Hedge funds (HFs) operate in a complex continually evolving competitive global system. Survival and success factors include having strong controls and an outstanding reputation to attract and retain both investors and the brightest people they can find, who are continually being fed better information from their contacts and by their data models, who can execute their decisions rapidly and without error, and who are able continually to test their ideas and to learn from their own and others’ experiences. They don’t have to get it right all the time, but they do have to survive and outperform their competitors (which include less risky investment alternatives) in bad times as well as in good. HFs are filled with the really bright guys (Masters of the Universe - to quote Tom Wolfe - e.g., the twenty year top performing ex mathematician James Simon of Renaissance Capital, although I think he may have taken a tumble over the last twelve months). These bright guys in use their superior modelling and risk management techniques in a highly leveraged way to make regular exceptional profits for their partners and themselves. Even so, only a few practitioners are believed to have begun to use models that take into account irrational and collective behaviour hence the frequent, recent, and regular whining about ‘once in a hundred year’ events and ‘25 sigma deviations’ (e.g. from a Goldman’s HF) but see recent reports in Nature and New Scientist and work by the Econophysics community.
While many of these guys have a strong maths or physics background, the rest of the market (and its regulators and risk managers) is filled with classical traders (typically chartists and people who sense what’s happening by talking to people and looking at patterns on screens) and people who have, since the eighties, been to business school where they were taught Standard Finance Theory (SFT) and its companion the Efficient Market Hypothesis. SFT comes from a world where computation is expensive and short cut assumptions (such as the use of only the first two moments of a distribution - mean and variance - (which was also assumed to be Gaussian) justified the arguments in favour of the random walk approach to investing, the CAPM, use of alpha and beta, adoption of the brilliant Black-Scholes derivative pricing formula based on the stochastic calculus and so on). These arguments have been rattling on for years (and it’s the conventional SFT approach that seems to have been adopted by regulators and risk managers) although there is lots of evidence to suggest that chartists make their money from adopting the opposite viewpoint - which seemingly is related to the market impact of traders themselves. It seems reasonable to suggest that, in the future, leading HFs can gain additional benefit from a smarter approach to modelling that begins to incorporate more of the micro (types of trader/institutional, their states and probable behaviours under various market conditions and its evolution) as well as the existing macro level of modelling.
Following some spectacular disasters in the eighties (and complaints about Japanese banks getting unfair advantages by excessive balance sheet growth for the capital employed), regulators came up with Basel I (and then Basel II) which imposed controls on banks’ risk-adjusted capital ratios but without accompanying it with proper investigation of how they were implemented (hence the drive by most banks to use off-balance sheet vehicles etc. to get round them and grow their earnings). Until recently nobody paid much attention to what happens at a system level when everyone is corralled into adopting the same mechanism - another unintended consequence of standardised regulation is seen in the raging arguments over the implementation of the ‘mark to market’ regime. One of the biggest issues in the current crisis has been a largely non-systemic approach to modelling and measuring risk i.e looking at an individual institution’s portfolio of instruments, their past correlations, risk parameters etc. as if it could, in a crisis, act in isolation when regulation has, of course, created systemic correlations which come into play as soon as systemic risk begins to arise. A smart HF (Paulson’s?) might have anticipated these probable behaviours and taken advantage of them at an early stage.
This lack of insight into complex system behaviour and its opportunities (and threats) may possibly be put down to the prevalence of a quantitative mind set derived from the relatively predictable world of mathematics and physics, which may be fine while the complex financial system is in one of its relatively stable states. When that is no longer the case (Taleb events?), rather more powerful mental models coming from biology (e.g. evolutionary behaviour) and from non-linear complex adaptive systems e.g. climate etc. may be needed; indeed one biological concept - punctuated equilibrium - could fit the current credit crunch very well - the post crunch world will be quite likely to have rather different dynamics (see references to the experiments of Doyne Farmer etc. in The Origins of Wealth).
Some points for today are therefore that survival and success in a complex evolving environment such as the global finance system require the continual development, deployment and effective use of models that reflect more features of reality than those used by competitors. Even though this may not be a guarantee of survival and success, it at least would be an indicator to investors and others of a greater chance of long-term survival. Some aspects of such models require both top-down (macro) and bottom up (micro) modelling. This is one of Simudyne’s key strengths and, when combined with powerful visualisation for better insights and powerful number crunching for ever closer approaches to real-time sensing and response, may be the ultimate differentiator of the HF of the future.
