Wall Street & Technology briefly discusses some survey results and concludes: “Wall Street’s Quants Feel Misunderstood.” There’s the obligatory quote from Dr. Wilmott:
“These numbers are alarming,” said Dr. Wilmott. “They indicate that even with the events of the past year, financial institutions are still not taking the importance of financial education seriously, especially as it pertains to improving relationships and understanding between quants and their managers.”
There’s some “alarming” statistics: since last year, quants feel that 86% of their managers have the same or less understanding of their quantitative roles.
But looking at the actual survey results, it’s not quite so bad. In fact, there’s a good deal of exaggeration, depending on how you frame the data. Only 4.5% feel that their managers have less understanding since last year – meaning a full 82% felt that the level of understanding is roughly unchanged. So this largely becomes a question of whether or not the present level of understanding is satisfactory. Most people’s knee-jerk reaction (and that of the original article) will unequivocably be, “Of course it’s not!” However, is that because managers haven’t kept up with quantitative advances, or because quants have run far ahead of their supervisors (and of where they need to be)?
I think it’s a little of both. Certainly, when Things Were OK, supervisors were less incentivized to follow the activities of the mathematicians under them. As long as the numbers danced (higher and higher), it didn’t really matter what they were. Meanwhile, each quant is incentively to pursue ever-more obscure models to squeak out minute bits of alpha. In the end, we wind up with quants doing overly-complex work for managers with too-relaxed supervisory roles. The question isn’t “Does your manager understand what you do?” as much as it is “Do YOU understand why you do what you do?”
The problem here is not that quants ran amuck and screwed up the system (see the replies to question #2), it’s that no one even knew what they were doing in the first place. The article is putting a normative spin on the survey results, but it’s silly to believe that if supervisors understood what quants were doing, everything would be fine. Just the same, if quants only worked within the limits of their supervisors’ knowledge, disaster would result as well (what’s the point of roles, anyway?). What is missing – and what surveys like this fail to address – is the need for proper communication of goals, objectives, methods and ideas. Yes, it might be hard for a mathematician to boil his ideas down to simple English or a supervisor to pick up some mathematical tenets, but the resulting clarity will be well worth the effort in either case.
So in the end, is it bad that quants feel like most of their managers only somewhat understand what they do? It’s hard to say. If the quants are doing their job “properly”, then yes. If supervisors are slacking off, then yes. But if quants are running ahead with inappropriate methods, then although the answer is still yes, the solution isn’t necessarily to educate the supervisors – it’s to teach them how to reign in the quants. Alternatively, it’s to teach the quants a little about their real business objectives.
Mike at Rortybomb has a post on the pros and cons of MBAs. Among his cons is the following:
They just wrote a post about MBA students being owned by their models. The general idea underneath this is that these models are too complicated to understand, and taught to be something that is true rather than something that approximates a lot of contingent data.
There’s a joke that goes around quant message boards. An MBA class at a highly ranked school has their esteemed teacher solve the Black-Scholes PDE, and they get the final answer:
at which point someone raises their hand and goes “can’t we cancel out the d’s?” Ha, right? “No, thats the symbol of the derivative, not a variable. Moving on….”
Now think about that for a second. What should we expect the student to walk away with in this situation? There’s too much emphasis on solving models rather than seeing models used. Often the final answer on the test is “write out the CAPM equation” rather than “discuss how you would use the CAPM in your work day.” Not the normal “list the X assumptions this model makes”, but actually thinking through the deployment in the field. I think shifting the focus from models as a kind of deep economic truth to just another tool you use in your day-to-day, like excel, would be very useful.
I agree, though I think if anything he is being too soft on naive model-users (for lack of a better term), though to be fair his purpose is to focus on what’s lacking in the curriculum. Modeling – or the use of models – can’t simply be part of day-to-day work unless those models are well understood in the first place. The article he links to puts it more aggressively:
Most MBA programs are taught in such a way that rather than owning the models, the models own students.Management research has become more thorough, rigorous, and technical, and it has developed tools based on complex models. Students in business school have to absorb many tools in a short time, so they aren’t inclined to delve deep into the inputs or the workings of the underlying models. They focus mainly on the outputs. When professors try to go into the details, students make it clear that they prefer the takeaways–not its derivation or caveats. In any case, faculty members, proud of the models they’ve developed or sharpened, aren’t eager to focus too much attention on situations in which their frameworks don’t work.
As a result of this little dance, MBAs join organizations with a toolbox full of models for which they primarily understand only the outputs. Worse, they believe: “I know a bunch of powerful tools that work in most, if not all, circumstances. I can therefore apply them aggressively, confidently, and to their fullest.”
This problem has been epitomized by the recent crisis in financial risk management. Salesmen pitching CDO’s don’t know that the underlying model is a single factor Gaussian copula, and moreover even if they didn’t they wouldn’t know what to make of the single correlation parameter. Finally, even crossing that bridge the model would still have failed since it was itself inadequate.
I must stress I am not speaking in favor of models per se. But modeling is an unavoidable consequence of dealing with data; a necessary simplification (I use the term loosely) to extract signal from noise. With the exception of looking at raw numbers, there is little we do in this age that does not involve data transformed in some way. It may not all be as complex as an obscure mathematical formula, but it is just as important to understand the underlying assumptions before taking the output for granted (or in blind faith as correct).
The realization that models are fallible should not drive us away from models. If possible, it should drive us toward better ones, or at least toward an understanding of what creates those failures. As George Box famously stated, “Essentially, all models are wrong, but some are useful.”
There is a firm that has two types of employees: salesmen and quants. The salesmen take information from clients, give it to the quants, receive a result and pass the new information back to the client. The salesmen have no idea what the client’s data means, nor what the quants do to it. In many cases, the salesman does not even fully comprehend the input or outputs, he merely acts as a messenger, formatting the data as it needs to be. If there is any problem with the final result, the salesmen are powerless to fix it since they can not identify where things went wrong. Conversely, the quants can not fix the problem because they have no conception of where the inputs come from or where they go; they only comprehend the intermediate step. This (real) firm is an excellent analogy to model use on a micro scale, where the calculation is a black box (the quant) and the user is feeding data in and receiving data out (the salesman).
Needless to say, the system is broken.
Tonight after squash I started to cook dinner, but after setting off the smoke alarm for what I must confess was the second time since I moved here, I headed over to Harry’s Steakhouse/Cafe (I’m not sure which one I go to. I think it’s the cafe. Whichever one is downstairs.). After a late match, I like to sit at the bar or one of the nearby high tables, have one of their awesome burgers and a beer (Heineken, yes) and bring a book. I stay and read until I’m tired, and then I come home and fall asleep. It’s pretty much a perfect after-squash ritual (If I’m not alone then it’s convenient to stay at the Harvard Club — the burgers are fantastic and surprisingly cheap; one suspects they take a loss on food to make up for gouging us on everything else…).
All of which was simply a long introduction to the only reason I’m still awake now — a great book I was just reading at Harry’s called (boredom alert) How I Became A Quant. So far (I’m only on the second chapter), this is one of the best finance books I’ve read, and so far it’s not even very much about finance. It’s basically a collection of stories by 25 prominent quants about how they stumbled upon their careers. Sort of like 25 mini-versions of My Life as a Quant stitched together. Anyway, the introduction opens with this:
Because you are reading this introduction, one of four things must be true. Your are a quant and are intrigued by the idea of reading the stories of others like you. You are not a quant, but aspire to quantness, and you are seeking some insight on how to achieve that goal. You are neither a quant, nor have such aspirations, but you want to understand the way Wall Street really works, perhaps to gain some perspective on the vast and unsympathetic forces affecting your life in mysterious ways. Or, missheleved among the science fiction and fantasy titles by a harried employee, the title has struck your fancy as, perhaps, a potentially satisfying space opera. There might be other things besides these four, but we can’t think of any.
So basically if Dave Barry wrote a finance book, it would turn out like this one. Or, uh, this one. Anyway, the first chapter – which has proven much funnier than the second – includes a pretty apt description of a certain school:
Harvard University, the school up the road that once wanted to merge with MIT and call the combination Harvard…
All around it’s shaping up as one of the better books I’ve read in a while (Hitchhiker’s Guide notwithstanding). And yes, that’s the Black-Scholes equation up there. The book seems quite obsessed with it.