Tag Archives: Risk Capacity

Leverage

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When I was *much* younger and a relative neophyte on Wall Street, the “carry trade” was a new trader staple.  It arose out of a 1985 agreement whereby the U.S., Japan, the U.K., France and West Germany sought to lower the value of the U.S. Dollar against the currencies of the other nations. It almost seems quaint now.

Basically, the trade involves a play on currency yields. It consists of borrowing a low-yielding currency (in those days, Japanese Yen) and investing in a currency that is offering a higher yield (in those days — hard as it may be to believe now — typically U.S. dollars).  The yield difference provided the profit (after costs). Currency risk provided the largest potential problem. 

Most traders saw this as easy money with low risk and “levered up” to give the trade some real juice since the initial transaction itself only yielded relatively small profits. Leverage was needed to accentuate and accelerate returns.  Thus a feedback loop of sorts was created and kept repeating, with leverage extending to ten times and often much higher.  This trade was a consistent and enormous success for a number of years. 

In 1995, five years after the Nikkei 225/property bubble had spectacularly burst, the Kobe earthquake hit. Japanese interest rates (which had been inching down since 1991) fell below 1%, and the Yen became a funding currency for various forms of speculation all over the world.  At that point, the Yen had been weak and kept on depreciating sharply for several months relative to the USD. Still mixed and weak economic data were coming out of Japan and short-term interest rates there were 0.25 percent while they were closer to 5.5 percent stateside.  Massive carry trade volume using the Yen led to sharp increases in leveraged positions by traders who had been shorting the Yen to make the carry trade bet.

But then the Yen started to appreciate – by 9 percent in one month alone – largely due to Russian debt problems — and the spread between Japanese and U.S. yields tightened dramatically in a flight to quality and liquidity.  One piece of good news from Japan (the Japanese government offered a plan to recapitalize its problem banks) and in a matter of 72 hours the Yen had appreciated by another 12 percent. Julian Robertson’s Tiger Fund lost $2 billion dollars in 48 hours on the Yen unraveling.  LTCM lost even more; it was forced to restructure its operations and was bailed out via a deal brokered by the Fed (and its positions eventually wound down over a year or so).

After years of success, the carry trade death spiral was quick and violent (only to be resurrected in various forms thereafter).  Ongoing carry trades unravelled as quickly as the Yen rallied; margin calls were triggered, levered positions went belly up and the entire financial system went into seizure. The Fed was forced to cut the Fed Funds rate in between meetings by 75bp (in spite of still solid domestic GDP growth) in order to avoid a financial meltdown, a collapse of U.S. financial markets and a global recession.  And, as always, the principals in the trades kept asserting that the models were fine, it was the markets (and the world) that were wrong.

Leverage — like excessive speeds on the highway — kills and often kills quickly.  Whether that leverage is undertaken by hedge funds (as in the example above, even though LTCM resisted being labelled a hedge fund), Wall Street banks (think Lehman Brothers) or retail consumers (think people with multiple investment properties in 2008), troubled times are very difficult to manage by firms and people laden with excessive debt, especially when the items purchased using the leverage (and acting as collateral) are declining swiftly in value.  And the more illiquid they are, the worse the problems become.   

It should be obvious but it bears repeating — relatively innocuous and seemingly low-risk trades can become death-traps in a hurry on account of leverage.  If you operate with borrowed money, you lack the luxury of trying to wait until prices “correct” if (when!) the lender demands his pound of flesh.  Even so, it’s a useful (and sometimes necessary) tool.  Use leverage wisely and never underestimate its risks. 

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My series on risk is available at these links:

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Complexity Risk Management — a lot like Jazz

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At the most basic level, complexity risk encompasses situations such as the Lehman Brothers collapse, where the management of a major investment bank did not fully understand the risks they are taking or the consequences of those risks or the Madoff scandal, where private investors did not understand their investments.  Situations such as these are a major problem in and for our industry.  But complexity risk today goes much deeper still.

Complexity risk has been brought to the fore most recently on account of several well-publicized market blow-ups relating to high-frequency trading. In late September, the Senate banking committee held a hearing on the issue and the Securities and Exchange Commission got into the act with a recent panel discussion.  Even Wall Street veterans have begun to question whether a market trading at warp speed is a good thing. In this context, we’re not merely talking about a distinction between trading and investing.  Instead, we’re rewarding trading to the exclusion of investing.  As Roger Lowenstein argues, “[i]f market signals are based on algorithms that become outmoded in a nanosecond, we end up with empty factories and useless investment.”

In exchange for providing the markets with more liquidity than they need, high-frequency trading has created a complexity risk problem of potentially enormous scale by subjecting markets to the much increased likelihood of more destabilizing crashes.  Moreover, prices may come to reflect (quite literally) the value judgments not of investors, but of high-speed algorithms. As Lowenstein points out, several publicly traded companies lost nearly $1 trillion of market value — albeit briefly — in a so-called “flash crash” in May of 2010 that the SEC said was triggered by a single firm using algorithms rapidly to sell 75,000 futures contracts.

Lawmakers in several countries are proposing to address this problem by imposing new restrictions on high-speed traders.  They are also considering modularity-enhancing options like the creation of shutdown switches that might be able to cordon off damage in a crisis. Lowenstein further argues that the better way to discourage this short-term market myopia is to take a page from anti-tobacco efforts: let high taxes discourage the undesirable behavior.  But the overall risks of complexity are broader and deeper still, as the 2008-2009 financial crisis aptly demonstrated.

As CalTech system scientist John C. Doyle has established, a wide variety of systems, both natural and man-made, are robust in the face of large changes in environment and system components, and yet they are still potentially fragile to even small perturbations. Such “robust yet fragile” networks are ubiquitous in our world. They are “robust” in that small shocks do not typically spread very far in the system.  However, since they are “fragile,” a tiny adverse event can bring down the entire system.

Such systems are efficiently fine-tuned and thus appear almost boringly robust despite the potential for major perturbations and fluctuations. As a consequence, systemic complexity and fragility are largely hidden, often revealed only by rare catastrophic failures.  Modern institutions and technologies facilitate robustness and efficiency, but they also enable catastrophes on a scale unimaginable without them — from network and market crashes to war, epidemics, and global warming.

Chaos, criticality, and related ideas from statistical physics have inspired a completely different view of complexity in that behaviors that are typically unpredictable and fragile “emerge” from simple and usually random interconnections among homogeneous components.  Since complexity science demonstrates that financial markets are unpredictable and fragile, the risks to investors and to the markets as a whole are both obvious and enormous.

While there are great benefits to complexity as it empowers globalization, interconnectedness and technological advance, there are unforeseen and sometimes unforeseeable yet potentially catastrophic consequences too.   Higher and higher levels of complexity mean that we live in an age of inherent and, according to the science, increasing surprise and disruption. The rare (but growing less rare) high impact, low-frequency disruptions are simply part of systems that are increasingly fragile and susceptible to sudden, spectacular collapse.  John Casti’s X-Events even argues that today’s highly advanced and overly complex systems and societies have grown highly vulnerable to extreme events that may ultimately result in the collapse of our civilization.  Examples could include a global internet or technological collapse, transnational economic meltdown or even robot uprisings.

We are thus almost literally (modifying Andrew Zolli‘s telling phrase slightly) tap dancing in a minefield — we don’t quite know when our next step is going to result in a monumental explosion.  One’s goal, therefore, must be first to survive and then to thrive in that sort of disruptive and dangerous environment — in other words, to be resilient.

Unfortunately, while today’s complex systems are generally quite good at dealing with anticipated forms of uncertainty and disruption (Donald Rumsfeld’s “known unknowns”), which makes them highly efficient, it is the unanticipated “unknown unknowns” that are so vexing and problematic.  Real crises happen when and where we least expect them and strike at the heart of a system. Thus the Lehman Brothers collapse wasn’t a problem of being too big to fail, but rather a function of being too central to fail without enormous cascading impacts. Its risk models were wildly inadequate yet considered utterly reliable — a classic unknown unknown.

As the complexity of a system grows, both the sources and severity of possible disruptions increases.  Resilient systems are not perfect or even perfectly efficient.  Indeed, regular modest failures are essential to many forms of resilience (adjusting and adapting are crucial to success).  In this context, then, efficiency can be a net negative and redundancy a major positive. Hedges matter.  Learning from mistakes is vital.

Meanwhile, the size required for potential ‘triggering events’ decreases in an increasingly complex world.  Thus it may only take a tiny event, at the wrong place or at the wrong time, to spark a calamity.  While the chances of any of these possibilities actually happening is individually remote, our general susceptibility to that type of catastrophe is surprisingly real.

Thus those who would attempt to manage risk in the aggregate and complexity risk specifically must take these fundamental features of network systems into account.  Sadly, this field is much more descriptive than prescriptive.  Zolli again:  “Resilience is often found in having just the `right’ amounts of [certain] properties – being connected, but not too connected; being diverse, but not too diverse; being able to couple with other systems when it helps, but also being able to decouple from them when it hurts. The picture that emerges is one of strategic looseness, an intentional stance of both fluidity (of strategies, structures, and actions) and fixedness (of values and purpose).”

This “Goldilocks” approach to complexity — everything needs to be at some relatively undefined “just right” level — makes it extremely difficult to try to manage.  There is simply no definitive blueprint for managing such risks.  But there are some patterns that are helpful.  For example, diversity, modularity (a problem with one component or the elimination of one outlet won’t scuttle the entire system), proximity, redundancy, flexibility and adaptability are all extremely valuable.  Within interpersonal systems, diversity, flexibility and mutual trust are vital to resilience and success.  So is decentralization and shared control.

Fortunately, diversification is already a well-established virtue in our world, even though its value is often honored only in the breach.   In this context, resilient diversity means fluidity of structures, strategies and approaches but it does not extend to goals, values and core methodologies.  An effective risk mitigation and management approach is thus much like playing jazz.  One must be able to improvise often and well but within an established and consistent structure.

Ultimately, reckoning with risk requires a firm grip on reality – both our inherent optimism and our inherent loss aversion must be tempered.  When things are not going well, until truth is out on the table (via transparency and trust), no matter how ugly, we are not in a position to deal with the problems at hand.  In the event of an unforeseen melt-down in a position, portfolio, market, or even a system-wide collapse, how prepared are you?  And no matter how unprepared you turn out to be (remember those pesky unknown unknowns), have you thought through how you can go about getting back on track after the calamity?

If you haven’t considered these questions carefully and systematically, I reckon that the chances of your getting into a whole heap of trouble, at some point at least, are surprisingly high.

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I dealt with these issues peripherally here.  My entire series on risk is available at these links:

Reckoning with Risk (8): Risk Capacity, Appetite, Tolerance and Perception

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A major concern of every investor relates to taking on risk.  We generally want to avoid it (in the sense of losing money) but we’re also ticked off when a risk-averse strategy underperforms.  What we’re really talking about is risk capacity, which (as I have noted before) is largely a joining of risk appetite and risk tolerance.  Unfortunately, the theoretical and the practical are often disconnected at precisely this point.

Risk appetite is about the pursuit of risk (in the probabilistic sense rather than in the sense of losing money). If I am at or near retirement and have saved what I need for it, my risk appetite should be small.  If I am 22 and likely have a long investment life ahead of me, it should be far larger.  Every investor should regularly and routinely ask what success will look like and then go about figuring out how best to get there.  That process will necessarily include a careful analysis of risk appetite (or need).  It begins with the neglected art/science of estimating expected returns for prospective portfolios.

Risk Tolerance relates to the degree of uncertainty that an investor can handle with respect to a negative change in the value of his or her portfolio.  Sadly, it seems as though our risk tolerance is highest when things are going best and lowest when things are at their worst.  I don’t need liquidity (until I do).  Ascertaining risk capacity requires an analysis and a merger of appetite and tolerance.  It is a function of capability (how much – as objectively as possible – you can carry) and maturity (your ability to cope with risk).  This maturity relates to emotions (will you panic and sell at the wrong time?) but also to control (how do you deal with uncertain outcomes?).

However, it seems obvious that these determinations are anything but static.  Every financial professional has had clients love a lower risk approach when they are afraid but decide it’s horrific when the market is hot (even for relatively brief periods) and the lower risk strategy underperforms.  Similarly, an extremely aggressive investor can suddenly decide that the aggressive strategy wasn’t right after all when the market heads south.  Clients will even look at individual securities or strategies within a diversified whole and criticize specific losses despite the overall portfolio’s solid performance in line with reasonable expectations.

But wait a minute.

As Michael Kitces has pointed out, a recent study using FinaMetrica data joins a growing body of research suggesting that client risk tolerance  is actually remarkably stable, and that what’s changing through the market cycle is not risk tolerance, but instead risk perception. This research discovered – with a large class of investors examined both before and after the 2008-09 financial crisis and carefully controlled to eliminate other factors – that only a very small number of investors reduce their risk tolerances during crisis.  That result is consistent with other research and FinaMetrica’s own analysis showing that despite much volatility within the world markets over the past 12 years, average risk tolerance has remained remarkably stable. The most significant implication of this research is that financial professionals struggling with unstable client investment behaviors should focus more on managing risk perception, rather than blaming changing client risk tolerances.  In other words, something constructive can be done about the problem.

Kitces argues that investor behavior is driven by two primary factors:  risk tolerance and risk perception. As he sees it, risk tolerance determines whether one is willing to take a specified risk in pursuit of a potential reward while risk perception is one’s subjective evaluation of whether a particular investment is consistent with that risk tolerance. 

On account of recency bias, we tend to focus excessively on what has happened recently to the exclusion of the broader context and to project the recent into the indefinite future.  Thus an investor who has suffered a significant drawdown (but one that ought to be within the expressed risk tolerance) will look to bail not because his or her risk tolerance has changed, but because s/he sees (via risk perception) the experienced drawdown as the beginning of an intolerable loss.

As Kitces argues (very persuasively), this will be a distinction without a difference to investors who have not carefully, comprehensively and objectively measured their risk tolerance.  Otherwise, it will be all but impossible to tell if the problem is that the risk tolerance was poorly assessed or that risk perception is the problem.  For advisors, managing risk perception means providing the ongoing communication (lots of it) necessary to allow the client correctly to perceive his or her investment risk and thus fight the natural tendency to over- and under-estimate the risks throughout the market cycle.  It must begin with realistic expectations before and as the portfolio strategy is undertaken and must continue throughout the management (both portfolio and client management) process. 

Nobody is happy about losing money.  But if expectations are in line with the portfolios created, drawdowns will be much more tolerable.  The key for financial professionals is to communicate, communicate and communicate some more about goals, objectives, risks, rewards and expectations.