Fight Back Against Black Swan Fatigue

In today’s lean, just-in-time, and over optimized world, it’s not uncommon for executives roll their eyes when the term “Black Swan” is brought up in risk management discussions.  That’s because even though preparing for extreme events makes logical sense, there’s also a cost associated with redundancy and robust disaster planning.  In addition, no one is ever judged a hero for saving the company from what never (or is never supposed to) happen.  Business executives must fight back against Black Swan fatigue, because in today’s interconnected and highly correlated world, the next extreme event could be the one that shoves your company off a cliff.

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When it comes to preparing for low probability but high impact events (i.e. Black Swans), the sad truth is most business executives will do nothing.  Why? Nassim Taleb, author of the Black Swan, explains; “It is difficult to motivate people in the prevention of Black Swans. Prevention is not easily perceived, measured, or rewarded; it is generally a silent and thankless activity. History books do not account forheroic preventive measures.”

Taleb is right. No one will be labeled a hero for keeping extra inventory on hand. No one will be characterized a hero for divvying orders among various suppliers just in case the favored and most cost effective supplier goes belly up. And spending money on strategy and risk management consultants to disaster and scenario plan for worst case developments? Forget about it. These are all just costs, and cannot be afforded in today’s bottom line economy, right?

Someone wise once said that risk management is much like insurance. You hate to spend money on it, but you’re darn glad you have it when all hell breaks loose.

But wait you say, don’t most business executives plan for disaster? Perhaps, but there’s a difference between hiring a consultant to produce a disaster planning report which promptly collects dust, and actually preparing for and assuming extreme events will occur as part of your overall business plan.  And even when managers believe they’re prepared for worst case events, sometimes it’s not enough—with potentially horrific consequences.

As detailed in the March 27, 2011 issue of the Financial Times, executives at Tepco’s Fukushima Daiichi plant were prepared for earthquake. In fact, they were also prepared for tsunami—having built a seawall 20 feet tall. What they did not expect is that the March 11, magnitude 9.0 earthquake would cause a tsunami wave 40 feet tall! The tsunami promptly washed away the sea wall and also the diesel powered generators cooling the spent nuclear fuel rods housed at Fukushima.

Executives at Fukushima had planned for disaster. They had built a 20 foot seawall. They had redundancy with backup generators in case the cooling system failed. And the nuclear plant powered down once the 9.0 magnitude earthquake hit. Everything worked as planned. But they were not prepared for the “unthinkable” extreme event.

Predictive modeling based on historical data will only take you so far. Even extrapolating with Bayes isn’t going to be of much use for “unknown, unknowns”.  As business managers we must fight Black Swan lethargy, especially when all oars in the boat are rolling towards lands of “optimization” and “cost effectiveness”. As managers, we must continue to sound the alarm, even though probability of the extreme event is of the smallest percentages.

Play up the risk of future Black Swans and then prepare for the extreme event. Here’s to hoping you’ll never be proved right.

Blasphemy? Quantitative Approaches Don’t Always Work Best

Ray Dalio’s Bridgewater Associates hedge fund, Pure Alpha II, is up 25% in year that hasn’t been kind to competitors. How did he do it?  Hint: it wasn’t through a purely quantitative approach.

Hedge fund manager Ray Dalio is a rare breed in financial investing. Dalio is known as a “macro” investor, or someone who takes a “big picture” approach to investing as opposed to math whiz “quants” who rely on quantitative/numerical techniques.

The July 25, 2011 issue of New Yorker, highlights Dalio’s investment methods as he looks for hidden profit opportunities; “(Dalio) spends most of his time trying to figure out how economic and financial events fit together in a coherent framework. His constant goal (is to) understand how the economic machine works.”

Dalio isn’t concerned with the nuts and bolts of companies. He doesn’t want to scrub the bowels of the machine to see how it works. And he shuns frequency based probability techniques used by financial quants to estimate whether stocks will move up or down in penny increments.

While other hedge funds and investment banks control risks with sophisticated Value at Risk (VAR) models and use of derivatives, Dalio suggests that studying the big picture is a better approach. “Risky things are not in themselves risky if you understand them and control them,” he says. Instead of statistical distributions, it appears Dalio is more focused on what he calls the “probability of knowing”.  He never places all his eggs in one basket, especially because he understands that a complex and global world can shift course in a moment’s notice.

This is not to say, however, that Dalio doesn’t use analytical techniques. Of course, Dalio crunches the numbers and uses computers for much of his work. But he’s not driven by making money with techniques such as high frequency trading, where super computers trade liquid instruments at near light speed. Instead, his algorithmic trading models are written with his investment philosophy of components and relationships in mind, and help supplement decision making for broad and big bets.

Dalio is doing much more than guesswork here, but it’s a different kind of analysis based on a rules based framework codified in thirty years of investment experience. “It’s the commitment to systematic analysis and investment (within the boundaries of his mental framework) that makes the difference,” he says.

The contrast between Dalio’s approach and those of data driven quants couldn’t be clearer. Quants model investment decisions based on math and use computers to move volumes of liquid securities thus making money on tight spreads. Dalio seeks to understand “larger underlying forces”, interrelationships and historical context. His main advantages appear to be a “top down” rather than “bottom up” approach to investing and the pursuit of a longer time line for decision making.

In 2008 during the worst of the Great Recession, Dalio was up 9.5%, in 2010 the fund was up 45%, and Dalio’s $122B fund is up 25% this year (2011) based on macro bets for Treasuries, Japanese Yen and Gold.

It may be blasphemy, but for one investor, a macro “big picture” approach is proving much more profitable than one that’s (normal distribution) probability driven.

Newsflash: Correlation is Not a Cause!

Just about every data scientist and statistician knows that correlation doesn’t necessarily confirm causation. However, popular business and social literature often confuse the two concepts. By understanding the maxim of “correlation is not a cause” more clearly, it’s possible to let loose creativity and imagination of the questioning mind.

Humans like to think and speak in declarative terms. For a sampling, imagine statements bandied about by pundits such as; “global warming is caused by humans”, or “the Financial Crisis of 2008 was caused by greedy bankers” or “Republicans lost the 2008 election because they didn’t pay enough attention to immigration issues.”

Psychologist and author Sue Blackmore says the simple reminder that “correlation is not a cause” (CINAC), would improve just about everyone’s mental toolkit. Case in point, in an Edge.org article, she gives an example of people filling up a railway station as a scheduled train approaches.  She asks, “Did the people cause the train to arrive (A causes B)? Or did the train cause people to arrive (B causes A)?” The answer she says is they both depended on a railway timetable (C caused both A and B)!

In linear systems, cause and effect is much easier to pinpoint. However, the world around us is considered a complex system where there are often multiple variables pushing an outcome to occur. Nigel Goldenfeld, a professor of physics at University Illinois, sums it up best: “For every event that occurs, there are a multitude of possible causes, and the extent to which each contributes to the event is not clear. One might say there is a web of causation.”

And author Richard Bookstaber says that it’s a difficult search to pinpoint cause in complex systems especially because, “a change in one component can propagate through the system to lead to surprising and apparently disproportionate effect elsewhere, e.g. the famous “butterfly effect””.

The concept that in complex systems there is a “web of causation” may not sit right with some individuals, especially since newscasters, publishers, and even a fair portion of scientists prefer to insist on simple declarations of true and false.  However, the very nature of complex systems is that every object is in some way linked to another with either weak or strong ties and often connections are opaque and mysterious. So even a correlation of one, may not necessarily mean A causes B.

Freeing ourselves from the shackles of CINAC thinking means we have the possibility to let loose our imaginations says psychologist Sue Blackmore. Each definitive report should be greeted with skepticism. And when “A causes B” is held up as the answer, Blackmore cautions, then the critical mind automatically gets to work thinking; “Maybe instead, B actually causes A! And if not, what are the other opportunities?”

It’s human nature to try and explain the world around us. However, when it comes to complexity, we should lead discussions with a measure of humility to include questions and possibilities rather than declarations of certainty.

Questions:

• How does our “aim to explain” end up stifling innovation and creativity?
• The US Securities and Exchange Commission posited an explanation for the May 6, 2010 “Flash Crash”, but experts are not buying their simplistic explanation of a single “trigger event”. What are your thoughts?

Forecasting Lessons from Heathrow’s Snowpocalypse

As evidenced by lessons learned from the Heathrow Winter Resilience Inquiry, when it comes to accurately forecasting future events, it’s possible to get the forecast mostly correct yet not properly prepare or respond to forecast results.

The period of December 18-23, 2010 was shaping up to be just like any holiday season at Heathrow Airport. Plenty of planes, plenty of passengers and thousands of Christmas gifts passing through airport scanners. However, an event was brewing on the horizon—a significant winter storm was on the way.

Airport company BAA manages six airports in the UK. And even with a storm forthcoming, the company had successfully managed and responded to winter events in February 2009, January and November 2010. As BAA managers examined weather forecasts, it appeared this particular storm could be managed, just like any other.

Indeed, in column for the Financial Times, columnist Michael Skapinker notes that, “(BAA) knew the snow would strike at the busiest time of the year… (and) with almost every seat sold, the airport was heading for the busiest weekend in its 64 year history.”

Thus the table was set: a big storm forecasted and thousands of passengers still on their way to the airport with no flights—as yet—cancelled. Add in a dash of cranky holiday travelers and you have a combustible mix for sure. And when the storm finally hit Heathrow, chaos ensued.

On Saturday morning, there was more than 9cm of snowfall, with 7 cm falling in an hour! Airport operators didn’t anticipate how a rapid accumulation of snow would snarl air traffic. Case in point, there wasn’t enough equipment to de-ice planes, leading to four thousand flight cancellations. Adding insult to injury, communication processes between BAA and airlines broke down, leaving airlines to tell passengers conflicting stories when flights would resume. And stranded passengers were forced to spend days and nights at the airport without enough blankets, food and water.

The Heathrow Winter Resilience report, commissioned by BAA to help them deal with future crises, cited the necessity for better preparation and planning to deal with harsh weather conditions. In addition, to improve communications the report recommended a single physical control center for major incidents to help prepare for passenger welfare.

All of these are good steps; however this case study highlighted two important things. First, it’s possible to predict—even accurately—that an event will happen, but sometimes it is difficult to anticipate the severity of impact. Second, while it’s important to anticipate “what will happen”, the second part of the equation or the ability to “act” on that knowledge with the right people, processes and technology is critical to success or failure.

Questions:

* What could BAA have done differently to “anticipate” the effects of the winter storm on December 18?
* Daily snowfalls increments of 7cm or more have only occurred six times since 1970 according to the Heathrow report. Was BAA “rolling the dice” in your estimation?

Why Predicting the Future is So Darn Difficult

Predicting the future is difficult—just ask George Soros. While Soros is often celebrated for the $1 billion profit he made in 1992 on a bet that the pound sterling would collapse in valuation, other trades ended up costing him almost as much money as he made.

As detailed in Sebastian Mallaby’s “More Money than God”, leading up to October 19, 1987, Soros’ Quantum fund had been up 60%. However, when “Black Monday” hit and the Dow Jones lost 22.6% of its value, Soros was in the middle of the mess deciding whether to sell or buy. While he held his positions through Wednesday of that week, on Thursday he abruptly changed his mind and sold positions worth $1 billion. Soros’ decision to unload his massive portfolio sparked other traders to also sell stocks and bonds, thus causing a downward spiral in markets. At the end of the day, Soros was out of the market; however his Quantum fund lost $840 million!

Alas, that’s the problem with gut decision making, you say. Soros should have used quantitative analysis, right? Even quantitative analysis can produce the wrong outcome.

According to Roger Lowenstein’s “When Genius Failed”, hedge fund Long Term Capital Management (LTCM) was chock full of the best minds in finance. Assembling PhDs in finance, mathematics, economics and more, LTCM partners built sophisticated trading models based on the assumption that while investors sometimes panic or get too optimistic, eventually markets settle towards equilibrium. And in moments of panic or too much optimism, LTCM’s partners believed there was money to be made.

Unfortunately, LTCM is a case study in over reliance on analytical models for decision making. Lowenstein writes, “LTCM Partners believed that all else being equal, the future would look like the past” and this—of course—turned out to be a calamitous assumption. LTCM bet heavily on models, often doubling down on investments that they believed had an infinitesimal probability of failing. The assumption underpinning these models was that markets are efficient and rational. And when markets proved otherwise, Lowenstein notes, “The fund with the highest IQs lost 77% of its capital, while the ordinary stock investor doubled his money during the same period.”

It is apparent in studying Soros and LTCM, that even the most experienced minds supplemented by analytical tools and techniques can make extremely poor decisions about the future. So why is predicting the future so difficult?

In Scientific American, author Michael Shermer has an answer. He says that the world is a “messy, complex and contingent place with countless intervening variables and confounding factors which our brains are not equipped to evaluate.” He says we should stick to short term predictions rather than those longer term trends which we so often get wrong.

Does this mean that any attempt to predict the future is for naught? Of course not, as there are definitely limited applications for prediction models in preventing fraud, recommending products, discerning customer defections, and more. Even three day weather forecasts are more right than wrong!

The real lesson is that predicting the future is hard, especially when we’re confronted with millions of potential variables. Deciding which variables to pay attention to, and weighting the importance of those variables is especially difficult.

And maybe then, the solution is not so much to spend countless hours predicting the future (especially for strategic decisions), but instead to expend energy preparing for it.

Questions:

* Do you agree with Michael Shermer that our brains are not equipped to evaluate our “messy world”?
* What other case studies have you encountered where either gut or analytical decision making was taken to the extreme?