated approaches to problems. A quant is to an academic mathematician as a research engineer is to a physicist.
As a quant, you can learn a great deal about how markets really work from traders and other practitioners. There are different types of intelligence. You may be the smartest person in any given room but the smartest people are often not the richest, including in finance.
You have to explain and motivate your work
It is not enough to come up with better models. You also have to be able to explain your work to people who don’t share your background. To be successful, you will have to motivate why people should risk their money on your work. If you cannot engage executive staff or traders with your work, this will limit you.
In academics and in research labs, you can be very successful even if you don’t take any effort to explain yourself to those outside your specialty. Financial firms employ a wide variety of people that quants need to collaborate with. To be successful as a quant, you need to be good at communicating to business people as well as other quants.
You need to gain an appreciation for finance
Finance is deep and there is considerable subtlety to the important problems. As with other areas of applied mathematics, understanding the complexities of the system you are trying to analyze and model is crucial. In my experience, it is very rare to be able to model phenomena that one does not understand. The constraints in finance are very different than in most fields of science and engineering. New ideas are incorporated by the markets, such that market dynamics evolve. Once a predictive factor is widely recognized, it often disappears as traders exploit the benefits. Further, finding exploitable anomalies in markets requires understanding all of the relevant factors. Many strategies that look good on paper are actually impossible to profitably implement once you account for time lags in execution, market effects (bid-ask spread and market depth), and taxes.
Highly-educated people often have trouble working on problems they find uninteresting. To be successful as a quant, you need to find financial problems intellectually engaging. Personally, I find economics and finance fascinating and there is no end to the intriguing quantitative problems. People who think that they will be successful as quants but who don’t really intellectually connect to the challenges may not be happy or successful.
One of the things that I like most about being a quant is that the world seems much more understandable when you can see how markets work. A desire to understand things is often a major element in why people go into STEM fields and that curiosity leads to many opportunities in finance (as well as in most other fields, of course).
As technology increasingly dominates financial transactions, the work opportunities for quants are burgeoning and go far beyond trading and portfolio management. Back when I entered finance, most quants worked at hedge funds or on trading floors at big firms. With the growth of fintech, there are quants working on all sorts of problems and in many types of firms, including tech startups. One emerging discipline is the use of alternative data (such as social media activity) to build or enhance credit scores, for example. This is a fascinating challenge that has the potential to provide substantial social good, as well as being profitable for the firms that employ these analytics.
Looking forward, the applications of quantitative methods in finance should continue to grow rapidly and there will be many more jobs. Problems like portfolio management and credit scoring lend themselves to multi-dimensional data-intensive objective models that computers are good at handling. Along the way, of course, there will be more disasters (like the epic collapse of Long Term Capital Management) in which quants put too much faith in their models and forget what I consider to be one of the most important adages in quantitative analysis: the map is not the territory. Or, as expressed by George Box, one of the fathers of time series analysis: all models are wrong, but some are useful.