Paul Wilmott Wants To Retrain and Reform Wall Street's Quants 198
theodp writes "What if an aeronautics engineer couldn't reconcile his elegant design for a state-of-the-art jumbo jet with Newton's second law of motion and decided to tweak the equation to fit his design? In a way, Newsweek reports, this is what's happened in quantitative finance, which is in desperate need of reform. And 49-year-old Oxford-trained mathematician Paul Wilmott — arguably the most influential quant today — thinks he knows where to start. With his CQF program, Wilmott is out to save the quants from themselves and the rest of us from their future destruction. 'We need to get back to testing models rather than revering them,' says Wilmott. 'That's hard work, but this idea that there are these great principles governing finance and that correlations can just be plucked out of the air is totally false.'"
Wow! (Score:5, Insightful)
Re:Wow! (Score:5, Insightful)
What a concept! Basing conclusions on experimental evidence from testing via trial and error rather than warping reality to fit your business model. That's incredible!
It will never last in the 'real' world...
Re:Wow! (Score:5, Insightful)
I'm glad you got insightful not funny. You are right. This is one of the case where experimentalism actually breaks down in the real world.
The problem is that the quant's model is in its self an input to the reality and the processes aren't statistical and stochastic. When I know (or even partly correctly guess) what model you are using for investing, then I can gain several benefits from altering my behavior. I can create false investment opportunities which match well with your model. I can predict when you will need to buy something and push up the price just before hand. I can guess when you will become over exposed to some asset and force you to sell too cheap.
The models are useful, but in the end lots of business stuff just has to come down to gut feelings and judgement. You also just have to do analysis which goes beyond the empirical (nobody has ever tricked us before) into risk control (what can we do to make sure nobody can do that in the future; how would we tell if they were trying to).
Re:Wow! (Score:5, Informative)
You are right, this is one important source of problems. I started out in quantitative finance back in the 1970s. (I worked as a research assistant for Fischer Black when I was in grad school.) The initial application of many of the quants' techniques were in markets like US equities, or listed options, where the assumptions that one participant couldn't affect the overall market much and that there were reliable sources of information were probably reasonable.
But if you look at one of the key "villains" in this last mess, the credit-default swap [CDS] market, it's an entirely different story. I have read Li's paper on the Gaussian copula function, and had a look at an implementation. What it is essentially doing is using a statistical sampling function to estimate the expected lifetime to failure (= default) for a population of debt instruments. Now, there is nothing wrong with the math per se; similar approaches are used in manufacturing for quality assurance. However, there is big difference: estimating the failure rate of, say, light bulbs does not in itself have any effect on that rate. But in the case of the CDS, the failure rate is being used as an input to the model that is used to price the swap. If the default rate estimate is too low (too optimistic), the prices will be too high -- and that, in turn, will lead to lower estimates of the default rate. In essence, there is a built-in feedback mechanism that can act as an error amplifier, a problem that is exacerbated by the lack of transparency and liquidity in the CDS market.
There's plenty of blame to go around. The managements, who should have known better, were bedazzled by the dollar signs floating out of their economic perpetual-motion machine. The quants knew the math, and their hubris led them to think that nothing else was needed. And the investors, while proving the truth of P.T. Barnum's Law of Applied Economics, forgot that there ain't no free lunch.
Too unnecessarily complicated! (Score:2)
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It seems to me this is a case where experimentalism works just fine in the real world. When the experiments are telling you that reliably predicting the market doesn't work, maybe it's because you can't.
As several studies have shown, the gut feelings and judgement don't really work either.
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All human decisions - even the most abstract or "rational" - have an emotional component; this has been experimentally measured.
Yes! I'm off-topic now but it is wonderful to see this assertion expressed on Slashdot. There's a tendency here to treat emotion as an uncomfortable byproduct of being a meat-sack.
In truth all the progress we have made is driven by emotion: we feel desire for some things (food, sex, Insightful moderations) and it gives us pleasure to try to figure other things out, which is really a desire for knowledge. We follow the creeds and models we do because we like them, and often the difference between philosop
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EXACTLY...
I work in the financial world, and I do modeling, and I understand the quants because I do much of the same work that the quants do. Except I am an algo-trader.
The reality with all of this quant stuff is that it works until it doesn't. This is the crux of the problem and I would say a large part of the industry JUST DOES NOT GET IT... For example the comment to test models is hogwash. This financial engineering is not science. Its a scientific approach to madness of crowds. And the reality is tha
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And this is missing my point...
I said, "I argued on the day of the invasion with a good friend of mine that it was stupid, idiotic for the exact reasons of why it is failing."
I am agreeing with you. BUT, people don't do the what if it did work. This is my beef with the financial industry, and the fact that you are applying your narrative, when in fact the opposite could have happened. What if Iraq worked, would you change your opinion? Would you be able to admit? I know I would have said, "ok I am wrong..."
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/mythbuster
or
In all seriousness, this does not sound like a field that needs saving from itself. If its common practice (which its not) for aeronautical engineers to tweak the fundamental laws of physics simply because they need them to make a non-working design work, maybe its time to get ri
Re:Wow! (Score:5, Insightful)
>
> or
Disdain for the "reality based community" [wikipedia.org] is nothing new.
> In all seriousness, this does not sound like a field that needs saving from itself.
>
> Something doesn't get to be common practice unless a good portion of the field believes that it is good to do so, or at the very least, not harmful.
I agree with your last statement in application to almost any discipline other than economics and (more specifically) finance. I must disagree with your first though. The ability of greed to short-circuit the mind's ability for critical thought is unparalleled as is the obstinate willingness of great swathes of people to swallow snake oil by the gallon on the merest suggestion of the slightest whiff of profit. My memory may be hazy, but I can recall at least two occurrences of a "new economy" in the last three decades. And of course each "new economy" marks a break with "outdated" beliefs/dogma/tradition (you know - like that outdated belief that you can't make something out of nothing, or that other one about a "turd by any other name would smell as sweet").
I don't doubt that given another fifteen years or so, we'll have forgotten the "hard lessons", the sincere abjuration of pernicious practices and every other skerrick of common sense. The new "new economy" will have arrived. Only a fool would fore-go the chance to make real money. May I suggest however, that you watch this infotaining interview [youtube.com] before you invest in the new "new economy".
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Steve Keen gets passively aggressively avoided by most mainstream economists in Australia. His models are too complex to work. He uses these exotic things like called "Differential Equations", and they are just too complex to work in the real world.
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My car has a differential. Maybe he should work more with car analogies.
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There is already a lot of information out there about business models HR concepts and the like. Heck many MBA programs encourage students to use them and experiment with them to find the best model. But real life makes it much harder. Long term growth is difficult for people. (are you really putting enough money away for retirement). The same thing with business, are you willing to invest your money in testing new models, which will overall have a better effect or put money what seems to get the best inve
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The problem is that management do not want quants who rigorously test models - especially risk models.
They would far prefer to be allowed to collect fees while business is doing well, and if they are taking more risks than they are supposed to, well, its not their money is it?
How about... (Score:5, Insightful)
... getting back to the real economy? Many financial products don't add anything to the real economy at all.
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financial products are not the only thing that went wrong in the eCONomy, look at GM and their demise, writing was on the wall when the hummer was released
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Nice job [newsweek.com] Newsweek (someone take a screenshot before the article gets pulled/corrected). His hedge fund didn't even exist in 2001 [caissacapitalltd.com]. And it's not a $170 million fund, it's roughly a 170 million British Pounds [guardian.co.uk] fund. I would add more, but his hedge fund was delisted from the New York Stock Exchange and the London Stock Exchange in January 2005 right after this fiasco [timesonline.co.uk] (and it's funny, except for one cryptic note [caissacapitalltd.com] ab
Re:How about... (Score:4, Informative)
Ok, the link didn't work. I'll try again:
December 9, 2004
Caissa founders in court battle
By Joe Morgan and Richard Irving
A FORMER Oxford University professor, a chess grandmaster and a software developer are locked in a court fight in New York over ownership of a £172 million hedge fund in which top City names such as Gavyn Davies have invested tens of millions of pounds.
Paul Wilmott, Ron Henley and Jonathan Kinlay, who set up a secretive hedge fund, Caissa Capital, accuse each other of trying to lure top investors to rival funds after the partnership collapsed in acrimony earlier this year.
The dispute erupted in September when Mr Kinlay, who has developed a computer program that highlights profitable trading strategies in volatile markets, filed a lawsuit in the Supreme Court of the State of New York against his two former partners. Mr Kinlay is seeking $800,000 (£414,000) in damages for alleged breaches of intellectual property rights.
Mr Wilmott, a former Oxford University professor, and Mr Henley, who trained the former chess world champion Anatoly Karpov, hit back with a $10 million counter-suit, claiming that Mr Kinlay had tried to wind up Caissa by luring investors to a new fund that he was secretly trying to set up without their knowledge.
The dispute flared up after Mr Kinlay returned from a holiday in Italy in August to find that Mr Wilmott and Mr Henley had given back $40 million to Caissa's biggest investor, Prisma Capital Partners. Prisma is run by Gavyn Davies, former Chairman of the BBC, Girish Reddy, former head of derivatives for Goldman Sachs, and Tom Healey, a star banker.
Mr Wilmott and Mr Henley claim that Prisma demanded the return of its money after Mr Kinlay approached its backers with a proposition to invest in his new venture, the Proteom hedge fund. It is understood that the partners were sceptical of the strategy Mr Kinlay planned to use, which essentially involved using the same technology that looks for patterns in genes to predict stock and bond market moves.
Mr Kinlay, meanwhile, alleges it was his two former partners who were trying to lure Prisma into a new fund that they were also trying to set up. Both sides deny the allegations.
After the initial dispute, the trio decided to go their separate ways and arrangements were made to write a separation agreement in August. On the basis of this agreement, Mr Kinlay agreed to sell his interests in Caissa Capital and Caissa Capital International, an overseas offshoot, to Mr Wilmott and Mr Henley. He also agreed to terminate software licences granting access rights to his trading program. He collected $377,759 from his former partners under the deal.
However, court papers allege that the separation agreement negotiated by Mr Kinlay was just a smokescreen while he asked a bank, which was helping him to set up the Proteom fund, to find a legal "rottweiler . . . who would sue his two partners and Caissa's investors".
It is alleged Mr Kinlay began to plot against his former partners almost a year earlier after deciding that neither "had the time nor the skill set to make a meaningful contribution".
Court documents allege that Mr Kinlay set up the Proteom fund because, as a minority partner in Caissa, he could not force Mr Henley and Mr Wilmott out. Mr Kinlay denies the allegations.
PARTNERSHIP BREAKDOWN SEEN AS CHESS CONFLICT
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Re:How about... (Score:4, Informative)
Here is the second page:
"There is nothing that would persuade me to remain in a partnership with someone as stupid, duplicitous and untrustworthy as you have proved yourself to be. As we discussed, I shall make arrangements for your exit from this firm at the earliest convenient opportunity."
Jonathan Kinlay to Ron Henley, July 20, 2004
"How like a game of chess this is. Except you are toying, not with wooden pieces, but with people's lives. And you are about to discover in this game it is I who am the grandmaster."
Jonathan Kinlay to Ron Henley, July 25, 2004
"White's attack has been successfully put down. Black sacrifices the terminally weakened pawn in order to open lines for the coming counter-attack. Meanwhile, the white knight has been neutralised and lies isolated and vulnerable at the edge of the board, waiting to be picked off by the black forces at their leisure."
Jonathan Kinlay to Ron Henley and Paul Wilmott, August 15, 2004
THE BRAIN'S WHO FELL OUT
JONATHAN KINLAY
The chief executive of Investment Analytics, a mathematical research firm that develops software programmes to exploit volatile stock markets.
He started his career at NatWest in the early 1980s but had left long before the investment bank found an £80 million black hole in its options trading book.
After a spell at Chase Manhattan, he joined the proprietary trading desk of EMC International, a European hedge fund, specialising in privately negotiated derivatives contracts. He is well known on the lecture circuit and has taught financial engineering at Carnegie Mellon in New York and at Oxford and Cambridge.
RON HENLEY
Few people span the diverse worlds of chess and high finance, and fewer rise to the top of both. Ron Henley is a chess grandmaster, but he is best known for training Anatoly Karpov, the former chess world champion. His interest in derivatives dates back to 1985, when he became a member of the American Stock Exchange.
He soon earned himself a reputation as a derivatives whizz kid, rising to become a specialist options trader for Cohen, Duffy & McGowan, one of the top options markets makers on the exchange floor. He is a regular chess commentator and runs an internet site that raises funds to sponsor young US players, including Irena Krush.
JONATHAN KINLAY
The chief executive of Investment Analytics, a mathematical research firm that develops software programmes to exploit volatile stock markets.
He started his career at NatWest in the early 1980s but had left long before the investment bank found an £80 million black hole in its options trading book.
After a spell at Chase Manhattan, he joined the proprietary trading desk of EMC International, a European hedge fund, specialising in privately negotiated derivatives contracts. He is well known on the lecture circuit and has taught financial engineering at Carnegie Mellon in New York and at Oxford and Cambridge.
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The real issue is why the farmer doesn't have the means to plant a crop and the bank does. Making money by loaning money to those who don't have it is self-propagating inefficiency.
If the market for food can't sustain farmers then it won't matter what the banks do.
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Finance adds a LOT to the economy. The basic idea of finance is deciding where the most "return" is - ie, what investments are the most productive. If you gave money to every entrepreneur who came asking, you'd be out of money by the end of the day.
Money has meaning. There is a limited supply, and that means you have to prioritize who gets it. It has to be in limited supply in order for it to have "meaning." In order to motivate people to do things, we give them money, instead of trying to guess that they w
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FAIL. You clearly don't know much about these financial instruments. Most financial products distribute risk in a win-win fashion from the seller to the purchaser, in the same way insurance policies do.
You can't blame it all on the qunats. (Score:5, Informative)
Quants only produce models that act in the way that traders expect, and traders do not want bad news. I've done a small bit of modelling before and you always reach a point where there's this one number that is completely made up, and you kinda set things up so the trader makes the call. In this sense, all these models that everyone talks about are not so much as analysis tools as they are communications tools - you sorta code the insight of the trader as to how he or she thinks the market will move. It's a very human business, not one of a bunch of computers run amok. Quants that say otherwise are just full of themselves...
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Me also thinks that a swim test [google.se] could do some good too - who can walk on the water can also earn handsomely in the world of finance if not then well - life is full or risks I see no reason why is it only the tax payer that has to carry the risk for them.
Re:You can't blame it all on the qunats. (Score:5, Interesting)
...you always reach a point where there's this one number that is completely made up...
***Try a sensitivity analysis using Monte Carlo techniques. That sounds hard but it isn't. Take the parameter that you have doubts about and give it a distribution (Gaussian or rectangular or something) with the mean at your best guess and the std deviation chosen to be big enough to cover the range it might reasonably vary over.
***Use Gaussian if you have an idea of what the parameter probably is but aren't exactly sure, rectangular if you really have no idea. A rectangular distribution says "I have no idea, the parameter could be anywhere within this particular range."
***Then run your analysis a million times with the parameter selected randomly from it's distribution for each run.
***That gives you a stochastic dataset of results. You can run simple stats on that data set to find the mean and std dev of the result value. You will then know how sensitive your results are to your poorly understood input parameter. If your 2-sigma output tells you the expected rate of return on a particular investment varies between -50% to +50% then you will know your model is pretty much useless and you will be doing a better job than the vast majority of professional analysts.
****Monte Carlo is great for those of us who don't care to learn the arcane minutiae of stat math. If you have a working model it takes an hour or two to extend it so you get stochastic results. Note that it's no harder to give a distribution to all your input parameters not just one. In which case you will be doing the kind of work that people who make 500 grand a year do.
Re:You can't blame it all on the qunats. (Score:5, Funny)
***Use Gaussian if you have an idea of what the parameter probably is but aren't exactly sure, rectangular if you really have no idea. A rectangular distribution says "I have no idea, the parameter could be anywhere within this particular range."
Guassian : So, make up three numbers, and assume that the middle number is -really- most likely.
Rectangular : make up two numbers, assuming that any number in between them is good.
In which case you will be doing the kind of work that people who make 500 grand a year do
ROTFLOL. Yes, but they dress more nicely.
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Guassian : So, make up three numbers, and assume that the middle number is -really- most likely.
Which three numbers would those be?
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Which three numbers would those be?
It ought to be pretty obvious to all of us that the middle number is inevitably 42.
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That's a given. It's the other two I'm interested in.
Generally we specify a Gaussian using two numbers: 42 and something called the standard deviation.
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mean, variance, and skew?
Of course, this means you're assuming that your data approximates a normal curve, or that you have a two-way transform (bijective!) that maps it to such a curve.
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If it's got non-zero skew then it's not a Gaussian, it's a skew-normal distribution.
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...you always reach a point where there's this one number that is completely made up...
This is true, but your proposed methods do not eliminate this. Yes, a sensitivity analysis can help. But the only advantage of prior distributions over parameters is that they encourage one to put all their assumptions on the table, whereas frequentist statistical methods (fixing parameters) tend to hide things. Other than that, you will always be subject to your modeling assumptions.
***Try a sensitivity analysis using Monte Carlo techniques. That sounds hard but it isn't.
Yes, it's easy to just "do"; doing it correctly in a way that never gives you false information or gives you acc
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Oh dear! Territoriality!
Didn't mean to trespass on anyone's specialty but the OP set the context of a enthusiast or amateur who is trying to do financial modeling. Isn't it better to get a quantitative statement that "my model is next to useless" rather than just a gut feel? I think so. Monte Carlo is a realistic way for a non-statistician to do that. Yes ok the analyst could also take partial derivatives analytically or numerically and apply a transformation to the input distribution and combine the varian
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***Use Gaussian if you have an idea of what the parameter probably is but aren't exactly sure, rectangular if you really have no idea. A rectangular distribution says "I have no idea, the parameter could be anywhere within this particular range."
Here you have exactly why the quants got things so wrong. If you have an arbitrary random variable with a finite variance, then a law of large numbers will tell you that it converges to a Guassian under repetition. That's what most educated people know.
The problem is: the odds of an arbitrary distribution with no bounds having a finite variance is zero. In finance and economics, we know this, and we make up so many excuses to use Gaussians instead of more general LLN collection families. Gaussians are so tr
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how about reforming pay? (Score:3, Interesting)
How about bringing their pay down in line with the pay of others (engineers and scientists) that do analysis of a similar level of difficulty? This is just a guess, but it would seem increased pay attracts people who want to make more money, not those that are genuinely interested in solving the problems in a field.
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Not entirely true, but certainly an interesting point.
Why not true, well, the noted hedge fund that was founded by a former Nobel-prize winner, then went spectacularly bust even before the current mess, springs to mind. Can't remember the details, but the guy was certainly a (dismal) scientist before he became just another pig at the financial trough.
All power corrupts, etc.?
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Do you mean Long Term Capital Management?
Founding members included Nobel prize winners Myron Scholes and Robert C. Merton. Imploded spectacularly in 1998.
http://en.wikipedia.org/wiki/LTCM [wikipedia.org]
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Ah yes, that's the one. Thank you.
Why should they? (Score:4, Insightful)
It's your money they are paying themselves with, not their own. Until YOU sit up and take notice, then actually DO something you're going to continue to get robbed. But hey, I'm making money off you as well, so don't worry about it "nothing to see here, move along".
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How about bringing their pay down in line with the pay of others (engineers and scientists) that do analysis of a similar level of difficulty?
...because a big wad of money is the only thing that can convince anybody with half a that high finance is interesting and any more worthy of research than (say) looking for mystical patterns in lottery numbers?
Your mission, should you choose to accept it, is to come up with some impressive and impenetrable mathematical diversion that makes a pyramid scheme sound like responsible investment.
Because, lets face it: if you tell your bosses that the way to make money is to identify emerging new products, inve
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How about you put your money where your mouth is an only invest in funds with managers paid only on that basis? Personally, I am going to continue to invest where I expect the best returns, regardless of fund manager wages.
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Hang on... (Score:5, Interesting)
I'm a long way from New York, so someone correct me if I'm wrong[*], but I've always understood the problem to lie more with the people feeding data into the equations, rather than with the equations themselves.
Now, I accept that risk calculations consisted of a great deal of voodoo because, as Taleb [fooledbyrandomness.com] tells us, they tended to ignore 'Black Swan' [wikipedia.org] events (where the 1 in a million catastrophe wasn't going to happen just yet) and saw patterns where only chaos existed, but as I understand it, the core of the problem was simple greed: money-hungry mortgage and securities dealers deliberately feeding bad data into the system.
So-called quants may be decidedly imperfect, but if someone's willing to game the system to make a buck, nothing the quant does can stop it.
If Wilmott doesn't have an answer to that, I fear that his efforts will only obscure the real problem.
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Admittedly without reading TFA, that sounds like his point - that what "quants" should be doing is developing good empirically good heuristic models rather than wanking over what are essentially hypothetical analytical ones based on complete SWAG parameters, where the parameters supplied by salesmen will invevitably be optimistic best case ones (and that's putting it charitably).
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Admittedly without reading TFA, that sounds like his point - that what "quants" should be doing is developing good empirically good heuristic models rather than wanking over what are essentially hypothetical analytical ones based on complete SWAG parameters, where the parameters supplied by salesmen will invevitably be optimistic best case ones (and that's putting it charitably).
That's fine, I'm not disputing that. What I'm suggesting, though, is that no amount of empirical testing will save you from someone who just plain lies with their input data - and that part is out of the hands of the people formulating the equations.
I don't for a second want to suggest that there's not room for improvement on the risk-measurement side. What worries me is that improvements on that side might be touted as a 'solution' to the problem, when the real problem is people willing to lie to make mone
Re:Hang on... (Score:4, Interesting)
You're not wrong, but I think the author referenced in the original post and you are addressed different parts of the whole problem of financial markets. The willingness of financial services salespeople - mortgage brokers, stock brokers, etc - to basically lie their asses off because there's so much money on the line is one problem.
"Quant" analysis of financial markets is, really, another, related problem. The same moral hazard of too much money to make cutting corners worth it exists, but the basic problem here is that many "quant" models are bullshit. Quantitive models for derivative securities can be realistically valued -- if and only if the risk of the underlying primary asset has been properly assessed (along with several other critical assumptions about the marketplace for the security -- but that's the JUDGMENTAL assumption fundamentally inherent in the models.)
Risk assessment is not actually that difficult -- insurance is built on the ability to do risk assessment. The real problem with the current financial problems were that NO ONE KNEW WHAT THE UNDERLYING PRIMARY ASSETS WERE and everyone operated on the belief that Nothing Could Ever Possibly Go Wrong (because no one could prove otherwise, because no one knew what the hell was actually going on).
This is and was every bit as monumentally stupid an assumption in the financial realm as it is engineering, computer programming, science, or any other real-world discipline.
I think what Wilmott is proposing is the development of models that are more reactive to real-world inputs, models that are much more Bayesian in nature in their ability to refine and revise their predictive nature based on actual events.
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As a correction, can you really have missed all of media noise around the equation that brought down Wall Street [wired.com]? That's a pretty big example of a broken model that directly led to a lot of the current chaos, rather than people feeding garbage into a good model.
Picking up nickels in front of a bulldozer (Score:5, Interesting)
The reason why the quants ignore Black Swan events is that they are not financially impacted by them to any real extent. They make their living from making small amounts of money using lots and lots of leverage. But I prefer Buffett's metaphor for this sort of practice: picking up nickels in front of a bulldozer.
As long as "quants" can pick up "nickels" in front of a bulldozer for a few years, they can retire and never have to work again, even if their parent companies (and the companies they borrow from) go bankrupt. Those "nickels" are many millions, their percentage of those "nickels" are still high enough to retire on. Of course, they risk billions in the process.
I suspect the only way to really curb the practice would be to either limit amounts of leverage or cause complete bankruptcy/imprisonment/physical harm somehow to those responsible when the bulldozer (the black swan) eventually comes along. Of course, these laws can't really be applied to those responsible for the GFC. Laws can and probably will be created, and then after a few generations those laws will be repealed as the creation of a few old fuddy duddies who didn't understand whatever "new economy" comes along, and the cycle will repeat.
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It was somewhere in-between. The model as such worked OK, but only if you assUmeD that each individual loan's probabilities acted in a vacuum. The fundamental flaw in the model is that there are some obvious cases where the loans will act in a coordinated manner that were simply (and pointedly) ignored, such as a bubble bursting.
THEN, knowingly using the flawed model, they plugged in increasingly dubious numbers and even fed the practically fictional result back in to the flawed model to get more rosy numbe
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" ... as I understand it, the core of the problem was simple greed: money-hungry mortgage and securities dealers deliberately feeding bad data into the system. ... So-called quants may be decidedly imperfect, but if someone's willing to game the system to make a buck, nothing the quant does can stop it. ..."
Good point, but I think you may be attributing too much of what happened in '07 (in the US) /08 (everywhere) to bad intentions, not that they don't exist. All my research about the mortgage crisis essent
Certificate in Quantative Finance (Score:2)
Who needs people with certificates in oxymorons?
The elephant in the room (Score:5, Insightful)
It wasn't. It isn't.
The reason why we have economic problems is the same old one from the beginning of time -- good old fashioned human greed.
Equations, and new software isn't going to change that. What you need to do is ensure that the people operating systems and processes are ethical and honest. It's really that simple, and also, unfortunately, that difficult.
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The reason why we have economic problems is the same old one from the beginning of time -- good old fashioned human greed.
Agreed. But 50% of that problem is that people have absolutely no idea what money is. It makes taking it away from them dead simple.
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Exactly! The situation in the economy, AIG, failing companies, the layoffs, outsourcing etc. is caused mostly if not entirely by corporate psychopaths which have a natural tendency to angle themselves into leadership position (they are very charismatic and manipulative) from which they can achieve the greatest benefit FOR THEMSELVES at the expense of others. Their total lack of "conscience" makes it so that any damage from their actions for them is unimportant. Even if it affects hundreds of millions of peo
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The reason why we have economic problems is the same old one from the beginning of time -- good old fashioned human greed.
Fine so far.
Equations, and new software isn't going to change that. What you need to do is ensure that the people operating systems and processes are ethical and honest. It's really that simple, and also, unfortunately, that difficult.
So what are we supposed to do in the meantime? My view is that this is just more snake oil. The whole point of markets is to reduce the problems from dishonesty and lack of morals. M view is that the problem is already solved. The phrase is "trust but verify". Auditing, accounting, etc serve the role of making sure people who handle our investments do what we want with them.
The problem with economics is (Score:5, Insightful)
The problem with economics is that is probably more a sociological study than a idealized science.
Economics talks of supply and demand and perfect markets.
Yet we all know the advertising and social herd behavior affect purchases much more than any real needs or demands.
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but people still manage to feed, clothe and house themselves far better in a market like ours where there's a plethora of consumer advertising, than in a planned economy where nearly all advertising is government propaganda.
So the lack of quality advertising caused the product scarcity in the soviet block and the downfall of communism? Please sir, tell me more.
:)
Or by far better do you mean that the women run around in skimpy mini skirts and pushup bras? (Cause that's totally better than frumpy clothes on the eyes).
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substitute "unbiased news" for " goverment advertising" and we appear to be on a path to test your theory
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A corporate economy is a planned economy as well. Every try to buy anything not made in China? It's hard not to give your money to Communists. Oh the irony.
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It really is no different than dealing with other extremely complex systems such as global weather patterns, biological systems, or quantum mechanics. Just as in other sciences predictions are made, but then new data comes in that overturns conventional thinking. The biggest difference is that the successes and failures of economic models are internalized more because of its direct effect on individuals. The
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I'm always amazed when non-economists make glaringly obvious comments as if they criticize the field of economic study altogether. It's as if PhDs around the globe never realized that we common folk have always known.
Or perhaps you don't know enough about economics to realize that these things you point out as criticisms are already handled in economics. Psychology is a huge driver in markets and, not surprisingly, it is central to the study of economics.
The One True Law of Finance (Score:5, Insightful)
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Analogously: if you want to make money at a casino, get a job as a dealer.
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There is this software mentioned in Douglas Adams' Dirk Gently book - name escapes me, "Reason?" - that, given the desired course of action, it spits out persuasive rational for the course, step-by-step. You tell it what you really want to do, and it tells you why it's perfectly logical and sensible to do so.
It's like the whole finance industry OD'ed on such software, and they called it "quants".
Btw, Tim, this doesn't belong under the heading "science".
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You don't understand what a quant *is*. A quant is an employee. He doesn't make the decisions about policy. He tries to guess what's going to happen. His guesses are based on quantitative models, hence the term quant.
The quant probably cares deeply about robust models and statistical significance. This doesn't mean that his manager does, or even understands what the terms mean. The quant is, after all, basically a statistician. (He's not the salesman. That character has some different title.)
The qua
Theocracy of Quants (Score:5, Insightful)
In another time, this would have been called what it is: theocracy, rule by theory.
Oh sure, they can try to be inductive, but there is always that old "correlation doesn't imply causation" gotcha isn't there?
The real solution to this problem with the social sciences was almost addressed by the Protestant culture that founded the US -- the Laboratory of the States -- but the incorporation of the slave states in the 1700s, with the resulting Amendment from Hell, the 14th, in the 1800s killed off that option entirely when "social science" sunk its fangs into the body politc in the 1900s.
"The Union" means everyone is a slave to the theocrats posing as theoreticians.
So now we're running uncontrolled experiments on nonconsenting human subjects in the guise of "public policy" of "liberal democracy" -- tyranny of the majority limited only by a vague laundry list of selectively enforced human rights.
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Theocracy [reference.com], from theokratia
Theory [reference.com], from theoria
I also tried to see if they had the same root in Greek, but theos and thea aren't related as far as I can tell. Please defend that correlation.
I'm not sure how that got you here:
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In your case, your state religion holds faith in the belief that there are no substantial negative social externalities to defining "marriage" in a way that is relatively untested in human history.
That's fine, as long as you allow people who do not share your religious beliefs to have their own human ecologies protected f
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with the resulting Amendment from Hell, the 14th, in the 1800s killed off that option entirely when "social science" sunk its fangs into the body politc in the 1900s.
And what does the 14th Amendment prohibit?
It a
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This has very little to do with social science vs "real" science. Most studies cannot use controlled laboratory experiments to extract meaningful information. The problem is almost always the same: once you bring the everyday scenario that you care about into the lab, it's no longer the everyday scenario you care about. To suggest that the "real" sciences don't have this problem too shows a significant lack of understanding of the "real" sciences.
It is all a question of leverage (Score:2, Insightful)
Hardest part is controling the emotions of greed and fear. When things are working, it is temping to make bigger bets. If the bets are too large, they will wipe out the account, or even fund when the natural and normal losses hit.
Risk Managment often goes out the window during good times.
the formula which distroyed wall street (Score:4, Interesting)
We did test the results... (Score:3, Interesting)
It just cost us tens trillion dollars to figure out that 30 years of free trading investment oriented capitalism wrecks your manufacturing base, leaves your country hopelessly in debt, and all these so-called free enterprise guys bitching about the UAW making 40k a year are actually not so free enterprise after all, when it comes to bailing them out, or protecting their businesses.
Comment removed (Score:4, Interesting)
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I would argue that the tax should be graduated, by how long you held the investment. The question in my mind is should it be a tax on the profits, or on the total investment. I lean towards a tax on the profits.
Say a tax on the profit of 40/sqrt(t) percent where t is measured in days. And any profits don't count as income for the purpose of income tax.
Just Gamble ... That works... (Score:3, Interesting)
Trillion dollar bet [pbs.org] now why did the world trade center get attacked, not once but twice...
I dont get it (Score:2, Insightful)
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I think you're underestimating the complexity and sheer randomness of the thing being modeled.
If you think that "a real mathematician" could handle that problem, then you are probably a part of the problem. Some day some mathematician may handle it. Perhaps. And it will be a century before their theories are sufficiently tested that a good engineer would trust them. And they STILL would fall far short of a mathematical proof.
Einstein said it (paraphrase)"To the extent that mathematical theories apply to
Doh! (Score:2, Funny)
John
what quants know (Score:3, Interesting)
It's not whether you have the right model (Score:3, Insightful)
it's if having a model is right. There's no reason to assume that prior market data contains information that can accurately predict the market in the future.
Models should be used for insight (Score:2)
rather than prediction. All models are both true and false to a greater or lesser extent and must be used with care. All models must be parametrized and those parameters are often uncertain.
In his classic work "Numerical Methods for Scientists and Engineers" Richard W. Hamming put it this way: "The purpose of computing is insight, not numbers."
And if you don't know who he is, shame on you.
If you believe your models, you're fooling yourself.
Its not a science (Score:2)
Wall Street, or more accurately, the financial business, isn't a science. Its about selling product. The models provide what a friend of mine in the biz calls "a compelling story" used to move the product.
This latest fiasco is a perfect demonstration of that principle. Investors (wealthy individuals, pension funds, sovereign wealth funds, etc.) demanded a supply of high return, zero risk paper. Investment banks produced things like mortgage-backed securities, where the risk could be stripped out of one cla
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Has he solved the problem of evolutionary models tending to get stuck in cul-de-sacs then? Somehow I doubt it.
I believe that he could get fairly good results with that approach, but I think that their quality is limited. (How did his fund do over the last year?)
OTOH, given that most financial analysts don't know what they're doing, and an ape (gorilla?) in Chicago beat the average analyst one year, that's not saying much. I'd say that his fund has the advantage of a cheap financial analyst who's no worse
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According to SciAM economics is not a science because it was incoherently built on generalizing physics equations (since invalidated) to a magical concept called "utility" resulting is a system that has no real predictive capacity and is constantly update only after the model proves to be worthless.
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Then whoever wrote that is a moron. It's the equivalent of saying that psychology is not a science because Freud made shit up. There are certainly psychologists who are as full of shit as Freud (likewise with economists) but that does not make psychology not a science.
A subject does not fall into science or not science. Methodology employed is what defines science. Some work in economics is science, and some is not, the same as in psychology.