Seth Klarman manages an estimated $23 billion for his hedge fund Buapost group. He has an excellent sense of risk and an ability to convey his thoughts. Here is an excerpt:

Nowhere does it say that investors should strive to make every last dollar of potential profit; consideration of risk must never take a backseat to return. Conservative positioning entering a crisis is crucial: it enables one to maintain long-term oriented, clear thinking, and to focus on new opportunities while others are distracted or even forced to sell. Portfolio hedges must be in place before a crisis hits. One cannot reliably or affordably increase or replace hedges that are rolling off during a financial crisis.

Risk is not inherent in an investment; it is always relative to the price paid. Uncertainty is not the same as risk. Indeed, when great uncertainty – such as in the fall of 2008 – drives securities prices to especially low levels, they often become less risky investments.

Do not trust financial market risk models. Reality is always too complex to be accurately modeled. Attention to risk must be a 24/7/365 obsession, with people – not computers – assessing and reassessing the risk environment in real time. Despite the predilection of some analysts to model the financial markets using sophisticated mathematics, the markets are governed by behavioral science, not physical science.

Full text with all 20 lessons is available here at the top notch blog, Farnam Street.

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With corporate balance sheets groaning with cash and liquidity appearing to have returned, the question remains; what next? Low rates and massive balance sheets for the next few years, or try to deflate central bank balance sheets.  Current interest rate levels and swollen balance sheets indicate central banks believe the crisis to be far from over. Or, perhaps like Japan the dominance of  central banks in the debt markets is now fashionable. It all feels quite uncomfortable and the Spanish party hasn’t even started yet.

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The more interesting graph is the one below.  It is a 60 month rolling annualized return alpha calculated by taking the last 60 months of hedge fund returns and subtracting the S&P 500 returns. This shows an annualized alpha experienced on a rolling 5 year period.  From the hedge fund index perspective it seems L/S hedge fund alpha has a negative trend and is due to hit zero by about 2019.

Now hedge fund alpha isn’t really running out, the more likely story is that over the last decade more and more managers having been piling into hedge funds recognizing them firstly as a great compensation class and then selling them to others as an asset class. The alpha hasn’t disappeared, but the number of the participants without alpha generating skill has likely grown so much as to mask the real alpha making it difficult to find the signal amongst the noise of the 2/20 crowd.

Generating long term positive returns means applying a stable allocation process over time and possessing a deep understanding of the source of value from that process. If you run a fund and want a good analyst or portfolio manager call this guy He has delivered 1,000 bps of alpha each year since starting out in 2007 without a down year and is looking for a place to put his analytical and portfolio management skills to work. Columbia University Press is publishing his book The Nature of Value later this year.

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The drawdown tool only makes sense with an ever increasing time series.  Quantitative analysts use volatility to express risk, but many decisions are made based on perceptions. Drawdown is a good way of understanding those perceptions and the patience that may be required with paper loss experience. Being a value investor helps some people to ignore price fluctuations while they focus on intrinsic value.

Plotting a drawdown series is also a good way to understand what holding an asset may feel like. 20 years can seem like a long time to get up to break even. The graph below doesn’t include dividend payments or the impacts of inflation on purchasing power of the asset.

The graph below is taken from a full article available on Seeking alpha entitled The trillion dollar trading system 1871-2011.

S&P 500 peak to trough drawdown 1871 to 2011

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“If you want to build a ship, don’t drum up people together to collect wood and don’t assign them to tasks and work, but rather teach them to long for the endless immensity of the sea.” 

Antoine de Saint-Exupery (1900-1944)

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Great video and highly entertaining ht:Paul Kedrosky. The phase space of nature is open ended which means we are part of unknown and mysterious unfolding. Enjoy the ride.

NECSI & MIT/ESD Seminar: Stuart Kauffman on “The End of a Physics Worldview: Heraclitus and the Watershed of Life” from NECSI on Vimeo.

My upcoming book will use some metaphors from ecology and adaptive systems to explain the basics of applied value investing. If anyone has special requests or areas of interest they would like to see in the book, put them in now.

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I jazzed up the model a bit to make it widgetier to play with and understand.  Just used a few cheap design tricks. I make no claims as to the integrity of the data or calculations. Caveat emptor folks as we say in Iowa don’t bet your farm on this. It is a 10 minute derivative work from the original DIY spread sheet with simple scrollbars for key variables and some charts.  Reuters has a more robust version here.

Here is an Excel Download in XLSX format. The DIY European Bank Stress Test V 2.0

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Credit is a function of the cost of money and one’s faith in the future return of that money. Credit like money is ultimately a social belief system. In 2008 the global credit market effectively had a heart attack.  The Federal reserve brought it back to life with cheaper money (low rates), trillions in swap lines and lending facilities. This was effectively like taking the paddles out and jump starting the stopped global economic heart.

The difference between an acute crisis and chronic process.

We experience the world one day at a time and for many one headline at a time.  These headlines often describe acute events or turning points.  The credit meltdown was one such event. The more important economic and political factors are chronic or process in nature. Like paint drying or a forest growing they unfold over time slowly avoiding the daily headlines that would lead to awareness of their potential impacts. These processes are right in front of us, but their slow steady monotonous motion blinds us to their potential impacts for increasing the risks of an acute crisis.

Below is the monetary base of the US showing deposits and currency in circulation.  This is near money. It is short term. You can see how the fed expanded near money since 2008 hoping that by making it more easily available things would get better. The hope was that after first saving the patient’s Lehman liquidity collapse (heart attack) the money multiplier effect would work its magic and keep the credit creation process alive and pumping.  By making near money available and cheap it was hoped far away longer term broad money would be created by the credit market.

Unfortunately it didn’t quite work out like that.  Here is the TCMDO(Total Credit Market Debt Owed) which shows all of the credit in the system including broad money.

The important factor is measuring the effectiveness of FED policy as an economic/credit stimulus is how TCMDO responds as a multiplier of interest rate policy adjustments and expansion of the monetary base.

This last graph shows that the FED is pushing on a string.

The credit market is shrinking as de-leveraging takes over and the FED’s attempts at expanding the credit market become less effective.  Less credit means, less throughput and declines in consumption etc. A central bank is merely a sometimes hand maiden for Credit creation/destruction. Ultimately credit is a free market function. When people don’t perceive opportunity or view themselves as over leveraged they stop lending / borrowing.

Each dollar of base money is stimulating less credit.

If one believes that credit expansion is required to reflate the economy and if one believes credit is generated by expanding the FED balance sheet then whatever expansion plans using multiplier of X in 2008 are likely to use a multiplier of perhaps 2X now. The danger is the likely call for a QE3 that can be justified in the trillions of dollars, the rationalization will be a cheapening of the dollar to help debt holders and domestic exporters while expanding credit supply via an expanded narrow money base.  The other side of the policy coin is of course is fiscal stimulus whereby the govt. spends money on creating economic activities, this is more akin to economic mouth to mouth resuscitation. A likely outcome will be a mix of fiscal and monetary policy over the next 3 years involving increases in federal debt which are purchased largely by the FED.

I am not advocating either policy, but rather suggesting likely outcomes based on historical precedents of fiscal policy and FED behavior. Neither of these actions is good for the dollar as a store of value, but the impacts of de-leveraging, declining GDP and subsequent deflation may give them appeal to many.

Source data is here:

Federal reserve St. Louis Adjusted Monetary Base (BASE)

Federal reserve St. louis Total Credit Market Debt Owed (TCMDO)

Ratio of debt to monetary base .XLS

All work above is property of Thoughtful Capital Group.

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The markit indeces shown below represent prime mortgage securities and thier current prices. Many people are now strategically defaulting or giving up their homes with negative equity.  This deleveraging process is now endemic in the US economy and may lead to further slowing down of throughput (GDP).  Please note these events are slow processes not headlines, they take years to work out.

John Steinbeck’s, The Grapes of Wrath is a purposely long read. The great depression wasn’t an event, it was a long and grinding process which likely changed 2 generations thought on debt and responsibility.

Below is an explanation of the PrimeX indices and their most recent prices.  These mortgages aren’t sub-prime liar loans, these are responsible people who are now in over thier heads due to un-employment and the de-leveraging or d-process in the economy. The mortgages below have high FICO scores, good LTVs etc. These are the “good mortgages” which are now melting down.

And so the long sad process continues…

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Let me know your thoughts. In the paper is a brief summary of the housing market analysis which I used for the 2007-2009 housing bubble while working for a small New York based hedge fund.  Current beliefs involve rapid de-leveraging leading to hopefully greater institutional de-coupling before a cascading systemic failure occurs.

Applying Actionable Systems Risk

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From the 1930s on into the 1980s, the American financial system resembled a well-run and orderly zoo. The various species — banks, securities dealers, insurance companies, etc. — were neatly caged within functional and geographical specialties, prevented and protected from competition with one another. Although competition remained active within each cage, specialized and benign keepers made sure it did not assume lethal proportions. And as in a real zoo, it was just as safe for the public to view a lion as a rabbit: deposit insurance and other safeguards were firmly in place. source: http://www-ceel.economia.unitn.it/events/monetary/wojnilower.html

The quote above is from Albert M. Wojnilower a new York based economist. I like the aspects of compartmentalization for the financial sector.  Compartmentalization is a standard design strategy for minimizing contagion and failure related to complexity and normal accidents.  De-coupling operational elements of a system makes the system more robust and less likely to succumb to cascading failures.

Trading financial efficiency (ROE and big bonuses related to monster institutions) for safety will take a strong regulatory stomach but is likely worth it in the long run.

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One project I worked with was to be the world’s largest gene gathering and research project. We were going to get genetic material and medical histories from people all over the world and then wait for sequencing to get cheap enough.  Unfortunately, the lab business model met the realities of the dot-com crash in 2001.

Genetics is going to be bigger and more profound than most any one can imagine.

In 1991 I co-founded a software start-up on the University of Iowa campus dealing with pictoral relational databases. In my spare time, I came up with a way to lose money using neural networks to trade t-bond futures.  One day a colleague asked me, “What do you think will be the next big technology revolution.”  Everyone had been chattering about hard disks, processors and memory, the same way normal people talk about the best cars or baseball players. I didn’t hesitate a moment.  I replied, “Thats easy. genetics and biology.”

The reason applied genetics and biology as a technology are going to be so profound, isn’t because of the technology, it is because of the human factor in terms of answers or questions.  Google can give you facts, your i-phone can tell you where you are etc. Genetics may let you know how long you have, what you may become and where you are going. It will do the same for others.  Genetics applied will pose more answers than we have social conventions to deal with and it will do it faster than anything we can imagine.  I drafted a book outline on the topic back in 2001, that may be worth a review today.

In the meantime check this out:

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Look out rogue traders exist in banks.

Shocker!  $x billion lost etc.

These headlines are not as worrying as they seem. The real worrying headlines are the ones not written. For every rogue trader loss there are significantly more bad actors and actions going on under the hood.  Institutionally the mega bank approach is antithetical to sound risk management.  Mega-bank structures mean that decision makers end up managing reports and check boxes instead of risk.

Banking isn’t complicated, it is a nice combination of high-wire tightrope walking and magic show.

The more leverage and complexity, the higher the wire. The greater the institutional complexity and size, the greater the need for magic (deception). What depositors and counter parties want is the veneer of a reputation for stability and sound practice. The machine or institution protects that veneer in the short-term at the systems expense in the long term.

In hiding the smaller (errors) rogues from others and itself institutions become blinded to the true nature of operational risk and complexity.  Current reporting on “rogue” activities in banks due to behavioral conventions is akin to flying an airplane with a fuel gauge designed to never report less than half a tank of fuel.

Designing a safe system means designing for components to fail safe.

One way to design a safe system is to acknowledge the reality that institutions die and fail all the time. It is important to acknowledge the foibles of human nature and institutional mortality. Greed and stupidity can’t be managed away, they are hard wired human traits. At best these traits impacts can be minimized or contained. Individual Institutions and banks don’t and shouldn’t matter to the overall economy. It is they economic ecosystem matters. To protect the system the rules needs to designed and managed in such a way that institutions can fail and disappear. The fact is that all institutions fail and disappear.

It is only our limited perception of time which makes large organizations feel permanent. Reading Kindelberger and other economic history is a good antidote to the permanency illusion or the overly great importance placed on today’s institutions. History humbles. History instructs. Sadly ego and vanity mean history is mostly ignored. Our time and place is unique, but probably not that exceptional when viewed in broader context.

Designing a system with smaller compartmentalized  and de-coupled institutionals is one solution. This means a trade off between resiliency versus efficiency. Society should choose, wether to have monolithic institutional titanic elephants with high ROEs (returns on equity) prone to systemic collapse, or smaller institutions with smaller ROEs prone to individual collapse.  Sustainability and resiliency often mean trading off concentrated monolithic efficiency for broader systemic resiliency.

The institutional decision tree below is likely human nature and SOP for most corporations not just banks.

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One point is well made about Jensen’s inequality and the nature of variance about the mean which significantly alters tail risk.

The way I think about these things is that a complex system has multiple components. Those components variances are not necessarily simplisticly additive. The potential for interaction to occur due to some form of coupling induces uncertainty in the systems outcomes.  The more components in the systems the greater the uncertainty of the outcomes. Additional portfolio diversification comes at the expense of knowing what is in the portfolio. If you don’t understand the sources of value or return, don’t add more of them.

This sounds obvious, but most people don’t quite get it. The addition of multiple components can vastly increase the number of “hidden” paths the overall system may take, these hidden paths include extreme tail variance.

Many systems and risks are assymetric in that thier positive variance is either de-minimus or trivial.  An airplane is a complex system made of multiple parts, those parts or components may each have a known failure rate, but the fact is that the overall failure rate of the large system is likely greater than the cumulative components failure rate, due to unforeseen systemic interactions.  The asymmetry of the system variance is obvious.  A normal functioning plane flys, a failed system crashes.

Interestingly a rule of complex systems is that they are almost never succesffully designed functional from scratch, but rather evolve from simple small systems that function.

The word evolve is logical here as one can think of a complex organism such as the human is made up of over 200 kind of specialized and differentiated/evolved cells which evolved over time into differentiated functions which allowed for a net symbiotic gain in individual reproduction.

From a risk perspective the rules fall out as the following.

• 1. Component risk is not additive at the system level.
• 2. Historical system risk and output behavior is inversely useful relative to the number of component connection/links in the system.
• 3. Asymmetrical risk in systems must be managed by adding capacity / tolerance for failure or minimizing / dampening the components interaction.
• 4. Be aware of the trade off between efficiency and robustness across components and across longer time horizons.

For robust design of a banking, biological, political, manufacturing etc. system This boils down, to keep it simple, keep it small, keep it buffered.

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The approach to understanding economies as flows and systems is quite powerful and one which I use to allocate at the macro and company level, +419% may 2009-aug 2011.

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The objective, the conservative, the hard nosed.  All these labels usually confer opinions of soundness of judgment and critical thinking upon those who bear them.

But, what if the subjective is more powerful and often more right than monolithic objective truth?

There is a field of statistics called Bayesian statistics deemed to be subjective by many. Most financial statistics and risk analysis uses the frequentist approach. Frequentists use historical data poured into a model to generate a probability distribution. This probability distribution is then assumed to reflect not only the past, but also the future probabilities.

The way we measure risk in finance with forms of price volatility is deeply flawed. Risk is assumed to be an absolute number or factor existing out there waiting to be found like Planck’s constant.  This pursuit or faith in an absolute numerical representation of risk. Finance and economics are ultimately social activities. There are no absolute truths for a given point in time.

Battle of the Frequentists and the Bayesians

The counter approach to frequentists is Bayesian inference. An important book about Bayesian history has come out. Its importance isn’t in pushing the science of Bayesian statistics forward, but rather in explaining the power of an idea so controversial and yet so useful it couldn’t be killed.

The Theory That Would Not Die by Sharon Bertsch McGrayne tells the tale of how Bayes theorem has quietly changed the world by solving some of the most vexing problems.  McGrayne’s research is only surpassed by her ability to tell an excellent story. Some of the amazing stories involving Bayesian analysis include:

• How Bayes helped win D-Day and may have shortened WWII by 2 years.

• The RAND think tank predicted the chances of a nuclear bomb accident with a sample size of zero in 1945.

• How Bayes theory found a Russian sub in the real Hunt for Red October in 1968?

• How the navy started looking for its lost nuclear bomb off the coast of Spain in 1966. The bomb was found 260 yards away from the Bayesian guess. The original search field was 140 square miles of ocean.

• Why and how was Bayes theory was almost killed by academics in the US?

• Hints that the multi-billion dollar Renaissance Technology Hedge fund is filled with Bayesians.

The power of Bayesian analysis should resonate with any financial professional. Here is a quote from Madansky a researcher challenged with finding the future rate of nuclear accidents when the sample size was zero:

“if you are willing to admit a shred of disbelief, you can let Bayes theorem work…Bayes is the only other theology that you can go to. It’s just sort of natural for this particular problem. At least that’s how I felt back then.”

Please note the term theology above. For statisticians there are 2 strong camps the frequentists and the Bayesian’s. These belief systems are at strong odds with one another. Almost all financial risk methods are based on frequentist approaches. Moody’s had the CDO pricing model that assumed all house prices in the US couldn’t go down because they never had.

Dead Turkeys are frequentists

The frequentist approach would fall foul of Taleb’s turkey model in which a turkey is fed and cared for 999 days. The frequentist turkey never predicts he will be at dinner on day 1,000 as the prior samples allow for no such possibility or event.

Bayesian analysis allows for counterfactuals, speculation and conjecture. This is a good thing for truth can be stranger than and past experience. How many 2009 risk models had the US losing its AAA status built into them as a scenario in the foreseeable future (5-7 years)?

The work using Bayes inferences and scenarios from the RAND institute went on to influence the design of safety protocols for the US nuclear arsenal. I like the idea of a tool like Bayesian inference being used for safety design. My hope is that such thought would go into designing rules and regulations for economic systems. As it is the thinking and thought behind banking rules is often crude or overly frequentist.

The statistician Tukey also makes an appearance in the fight to keep Bayes theorem alive and applied.  At one point he articulates the value of subjectivity and multiple views of truth, something finance and risk management need to acknowledge as worthwhile and valid.

Tukey explains calling objectivity an “heirloom” and “a fallacy… Engineers are not expected to design identical bridges or aircraft. Why should statisticians be expected to reach identical results from examinations of the same set of data?”

If you manage money, risk or have to make predictions, I would strongly suggest reading this gripping book, The Theory That Would Not Die. After completing it, if you are like me you will run to your browser to find Bayesian tools and tips. Your objective view of the world may never look the same again.

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