The BP oil spill and the disaster estimations - Part 1/3

The BP oil spill is already the biggest oil spill in the US and is on its way to becoming an unprecedented industrial disaster, given the environmental impact of millions of barrels of oil gushing into the Gulf of Mexico. Even the most hardened of carbon lovers cannot but be moved at the sight of the fragile wildlife in the Gulf literally soaking in the oil. The ecosystem of the Gulf states which were already ravaged by unrestrained development and the odd super-cyclone is now being struck a death blow by the spewing gusher.

Could the specific chain of events leading up to this spill have been predicted? The answer is no. But that doesn't mean that the outcome could not have been anticipated. Given the technological complexity that some of the deep-sea oil drilling operations typically involve, there was always a measurable probability that one of the intermeshing systems and processes would give way and result in an oil-well that was out of control. As Donald Rumsfeld, Secretary of Defense in the Bush II administration put it, stuff happens. But where there has been an abject failure of human science and industrial technology has been in underestimating the impact of this kind of an event on a habitat and overestimating the power of technology to fix these kinds of problems.

Fundamentally, the science of estimating the impact of disasters can be broken down into three estimations:
one, an estimation that failure occurs
second, the damage expected as a result of the failure
the third, (which is probably a function of the second) are our capabilities in fixing the failure or mitigating the impact of the failure.

In this post, I will discuss the first part of the problem - estimating the probability of failures occurring.

There is a thriving industry and a branch of mathematics that works on the estimation of these extremely low probability events known as Disaster Science. The techniques that the disaster scientists or statisticians use are based on the understanding of the specific industry (nuclear reactors, oil drilling, aerospace, rocket launches, etc.) and is constantly refreshed with the our increasing understanding of the physics or science in general underlying some of these endeavours. The nuclear-power industry's approach analyzes the engineering of the plant and tabulates every possible series of unfortunate events that could lead to the release of dangerous radioactive material, including equipment failure, operator error and extreme weather. Statisticians tabulate the probability of each disastrous scenario and add them together. Other industries, such as aviation, use more probability based models given the hundreds of thousands of data points available on a weekly basis. Then there are more probabilistic approaches such as tail probability estimation or extreme event estimation which uses math involving heavy-tailed distributions for the probability estimation of such events occurring. Michael Lewis in his inimitable style talked about this in an old New York Times article called In Nature's Casino.

One variable that is a factor and often the contributing factor in many such disasters is human error. Human error is extraordinarily difficult to model, just based on past behaviour because there are a number of factors that could just confound such a read. For instance, as humans encounter fewer failures, our nature is to become less vigilant and therefore at greater risk of failing. Both lack of experience and too much experience (especially without having encountered failures) are risky. The quality of the human agent is another variable that has wide variability. At one time, NASA had the brightest engineers and scientists from our best universities join. Now, the brightest and the best go to Wall Street or other private firms and it is often the rejects or the products of second-rung universities that make it to NASA. This variable of human quality is difficult to quantify or sometimes difficult to measure in a way that does not offend people on grounds like race, national origin, age and gender. Let us suppose that the brightest and the best joining NASA previously came from colleges or universities where admission standards required higher scores on standardized tests. Now we know that standardized test scores are correlated with the socio-economic levels of the test takers and hence to variables such as income, race, etc. So now if NASA goes to lower rung colleges, does it mean that it was being more exclusive and discriminatory before (by taking in people with average higher scores) and is now more inclusive now? And can we conclude that the drop in quality now is a direct function of becoming more inclusive on the admission criteria front? It is never easy to answer these questions or even tackle the question without feeling queasy about what one is likely to find while answering the question.

Another variable, again related to the human factor is the way we interact with technology. Is the human agent at ease with the technology confronting him or does he feel pressured and unsure from a decision making standpoint? I have driven stick-shift cars before and I have been more comfortable and at ease with the decision making around gear changes when the car-human interface was relatively simpler and spartan. In my most recent car, as I interact with multiple technology features such as the nav system, the bluetooth enabled radio, the steering wheel, the paddle shifter, the engine revs indicator, I find my attention diluted and I have seen that the decision making around gear changes is not as precise as it used to be.

On Knightian Uncertainty

An interesting post appeared recently attempting to distinguish between risk and uncertainty. The view was proposed by an economist called Frank Knight. The theory proposed by Knight is that risk is something where the outcome is unknown but whose odds can be estimated. But when the odds become inestimable, risk turns to uncertainty. In other words, risk can be measured and uncertainty cannot.

There are economists who argue that Knight's distinction only applies in theory. In the world of the casino, where the probability of a 21 turning up or the roulette ball landing on a certain number can be estimated, it is possible to have risk. But anything outside simple games of probability becomes uncertainty because it is difficult to measure the uncertainty. The real world out there is so complex that it is indeed difficult to make even reasonably short term projections, let alone the really long term ones. So what is really the truth here? Does risk (as defined by Knight) even exist in the world today? Or as the recent world events (be it 9/11, the Great Recession, the threatened collapse of Greece, the oil spill in the Gulf of Mexico, the unpronounceable Icelandic volcano) have revealed, it is a mirage to try and estimate the probability of something playing out with remotely close to the kinds of odds we initially estimate.

I have a couple of reactions. First, my view is that risk can be measured and outcomes predicted more or less accurately under some conditions in the real world. When forces are more or less in equilibrium, it is possible to have some semblance of predictability about political and economic events. And therefore an ability to measure the probability of outcomes happening. When forces disrupt that equilibrium and the disruptions may be caused by the most improbable and unexpected causes, then all bets are off. Everything we have learnt from the time when Knightian risk applied is no longer true and Knightian uncertainty takes over.

Second, this points to the need for the risk management philosophy (as it is applied to a business context) to not only consider what the system knows and can observe but also the risks that the system doesn't even know exist out there. That's where good management practices such as constantly reviewing positions, eliminating extreme concentrations (even if they appear to be value-creating concentrations), constantly questioning the cognitive thinking - can lead to a set of guardrails that a business can stay within. Now these guardrails may be frowned up and even may invite derision from those interested in growing the business during good times, as the nature of these guardrails are always going to be to try and avoid too much of a good thing. However, it is important for the practitioners of risk management to stay firm to their convictions and make sure the appropriate guardrails are implemented.

Interesting data mining links

1. The NY Times recently had a piece on how data is increasingly part of our life. Link here.

2. The Web Coupon - a new way for retailers to know more about you. Link here.

3. On Principal Components Analysis. Link here.

The future of publishing - and a new business model

The demise of an ages-old business model and the emergence of a new one to take its place is always an exciting thing to watch - unless you are part of the age-old business model on its way to its demise. There are old assumptions challenged, changes in the way consumers consume, the emergence of a technology trigger, new financing patterns, new winners and losers. Fascinating to someone looking-in from the outside.

An industry that has pretty much been under attack since the coming of the Internet has been the print and the publishing business. But what threatened to be a slow roll of a snowball (obviously to be replaced with new ways of consuming and disseminating information) has taken the form of a rapidly growing avalanche after digitized books and the digital book reader (the Kindle, predominantly) have become mainstream. As is to be expected, there are powerful players working to pull the rug from under the feet of the big publishing and media companies. First Google with wanting to digitize every book ever published. Amazon then came with the Kindle that cut out printing costs from the value chain and make books much more affordable for end-consumers. Of course, the elimination of the printing, warehousing and the physical distribution process would mean massive job-cuts in the big publishing and printing houses, not to mention a necessary shrinking in the margins retained by the publisher from the printing price of the book.

An interesting article in the New Yorker talks about the demise of publishing at the hands of the digital giants in more detail. Link here Amazon, Apple and Google are the big digital players jockeying for position in this market. A few years back, Microsoft would have been a contender as well but repeated failures to crack the consumer space (where MS does not have a monopolist advantage) has resulted in a little more of circumspection.

Bank Regulation in the Canadian context - Part 2

To paraphrase from my previous post on the subject (link here), the stock prices of Canadian banks outperformed large American banks during two separate periods through the late 90s and the 2000s. One was a benign period from 1998 to 2005, and the other was the period from 2002 to 2009 (which culminated with the Great Recession), i.e. a combined good and bad period. However, Canada all through this period faced tighter banking regulation than the US banks. What worked in the Canadian example?

Per the FT article, there were three factors involved. And extrapolating from these factors, my belief is that it translated to one important difference in the operating philosophy of Canadian banks vis-a-vis US banks, or for that matter, even the ones in the UK and continental Europe.
- The first factor was a simple regulatory framework. The US famously had an alphabet soup of regulatory agencies that were competing for banks' business. Canada by contrast had a very simple set up. One agency to serve as the central bank - responsible for the stability of the overall system, one as a banking supervisor, one agency for consumer protection and the finance ministry that set the broad rules on ownership of financial institutions and the design of financial products.
- The second factor was a set of really simple and easy-to-follow risk guardrails on individual institutions, having little to no room for flexibility. the first such rule as a requirement of 7% of assets to be maintained as Tangible Common Equity or TCE. Now, 7% is quite a conservative number when compared with the 4.5-6% that US regulators have been comfortable at different points in time. Additionally, the OSFI required that the capital maintained be of the highest quality - shareholder equity. The Canadian regulators require that 75% of TCE should be comprised of shareholder equity. There is no room for quasi-equity products like preferred shares (which, incidentally have not turned out to be very useful from a capital standpoint for US institutions). Finally, the third requirement was a leverage cap of 20:1. Compare this with US banks that have consistently maintained higher leverage ratios in an attempt to expand investments and improve returns to stakeholders in an environment supposedly insulated from risk.
- Finally, a third important factor were the dealings between the Canadian bank regulator and the banks when it came to following rules. The Canadian system was based on principles, rather than narrowly following specific rules. It is about the spirit rather than the letter of the law. The head of the OSFI regularly met with the bank CEOs and was a frequent attendee to board meetings, especially in the ones having the non-executive board members attending. The bank CEOs on their part took interest in maintaining a stable system and paid serious attention to the advisement of the regulators.

Now, I am attempting to fill in the blanks beyond this point. My hypothesis on the operating philosophy of Canadian banks is that these simple and non-negotiable guidelines did not leave too much room for adventures such as optimization around edges, getting into illiquid and structurally untested asset classes (like the synthetic ABSs and MBSs), etc. Canadian banks realized that the one safe and reliable way of making money would be to focus on consumer/ business borrowing needs and meet them with simple lending products. The returns from a plain-vanilla banking business which centered around taking deposits and lending them directly to consumers and businesses, were secure and good enough to generate a healthy return on capital for these banks. Which then got captured in a healthy stock price. The creativity and the management talent of the bankers went towards meeting customer needs, as against getting into even more arcane areas of structured finance.

What does this all mean for risk management and its application? There is a myth out there somewhere that tighter regulations tends to dampen shareholder returns. The high-impact downside resulting from tail-events is prevented, but that is at the cost of profits during more normal times. However, that doesn't seem to have been the case considering the performance of Canadian banks. Canadian banks were more tightly regulated than US banks, i.e. risk management was tighter. But the banks clearly did not suffer as a result. Rather a principles-based risk management practice resulted in greater co-operation between banks and the regulators, allowed the banks to focus on the long-term drivers of value in banking and ultimately returned better returns to shareholders.

Bank Regulation in the Canadian context - Part 1

The fallout of the 2008-09 Great Recession in terms of failed banks, lost jobs, shuttered plants, bankrupt companies, is news to all by now. What started off as a repayment crisis had an amplified impact on the overall economy - driven by reckless risk-taking by big banks, over-leveraging and ultimately pursuing a path that seems to suggest that they believed they were too big to fail. Which turned out to be the case ultimately. Read bailout of AIG, the arranged marriage for Bear, government takeovers of Fannie and Freddie and so on.

The contagion has not been limited to US banks and institutions by any means. European Banks (UBS, Deutsche and Societe Generale), British banks, Irish and Icelandic banks - all showed similar behaviours, similar disdain for any considerations of their long-term health believing themselves to be too big to fail. One glorious exception in all of this has been large Canadian banks. As compared to some of their US and European rivals, these large banks have been the very paragon of well-managed and well-run financial institutions and have hardly suffered a blip to their profitability or needed any government largesse over the Great Recession to survive. In fact, Canada is the only G7 country to survive the financial crisis without a state bail-out for its financial sector.

(The top 5 Canadian banks are Royal Bank of Canada, Scotiabank, Toronto-Dominion Bank, Bank of Montreal and the Canadian Imperial Bank of Commerce. Besides cornering nearly 90% of the Canadian market, these banks are in reality large international banks with operations in 40-50 countries, and stock listings on multiple exchanges. A quick primer on Canadian banks is here.)

What caused the Canadian banks to survive? An immediate reaction (which incidentally would be wrong) is that Canadians are somehow too nice to participate in the kind of no-holds-barred plundering practiced by the American banks. They play a soft form of capitalism, one that protects the downside but also somehow limits the upside. Hmmmm, not entirely true. The net shareholder returns of Canadian banks have exceeded that of UK and US banks in the last 5 years, as evidenced in the graph below.


What about returns over a larger time period? How do the top Canada banks compare to the top US banks in terms of stockprice performance?

Looking at a 7 1/2 year period from mid-2002, the total returns on a basket of large Canadian banks (the ones mentioned above) was 144%. In the same period, US large banks (Citi, Chase, BofA, Wells, Goldman, Morgan Stanley) had a return of a paltry 2%. OK, the US banks returns were decimated because of the recent credit crisis. The market over-reacted maybe. If you look at returns from a period from Jan 1998 to Dec 2005, when we were having a so-called 'Goldilocks' economy, the story isn't too different. US bank stocks rises to a more respectable 69% but the performance of Canadian bank stocks improves even more to 183%.

Table of stock price performance for top Canadian banks - followed by US banks
(Boom and Bust Period)

	June 2002	Feb 2010
RBC	16.06	50.44
TD	23.19	59.56
CIBC	32.85	59.135
BofM	21.86	48.86
Scotia	17.41	42.87

	June 2002	Feb 2010
Chase	22.25	38.39
Wells	25.2	26.71
BofA	35.18	14.47
Citi	28.68	3.18
Goldman	73.35	152.49
MS	35.62	27.13

Table of stock price performance for top Canadian banks - followed by US banks
(Boom Period only)

	Jan 1998	Dec 2005
RBC	12.15	39.27
TD	17.49	52.55
CIBC	24.02	65.8
BofM	20.22	55.94
Scotia	16.6	39.93

	Jan 1998	Dec 2005
Chase	51.29	48.3
Wells	18.22	35.56
BofA	29.94	46.15
Citi	24.78	48.53
Goldman	73.72	133.26
MS	29.19	56.74

(Goldman Sachs and Bank of Montreal did not have full information over these periods. But having them in the numbers - or taking them out - doesn't change the story.)

SO what can explain the better performance of Canadian banks? What allows them the ability to not only perform better through the cycle but also do so with mininal government handouts? The answer is superior risk management and that will form part of the next part on this subject.

Christya Freeland of FT.com has a fascinating article on the subject and the link is here.

A serious problem - but analytics may have some common-sense solutions

My family and I just got back from a India vacation. As always, we had a great time and as always, the travel was painful. One, because of its length and also because of all the documentation checks at various points in the journey. But in hindsight, I am feeling thankful that we were back in the States before the latest terrorist attack on the NWA jetliner to Detroit took place. A Nigerian man, Umar Farouk AbdulMutallak, tried to set off an explosive device but thankfully did not succeed.

Now apparently, this individual was on the anti-terrorism radar for a while. He was on the terrorist watch-list but not on the official no-fly list. Hence, he was allowed to board the flight going from Amsterdam to Detroit, where he tried to perpetrate his misdeed. The events have raised a number of valid questions on the job the TSA (the agency in charge of ensuring safe air travel within and to/from the US) is doing in spotting these kinds of threats. There were a number of red flags in this case. A passenger who had visited Yemen - a place as bad as Pakistan when it comes to providing a safe haven for terrorists. A ticket paid in cash. Just one carry-on bag and no bags checked in. A warning coming from this individual's family, no less. A denied British visa - another country that has as much to fear from terrorism as the US. The question I have is: could more have been done? Could analytics have been deployed more effectively to identify and isolate the perpetrator? And how could all of this be achieved without giving a very overt impression of profiling? A few ideas come to mind.

First, a scoring system to constantly upgrade the threat level of individuals and provide a greater amount of precision in understanding the threat posed by an individual at a certain point in time. A terror list of 555,000 is too bloated and is likely to contain a fair number of false positives. This model would use latest information about the traveler, all of which can be gathered at the time of travel or before travel. Is the traveler a US citizen or a citizen of a friendly country? (US Citizen or Perm Resident = 1, Citizen of US ally = 2, Other countries = 3, Known terrorist nation = 5) Has the person bought the ticket in cash or by electronic payment? (Electronic payment = 1, Physical instrument such as a cheque = 2, Cash = 5) Does the person have a US contact? Is the contact a US citizen or a permanent resident? Is the person traveling to a valid residential address? What are the countries the individual has visited in the last 24 months? And so on. You get the idea. Now the weights that have been attached are quite arbitrary to start, but they can always be adjusted as the perception of these risk factors change and our understanding evolves.

Now what needs to be done is to update the parameters of this model every 3-6 months or so. Then every individual on the database as well as very person traveling needs to be scored using this model and high scorers (high risk of either having connections to terrorist network or traveling with some nefarious intent) can be identified for additional screening and scrutiny. These are the types of common-sense solutions that can be deployed to solve these types of ticklish problems. When the size of the problem has been reduced from 555,000 people on whom you need to spend the same amount of time, to one where the amount of scrutiny can be sloped based on the propensity to cause trouble, the problem suddenly becomes a lot more tractable.