[OPE-L] quant analysis

From: GERALD LEVY (gerald_a_levy@MSN.COM)
Date: Mon Dec 10 2007 - 09:57:42 EST

> Does not compute: How misfiring quant funds are> distorting the markets> > By Anuj Gangahar> > Published: December 9 2007 19:59 | Last updated:> December 9 2007 19:59> > George Pelham-Box, one of the most influential> statisticians of the 20th century, once remarked that> "essentially all models are wrong, but some are> useful".> > During the global capital markets turbulence of> August, several celebrated quantitative hedge fund> managers were forced to admit that their long-held> investment models were indeed wrong and, at least that> month, not especially useful.> > The performance of these so-called quantitative funds,> which trade using statistical models designed to> identify patterns in financial markets, is> increasingly important because they account for such> huge trading volumes: Tabb Group, the US consultancy,> predicts that by 2010 algorithmic trading, one aspect> of "quant"- based investing, will account for half of> all US equity trading.> > Since August, when many quant funds lost badly, the> specialists who developed these models have been> fine-tuning them to try to iron out the bugs. So far> this has been unsuccessful. According to some> estimates, the amount of money managed by quant funds> has dropped by up to 40 per cent in the past six> months as the drawbacks of the strategy become> apparent.> > Furthermore, concerns that the models have been> playing havoc with markets persist. The tell-tale> signs are sudden movements in US equity indices, most> frequently as a result of a sell-off in the last hour> of trading sessions, which are accompanied by steep> increases in volume as the computer-based trading> programmes sell huge numbers of shares quickly. These> late-day swings have occurred more recently as well,> suggesting that the August pattern was not a one-off.> > Traders themselves are undecided about what causes> such spikes and how exactly they are connected to> quantitative trading. Andrew Wilkinson of Interactive> Brokers, the direct-access broker, said the swings> could be related back to the "Vix", Wall Street's> so-called "fear gauge", which is used by many as the> primary benchmark for equity market volatility. It is> derived from the price of buying protection on the> options market against swings in the S&P index.> > US equity market volatility> > It follows that the more the Vix rallies, the more> fearful traders are, and the less they are prepared to> bet on its direction. Traders simply stop trading the> Vix because they get anxious. Quantitative models> programmed to sell in huge volumes when certain> activity levels in the Vix are reached then kick in,> leading to the sell-offs and volume spikes. "It is as> though the market is playing some game of chicken."> > Other critics offer a more fundamental explanation for> the recent problems endured by quant models  lack of> innovation and unrealistic assumptions. Nassim> Nicholas Taleb, author of the best-selling books The> Black Swan and Fooled by Randomness: The Hidden Role> of Chance in the Markets and Life, go so far as to say> that the very principle of using quantitative models> based on history is bound to fail and should be> abandoned.> > Dimitri Sogoloff, president and chief executive of> Horton Point, a US-based quant hedge fund, believes> the failure to apply cutting-edge scientific> principles to quantitative investment has contributed> to its downfall. He says the lack of recent crossover> of scientific theory to economics and finance has> damaged the ability of quant models to maximise their> predictive power. Mr Sogoloff calls for the> development of so-called "algorithmic alpha> generation"  a product of a truly innovative,> uncorrelated and dynamically adaptable investment> process.> > But Paul Alapat of Amba Research, a Bangalore-based> quant research house that serves many of Wall street's> largest banks and hedge funds, argues that the> mathematical, engineering and physical science> backgrounds of many quant managers and their teams has> contributed to their strategic dilemma. "Engineers and> mathematicians are programmed to think in a very> precise and rules-based way. I think they need to> temper the physics with psychology."> > He draws parallels with the dilemma faced by investors> in property. "Everyone knows there is a housing> bubble, but the problem is that if you take your money> out even a day before it bursts, you lose out. With> quant funds, when market moves are sudden you can be> forced to liquidate valuable positions, and this is> intrinsically difficult to do."> > August was by no means the first time that> quantitative trading strategies have caused trouble.> Long-Term Capital Management, the hedge fund that> famously collapsed in 1998, boasted some of the> founders of the field among its senior executives. But> the explosion of over-the-counter derivatives> products, including those at the heart of the current> credit crisis, has made such strategies more> accessible to a far wider range of investors.> > The rise of trading based on mathematical models has> led to criticism by other investors that these giant> automatons were distorting market behaviour and making> it harder for less sophisticated investors to react to> opportunities in time. Such criticism has died down.> But a deeper question remains. Can they outsmart human> beings  particularly when markets move unexpectedly?> > The lessons of August are stark. Quant managers learnt> that the models  many of which had been years in> development  were flawed. Renaissance Technologies,> perhaps the most celebrated quantitative hedge fund,> and Goldman Sachs, the investment bank, reported steep> losses.> > One quantitative analyst says: "August demonstrates> what we already suspected  that quant models cannot,> whatever their complexity or relevance, adapt to> brutal changes in market conditions. Quant funds hire> physicists, mathematicians, astronomers and computer> scientists and they typically know nothing about> finance."> > Companies also learnt that whatever parameters they> had been using to make their predictions, a host of> their peers and rivals had been barking up precisely> the same tree, meaning models they thought were exotic> and cutting-edge were in fact mundane. This was partly> because the pool of quantitative investment> professionals who are truly at the cutting edge is> still very small. Mr Alapat says: "If a quant fund has> a proprietary model that is successful, it gets> mimicked very quickly."> > Quant funds go to great lengths to hang on to their> specialists, knowing that the second they depart they> will use the same strategies at a competing firm,> effectively diluting returns by chasing the same> opportunities to make money.> > Whatever its flaws, however, quantitative trading> still has many advantages over its chief competitor,> the human brain. From the individual who holds on to> losing stocks for too long to overconfident money> managers who mistakenly think they can predict> financial trends, human nature is capable of placing> bad bets time and time again. Psychology and raw> emotion often rules the stock market.> > Quantitative model makers point out that their> products have had notable successes. Renaissance> Technologies, for example, made average annual returns> of 38 per cent between 1989 and last year.> > But even when a trader is using a mathematical model> rather than gut instinct, the question of when to use> a mathematical model still comes down to judgment. It> is this decision, particularly when markets are> volatile, that can be the most fraught with difficulty> and potentially most damaging to quantitative> strategies.> > The use of computer programs based on proprietary> algorithms  to which even their investors are not> privy  to make day-to-day investment decisions is> often known as black-box investment. A "black box"> contains formulas and calculations that the user does> not see nor need to know or even understand in order> to use the system.> > "Black box models have a limited shelf life," says> William Strazzullo, chief market strategist at Bell> Curve Trading. "When they work it's good but over time> relationships [between the factors included in the> model] inevitably break down."> > "Markets are in a state of constant flux and there is> a danger that when the model breaks down, it's too> late," says Mr Strazzullo.> > Some participants are trying to adapt black box models> to take into account factors that are not strictly> financial  such as management behaviour and sometimes> even press coverage and its impact  in an effort to> remain a step ahead of the quant crowd.> > One trader says that in his view, August was a special> situation that could not be used to draw the> pessimistic conclusion that quant models are fatally> flawed. "In August you had a bunch of hedge funds that> were essentially market-neutral and which had huge> positions, which then developed funding problems due> to the situation in credit."> > Last orders> > Of the most recent spikes in volatility, Gary Ardell,> head of financial engineering at BNY ConvergEx Group,> says: "It looks like August again. People are selling> the crown jewels. All the things that are supposed to> move higher are falling. We are generally approaching> the open of the trading day from a neutral position> and by 9:45 decide which kind of trading model should> be used."> > In other words, many quant funds have been forced to> sell some of their most successful positions in order> to raise money, as they are unable to borrow in credit> markets. For other investors who run their models over> a longer time horizon, trading since August has been> fraught with pitfalls. Models need constant> fine-tuning in order to account for shifts in the> investment landscape.> > Daniel Stroock, a professor at MIT, said in a recent> study that the role of quants in the market was> analogous to the role batfish play in keeping coral> reefs tidy.> > Just as batfish do not construct the reef but are> essential to its health, quants do not create the> structure financial markets depend on but do preserve> the conditions that make markets function. So, he> argues, it would be misleading to suggest in any way> that quants were responsible for this summer's> meltdown in the subprime-mortgage market or for the> broader troubles that followed.> > The functioning of financial markets, he says, relies> on the general acceptance of certain assumptions. One> of the most important is the so-called "no arbitrage"> assumption: that if there is a "free lunch" to be had,> someone will eat it. If a trader can make risk-free> profits by buying one security and selling another, he> will do it. If a computer programme can do this> faster, then it will get the lunch.> > Thus, he says, it is essential that the assumption be> correct, and an important role of the quant is to make> sure that it is. By scrutinising huge volumes of> financial data, he says, quantitative funds spot> arbitrage opportunities and alert their fund managers> before others have a chance to act.> > Whether quants have a future, however, depends on how> far investors burnt by the losses of August and the> subsequent flight to less risky-looking assets can> share Mr Stroock's confidence.> > > Additional reporting by Michael Mackenzie> > Copyright The Financial Times Limited 2007

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