Why so little quant passion?

Deconstructing the process that brings PhDs into quant roles

Quant¹ profession is quite interesting in how infrequently it is a choice of passion, rather than convenience, for its members. Until recently², almost universal background of a quant was that of a math or physics PhD getting disillusioned by academia, deciding to quit, and then deciding to be a quant, often hastily, from a tricky position not conducive to clear thought, using very dubious process, often based on wrong premises (more on this below). It’s perfectly possible to first take a misguided course first and then find what one actually cares about: plenty of people do undegrads or lawschools or medschools to please their parents or wanting a safe choice or for many other reasons that rarely survive the test of time. However, it’s the typical way those PhDs go about making the transition to quant decision that results in them making this decision despite the lack of passion. Let me explain a few common patterns in that decision process, which would clarify why I believe this process is typically so biased. Not every one of the patterns applies to everyone of course, but afaik³ they are all pretty common.

A typical math or physics PhD deciding to quit academia and pursue a quant job:

1. Often thinks more about whether to quit or not quit: “academia vs industry” is how it’s often framed. Focus is on letting go of that long-held dream of academic career. On saying “no” to academia. It’s traumatic. It will take years until one is really past this trauma and have let go. However, the decision on what else can one do if not academia has to be processed at the same time: funny how humans work, for many it literally happens at the last year of PhD (as until then it was all kinda clear path ahead/no decision to be made/autopilot), if not last months. It’s no surprise then that the decision, maybe most important decision of one’s life, gets something of a short shrift, and is made in a rather dubious psychological state.

2. Doesn’t know how to think about choosing a career⁴. In a sense, those PhDs are the least prepared to make this choice: they likely were passionate for science early on, pursued it with abandon and often little thought, were never really adrift and lacking clear passion or direction, which is a condition that gets most people thinking about what they actually want and who they are. And now they see their true passion “blocked”⁵ and have to suddenly choose something else?!..

3. Is scared and doesn’t believe in themselves. They just dunked a decade or so of their life into some esoteric branch of pure math and kinda know that’s not gonna help them much in “industry”, so they are down on themselves for making that terrible choice, but also feel very behind and lost (what a change from the “I’m top of the class gonna do PhD and be amazing scientist maybe win a Nobel” kinda thinking that got them here). It is true that many of those people are not immediately employable - and they are afraid of that. What they don’t understand is that they can become employable in a wide variety of domains with some effort and a year or two of time - it really doesn’t take another PhD to enter “industry”. Again, psychological context in which this all happens contributes to short-termism and scarcity mentality: instead of thinking “what do I really want to do” and figuring out how to get there (bridge job? masters degree?..), they are thinking “who will take me right now so I can escape this fiasco”. And this is where qfin swoops in.. [see next item]

4. Views Quant as a default choice. Most math & phys PhDs know, or quickly find out, that quant is a thing math and phys PhDs can get, right out of PhD, with quite limited preparation. Other paths mostly require more work. Tech should’ve been a more viable alternative, but somewhat surprisingly, despite being in many respects a better fit for many of those PhDs, is really not that welcoming⁶. As math/physics PhDs are quant industry’s main talent pipeline (and for no other industry that’s the case), it really makes it pretty straightforward. You’re not expected to know finance, or even much coding⁷ . One particularly misleading belief I’ve seen, and that seemed quite material to many of those PhDs’ decisions, is that “quants do math”. As many of those PhDs stick to their “sunken cost fallacy”, often the linchpin of their thinking about what to do next is not “what do I want to do” or even “what I can do” but simply “what field can I possibly use what I know in”. Pre-GFC industry lore (when “doing math” was more of a thing) and industry eagerness to sell itself (“we’re solving hard technical problems”, promotional videos with some whiteboarding mathy stuff, etc) fall on a fertile ground in the minds of folks wishing to believe they can “do math” somewhere that is not miserable academia. And so they go. Unfortunately it’s a myth. Afaik most quants barely don’t use much math (see also point 1 below), and there is an inverse correlation of math usage and quality/career prospects of a quant role (more math in banks but banks pay less/have weaker people than hedge funds and props, more math in exotics which is a stagnating/shrinking business, more math in valuation models which are a bit middle office, less math in alpha-research with more focus on data analysis and finance and occasional stats). Maybe for some this might work, getting crumbles of an old dream where they can (see also 1 below). For most it’s healthier to move on and find a new dream. If “doing math” is a priority, other roles might also be better outlets, if not as convenient to slide into: there are actual research or research engineer roles at various tech and engineering companies.

5. Lacks perspective, and often really not in a position to have perspective. If one has a stable life setting, some money and time to figure things out, they’d probably be quite thoughtful about deciding what job they want to do, spend time exploring themselves, understanding their values, etc (see book ref in the footnotes). However, those PhDs are scared and feel time constrained (especially as most are international and really need to line up a job asap least they have to go back to their home country most really don’t want to), they need a solution asap.
   They are still processing the loss of an old passion and at best not ready to look for a new one (and so stick to simple but limited “external view”⁸ : what’s easy to get, what pays best, what other people are doing etc), and at worst trying to hang on to shreds of a dream (I can still do math as a quant!) instead of moving on and seeing reality clearly.

6. Is ashamed, or at least somewhat shunned, so often goes through the process alone. This is quite personal, as I somehow gotten quite passionate about advising folks, and PhDs in particular, on r/quant, about entering the quant field. It’s quite terrifying a lot of those people seem to not have better sources than some, however credible sounding, online stranger, and mostly seem to get their information from random online readings. Stark contrast with business people executing a career transition by doing a lot of informational interviewing and making sure to have talked to multiple professionals working in each of the various roles they are potentially interested in. It isn’t surprising academia doesn’t prepare folks for real life of course, but sad nevertheless.
   If those PhDs were alone that would be half bad. In reality they are usually surrounded by fellow PhDs who are still continuing their academic journey: and many are often “true believers”, and look at any exits as “betrayals” and on industry and Wall Str in particular as “evil”.. Hence, many PhDs I advise during the transition are quite isolated, and many actively hide their job search.

Some of the consequences of that lack of passion and that faulty decision-making process that one sometimes sees are:

1. Folks living in the past/pretending the job is about math: sometimes it’s not very successful folks spending too much time reading “fancy” papers or focusing on not very material technical details even past the very junior stage (at which this is extremely common which is by itself telling) instead of learning to “be commercial” and understand what the business needs and what the job really is about; sometimes it’s the opposite, bank MDs with cushy sinecures sometimes literally spending half their time doing research and writing “math finance” papers which they probably know aren’t that business relevant; you can think of Dupire’s group/seminar at Bloomberg as something of a center of that kinda activity - and some of this is plausibly a hangover from the 90s to GFC quant finance golden era where new valuation models did really matter for the business, for better or worse (remember CDO-squared)

2. Little focus on internal vs external. When you think about it, it’s kinda striking how different the attitudes are in tech vs quant finance. In the former people get excited about interesting technology or stack one gets to work with, or companies trying something unusual, or attempting to solve worthy problems (online education for everyone! virtual reality! artificial intelligence!). Whereas in qfin what matters is how much money you are making, and to some extent, how prestigious your firm is. I don’t see young quants being “passionate about automating credit markets”, but only quants eager to go to the most prestigious place possible (DE Shaw!) and get to the top of the totem pole (alpha research! etrading!). Sure, you can say, these are cultural differences between industries.. what’s interesting to me is that these cultural differences really make most sense in the context of “nobody’s passionate about qfin” view I’m advocating here.

The reason I started thinking about this note is this question that bugged me: why are there no good quant communities⁹? why do even quants I meet in unrelated contexts often not eager to talk quant stuff? The model presented here, of somewhat disillusioned people not really passionate about the stuff they do, having ended up in the industry via a tricky transition out of academia, followed by some lock-in and inertia, might explain why.

1. Quant here, and most elsewhere on this substack, refers to quant researcher, not quant trader.

2. In recent years the sociological landscape changed a bit with increased prevalence of MFE and undergrad-only backgrounds. The latter are actually often very healthy, if they stick around. But the 90%+ mainland China sourced MFEs probably don’t add much in terms of passion.

3. Based on my experience advising multiple PhDs quitting for quant roles over the last few years, as well as my own experience and that of a small cohort of math PhDs from my own PhD program making the same move as me. If you think I’m wrong or missed something - post your story and observations in the comments!

4. Check this book out.

5. the attitude is rarely taking responsibility, a la “I made a wrong choice pursuing this, gotta learn my lesson and do better next time”, but is usually either blaming the world “important science not getting much funding those terrible politicians” or fate “I’m not smart enough to make a decent school and I’m not gonna go to Iowa”

6. exit to tech can be done with a proper amount of preparation of course - but way more than a couple months of interview prep quant interviewing requires; data science is somewhat easier and might’ve been on par with qfin for a few “shortage” years sometime in 2010s, but since then became less accessible. “tech not welcoming vs qfin” is something I think about quite a lot (given how my own path, qfin first then (likely) tech, seems to be turning out), and won’t claim to fully understand. It’s kinda weird tech is quite willing to take folks with random degrees from coding bootcamps but not obviously smart top-10 schools math PhDs. Maybe it values concrete skills over general smarts for some reason. Maybe it wants some demonstration of passion/commitment. More likely it’s just an accident of history: tech is an old and big industry with multiple massive pipelines of talent, math/phys PhDs would be a rounding error in their recruitment anyway, so nobody took a moment to understand it might make sense to hire them even without a github and many coding classes. Whereas math/phys PhDs ARE the main pipeline for quant roles, so it’s all pretty streamlined for them.

7. During my onsite at one of the big banks the algorithmic coding portion of my interview consisted of implementing binary search in a sorted array on a piece of paper..

8. https://www.benkuhn.net/overconfidence/

9. I don’t think industry culture/secrecy obligations are a sufficient explanation: traders and investment analysts and other finance professionals all do have communities and often do love their stuff