The Essence of Being A Quant
During the holidays, a person gets a chance to reflect on the more philosophical matters. In my case, one question that stood out was to define the essence and importance of the profession to investment management. I began to realize that the term itself or even the profession is poorly defined and articulated even within the industry. The first time I was asked what a “quant” was, I simply explained that they were number-crunchers that created systems for trading money. The reality is not far off from this simplistic explanation. But having read and heard a lot of different explanations (and accusations!), the essence of being a quant is to translate any possible unit of useable information for either financial forecasting, algorithm development, or rules-based systems for trading- lets call this broad class simply “trading models“. This includes (but is definitely not limited to): 1) fundamental data 2) technical data 3) economic data 4) news 5) weather 6) or anything that might be considered useful or predictive. The analytical tools of the trade have become highly cross-disciplinary and come from a wide variety of fields such as (but not limited to): 1)math 2) statistics 3) physics 4) economics 5) linguistics 6) psychology 7)biology. A lot of the common methods used across fields fall in the now burgeoning interdisciplinary field of “data mining and analysis.”
A quant is simply interested in creating a quantifiable method for developing trading models. If a concept is not quantifiable it is often because it is either 1) not clearly defined or 2) simply not testable. These principles are generally respected by all scientists regardless of discipline. There is truthfully no area that should not be of interest to a quant, there are just areas that are more or less fruitful or simply worth prioritizing. Since financial price data of many time series are of high quality and readily available with long histories, this is a natural area of priority. Why then do quants seem to frown or pick on technical analysis which makes use of the same data? The answer is because most of the original technical analysis literature and media falls into the two categories identified as being difficult to quantify. Often the concepts and ideas are highly elaborate with a wide range of extenuating circumstances where the same conclusion would hold a different meaning. This implies a highly complex decision tree approach (assuming all of the nuances can be identified or articulated). The downside to believing in traditional technical analysis is twofold: 1) a lack of statistical robustness 2) the flawed assumption that markets are stationary- we can rely on gravity but we cannot rely on any measurable financial phenomena to always follow the same set of laws or rules since they operate in a dynamic ecosystem of different market players; asset managers, central banks, and governments constantly try to influence or anticipate the actions of eachother. While that may sound harsh— it does not mean that we should abandon using technical indicators or ideas. It simply means that indicators or ideas represent testable inputs into a trading model that can and should be monitored or modified as conditions change.
What about the die-hard fundamental analysis approach? They are more similar to traditional quants (thanks to value investors for example that often create quantitative rules of thumb that are easy to test) and tend to use statistical analysis or some form of quantitative application in portfolio management regardless of their level of discretionary judgment. However, they are also guilty of some of the same flaws as technical analysts because they often rely on concepts that are either not observable or not testable from a practical standpoint (and hence not quantifiable). For example, if a portfolio manager talks about the importance of having a meeting with management and assessing their true intentions for making corporate decisions– this is not really testable for a quant. Neither is the leap of foresight that an investor has about whether a product that has never been sold will be popular. The downside to believing in a purely fundamentalist approach is that the relative value of the insights that they claim are important is very difficult to assess or measure. Regardless of how important these individuals claim (and rationally product foresight is potentially a real skill) their qualitative or intuitive insights are, they must be separated from the style or factor exposure that is quantifiable (that is taken on either intentionally or unintentionally) to determine some baseline of usefulness. For example if a portfolio manager claims to buy large cap value stocks with high quality balance sheets, but uses additional “judgment factors” to narrow down their list for the portfolio, their performance should be benchmarked against a stock screen or factor model that approximates their approach. This gives some insight as to how much positive or negative value has been added by their added judgment. In many cases this value tends to be negative–which calls into question the utility in paying a portfolio manager such exorbitant compensation.
In truth the quant is as much a threat to the classic portfolio manager role as the machine is to human labor. A quant can manufacture investment approaches that are far cheaper, more disciplined, have greater scale, and are more reliable. The more advanced and creative the quant is, the more information can be quantified, and the more approaches that can be replicated. A quant’s best friend is therefore the computer programmer who performs the very important task of creating the automated model. Unlike a machine, once a model is created, it can and should be frequently improved and monitored. How this process is done distinguishes the professional from the amateur quant. A professional quant will make sensible and robust improvements that will improve a model’s prospects for dealing with uncertainty, regardless of what the model performance is in the short-term– whether it is good or bad. The amateur quant will make cosmetic improvements to backtests of the model by primarily tweaking parameters in hindsight, or simply make adjustments based on short-term performance that would have caught the latest rally or avoided the latest decline. Here are a few other key differences between the pros and the amateurs when it comes to quant: The professional starts with a simple and clear idea, and then increases complexity gradually to suit the problem within an elegant framework. The amateur tries to incorporate everything in an awkward framework at the same time. The professional will seek to use logic and theory that is as general and durable as possible to guide model development or improvement. The amateur strives to create exceptional backtests and will be too data-driven with model development and refinement decisions. The professional takes a long time to slowly but earnestly improve a model fully aware that it can never be perfect. The amateur either wants to complete the model and proceed to trading immediately, or in the opposite context they are so afraid of failure that they 1)always find something that could go wrong 2) are overly skeptical and easily persuaded by peer hyperbole over fact 3)are addicted to finding new avenues to test. Finally–and perhaps most importantly towards impacting performance: The professional will not give in easily to outside influences (management, clients etc) to make adjustments for ill-advised reasons unless there is a long-term or clear business case for doing so (the typical trades that the model takes on are perhaps difficult to execute in practice). The amateur will buckle to any pressure or negative feedback and try to please everyone.
If the last paragraph sounds arrogant, I would be happy to admit being guilty of one or more of such amateur mistakes earlier in my own career. But this is much like the path of development for any field of expertise. In truth, almost any “professional” quant learns these lessons whether through peer instruction or the “school of hard knocks”. But one of the positive benefits of experience and honest self-assessment is that you can learn how to lean in the right direction. Without being honest with yourself, one can never get better. To end on a sympathetically qualitative note, it is useful to think of a professional quant as also sharing the qualities of a martial artist: a quant must also have solid control of their own mind and emotions as they relate to working with trading models to be able to rise to the highest level. When practiced well, this is not a frenetic and purposeful state, but rather what appears to be a focused but almost detached state where the decisions are more important than the outcomes. One really good idea in a relaxed moment is superior to a hundred hours of determined exploration. The benefit to such a state tends to be good and consistent outcomes with regards to model performance. Ironically, too much focus and energy invested in the outcome (performance) has the opposite effect. The psychology and emotional maturity of a quant can be as or more important as their inherent talent or knowledge towards driving investment performance. Of course this hypothesis is subject (and should be) to quantitative examination.