Quant?

Most individuals on this thread have next to no idea what quants do. Quants do not all focus on one area. Quants denote people who conduct analysis using quantitative tools. Quants may be involved in:

A) Securitization: creating new products is a huge thing for the various banks. Quants are in the business of developing custom solutions for clients, building pricing models, building tools to assist traders in the analysis. For example, quants create mortgage prepayment models to help traders price and trade mortgage backed securities.

B) Risk management: Quants are often deployed in building risk management systems. Quants may analyze Credit risk, liquidity risk, market risks and create models which better identify these risks and monitor them.

C) Trading: Quants do not solely assist in building predictive models. They are only one type of trading that banks do. Most quants work on desks building tools to better assist traders. They might code up a new pricing model, or a new hedging strategy. There are quants who are also involved in algorithmic trading. Even here, they can be broken down into two divisions where one focuses on "flow" aka building systems which automatically make markets (very prevalent in equities, vanilla derivatives etc). Or they might work on one of the prop desks where they build predictive models which is what most people on this thread are talking about.

And why in the world are business schools being discussed here. Quants do not need an MBA. MBAs are simply not quantitative enough (and many people would say, they are simply a networking tool). You are better of getting a MFE or a MA/PhD in a quantitative subject (from a recruited school of course).

 

<b>Here's an article I did with one of the first and best known quants, it may help assist you with understanding exactly what quants are and do, enjoy! </b>

I am pleased to be joined today by Dr. Emanuel Derman. He is one of the first high-energy particle physicists to apply his knowledge to the financial realm. He specializes in quantitative trading which is the designing of mathematical models for the purpose of making buy/sell decisions. The scientists who build these models are known as “Quants” in popular parlance. Dr. Derman: is the best-known Quant on Wall Street and is credited with co-authoring some of today’s most widely used and influential financial models. He spent 17 years on the Street and was managing director/head of The Quantitative Strategies Group at Goldman, Sachs, and Co. Recently, he authored the popular book, My Life As A Quant, which documents his growth from academia to the rough and tumble world of Wall Street. I am looking forward to this interview, let’s get started!

Dave: Thank you for joining me today, Dr. Derman.

Dr. Derman: I'm very pleased to be here.

Dave: Let’s start by talking a little about your background. What was the impetus that moved you, a physicist, from academia to Wall Street?

Dr. Derman: To tell the truth, it was entirely unplanned. I started out as a very ambitious theoretical physics student in the late 1960s, when I came to graduate school at Columbia University in New York, from Cape Town, South Africa; I wanted to devote my life to great things, like Einstein or Schrodinger, Gell-Mann or Feynman, all famous physicists who uncovered deep and transcendental truths about the universe. But physics is very tough, a very meritocratic field. I got my Ph.D in particle physics, published a fair number of papers, worked as a post-doctoral researcher and then an assistant professor in good schools for a number of years, and slowly found out I wasn't as smart or creative or lucky as I had hoped. Plus, by the early 1970s there was a severe shortage of academic jobs.

My wife was in academic life too, and we kept moving around the world trying to find positions in the same cities, but only succeeding half the time. Finally, it grew too much for me. In 1980 I was a professor in Boulder Colorado with a wife and two-year-old son in New York, and after one year I decided I'd had enough. I quit Boulder and particle physics and took a job at AT&T Bell Labs in Murray Hill NJ to be with my family in New York.

I spent five years there working in a big corporation and didn't like it too much after the freedom of academic life, and then I moved to Wall Street and Goldman Sachs, which was beginning to hire physicists to work on financial problems.

Dave: What exactly is “particle physics”?

Dr. Derman: Particle physics is the study of the smallest subatomic particles in the universe, and the attempt to discover the laws and concepts that describe and govern them. Particle physicists tend to think it's the most fundamental area you can study, though other kinds of physicists or scientists might be willing to argue about that.

Dave: The transition from academia to Wall Street must have been difficult. Can you share several of your first experiences?

Dr. Derman: People are impatient on Wall Street and don't respect age, which may be good. I was close to 40 when I moved to work there, and the first guy I worked for on Wall Street asked me to enhance a Black-Scholes-style bond options model he had built. I started out slowly and carefully, tackling the problem the way I used to in physics, with care. After about a week, he got impatient. “You know,” he said sharply, “In this job you really need to know only four things: addition, subtraction, multiplication and division — and most of the time you can get by without division!”

Dave: I understand when you first started being a Quant had negative overtones. Did you experience any ….

Dr. Derman: Quite a few that I mention in my book. In 1985 when I started I quickly noticed the embarrassment involved in being “quantitative”. Sometimes, talking in a crowded elevator to another “quant,” you might start to say something about the duration or convexity – the derivatives – of a financial instrument. If the person you were talking to had been at the firm a little longer than you, he – it was usually a he – would cringe a little, and rapidly try to change the subject. “See the Yankees game last night?” he might ask, or “Futures dropped more than a handle today!” he might exclaim, the sort of things a real bond trader might say.

Soon, you began to realize, there was something a little shameful about two consenting adults talking math in a crowded elevator; there was something awful about being “outed” as a quant in public. People in the elevator just looked away.

In those days they paid up for quantitative skills, but you were still somewhat reluctantly accepted. Once, a friend and I were talking on the trading floor when one of the convertible traders walked between us, momentarily. Suddenly he grimaced and winced; he clutched his temples with both hands as though a sharp pain had pierced him and exclaimed, “Aaarrgghhhh! The force field! It’s too intense! Let me get out of the way!”

All of this was, of course, simultaneously patronizing and nevertheless a little complimentary, and therefore quite irritating.

Dave: What occurred that moved quantitative finance from the fringe to the hottest topic on the Street?

Dr. Derman: Three things. First, the arms race in securities’ design and investment strategies, the increasing sophistication that has led to quantitative trading strategies being an attractive way to try to make money in more subtle ways. Second, the rise of hedge funds like LTCM – and their capacity to wreak destruction when they rely too heavily on their models – has given quantitative finance a paradoxically greater respect. And third, there really have been advances in quantitative finance that allow investors to slice and dice the risks they want to take more accurately.

Dave: What exactly does a Quant do?

Dr. Derman: Use a combination of (i) financial and business understanding, combined with (ii) mathematical models of securities and markets that they (iii) implement as software on computers to value securities and search for rich or cheap ones in the marketplace. Especially, quants work on complex securities like options or convertible bonds whose value is subtle and less apparent, and can sometimes be extracted only by careful systematic formula-driven trading.

Dave: Are you able to explain how physics and finance mesh?

Dr. Derman: Yes, briefly; they both share the same language. Most of the more or less successful attempts to model securities and the way their value changes as markets move has been inspired by physics, and uses its techniques – algebra, calculus, Monte Carlo simulation, the idea of equilibrium, the idea that there are laws that describe these things. The two fields are deceptively similar-looking.

Dave: I really like your statement that physics is playing against God and finance is playing against God’s creatures. It’s brilliant ! I want to delve a little deeper into the meaning of this statement. Are you saying that pure physics represents a perfect world whereas finance reflects the uncertainty and foibles of the far from perfect mind of man?

Dr. Derman: That’s very well put, yes.

Dave: It seems difficult to mesh an imperfect discipline like finance with physics. Please explain the interrelation.

Dr. Derman:; Well, my book is partially about my belief that they don’t mesh as well as people in finance think. As a physicist, when you propose a model of nature, you’re pretending you can guess the structure God created. It sounds eminently plausible. Every physicist is a sort of great pretender; he believes he has a small chance of guessing right, or else he wouldn’t be in the field. But as a quant, when you propose a new model of value, you’re pretending you can guess the structure of another person’s mind. When you try out a new yield curve model, you’re implicitly saying something like “Let’s pretend people in markets care only about the level of future short-term interest rates, and that they expect them to be distributed normally.” As you say that to yourself, if you’re honest, your heart sinks. You’re just a poor pretender and you know immediately there is no chance at all that you are truly right..

Dave: What do you consider to be the primary differences between traders and quants?

Dr. Derman: Quants and traders have fundamentally different temperaments. Quants come from an academic background where they need to like to do one thing deeply and well, and not stop till they are finished. Work on the Street often needs several quick approximate answers. The hardest adjustment, when I moved to Wall Street, was to learn to do many things in parallel and not too badly, to interrupt one urgent and still incomplete task with another more urgent one, to complete that, and then “pop the stack.”

Quants are deliberate by nature. Traders have to be opinionated, visceral, fast-thinking and decisive, though not necessarily always right. It takes a long time to learn to talk to traders. This isn’t helped by the fact that they’re always busy and distracted, and it takes an hour of uneasy hovering around them to have five minutes of punctuated exchanges. If you want to convey information to a trader, you have to learn to start from the conclusion, to be very articulate, and to be brief. Those are good skills in general.

 

Traders and quants tend to think differently about financial value. Traders need to communicate so incisively because the essence of good trading is responding intelligently and rapidly to variation (and the threat of variation). Traders think naturally about change. When they consider an option, they think about what may happen to its price tomorrow as markets move. Traders think about hedges. Quants think more about current value and how to compute it as an average over all scenarios.

Dave: Have you ever met a Quant who actually traded ? If so tell me a little about this person. If not, is it possible that a Quant could become a successful trader?

Dr. Derman: Sure, I have, several in fact. It was rare years ago but it’s becoming more common now, and there are too many to tell about. A quant can become a successful trader, especially a trader of complex derivative products whose value has to be hedged according to a formula to extract a profit. But you don’t have to be a quant to be a trader; you have to be able to understand the models to trade, not necessarily to write them. You don’t have to be able to solve Newtonian mechanics problems to ride in the Tour de France either – it might be a disadvantage, in fact.

Dave: Is there a difference between risk managers and quants?

Dr. Derman: Yes, indeed. Risk managers have to understand risk and how to monitor it, and how to decide whether risk taken is commensurate with reward proffered. That’s a task for which a quantitative background is immensely useful, but it’s not a purely quantitative task; risk management takes not just quantitative skills but lots of common sense, judgement, and social skills. Dealing with traders isn’t easy.

Dave: Let’s move into the actual application of physics onto finance. What is the role of models in finance.

Dr. Derman: To my mind, the predominant use of models in finance is to find the value of securities whose prices you don’t know by analogy with securities whose prices you do know.

If I can tell a story I learned as a kid in bible school: Hillel, a famous sage, was asked to recite the essence of God’s laws while standing on one leg. “Do not do unto others as you would not have them do unto you,” he is supposed to have said. “All the rest is commentary. Go and learn.”

I think you can summarize the essence of models in quantitative finance models on one leg too: “If you want to know the value of a security, use the known price of another security that’s as similar to it as possible. All the rest is modeling. Go and build.”

Where do models enter? It takes a model to show that the two different securities are similar, meaning that they have identical future payoffs under all circumstances. To demonstrate payoff identity, (1) you must specify what you mean by “all circumstances,” for each security, and (2) you must find a strategy for creating a replicating portfolio that, in each future scenario or circumstance, will have identical payoffs to those of the target.

Most of the mathematical complexity in finance involves the description of the range of future behavior of each security’s price.

Dave: What are the several different types of models in physics?

Dr. Derman: Physics has models by analogy too – physicists call these phenomenological models, models in which you try to describe one set of phenomena as really being like some other phenomenon you already understand. These theories describe the world in man’s terms, using his analogies. An example is the liquid drop model of the nucleus, where you think of an atomic nucleus as rather like an oscillating drop of fluid, even though you know that is only an analogy, and that at a more reductionist level it’s composed of other particles and it’s not a liquid at all.

But the really deep models in physics reach the status of theories, what we call fundamental models. Theories say “These are the laws of the world.” They try to describe the dynamics of the world in God’s terms; they seek eternal truths. Examples are Newton’s laws, quantum mechanics, general relativity, even string theory, if you believe it.

Dave: Which one of these models is most likely to be applied to the financial realm and why ?

Dr. Derman: Most financial models are models by analogy, I’m afraid, and analogies are always imperfect. It’s amazing that physics works as well as it does, quite mind-boggling.

Dave: What type of model is the Black – Scholes ?

Dr. Derman: Black-Scholes is perhaps closest to a fundamental model, but it’s really a piece of brilliant engineering, a recipe. Black-Scholes tell you how, under certain idealized but not totally ridiculous assumptions, you can create a stock option out of a (dynamic, constantly changing) mixture of money invested in stock and money invested in an interest-bearing bank account. Since it tells you how to create it, you can calculate the cost of creation and hence the fair price to charge for it.

Dave: Is Black- Scholes still the most successful, if I can use that term, financial model?

Dr. Derman: Yes indeed.

Dave: Is the world of options still the primary place financial modeling is used?

Dr. Derman: Yes, but the world of options keeps growing, The history of quant employment on Wall Street is the history of extensions of the Black-Scholes model to new domains, from stocks to bonds to commodities to credit to ….And of course, statistical trading, statistical arbitrage, makes heavy use of financial modeling of a more statistical or econometric kind.

Dave: Please explain the invariance principal and how it relates to risk.

Dr. Derman: The invariance principle is something I formulated in a recent paper to try to understand in physics terms the relation between risk and return in financial theory. The invariance principle states that factors or portfolios with the same residual (i.e. undiversifiable unhedgeable) risk should have the same expected return. You can use this principle to derive the familiar truism that more risk should lead to more expected return, and to the Capital Asset Pricing Model. The invariance principle was really just an alternate way of understanding these results.

Dave: I find your views on time in trading to be fascinating. Each stock has its own intrinsic rate for trading opportunities to occur. This intrinsic rate is based on trading frequency and not the clock. What is the exact definition of intrinsic time as it applies to stocks?

Dr. Derman: Intrinsic time isn’t really my invention at all. I wish it was. I think it goes back to Peter Clark more than thirty years ago. The idea is that stocks trade at different frequencies, even variable frequencies, and that what matters is not how much time has elapsed but how many trades have elapsed, irrespective of calendar time. The hypothetical clock that counts trades rather than seconds is counting intrinsic time.

Dave: Can you further explain this concept and how it may be applied by a trader?

What I attempted to do was apply the invariance principle – equal risk, equal expected return – to trades in intrinsic time rather than in calendar time, calculating risk and return per trade rather than per second or day. When I did this, I found that expected return for investors increased with the trading frequency; greater expected return leads to more trading which leads to greater expected return, and so I hoped I could explain the stock bubble of the late 1990s as a sort of feedback frenzy in these terms. If you see more frequent trading you expect a greater return proportional to the square root of the trading frequency in this model.

Dave: Is there a simple model a non quant can apply to their personal portfolio to better understand risk?

Dr. Derman: That’s a tough one. One thing I would say though is that people and trading desks should focus as much or more on disaster scenarios more than on risk in general; risk involves probability estimation, and that’s notoriously difficult for humans in social sciences like finance. So forget about probabilities of things happening and instead, try to understand what scenarios can hurt you, and how much, and then decide whether you can stand that magnitude of loss, and if not, then perhaps you should lay off some of that risk, irrespective of the probability of it occurring.

Dave: Do you have advice to anyone who has aspirations of becoming a Quant?

Dr. Derman: Learn the math, finance and programming, and then learn a lot about business too. And try to understand the economics and “physics” and intuition behind the models, not just the math. And learn to communicate what you know clearly and concisely and metaphorically. And then have patience, but not too much patience. Life is short.

Dave: We are almost out of time, do have any final words for my readers?

Dr. Derman: It’s really been interesting and a pleasure for me talking with you, and I appreciate the opportunity. I hope your audience finds it useful. When I wrote my book I was never sure to what extent I was indulging myself and to what extent I was writing about common experiences, and I’ve been gratified to have some people tell me that some of it indeed about common experiences.

Dave: Thank you for joining me today, It’s been a pleasure !









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"Dr. Derman: Learn the math, finance and programming, and then learn a lot about business too. And try to understand the economics and “physics” and intuition behind the models, not just the math. And learn to communicate what you know clearly and concisely and metaphorically. And then have patience, but not too much patience. Life is short."



Sums it up for me. Thanks surf for that great read, I might get that book - My life as a quant.

 

anytime, squad. thanks for the kind words, i recommend the book-- it's good reading! here's a little more on Dr. Derman.
http://www.ederman.com/new/index.html

best wishes,
surf

 

I agree with your points A, B, and C. I am sure that the original poster appreciates your insightful response also.

Here I disagree with you. The term "business school" does not automatically mean that there is an MBA designation, although some MBA designations are certainly acceptable. Applicable graduate programs are offered through the business schools at many colleges and universities, namely programs with designations such as "MS Finance", "MS Financial Engineering", "MBA with Finance Concentration", and so forth. Some consider the business concentration as advantageous while others prefer the scientific concentrations. Either way you will need to demonstrate that you understand the math and the markets. The point I was trying to make is that a graduate degree will be preferred over double undergraduate majors any day, therefore the original poster should save the money and the effort and go for a single undergraduate major.

-segv

 

Hey Segv.

Thanks for the post. I think that your recommendation is pretty valid, and I am planning on going to grad school (business, etc.) in majoring for maybe financial math, financial engineering etc.

Although at the undergrad level I still want to double major. Now, this is important b/c money is not an issue. I want to understand the markets little bit more. In the finance major eco is thrown in as well. And math is self explanatory. Although I acknowledge your tips, I guess that I will double major nonetheless.

 

I see that while away the thread got sorted out to the satisfaction of OP. I will just add few points as promised:


It is helpful to look at quant jobs being split as EQarb suggests. It is even more important considering that B. is paid from different pool than A./C. - meaning that it is paid less.

B., which is usually called model review or model validation, is likely easiest to enter but it has its risks.
First make sure that the group is not responsible for internal model. Internal model people deal largely with VaR concept and it is a complete mess everywhere. They are far from products, far from markets and math required is "not very advanced".
Second check where the group sits. Hiring manager will tell you a lot of BS about relationship with the desk etc but unless the group sits next to the desk or nearby (e.g. not another building) forget the job. In fact a good rule of thumb is that you should always be on the same floor as traders you cover...
Note that the group is less "active" in creating new models. The group largely review the work already done by group C. Nevertheless it is a good training ground with less stress and good potential - you can always easily move to group C. if you are good enough.
C. is the place you should aim for if you get a shot. It gives you good product exposure and close contact with the markets. You will likely develop new models, fix old ones, fix spreadsheets, help to price deals, write functions, slave to traders etc.
From there there is a chance that you will fulfill the second part of your objective but only after you become a trader because....let's face it - as a quant you will never launch any fund - probably not even a small laundry...hahaha - looking forward for raging comments from quants...
You will likely not have a shot at A. - it is for seasoned quants and/or traders - nothing for newbies.

With respect to education; in over 10 years in this business (which makes for quite a good statistical set) I would say that non-PhD quants I met make for much less than 10%...oh and MBAs make exactly for 0%! From this statistic I do feel I need to wish you good luck at least twice. gl gl

 

dhpar.

Wow thanks.
So if I wish to open a fund in a future the best way is to be a trader as well. I never thought of that like that but why not shoot for the top right.

That pretty much sums up a lot. Last two pages were very well informative. Thanks a bunch.

 

The term business school as jack used it denoted MBA programs. Generally business schools do refer to MBA programs. Many of the best MFE degrees are located in the math or economics department (i.e. Princeton, Chicago). And MBA with a concentration in finance is not NEARLY as quantitative enough to actually cut it as a quant. These guys have a hard time doing DCFs. They aren't studying stochastic calculus at these places, I can assure you of that. The only MBA program which might hold some clout (due to the inherent "hardcore" reputation of the school) is Chicago's. Harvard's MBA program isn't spitting out quants any time soon...they are too busy honing their "leadership" skills.

Fact of the matter is Segv, as terrible as it sounds, solely an econ major from Baruch isn't landing him at Berkeley, Princeton, and Chicago. Most econ majors do not take the necessary maths to even make the cut at those top programs (however, pursuing econ at the graduate level requires significant mathematical aptitude). Thats why I would say he needs to really push himself and excel to boot.