The Impact of that Low Frequency High Impact Risk

I’m often fascinated by the fact that people perceive poker players as gamblers but people making business decisions outside a casino as sane, calculated risk-takers. If only that were true! Negative expectation bets are placed around the boardroom table just as often as the poker table. It’s just that every time a company goes pop, it’s considered to be an exceptional event that cannot possibly repeat itself in that form.

Consider for a moment a hypothetical game of roulette in which every number across the table appears to be offering a 20% return. Put your money on black and red comes up… you get 20% back. Put your money on 12 and 33 comes up… you get 20% return. Feels good, but there’s a catch.

In this apparently profitable game, if the 00 hits two spins in a row, you will be liable for 1000 times whatever you have on the table on that spin! Be clear about this… if you have $50, you will owe $50,000! OK, you reason but the chances of 00 hitting twice in a row are incredibly slim. 2.7% x 2.7% to be precise or 0.072%, roughly 7 in 10,000 spins. At 30 spins an hour, you could happily play all month without reasonably expecting it to happen! But what is your Long Term Expectation now?

Well for every dollar you invest in this situation now:

99.93% of the time you’re going to win $0.20 – that’s an upside Expectation of $0.199

0.07% of the time you’re going to lose $1000 – that’s a downside Expectation of – $0.70

Thus, giving you an overall expectation of $0.199 – $0.70 which equals – $0.5001. We’re losing nearly half a dollar for every dollar we invest.

Traditional risk management is about the need to prepare for this highly unexpected event. When United 93 went down in Pennsylvania, the impact extended well beyond the grief of the families of the brave deceased. It hit United Airlines too in ways that it could never have imagined. In 1992 one of the most profitable companies in the world posted the biggest corporate loss in history. The world had changed and IBM had failed to change around it. In 2008 Lehman brothers, RBS, Merril Lynch and some of the biggest financial institutions hit the wall and others, even Citigroup, came to their knees.

When Black Swans happen, their impacts can be devastating. They come from left field – from off the charts – and wipe out years of growth and achievement. The most important question, when they happen, is not could someone have prevented the risks. Naturally someone could have. Everyone can prevent something but no one can prevent everything. The real question is whether or not the risk that the organisation was taking on was priced correctly. In other words, was the company playing poker with a positive Long Term Expectation despite Short Term Losses? Or roulette, with some profitable Short Term wins but a negative Long Term prognosis?

In 1998 when the US banking sector started extending mortgages to the sub-prime sector part of the reason was definitely an imperative for profit. The oft untold side of this story, however, is that the other reason was a mandate from the Clinton administration to start making loans available to the underclass of American society. This socially well-meaning policy came with political strings attached, however. It just simply would not have looked good to charge one rate of interest to the predominantly white middle class and another – significantly higher one – to the predominantly black working class. And anyway, the economy, and house prices, were strong… So a compromise was reached. The rate of interest was perhaps not as high as it should have been, but for a long time, no one seemed to mind.

Whether or not the rate charged was correct given everything we knew at the time, the fact remains: In the same way that the world’s best skydiver could die tomorrow due to uncertainty, so can the world’s best run companies go under. In Why Most Things Fail Paul Ormerod observes that of the 100 biggest companies in the US in 1905 only 36 of them still exist today! How many of our 100 biggest companies will be with us in the next century, or the next decade… or next year?

Posted 02:14pm by Caspar and filed in Decision Making, Risk

The decision making process – calculating risk

I’m often minded to think back to the “financial crisis” and the causes and fall out therein. As an exercise in uncertainty, you don’t have to go much further than the life of Sir Fred Goodwin. Once revered as the banker who led Royal Bank of Scotland from relatively small provincial institution to the fifth largest bank on the face of the planet. Now reviled as the man who bankrupted it in the process of doing so.

Getting to grips with the fundamental aspects of risk what we now have to understand is that that even if Fred Goodwin were the most competent and brilliant banker on the planet… the law of uncertainty says that there is still a probability that what happened would have happened. It has before and it will again. That’s not to say that Goodwin did all he could to prevent it. But it is to say that no-one could have done everything. For in any system there will always be things we cannot control: “A man does all he can and his destiny will reveal itself.”

The most important question as viewed through this lens is, therefore, did they do all they could? Were the models used by financial institutions accurate? Benoit Mandelbroit has been warning for a long time that they aren’t. According to conventional wisdom taught around the world, he says the chances of the fall in stocks on August 31st 1998 at 1 in 20 million; the odds of the Dow’s fall by 7.7% the previous day 1 in 50 billion and the odds of black Monday 1997 1 in 10 to the power 50, “odds so small” he says “they have no meaning”.

Nothing is certain. Everything has just a probability. The important thing, where the world’s finances are concerned, is to estimate the probabilities effectively. If bankers think the chances of meltdown are less likely than they actually are then obviously they’re going to take more risks than if they estimate them accurately. Likewise in life, if we don’t know the probabilities of outcomes associated with what we do then we’re going to make similar uneducated, potentially poor, decisions.

Ultimately, whenever we do anything our minds consider future possibilities much like an actuary calculating the value of an insurance policy. At a subconscious level we understand and accept uncertainty and do a multitude of tiny little calculations that will ultimately decide what we do. Specifically, we are always multiplying probability by outcome or impact of an possible event: we know the probability of our plane crashing is very low, but we feel that the actual impact of such an event would be huge combining as it does not only the chance of dying in a fireball but also falling 30,000 ft beforehand! Probability times potential outcome here (to give a result that will of course differ for each of us) usually creates a number high enough to cause concern but not enough to stop us making the decision to “take the risk.”

Asking someone on a date is a very different kind of risk. On this occasion, the overall potential impact of the downside is indignity or shame of possible rejection. Not as great, perhaps, as dying in a fireball. But the probability of experiencing it is higher, hence the fear that many of us have of it and why many are less inclined to do it than get into an airplane – or even jump out of one.

The exact probability we’d put on being rejected is going to differ from person to person and situation to situation. In order to make this decision, then, we need not only to make some kind of assessment of the chances of a negative response but also how significant the pain of this possible rejection might be – something that will depend on a large number of private, personal, considerations, not least whether we’ve experienced it before.

But there’s another consideration we take into account in order to make this and every other decision that we ever make: for pain and hurt are not the only possible outcomes of this action. There’s also the potential reward of success – and of course the probability of experiencing it.

And by putting these two probabilities and outcomes together – success and failure – we come up with an expectation which ultimately will define what we decide to do. Whether or not our decision turns out for the best or course is a very different matter!