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Quant, Quanto, Quando
According to this study by the CFA Institute, three quarters of hedge fund managers think the outlook for computer-driven strategies is troubled. (HT Fintag).
The survey’s findings do reflect part of the long-held view that quant strategies have had their day. If pre-crunch those strategies were plagued by diminishing returns and a crowded marketplace, post-crunch they’ve been plagued by big losses, investor withdrawals and a few spectacular collapses.
By and large, it’s inadequate models that are blamed. But, as the CFA study’s authors write:
The conclusion of this discussion is that what appears to be model breakdown may, in reality, be nothing more than the inevitable fat-tailed behavior of model errors.
The models, in other words, are perfectly accurate, but inadequately applied.
Here, of course, it’s probably worth making a distinction: broken and inaccurate modelling has characterised a lot of the travails in the markets over recent months, but not always at the hedge funds.
Rating agencies in particular, were using some pretty outdated assumptions. In rating products such as leveraged super seniors, CDOs squared, or market value structures like CPPIs and CPDOs, rating agencies were using spread modelling techniques several years out of date; none of which had tails fat enough. Given how critical market liquidity was to all those structures, it becomes meaningless to talk of what happened as a “25-sigma event”.
Anyway, as the studies authors conclude, quant funds are most certainly not dead. The trick for them going forward will be to move beyond linear models based around Gaussian (or normal) distributions and instead trying to think of models which can move from “normal” market environments to “fat tail” environments dynamically and adjust investment strategies accordingly:
Eliminating the tails from noise would be an exceedingly difficult exercise. One would need a model that can predict the shift from a normal regime to a more risky regime in which noise can be fat tailed. Whether the necessary data are available is problematic.
Related links
Quants adapting to a Darwinian analysis - FTfm
Quando, quando, quando - for the uninitiated
Market Timing System using Grammatical Evolution - Alea
Predicting the credit crisis - FT Alphaville
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Danger of trhowing the baby out with the bath water here - models have been proven to work but as any scientist/physicist will tell you maths is used in the real world to produce a theory to explain events. A theory is not static - it remains until another better theory comes along. Newton replaced by Einstein - but ultimately Newton works pretty well for approximations in most circumstances - just not when you start approaching the speed of light. Models need to be correctly applied (i.e. accurate inputs not the rubbish that was fed in last year) to suitable scenarios and only applied where appropriate. In my view quantative analysis will evolve (probably to become even more complicated) following the events of last year, it will not disappear.
You mean there is no free lunch, even if you study maths?
or you mean Soros’ reflexivity has validity…
It appears that most all ’successful’ strategies in this recent bubble were really just leveraging negative skew, relying upon ever-increasing supplies of credit to get increasing growth rates. The scale of the scheme is the most interesting dimension, and I believe it is at least a 25 year cycle that is ending.
Are we talking about (1) quant trading strategies or (2) quant risk models here?
Regarding (1) many of the strategies are simler than their owner’s would like to make out. Some will suffer, some won’t. Plenty of research will go into the next wave of models. The only generalisation I would make is that having “quant” or computer driven in your marketing material might become less fashionable.
Regarding (2), sure the models used for determining risk during this latest meltdown have failed a test. However you’ve got to question the mentality of the people making the company-wide decisions rather than the egg-heads. Remember such drivel as “there has never been a nationwide decline in the median house price since the great depression.” If that was good enough for the CEO’s then they deserve to be taking the writedowns.
Like Chinese whispers the message conveyed by a financial model gets distorted as its use is expanded to price each new generation of product. In addition, the understanding of the model is less well understood the further up the management hierachy you go. So, when real events start to contradict the model’s forcast the first reaction is inaction, as no one can believe that the model is dead. And then management acts like the audience at a performance of Peter Pan, “If we clap loud enough Tinkerbell (the model) will live” Only much later as the losses mount does reality start to sink in and management realises that the belief that models can fly is just a fairy-tale.
Get the modelers out of finance. This is ridiculous. Risk is “felt”, managed and taken on by businessmen not by kids playing with formulae learned in a classroom or an astronomer applying his models for counting stars to pricing. A model is only there to be one of the tools of decision making; The day 2 bits modelers were anointed as geniuses (because older financiers could not admit they do not understand the science behind it is the day) and their “findings rendered the sole basis of investment decisions is the day finance signed its death wish. Models as currently constructed perform acceptably when all goes well and you do not need umpteen derivative models to make money in that scenario. All are so busy eliminating noise that they develop models that are completely insulated from reality. Good old and eternal business sense and proximity to direct clients and general economy beat all Guassian and non-Guassian models. An underpaid tax accountant will tell you more about what will happen than any snotty over paid kid and modeler living in lala land.
[…] Meanwhile FT Alphaville reports on a study by the CFA Institute, indicating that three quarters of hedge fund managers think the outlook for computer-driven strategies is troubled. “The survey’s findings do reflect part of the long-held view that quant strategies have had their day. If pre-crunch those strategies were plagued by diminishing returns and a crowded marketplace, post-crunch they’ve been plagued by big losses, investor withdrawals and a few spectacular collapses.” […]
It’s all well and good to talk about moving to “fat-tailed” distributions: this doesn’t recognise the reality that the reason Gaussian distributions have remained so popular is that the mathematics becomes much more difficult when you use non-Gaussian distributions.
In particular, using suitable non-Gaussian distributions requires you to understand that the earlier promises you made about the extent to which you can manage risk are just not possible.
What they are useful for it to help you understand that your investments are quite risky and that you should expect large deviations from your central expected return.
Predictors of what?
Death to the VAR and away with Monte Carlo. Pricing models as currently known have miserably failed. In fact thet simplest forward/futures rate calculations are far better predictors than BS (adequate acronym) models and other Ito’s lema based formulae. Quants as currently used are reminiscent of Enron and its insistance that their startegies were too sophistivcated for laymen to understand; And people fell for this stuff. Wake up and smell the mint tea.