Risk means different things to different people; its essence is embedded in the eye of the beholder. While the concept of fate has long been established, our understanding of risk is still quite new in historic terms. The gradual removal of religious shackles and superstitions during the Renaissance helped shed the belief that destiny is mapped out and unchangeable. Curiosity, freedom of thought and experimentation meant that it was only a matter of time before a greater understanding of risk was sought out in the early modern era. While the initial impetus was provided by Italian thinkers, it was the French, with their love of mathematics, who got their teeth into the subject. French mathematicians Blaise Pascal and Pierre de Fermat founded decision theory in the mid-17th century, an impressive advance in understanding. Pascal took probability theory and linked it together with decision making under certain conditions. Pascal’s Wager is classic probability insight here. Faced with the decision as to whether to believe in God or not, the rational choice is to live as though God exists. Should he exist, the player stands to receive infinite gains (as represented by an eternity in heaven). The downside to taking this approach only to find out he doesn’t is finite, sacrificing some short term earthly pleasures, whereas the flipside is to face an eternity in hell.
Risk is uncertainty
Risk, ultimately, is about the future, and the inherent uncertainty built in to what lies ahead. In that sense, it cannot be about the past – unless of course you believe the past can predict the future. Yet regardless of this we have devised methods that exploit numbers to analyse what happened in the past. Extrapolating from the past can help inform the probability of future events; however, it can only go so far in the real world. Uncertainty cannot be eliminated as the limits to our knowledge and understanding are huge. Measuring risk is therefore fraught with danger. Scientific proxies like volatility and standard deviation metrics are helpful, but they are just that, proxies.
Financial markets are not an easy model because they are constantly in flux, and go through both cyclical and structural changes. This dynamism occurs because it is constructed and driven by humans. Yet the mathematics behind our understanding of it, for the most part, remains both backward looking and static. A practical reminder of these limitations was served up in the form of the biggest financial crisis in a generation.
The sheer scale of the task in understanding dynamic systems, like the weather, helps explain why no revolutionary changes have been made. Yet encouraging progress is being made in certain areas. Physics professor Didier Sornette has led us into the realm of big data. One approach to dealing with the problem of vastness, intelligibility and uncertainties is, to some degree, to ignore it and to focus on what you believe to be really important. As data visualisation techniques improve, contextualising and sorting such large swathes of data should become far easier.
Portfolio risk
The core types of risk that permeate the world of investment are risks that revolve, within and around credit, liquidity, markets, compliance, regulation, politics, legal, catastrophes, and reputation. Making the distinction between more introspective portfolio risk and the broader risks that affect the financial and economic system is important because, while they are inextricably linked, what really matters to a client is whether or not their investment portfolio performs as expected and allows them to enjoy the benefits that those expected returns offer to their lives. Much of the investment industry is unhealthily wedded to the concept of benchmarks. They do serve a purpose in terms of measurement of performance (relative to peers or a pre-determined index). However, more often than not clients are more interested in absolute outcomes as opposed to some relative measure – setting a benchmark may or may not aid the delivery of these absolute returns. Furthermore, the existence of benchmarks may encourage fund managers to hedge their career risk by herding to the consensus – as Keynes said (General Theory – 1936, pp.157-158) “worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally.” The evolution of the investment industry is relatively recent and has occurred with the ‘efficient market hypothesis’ at its core. Intellectual giants such as Eugene Fama and Harry Markowitz put forward the notion that equities are always correctly priced because every market participant has all relevant and available information at their fingertips. Most practitioners would raise an eyebrow at these assertions. Yet that didn’t stop others building out the concept further with more mathematical modeling, such as Black Scholes. As is so often the case, the great minds who create such complex and sophisticated tools know full well their limitations – it is often perhaps the lesser minds that follow trailing behind, who fail to grasp the fallacies and plough on as if the outputs are gospel. It is no surprise that since the 2007/08 financial crisis these models, based on assumptions divorced from reality, have come under increasing scrutiny. A renewed hunger to build more accurate representations of the financial world has surfaced. And while there is still a way to go, the construction of more useful portfolios for clients is encouraging. The deviation away from traditionally ‘balanced’ equity and bonds, and the use of alternative assets such as property, infrastructure, private equity and hedge funds, has undoubtedly resulted in more effective risk-adjusted returns for clients as the benefits of holding uncorrelated assets has kicked in. We should, however, remain mindful that the benefits of diversification do tend to fade in times of stress – as correlations converge. And so, of course, portfolio diversification is a good thing; we should take that as read. But diversification by buying poor quality assets is a bad thing, potentially taking investors along the road to penury.
A parting word
Risk is always going to maintain an air of mystery. Its elusive tendencies will unlikely ever be fully conquered. We should remember this when presented with spuriously accurate risk numbers to two decimal places. However, by thinking more intelligently about it, accepting our cognitive limitations when trying to tame it, we can make progress, by making better policy decisions at the macroeconomic level and better investment decisions at the portfolio level. And so, as we wait for the academics and industry professionals to invent the ‘mathematics of surprise’, we must continue to adopt a practical and thoughtful approach to dealing with risk.
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