• C++ Programming for Financial Engineering
    Highly recommended by thousands of MFE students. Covers essential C++ topics with applications to financial engineering. Learn more Join!
    Python for Finance with Intro to Data Science
    Gain practical understanding of Python to read, understand, and write professional Python code for your first day on the job. Learn more Join!
    An Intuition-Based Options Primer for FE
    Ideal for entry level positions interviews and graduate studies, specializing in options trading arbitrage and options valuation models. Learn more Join!

Sylvain Raynes: The State of Financial Engineering

Editor note: Sylvain Raynes is a founding principal of R&R Consulting, a structured credit metrics consultancy founded in 2000. Most recently, Dr. Raynes worked on the originations teams of UBS and CSFB. He developed methods for standardizing the credit risk analysis of exotic ABS the mid-1990s while in the Structured Finance Group Moody's Investors Service. Earlier, at Goldman Sachs he was involved in the statistical modeling of Derivative Product Companies, and at Citicorp he was responsible for the design of the credit scoring model for Citi's then-$30 BN credit card portfolio. Dr. Raynes has a PhD in aerospace engineering from Princeton University and an M.S. in Numerical Methods from Von Karmen Institute in Brussels.

Sylvain Raynes | Dec 4, 2008

We are the CDO makers,
and we are the dreamers of dreams.
Betting on lone loss triggers,
and trading in eclectic teams.
Spread losers and yield forsakers,
for whom the pale bonus gleams.
Yet we are the sole underwriters,
of the deals, forever it seems!

Fantasia on a Theme by William Blake

All over the world, it has become fashionable for Universities and Colleges to offer Masters degree programs in quantitative finance or financial engineering (FE), a code word meaning the solution of the Black-Scholes option pricing differential equation in as many ways as possible. To do so, students are taught to use basic techniques in numerical analysis whenever the equation is either non-linear or does not lend itself to the standard analytical solution. As a precursor to this main task, the program usually includes a course in stochastic calculus during which Ito's celebrated lemma is discussed, proved and used.

In general, the cost and length of such programs are remarkably similar despite the variability in the quality of the teachers and the brand name of the institution. In many cases the FE program is one of the biggest money makers at the University, if not the biggest, enabling schools to charge somewhere between $25,000 and $35,000 for eighteen months of night classes taught by top professionals in the field. Many such people are in fact refugees from bulge-bracket Wall Street firms looking for something to do before heading out to pasture in Florida and Arizona. In addition to being crassly commercial in their approach to knowledge transfer, often resorting to advertising their own former firm in the classroom, they are willing to accept much less in compensation than full-time professors and never become management headaches to the institution. Even Ivy League schools like Princeton University, who swore up and down they would never play this game, are now happily teaching finance and deriving significant incremental income from a fully depreciated curriculum.

The techniques taught in quantitative finance are completely standard in other fields. In most cases, the only exciting thing about the curriculum is that one day these methods might be applied on Wall Street to the calculation of cash flows. If they were instead applied to the making of widgets or the collection of tomatoes, it is a fair bet that nobody would be interested in them, and certainly no university would be able to charge $35K to learn them. In many other cases, schools with no MBA program have succeeded in manifesting an MBA curriculum out of thin air under the FE banner. At this point, financial engineering does not appear to have any specific meaning, or perhaps it means whatever it takes to get people through the door.

It is a plain fact that the field of quantitative finance has not made a single fundamental step forward over the past twenty years, not to mention that Black himself, by his own admission, had nothing to do with the equation that now bears his illustrious name. The BS equation was first formulated and solved by Casey Sprenkle some ten years before Black's famous 1973 paper in the Journal of Political Economy. Regrettably, it is still politically incorrect to give due credit to someone who made a real contribution to finance. Unlike those of some of his associates, Black's reputation hardly hangs on one paper.

Statistics and numerical analysis have nothing to do with finance per se but are merely tools of financial analysis, just like accounting statements and legal opinions. Finance is quantitative by definition; there is thus no need to add an adolescent adjective to the word. This is like saying aerial flight or wet swimming. Although people employed as aerospace engineers use computers on a daily basis, none would describe him- or herself as a computer programmer.

But if this were about mere semantics, it would not be worth mentioning. Unfortunately, FE programs are also drifting farther and farther away from their purported subject matter. In effect, quantitative finance has entered the scholastic stage whereby numerical techniques are taught completely out of context as if a deal were somehow a differential equation that could be solved for the right solution. In fact, there is no solution to a deal as there is to a differential equation. At this point, analysts are talking about investing angels dancing on financial pins. Even worse, professional societies devoted to financial engineering are in reality pressure groups acting on behalf of various financial constituencies, like hedge fund managers seeking to get the regulators off their proverbial backs. Although every American citizen has the right to lobby whomever he pleases, this cannot exactly be described as furthering the field of financial engineering or building esprit de corps among its members.

Students thinking themselves financial experts simply because they can solve the BS equation in a few minutes (there is apparently no other one around) are being misled by their own mentors and teachers into the naïve belief that this amounts to finance. Seminars with magical titles like How I Became a Quant or A Quant Roundtable only serve to perpetuate a myth, the myth that finance is about differential equations or positive definite matrices. Anyone even remotely acquainted with the practice of finance knows full well how far removed such mathematical topics truly are from the real subject matter and the day-to-day bubbling within the cauldron of finance.

A deal only happens when various constituencies (lawyers, investors, bankers, rating agencies sometimes, regulators, accountants, etc.) are able to come together under a unified framework. This is not the time to deliberate or lecture on cross-correlation and conditional VAR. On the contrary, any such talk is completely counter-productive and propagates the negative stereotype commonly attached to those that engage in it to impress or intimidate their neighbors. Rather than repel them, a savvy financial engineer ought to find a way to bring all the parties together, thereby placing him- or herself at the center of the deal instead of its margins. However, this pre-supposes the emergence of a new language better suited to deal making, i.e. not the current one extracted lock, stock and barrel from theoretical physics. To be on a fantastic journey is great only as long as everybody else is on board with you.

Too often, students who would otherwise have something valuable to contribute are being led down the primrose path at the instigation of people for whom this is, at best, a hobby. The promise of high-flying jobs in New York is all that has propped enrollment at its current levels, since once on the job, graduates of financial engineering programs quickly become aware that it is MBA graduates, and not themselves, who are destined to breathe the rarefied air of boardroom deal-making. In fact, the label quant has now become quasi-pejorative, the practical equivalent of geek or inconsequential number-cruncher. Deal-makers do not want such people in front of their clients, if only for fear of hearing naïve prognostications of hetero-scedasticity and Gaussian copula bandied about before befuddled investors (and even before lunch).

Not surprisingly, business school professors often warn their students about not being labeled a quant if they ever want a career in the community of finance. Given the current state of affairs, we could not agree more. Every financial professional worth his salt should be numerate at least to the extent necessary to do his or her own deals, and ideally more. If solving a differential equation is what it takes, then so be it. Unfortunately, it rarely if ever does. Professor Joel Hasbrouck of the Stern School of Business at New York University has posted a rather blunt Power Point presentation entitled Quantitative Finance on the Internet. Despite the obviously self-serving and competitive nature of his remarks, he is largely and sadly correct.

Many students pursuing financial engineering degrees already have technical degrees in other fields and are merely looking to acquire a brand name enabling them to join the party on Wall Street and earn much more than they would in their own field, assuming they would even find a job there. For them, most of the course work in the program is useless or repetitive at best, since they already know what they need to know to perform. Others are liberal arts majors with essentially no background in numerical analysis. They find the program hard and commonly team up with students from the first category to make sure they can do all the assignments. To such students, names like Crank-Nicholson and McCormack easily acquire mythical status, being by definition the way to solve the BS equation numerically. They have no way to gauge whether these are just two methods among many or the pure, unadulterated truth.

In some cases, students are blindly taught to use techniques that only work because the BS equation is parabolic. The course assignments we have looked at are remarkably commercial in intent and mainly serve to stifle basic creativity by intimating, for instance, that what Risk Metrics (a commercial vendor of analytical services) does is the right way to model credit or market risk. This is thoroughly and sleazily transparent, anti-intellectual and only fosters cynicism in the student body when it is perfectly clear that there is no such thing as a right answer in finance, unless by that term one trivially means that no logical mistakes have been made.

What is obvious and regrettable is that no effort is ever made to teach numerical analysis as a proper and rigorous discipline. Instead, students literally learn numerical recipes and are no more equipped to handle reality than someone equipped with a driver's license when their breaks down. One also gets the disturbing feeling that the majority of teachers involved in quantitative finance have limited knowledge of either finance or of the elements of numerical analysis. Unfortunately, too many professors in that field are there for the same reason that the school can charge enormous tuition fees, i.e. they can earn much more for teaching the same material in finance than they would in the original field. It's hard to turn down $150,000 for teaching either statistics or the numerical solution of partial differential equations on a full-time basis when your equally competent buddies from graduate school are doing the same thing elsewhere for $50,000.

Although we freely admit that we have not performed the monumental task of a complete inventory and comparative analysis of financial engineering curricula, a cursory review of the most popular ones reveals what seems to be their central dilemma, which is how to fill 18 months of teaching with a single topic: stochastic calculus and its applications. The general answer appears to be to fill the remaining 14 months or so with subsidiary material peripheral to finance but available for much less elsewhere. Here, the main target is numerical analysis. In the latter domain, although students are taught a few useful techniques, the elements of numerical analysis are not addressed, perhaps because this would take up too much time and force the school to hire teachers who would demand higher pay and thus decrease yields.

The upshot is that the curriculum is always sitting between chairs but never on any one of them. One meanders across finance discussing swaps, default swaps and various options (instruments that hardly require Herculean intellectual prowess to grasp), engages in endless and meaningless debates on the eigenvalues of correlation matrices, and then dabbles in numerical analysis by learning basic methods applied to the BS equation taken as the last word in finance for the remainder of mankind's existence. At no time, as far as we can tell, are students taught how to construct a numerical method from scratch or how to tell if it will work or fail.

Throughout our Internet search, the following topics were absent from the syllabi of the numerical analysis courses within the financial engineering curricula of the academic institutions we reviewed:

1. Z-transforms and Laplace transforms
2. Banach and Sobolev spaces
3. Fourier series and transforms (one exception)
4. Lax equivalence theorem (same exception)
5. Von Neumann stability analysis
6. Courant-Friedrichs-Loewy (CFL) condition
7. The Nyquist sampling theorem (useful in Fourier analysis)
8. Convergence analysis
9. Error propagation analysis
10. The Weierstrass approximation theorem
11. The interplay between truncation and discretization error

It is simply not possible to claim expertise in numerical analysis if one does not have at least a passing acquaintance with the above foundational elements. However, learning these things takes time. On the other hand, if the goal is not to become knowledgeable in numerical analysis but simply to learn a sundry assortment of basic methods, there are much cheaper ways to do this, for instance in the mathematics or computer science department of the same school. Numerical analysis is a well-formed discipline that does not need finance to give it credibility.

The consequence of all this is that today, and through no fault of their own, students with degrees in financial engineering are ill-equipped to face the rapidly changing face of finance. Once ensconced in their jobs, they are quickly marginalized and relegated to the role of glorified programmer until being eliminated in the next headcount reduction because (with unfortunate justification) they are not considered producers.

It would be a different matter if financial engineering were just a code word for numerical analysis with finance used as a marketing mechanism to attract people to the field. It would be equally acceptable if financial engineering were devoted to the actual practice of finance instead of being largely an obsession with one equation, no matter how interesting it might be. Someone who has never done an actual deal can hardly be expected to know how deals are done, let alone teach how to do them. Likewise, a manager who used to supervise twenty-five Ph.D.s in some research department on Wall Street has as much knowledge about deal making as an usher at Yankee Stadium has about baseball. On the contrary, it has become painfully obvious that these managers, if the term can be used at all to describe this level of incompetence, are precisely the people who truly need supervision instead of underlings who, at bottom, never make a single decision that could take their firm down.

Financial engineering never grew up within finance; it was taken over by physics. This is not surprising given that the same thing happened to economics 100 years ago. Unless the field re-invents itself pronto and starts becoming relevant to what people actually do out there, graduates with newly minted financial engineering degrees hoping to see a decent return on their own or their parents' sizable investment will continue to be sorely disappointed by their actual career prospects, and will keep wondering where in God's name they went wrong.Regrettably, the answer is: nowhere.

Source: The Spectrum - The State of Financial Engineering

__________________________________________________________________________________________________________​

The Answer is the Question, the Question of the Deal
Sylvain Raynes | Dec 5, 2008

My recent post on the sorry state of financial engineering seems to have touched a raw nerve in the FE community.
I will not attempt to respond to anonymous comments since the US constitution gives every citizen the right to look his accusers in the eye. People who wish to remain in the dark usually have nothing to contribute themselves, and that's fine of course. Besides, I am not here to try to win a popularity contest. Are you?

The most interesting puzzle is why someone who apparently teaches FE himself would bad-mouth his own occupation. It seems counter-productive and nonsensical at best. Perhaps you should reflect on this for a while before rushing in with sour grapes. By the way, please leave Baruch College out of this. Institutions don't teach classes, professors teach classes. I wrote this on my own time, and nobody else but me needs to feel responsible. Taking responsibility is, however, what we are really talking about.

I wrote this post four years ago but was waiting for this easily predictable cesspool (the credit crisis) to point out that if financial engineers had been actually doing what they claimed to be doing, this mess would never have happened.

In the early days of bridge building and aerospace engineering, many bridges and airplanes crashed. That's ancient history for you guys, but very relevant. Thereafter, the field improved rather quickly and, magically it seems, bridges and airplanes stopped crashing. The same thing could happen for structured deals.

In the end, it will not be possible for financial engineers to walk way clean from a trillion dollar disaster by saying they had nothing to do with it. They had a lot to do with it. What matters now is not to try and exculpate ourselves like the French cop in Casablanca, but to start getting to the heart of the matter. This means that we need to engage the field and find out how to become relevant to the mainstream segments of American finance.

These people manage other people's money (yours, for instance) with essentially zero knowledge of structured finance and of what they are, in fact, investing in. They don't have a Ph.D. in anything and are just trying to feed their families, and yours too. Why don't you help them figure out what the Hell is going on out there, instead of speculating on the transcendental meaning of copula functions, and on how to invent the next one?

I would love to be proven wrong about FE graduates. What is it they say in Missouri? Don't tell me, show me.

-- Sylvain Raynes

Source The Spectrum: The Answer is the Question, the Question of the Deal
 

Attachments

  • JH_quantitative_finance.pdf
    128.4 KB · Views: 778

doug reich

Some guy
The obvious question is: how does Prof. Raynes defend himself while obviously being a cog in this FE machine he deplores? Or is Baruch "different"? Certainly, they charge less, but is the material better as a result? (I withhold judgment on the program's chacterization with only one semester under my belt.)
 
Anyone know where I can find a copy of that Joel Hasbrouck PowerPoint presentation? I did a quick Google search, but all I found were various repostings of this (rather awesome, IMHO) Raines essay.
 
So has it been a specific truth in the current market condition that quants have "once ensconced in their jobs, they are quickly marginalized and relegated to the role of glorified programmer until being eliminated in the next headcount reduction because (with unfortunate justification) they are not considered producers." ???

I can understand his statement about being the glorified programmer. However, have quants become a priority in going on the chopping block to reduce headcount like he seems to say because they are "not producers"?

As to his statement "graduates of financial engineering programs quickly become aware that it is MBA graduates, and not themselves, who are destined to breathe the rarefied air of boardroom deal-making."

Dealmaking seems to be in the realm of either the "Sales" portion in Sales and Trading or I-banking (M&A, corporate advisory, underwriting). I did my BS in finance in a school that is well known for its MBA program and was able to take almost all of my business classes cross listed with the MBA program so sat in class with a bunch of MBA's. I can see why these MBA's would be considered for sales and dealmaking positions because they present well, come across well, are polished etc. Basically they are good BSer's. Barack Obama types if you will. An MBA program is about as technical in terms math and quant stuff as the securities analysis class which shows you how to calculate a weighted average cost of capital and the speculative markets class which superficially shows you how to value an option using BS or binomial tree.

However for pure trading, analysis roles, and other jobs needing hard technical skills, I can't see why anybody would want to take an MBA over somebody who has a solid background in math like an MFE.
 
I'll post the Power Point slides cited in the article as soon as I obtain it.
The figure "$25,000 and $35,000 for eighteen months" cited is rather low. In fact, I'm not aware of any US program except the Baruch MFE that charges less than $35K for 18 months program.
Here is the latest tuition data I have
Baruch: 9K for NYC residents and 18K for others. (Source)
NYU: $1,206 a credit for 36 credit program = $43,416. (Source)
Columbia FE: $1248 per point for 36 pts program = $44,928 (Source)
UCLA MFE: $50,00 for the one-year program (Source)
UCB MFE: $49,725 for one year program (Source)
 
An MBA program is about as technical in terms math and quant stuff as the securities analysis class which shows you how to calculate a weighted average cost of capital and the speculative markets class which superficially shows you how to value an option using BS or binomial tree.

However for pure trading, analysis roles, and other jobs needing hard technical skills, I can't see why anybody would want to take an MBA over somebody who has a solid background in math like an MFE.

But what if the math is irrelevant? What if during boom times, it was merely used to cover the emperor's nakedness? What "hard technical skills" does a quant really bring, other than programming? The PDEs, time series analysis, numerical analysis, etc. may be so much redundant fluff these days. Finance -- like economics -- may not really be amenable to mathematical treatment. Raynes is right. On a side note, I'm aware of one nincompoop of a professor who, under the guise of teaching a course in math finance, is teaching measure theory (as he doesn't know any finance). Does anyone think the graduates of this "program" are going to be hired by anyone?
 
Well as a loose example, options market makers like to recruit engineering/physics/math majors. Yeah sure, the MFE is overkill, but I noticed for positions like this they like people who can do math (well more like elementary calculations far away from the level of PDE's, etc) and do it very quickly. I know MBA's sometimes get these positions as well, but given that there will be a greater intersection of finance and math and CS, people hiring for jobs like this would be in my opinion more likely to hire an MFE over an MBA. But sure, a young college grad in applied math, moldable and trainable might be most ideal.
 
"1. Z-transforms and Laplace transforms
2. Banach and Sobolev spaces
3. Fourier series and transforms (one exception)
4. Lax equivalence theorem (same exception)
5. Von Neumann stability analysis
6. Courant-Friedrichs-Loewy (CFL) condition
7. The Nyquist sampling theorem (useful in Fourier analysis)
8. Convergence analysis
9. Error propagation analysis
10. The Weierstrass approximation theorem
11. The interplay between truncation and discretization error"

Why the hell would one want to learn about Banach & Sobolev spaces when doing numerical analysis? I'm just a beginner in the field but still, I can't imagine...
 
Why would one want to learn about Banach & Sobolev spaces when doing numerical analysis?

A Sobolev space is a particular example of a Banach space, and useful for solutions to PDEs. Function spaces arise naturally in differential equations; e.g., the method of successive approximations for ODEs really involves convergence of a Cauchy sequence of functions -- living in a function space -- to some limit.
 
On a side note, the following little snippet can be found here:

[FONT=Verdana, Arial, Helvetica, sans-serif][SIZE=-1]
He has some knowledge of the physicists and mathematicians who put together the Wall Street "instruments" that have triggered the present crisis. Many of these people knew that they were simply providing their bosses with simulations that proved what the bosses wanted proven. And many of these former academics openly referred to themselves as "whores." It may be dangerous to let too many physicists go unemployed.
[/SIZE][/FONT]
 
I edited Andy's link in the first post to the original context the article was taken from (R&R's blog at the Spectrum). Professor Raynes cannot respond here, but R&R values your thoughts and commentary, and the editors there would welcome a direct dialog on their blog.
 

bob

Faculty (Undercover)
But what if the math is irrelevant? What if during boom times, it was merely used to cover the emperor's nakedness? What "hard technical skills" does a quant really bring, other than programming? The PDEs, time series analysis, numerical analysis, etc. may be so much redundant fluff these days. Finance -- like economics -- may not really be amenable to mathematical treatment. Raynes is right. On a side note, I'm aware of one nincompoop of a professor who, under the guise of teaching a course in math finance, is teaching measure theory (as he doesn't know any finance). Does anyone think the graduates of this "program" are going to be hired by anyone?

Unfortunately, I believe this response is a perfect illustration of the misinterpetation this piece invites. Sylvain has as usual caparisoned his rhetorical cavalry richly, but in this case seems to have marshalled them with rather less care.

To conclude on the basis of an essay bemoaning the shallowness of the common FE curriculum that math--not the math that is taught, but math as a tool--is useless in finance is, I assure you, not the intent here. If you will not accept my word on this, having known Sylvain a good deal longer than I have known anything about finance, then perhaps you would care to inspect the piece itself:

...any such talk is completely counter-productive and propagates the negative stereotype commonly attached to those that engage in it to impress or intimidate their neighbors. Rather than repel them, a savvy financial engineer ought to find a way to bring all the parties together, thereby placing him- or herself at the center of the deal instead of its margins. However, this pre-supposes the emergence of a new language better suited to deal making, i.e. not the current one extracted lock, stock and barrel from theoretical physics.

Granted, it is easy to miss such moments because they yearn toward something that does not yet exist. Currently--perhaps inevitably--we can do no more in this language than clear our throats. To try speaking it at the moment is to be the one who wonders aloud during the party whether the building is up to fire code, but cannot tell you how to get out once the blaze begins.

While it may be true, as Sylvain opines, that progress has been slow in this fledgling field, I would not agree that what has been done is therefore useless; nor do I personally feel that this line of inquiry is merely a cul-de-sac. The fact that work considered "fundamental" in the field can be found with publication dates within the past thirty years is, to me, not a sign that the field is small, but instead that most of its extent remains unmapped. An unhealthy obsession with a single equation is perhaps an obstacle for FE's as such to be the ones who do this work, but we can perhaps content ourselves with the idea that, if it is indeed worth doing, then it will be done.

Flannery O'Connor, asked once whether academia was properly nurturing young writers, answered this: "Everywhere I go, I'm asked if I think the universities stifle writers. My opinion is that they don't stifle enough of them. There's many a bestseller that could have been prevented by a good teacher." Perhaps Sylvain, a good teacher himself, has taken a page from O'Connor's book here, but I'm afraid that it lends itself to being read as anti-intellectual invective, when in fact it is precisely the opposite.
 
I edited Andy's link in the first post to the original context the article was taken from (R&R's blog at the Spectrum). Professor Raynes cannot respond here, but R&R values your thoughts and commentary, and the editors there would welcome a direct dialog on their blog.
Thanks Max.
I found a few sites quoting this piece as well as the R&R blog but didn't get to change the link yet.
Not sure if you can ask the editors there about the Powerpoint slides cited in their piece. Do they have a copy, a link to it?
 
To conclude on the basis of an essay bemoaning the shallowness of the common FE curriculum that math--not the math that is taught, but math as a tool--is useless in finance is, I assure you, not the intent here. If you will not accept my word on this, having known Sylvain a good deal longer than I have known anything about finance, then perhaps you would care to inspect the piece itself:

But surely -- unless my interpretation is much awry -- he is making the same point:

However, this pre-supposes the emergence of a new language better suited to deal making, i.e. not the current one extracted lock, stock and barrel from theoretical physics.

At least here he is not contending that any kind of mathematics will provide the underpinning for a new financial engineering; his private communication with you, I am not privy to.

My own thinking ( or what I try to pass off as such) at the moment goes further. I do not think the present financial system will survive in any recognisable form. The mathematical and statistical tools quants have been using have "worked" only in the particular artificial conditions that have taken shape over the last thirty years as the political project of neoliberalism. Once the ground rules change, the mathematical tools used hitherto will suddenly become irrelevant. Thus, for example, PDEs are great fun and have a rich body of theory behind them -- but their use in finance will probably cease abruptly. Indeed, as Donald MacKenzie argues, in An Engine, Not a Camera, the use of the B-S equation in finance was fortuitous, and it served not so much to shed insight on markets as to permit their creation. It's an interesting question as to the extent to which math and stat tools model the markets in any real sense. They are often used as camouflage to impress the unwary. Even when they are used for authentic modeling purposes, it's to use them phenomenologically along the same lines as thermodynamics -- but working only for the particular circumstances of the recent past.

Anyway, these are my half-baked opinions. And I could well be wrong.
 
My own thinking ( or what I try to pass off as such) at the moment goes further. I do not think the present financial system will survive in any recognisable form. The mathematical and statistical tools quants have been using have "worked" only in the particular artificial conditions that have taken shape over the last thirty years as the political project of neoliberalism. Once the ground rules change, the mathematical tools used hitherto will suddenly become irrelevant.

Well, yes. But that's a bit like saying "All maps are useless" because you can't navigate the streets of London with a New York subway map. A model of a market can only be expected to model the market it is designed to model. If the market changes, one would expect the model of that market to have to change, just as the New York subway maps will all have to change once the Second Ave Subway gets built. (I know, I know...)

Not only that, but models (like maps) are only designed to work to a particular scale. A map of the world isn't useless simply because I can't get driving directions from it, just as the New York subway map isn't useless because it won't show me where Afghanistan is. They're different models of the world, with different focuses and differing scales. I think the problem our field faces is not the limitations of the models but a lack of understanding of what those limitations are. All models are wrong, a professor of mine once wrote on the board, but some are useful. The trick is remembering that you can't use a street map to find your lost apartment keys.
 
Edit... Yes like Professor Raynes states, in the early days bridges collapsed and planes crashed. Doesn't mean engineers didn't change history. But I have to admit, pretty costly baby steps.
 
Prof. Raynes just posted a followup to the comments about his "State of Financial Engineering" opinion piece. I added it to the first post and link to source.

I was just looking at his blog and saw some more interesting posts. In particular this one, though it's only tangentially talking of quant finance. The style is lyrical. I see he mentions Karl Marx. Sales of Das Kapital have gone up markedly in the last few months in both Europe and the USA (albeit from a very low level). In these difficult times, I now have the courage to confess Marx is one of my favorite writers and has more to offer by way of insight into what's happening today than any craven worm of a bourgeois economics professor. Quant finance as we know it is probably dead. We're now in the early stages of a "crisis of capitalism."
 
Top