The original Black Swan story was interesting because it correctly used an ecological model and behavorial shift to show a "surprise", ie. the discovery of a Black Swan. The ecology was the new environments which allowed for the discovery of the black swan and the behavioral shift was the exponential rate of increase in exploration of those new ecologies. The swan metaphor is more apt than first appears.
Europeans had only seen white swans up until the 18th Century and could have created a White Swan Swap, if they possessed our contemporary financial nous.
Imagine our smart 18th century Dutch banker, Don Dutchman betting with Bob Blackswan that there are no black swans and never will be. Don wagers with Bob Blackswan, that they will never capture evidence of such a creature.
Don and Bob agree to an ongoing wager, that for every year, there are no known Black Swans Bob will pay Don 1,000 guilders, a princely sum. If a Black swan is ever spotted Bob gets 1 million Guilders a 1:1000 payoff. The wager is drawn up and copies of the contract exchanged. A bilateral black swan default discovery swap has just been created with a notional value of 1 million guilders.
Bob Blackswan believes in Black swans and Don Dutchman doesn't. Bob and Don have just created a sort of Black Swan default swap.
Before the arrangement was settled Don had his accountants and experts comb the evidence and historical record of white swans just to be sure. Don had astronomers and mathematicians calculate the "odds of a Black Swan" using the hot new Bayesian approach that all the 18th century quants were trying.
Don has created the perfect bet, no black swan has ever existed and the statisticians (quants) tell him the probability of finding such a creature approximates zero. Don Dutchman believes he is in for free money and may even set up a Black Swan insurance company or bank selling these black swan swaps if he can find enough Bob Blackswans to pay 1,000 guilders a year for the privilege.
Negative Optionality lies out of the box.
Bob has a different opinion and is betting on negative optionality. Positive optionality is found in companies or bets in which there is an internally perceived "option" or bet embedded within the company that allows for extra upside.
For example people purchase bio-tech companies not on cash flows per se, but rather the optionality of some new wonder compound eventually becoming a cash stream via the FDA process. People probably buy shares in apple computer based on the next hot " i-something" being released.
Negative optionality is the inverse of this, it is looking at something of current value and pricing the potential for the loss of that value based on an external event. This even might remove market share, eliminate the market with a substitute or in some other way diminish the economic value of the investment. Value investors see moats as negative option protection.
Don Dutchman looks at his pile of historical white swan data, finds a few off-white colored swans, but assesses his risks as low. To be sure of his bets, he back tests 150 years of historical swan birth records using millions of swan samples. All the samples come up white swan.
Don even creates a "whiteness" measure for swans tracking the variance of the whiteness. The greyest newborn swan eventually turns white. Variance in Don's sample is so low that when he plots his swan variance relative to the risk of "blackness" he perceives it as negligible.
The variance among swans or Don's VaR (value at risk) is incredibly small. Don calculates his risk as so small that he takes Bob's Blackswan's bet and runs around town with it looking for other takers. Each year Don is going to rake in 1,000 guilders from each bet holder. The low variance in white swan samples means that seeing a Black Swan is a 25th standard deviation event greater than 1:1 trillion odds against. Don is betting 1:1000 against, a sure thing.
Bob thinks outside of the box and outside the swan environmental data set, which is limited to Europe (the center of the 18th century universe). Negative optionality manifesting itself as a black swan event rarely comes from within a historical data set. Black Swan size impact negative options are environmental / contextual. Things operate in multiple shifting environments, or frameworks: political, geographic, technical, scientific and human behavioral.
Bob is aware of 2 factors in his favor. He knows that he just needs a single black swan sample to win his bet. Bob also knows things are changing in in the "known" world. It is 18th century Holland and the world is opening up due to travel and trade. Everyday, new creatures and exotic lands are being discovered, the swan environment is growing exponentially.
The negative optionality is increasing exponentially with each new exotic environment. Every exploring ship or expedition that comes in from a new environment is a negative option that might help Bob win. The number of exotic species discovered is increasing exponentially and each new find is a negative option that might let Bob win his bet.
Eventually Bob wins the bet due to understanding the environmental drivers of negative optionality.
Negative optionality sits outside of historical price and is therefore not measurable by historical data. The only way to "price" the risk of negative optionality is to measure the "drivers" of price or value properly. Don looked at today's swans and the historical sample, Bob thought in terms of environment, evolution and geography as drivers of negative optionality allowing him to win the bet.
Consider the simple pundit quoting this and that fact about the US stock or housing markets or the economy. A sample size of one isn't a serious analysis, certainly not worth betting the farm on.
The drivers of a value are important. Unfortunately few risk managers are polymaths or interested in drivers, they are hired for their math skills or PHDs more than their imaginations, so they molest the data they have with ever greater finesses until it tells them what they wish to hear.
Behaviorally the brilliant PHd risk manager is being asked to fit the data to the need.
The New risk manager is always wrong, because the group says so.
Consider that on the first day of the job a young risk manager walks into his boss' office, presented data from 30 housing markets globally and said, I think there is a 1:10 chance our bank will fail. We should cease mortgage securitizations operations immediately. It probably happened. Collective myopia isn't all encompossing, just strong enough to facilitate belief systems that reject challenges to the group needs and beliefs.
There will be more bubbles and people who see them for that, but group behaviour will ignore them at their peril. Read Kindelberger's mania's panics and crashes. We aren't that special, we like every other culture alive, just needs to believe so.
The bank above couldn't accept or internalize a 1:10 failure rate and so listens selectively to those who deliver the most complex and obtuse narrative backed by "rigoruous" equations about why the bank would succeed and CDS and CDO's were money machines. Group's select for belief support and re-inforcement especially if it re-inforces a recently successful narrative.
In many cultures, high priests or astronomers and mathematicians promised a good harvest or success in battle. Not much has changed, we are still social animals who need a sufficiently complex narrative delivered by an "expert" to further the group.
Group behavior and need is one of the things a a good risk manager should assess and report on. If everyone in the group needs real-estate or oil to go up, there is a risk that no one is looking out for the other side of the bet. If that group's need has been wish fulfilled a few times, it becomes an unchallengeable belief.
If a risk manager is any good, most people won't like them, because they challenge fundamentally held personal and group beliefs that may be highly successful. Positive result deviance can be more dangerous and telling than small negative deviance, for it can re-inforce dangerous behaviour. If you want to find trouble before it happens, watch a group who just made a bunch of money and is scaling up to really "go for it", without understanding what "it" is or what makes "it" viable. If possible ask, "how high is up and why". Everything has an upper bound limit.
I am not against PHd's, I managed 70 of them in an advanced research institute. Like any group of people about 10% of them were deeply interesting and creative in addition to being scientists. Others were plodders, bright but plodders. Cross field thinking imaginative polymaths are where the "good" stuff gets discovered. Feynman came up with his electron spin equations by watching a plate on the floor. He knew how to see and how to think critically. Watch this video to learn a bit about that.
Where do negative optionalities that lead to Black Swans come from?
Negative options are everywhere. Luckily, we have to worry most about the fast moving ones, i.e. the cases where these "extreme" options start being created exponentially. Some types of negative optionality impact what I call "oxygen risk", the risk that something ubiquitous and taken for granted is removed, like oxygen.
Consider cheap plentiful memory chips mostly produced from Taiwan. The probability of something happening, earthquake, typhoon, china etc. is not negligible, the follow on impacts would be huge. The probability in any given year of such an event is probably >1%, the same goes for earthquakes on the West Coast of the US and yet everyone will be shocked. Hint: a 1% annual risk has a roughly 1:3 chance of occuring over a 33 year investment horizon.
Technological innovation due to its' exponential growth curve is one of the greatest current negative option creators. Technological innovation for many firms is the Black Swan gun pointed at them. Technological innovation is also Schumpeter's growth engine.
To really understand the power of technological change in society read The Singularity is Near, the best book on the future out there and something the average MBA/PHd risk manager can't/won't think about.
Example:Pharmaceutical companies are under a negative option Barrage
I used to manage what was to be the world's largest gene sample gathering project. Gene Sequencing is an example of technology negative option. The cost to sequence a gene is declining on an exponential curve. Each decline in costs makes sequencing cheaper, what cost $3 billion in 1999 will cost $1,000 in 2011.
As the cost to sequence drops, the number of experiments for drug efficacy or adverse affects increases. Each study done with a SNP type analysis is a potential bullet "negative option" cleaving off the addressable market of a current drug. The cheapening sequencing curve is creating a huge environmental shift in drug markets that you can't see, yet. The number of gene studies will grow exponentially and with each one, current potential market size could shrink. Drugs will be safer for patients, but big pharma is under an incredible negative option attack, that will show up as a black swan.
Knowing how to see negative options growing exponentially is a powerful thing. When everyone believes or more importantly needs to believe the same thing, negative options abound. Today's ridiculous conjecture, could be tomorrow's, "whocouldanodeit?".
Technology advances along exponential curves and throws negative options up all the time. Technology and behavior represent environmental shifts similar to the "un-discovered" lands of Australia. Think about the behavioural shift about "home ownership". From home to part ATM, part get rich quick lifestyle. Culturally this is a big glowing warning sign. Everyone needed and believed home prices would increase.
It is important to recognize ethnocentric biases and beliefs. To the Aborgine, Black Swans weren't that rare. He who writes the history books defines the black swan. The black swan created by a negative option could be someone elses gain and good fortune.
Many technology firms trade at market premiums as "growth" companies all the while being directly under attack as their environments shift. Consider the telcos,cable firms and networks with their walled gardens. Every year they inch closer to being commodity bit pipes and being valued as utilities delivering an ever cheapening commodity. The negative options of cheaper faster pipes grow exponentially making a controlled digital experience seem all the less relevant. AT&T 2011= AOL 2007, discount accordingly. What got you here in technology won't get you there going forward.
I just finished Michael Covels' book, Trend Following. An interesting statistic on page 335. The average lifespan of a "winning" stock is 9.35 years. If true an 8% dividend yield is required for breakeven. Choose wisely.
If you want to hire a risk manager, hire a ploymath generalist with imagination. Going deeper with the same data, isn't helping, thinking outside of the box and about the drivers that make a portfolio or exposure work is important.
Scenario planning at Shell was a great revolution and yet math people don't like narratives so much and story telling isn't viewed as serious enough for a bank. Bringing a biologist, social scientist, historian, organizational psychologist or other type onto the risk management team and then don't let the math geeks bully them. Read the living company to understand how difficult it is to change organizational beliefs.
Scenario simulations, historical narratives, scenarios with a "b" team taking the other side of the bet might tell you a lot. Just remember, the guys who made money and its usually guys last year didn't just gain 50 IQ points, they were most likely in the right place at the right time.
Luck and intelligence are often confused in Finance. From an anthropologist's perspective chest thumping males celebrating a bonus are just showing a standard primate victory display, but not necessarily possessing greater prowess in predicting the next quarter or year.
After all who could have imagined that house prices across the US would go down simultaneously, didn't seem to be the quant looking inside the shiller index. Even a bad historian could have had a bit to say about it, but the group probably wouldn't have listened as it would have been, "just a historian". Economic's current heavy reliance on math is really just a way of denying the fact that it is just one of the social sciences with serious physics envy.
P.S. for those of you who scoff at the Black swans analogy as simplistic, how about a glow in the dark rabbit? Biotech is going to change everything it means to be human, que the swan machine.
