Designing better futures

The Miral Scale: measuring the ideas in the heads of humanity and why Facebook is a 9.17

 

The Miral Scale is a way of measuring how many people in a given time in the human species share a collective concept or idea. As the world becomes more connected and shares experience the global concepts of hive mind and connected become more relevant. The social graph as expressed by Mark Zuckerberg, founder of Facebook is a concept of a floating social network, but hasn't been quantified or grounded.

The human experience is becoming a ever greater more about shared and linked experience. Concepts that reside in human consciousness have been referred to as Memes, a themetic expansion of the concept of the gene from biology, put forth by Richard Dawkins in his book, The Selfish Gene. Memes spread via culture and shared experience.

The Miral Scale = Memes * Humane species hive mind

When a virus spreads through a host species it is called viral. The concept of the miral scale is that memes spread through the human species and are said to be Miral. The Miral scale measures the number people who have a specific concept in their mind at a certain time.

Miral spread

meme concepts are granular and can be infinitely large, small or complex. They can be visual, audio, conceptual, tonal etc. The only important component is that a meme is present and can be expressed or experienced and therefore spread. Importantly a person may or may not have the consciousness of carrying the meme.

Meme

Memes can be described in simple or complex fashion. Red cube, cube and green cube are 3 different memes which may register with the viewer as unique. In the same way the exposure to Lima beans may be associated with a physical response associated with the visual que of a Lima bean. Thus the Lima Bean can be good, bad, recognized or un-recognized. This is an example of a compound meme.

Memetic concepts can link or radiate from a central concept in a infinite variety.

The Miral Scale

The Miral scale ranges from 0-10 and is logarithmic ranging from 0 to the number of conscious members of the species. Thus Miral scale ranking circa 2010 would look something like the following table:

Miral level	0.0		1.2		2.0	3.0	4.0	5.0	6.0		7.0	8.0	9.0	10.0
Meme carriers	0		7		68	680	6,800	68,000	680,000		6,800,000	68 million	680 million	6.8 billion

 

The Miral scale changes as a function of the size of the species. For example it is estimated that there were 1 billion people alive in 1800. Thus the high end of the scale 10.0 would have equated to 1 billion people. http://en.wikipedia.org/wiki/World_population

A meme's impact on the Miral scale changes overtime

New concepts and ideas arrive, fads explode, old cultures and ways of thinking die with those cultures and people who carried them or are forgotten from living memory. Artifacts may bring them back.

Facebook supposedly has 500m members. If one assumes each is a unique person and that for every member of Facebook there is another person who is aware that Facebook is a website. Then there are an estimated 1 billion Facebook is a website meme carriers, putting Facebook at 9.17 on the Miral Scale. It is estimated 116 million people have registered for Friendster, assuming a 2:1 awareness to registration ratio gives it a Miral scale of 8.69. Assuming 200 Million MySpace registrations and a 2:1 awareness factor yields a Miral Scale rank of 8.77. Younger fast growing services such as Foursquare are estimated to have just north of 1 million users. Assuming a 2:1 awareness to user ration would give it a Miral scale rank of 6.47.

Service	Estimated users	Ratio of user:meme awareness	Miral scale
Facebook	500m	2:1	9.17
MySpace	200m	2:1	8.69
Friendster	116m	2:1	8.77
FourSquare	1	2:1	6.47

 

A brand is an example of commercial culture hacking to increase the brand's Miral Scale

It is easy to imagine that the biggest brands have some of the largest Miral scale rankings, Coke for example is almost a human universal in terms of its logo. Anthropologists and sociologists can point to a few human universals such as the relationship of mother, father etc. that may be titled differently but would be considered 9.99 on the Miral Scale as meme or concept held as human universals.

Memes, bits & pixels

Different technologies, cultural expressions, idioms, beliefs and traditions all rise and fall over time. Meme's can exist within or beyond a language construct, they can be experiential. For example various groups hearing a famous piece of music such as Beethoven's 9 symphony may have recall it and be able to distinguish it from other "classical" music, but may not be able to label it. Meme's transcend linguistic labels but are typically expressed using language. Written meme's or experiences can be exclusively shared within the domain of linguistic expression. Reading about a scene in the Harry Potter series can be considered distinct or related to the visual expression, depending on how one wishes to define the meme.

Memes work in a similar fashion to the concept of the Bit or pixel in computing. The bit or pixel is a finite and exact thing, which appears to have little value outside of a multitude of bits and pixels. Bits and pixels in context allow for a multitude of constructs and expressions. These absolute ideas such as bits, pixels and memes posses significant importance.

Memes are not static or absolute, but vary over time. The Miral scales ebbs and flows and may disappear altogether only to reappear via the experience or discovery of old cultural artifacts, new expressions or the synthesis of new ideas.

History is by definition the slowly fading memetic stream. History rediscovered rebirths old memes increasing the Miral scale.

MemeQ

The human mind being finite has a potential memetic carrying capacity which is probably a function of both the memory and the ability to synthesize and store memories dynamically in new contexts.

One could argue that there is probably a normative memetic carrying capacity similar to an IQ, a memeQ if you will that varies with developmental and physical potentials achieved.

Home Sapiens Sapiens HiveMind MemeQ

The concept of the Hive mind is referred to when sci-fi writers or others like to discuss a collective consciousness. Connectedness is assumed to have a lot to do with intelligence, but most cognitive scientists agree we are far away from a truly generalized theory of intelligence. The miral scale only indicates points of memetic awareness and nothing else.

That being said, If one assume the normative level of the memeQ to be set at 100 per individual, then the Species has a memeQ storage capacity of 100*6.8 billion or a hive mind capacity of 680 billion memeQ's in 2010. It can of course be argued the definition specious metric of memeQ is tautological, nonetheless it is a starting point for a debate.

There is no way to measure the memeQ of an individual at this point and no particular way to extract or relate it to other general theories of intelligence, memory, creativity or cognition in general.

MemeQ potentials

Intelligence like many human attributes is a genetic potential. That potential is realized within its range relative to the nutrition, education, support and even stimulus of the environment around in which the individual develops.

Lack of micro-nutrients such as iodine for example can lead to the loss of 10-15 IQ points. Growing up in a household with a smaller vocabulary has been shown to produce children who have smaller vocabularies. Why vocabulary size and intelligence aren't known to be correlated with memetic potential, it may be indicative of the fact that human miral capacity is potential itself which has a developmental range for each individual.

Why should anyone care about a Miral Scale or memetic potentials.

To paraphrase Winston Churchill, if the Empires of the future are the empires of the mind, then understanding memetic spread and capacity within groups will be important. Marketing, education, culture, and even what some may consider the march of human progress is most likely about developing and honing the memetic potential of individuals and groups. Ignorance, unrealized capacity and lost opportunity at the individual level is more and more about lost opportunity for the group. The next great discovery in medicine, energy, law or culture is inevitably assumed to be greater when shared by everyone, understanding how memes spread, are managed and evolve will be one of the keys to the Empires of the present and the future.

Excel tool for expressing a Miral scale estimate.

Free Excel tool for calculating a meme score. Here is a free excel tool for converting a Miral rank to the number of people estimated to carry the meme.

 

 

 

 

 

information is life: healthcare 2.0

I have a fraternity brother who is slowly losing his children to a rare neurodegenerative disease called Battens syndrome.  He and his wife are watching their 5 year old son fade before there eyes.  One of their twin daughters will go through the same process.  They are doing everything they can. 

If you know someone in such a situation here is a video explaining an amazing example of open source software for collaboration to discover better therapies and solutions.  This is insight into open medicine that lets everyone help is a fascinating example of well thought out design, information sharing and participation that may improve many lives.

 Imagine what will happen in the very near future when geneology sites with millions of people's profiles (see here) potentially start allowing the uploading and cross posting of raw DNA data while other build tools to study phenotypic expressions of genetic potentials.

The technology is here now and the cost points and pieces are all falling into place.  Open genomic posting and analysis in 3-5 years (800-1,200 days) will be an incredibly impactful business.

James Montier: refreshingly honest about Finance as mostly junk

James Montier doesn't wear ties or suits etc.  He doesn't look smooth or polished.  He isn't the type you would put in a line-up of likely bankers, and yet he is refreshingly honest and knowledgeable about investing. 

Montier is an economist and global equity strategist at Dresdner Kleinwort Wasserstein, a German-owned investment bank. 

Montier has common sense and the ability to sum up some basic arguments very well.  If you are interested in investments, looking at an MBA etc. you find this video very interesting.

A Fast Company bio is here.

James also doesn't scream or yell seeking to be a personality, he just has a very honest voice.

There are some excellent points made in this video.  Well worth the time.

Some Quotes from the video:  "We intentionally screw people.  We have become marketing bitches.  Even Fama and French don't believe in the Efficient Market Hypothesis."

oh and Montier's synthesis of economic data with leading-edge behavioral research got him named London's "best global strategist" last year.

Average performance in emerging markets can just be Mean.

The Economist had an article about Emerging markets bouncing entitled Counting their blessings.

The table to the left was presented.  Looking at it would tend to make a person believe that those emerging markets are on fire and really paid off over the last 2 years.

While a nice bounce has been had, the truth is a little less prosaic.  The fixed unit measure of a year starting on Jan 1 to Dec 31 is a cultural creation and can play tricks when we use short cuts like average annual returns (ask your fund manager about this).  While many think in yearly terms like this, few actually invest at the beginning and ending of the year.

The data as presented at first glance might lead one to believe that buying and holding for that period of 2 years would put one in a pretty fine position.

 But, there is a little something called siegels paradox which means that a gain relative to a given loss is not symmetric. This effect is amplified with volatility and kicks in significantly over a 13% loss. 

Example: If you lose 50% you need to make 100% from that point on to make it back up. 

Here is the table to the left with the arithmetic mean (average) and the Geometric mean (compounded) value presented.  Buy and hold is showing losses in 7 of the 8 markets presented for the respective period.  

Yes, there was a great bounce, but not high enough for most to be above water.  After factoring in the cost of capital at a few percentage points,  average fund management performance usually 2-4% below index and fees of 1-2% one is seriously defeated. 

Another sad fact is that almost all investors under perform the vehicles they invest in.  This can be shown in studies of money flows, where people buy high and sell low. 

My point is it ain't what you know, its what you know that just ain't so.

Thinking in Prime Numbers

Visualizing relationships, be they mathematical, cultural or physical is a powerful skill.  Here is a great site about an interesting quest for visualizing prime numbers.

 I am a big fan of Phi, the Golden Ratio.  One of the best books on this is Mario Livio's The Golden Ratio.  Livio is a serious mathematician, historian and story teller who brings alive the beauty of seeing how things relate.  The golden ratio shows up in many optimal packing and density solutions involving complex systems, it abounds in nature from the circling of an attacking hawk to the positioning of leaves on stems. 

My guess is we haven't scratched the surface of applied uses for constants like Phi or Feigenbaums constants.  For those interested in how growth and form emerge from nature and complex systems I would suggest studying morphogenesis, starting with the classic On Growth and Form.

All of the above end up relating to and explaining a simple elegant shape such as the a nautilus shell.

18 rules of complex system failure

 This list below was on ZDnet and is a cut and paste job from this Brief Paper "How Complex Systems Fail"  by Richard I.  Cook.   I suggest reading Normal Accidents if this field of extreme risk interests you.

In finance complex failure at the largest level is called systemic risk and it is what we are currently facing. 

How many items below relate to a recent  failure or proposed solution?  (real estate, CDS and AIG, US DEBT bubble, stimulus...) the comments in parenthesis are my opinions.  My own solution is a lo-fi finance approach.

1. Complex systems are intrinsically hazardous systems. The frequency of hazard exposure can sometimes be changed but the processes involved in the system are themselves intrinsically and irreducibly hazardous. It is the presence of these hazards that drives the creation of defenses against hazard that characterize these systems.

(Finance is nothing more than risk allocation)

2. Complex systems are heavily and successfully defended against failure. The high consequences of failure lead over time to the construction of multiple layers of defense against failure. The effect of these measures is to provide a series of shields that normally divert operations away from accidents.

(robust checks and balances need to be in place at all levels.  Innovation is frequently a form of subverting these for the sake of efficiencies (capital, tax or otherwise).   Risk eventually shows up.

3. Catastrophe requires multiple failures - single point failures are not enough. Overt catastrophic failure occurs when small, apparently innocuous failures join to create opportunity for a systemic accident. Each of these small failures is necessary to cause catastrophe but only the combination is sufficient to permit failure.

(The media reflecting audience desire seeks to identify point failures. There is rarely just a bad guy or broken part. )

4. Complex systems contain changing mixtures of failures latent within them. The complexity of these systems makes it impossible for them to run without multiple flaws being present. Because these are individually insufficient to cause failure they are regarded as minor factors during operations.

(people and organizations fail all the time.  All things fail eventually.  Systems need to allow for system failure.  To paraphrase someone else: Capitalism without failure is like religion without hell.  It doesn't really function as social tool.)

5. Complex systems run in degraded mode. A corollary to the preceding point is that complex systems run as broken systems. The system continues to function because it contains so many redundancies and because people can make it function, despite the presence

(Optimization comes at the expense of safety: 100:1 leverage or some other "innovation" may seem optimal, but may be unstable.)

6. Catastrophe is always just around the corner. The potential for catastrophic outcome is a hallmark of complex systems. It is impossible to eliminate the potential for such catastrophic failure; the potential for such failure is always present by the system’s own nature. of many flaws.

(Only the paranoid survive, find a culture or organization that is fat and happy and you will find un-acknowledged risks.)

7. Post-accident attribution accident to a ‘root cause’ is fundamentally wrong. Because overt failure requires multiple faults, there is no isolated ‘cause’ of an accident. There are multiple contributors to accidents. Each of these is necessary insufficient in itself to create an accident. Only jointly are these causes sufficient to create an accident.

(See earlier comment in regards to the media, this also applies to congressional committees, which are typically witch hunts.)

8. Hindsight biases post-accident assessments of human performance. Knowledge of the outcome makes it seem that events leading to the outcome should have appeared more salient to practitioners at the time than was actually the case. Hindsight bias remains the primary obstacle to accident investigation, especially when expert human performance is involved.

(This is the forehead slap effect, that accompanies after a bubble event.)

9. Human operators have dual roles: as producers & as defenders against failure. The system practitioners operate the system in order to produce its desired product and also work to forestall accidents. This dynamic quality of system operation, the balancing of demands for production against the possibility of incipient failure is unavoidable.

(Unfortunately positive feedback in the form of earnings, bonuses and industry recognition, amplifies this bias.  A fair assessment of skill rarely gets in the way of ego amplified by culture.  Cultures be they national, organizational or group that assume instant monetary reward directly equates to insight or skill almost always fail due to this bias.)

10. All practitioner actions are gambles. After accidents, the overt failure often appears to have been inevitable and the practitioner’s actions as blunders or deliberate willful disregard of certain impending failure. But all practitioner actions are actually gambles, that is, acts that take place in the face of uncertain outcomes. That practitioner actions are gambles appears clear after accidents; in general, post hoc analysis regards these gambles as poor ones. But the converse: that successful outcomes are also the result of gambles; is not widely appreciated.

( Few do post-mortems on successful outcomes that deviate from the norm.  These are better than post mortems on failures as  they paid for themselves.)

11. Actions at the sharp end resolve all ambiguity. Organizations are ambiguous, often intentionally, about the relationship between production targets, efficient use of resources, economy and costs of operations, and acceptable risks of low and high consequence accidents. All ambiguity is resolved by actions of practitioners at the sharp end of the system. After an accident, practitioner actions may be regarded as ‘errors’ or ‘violations’ but these evaluations are heavily biased by hindsight and ignore the other driving forces, especially production pressure.

(See CDO's, black boxes and any obfuscation.  Wall street is excellent at packaging and selling things, fairly mediocre at purchasing them.  See the mutual fund industry performance among others in these regards.)

12. Human practitioners are the adaptable element of complex systems. Practitioners and first line management actively adapt the system to maximize production and minimize accidents. These adaptations often occur on a moment by moment basis.

(wrong incentives, confusing short term motivations versus long term risks are part of the system. We need to design, organizations and regulations with this in mind.)

13. Human expertise in complex systems is constantly changing. Complex systems require substantial human expertise in their operation and management. Critical issues related to expertise arise from (1) the need to use scarce expertise as a resource for the most difficult or demanding production needs and (2) the need to develop expertise for future use.

(Expertise is a false notion in complex systems due to their changing nature.  For this reason anything seeking to "optimize a system can make it unstable.)

14. Change introduces new forms of failure. The low rate of overt accidents in reliable systems may encourage changes, especially the use of new technology, to decrease the number of low consequence but high frequency failures. These changes maybe actually create opportunities for new, low frequency but high consequence failures. Because these new, high consequence accidents occur at a low rate, multiple system changes may occur before an accident, making it hard to see the contribution of technology to the failure.

(see above and regulatory reform etc.  The law of unintended consequences runs deep in complex systems.  The ratings agencies offering stamps of approval to structured products is a classic case of this.)

15. Views of ‘cause’ limit the effectiveness of defenses against future events. Post-accident remedies for “human error” are usually predicated on obstructing activities that can “cause” accidents. These end-of-the-chain measures do little to reduce the likelihood of further accidents.

(Establishing boundary conditions etc. that assume point failure, human error (greed,stupidity and crowd blindness) need to be built in.)

16. Safety is a characteristic of systems and not of their components. Safety is an emergent property of systems; it does not reside in a person, device or department of an organization or system. Safety cannot be purchased or manufactured; it is not a feature that is separate from the other components of the system. The state of safety in any system is always dynamic; continuous systemic change insures that hazard and its management are constantly changing.

(see above large stable systems are the results of small stable systems. Consider the role of audit integrity and other sub functions of the financial system.)

17. People continuously create safety. Failure free operations are the result of activities of people who work to keep the system within the boundaries of tolerable performance. These activities are, for the most part, part of normal operations and superficially straightforward. But because system operations are never trouble free, human practitioner adaptations to changing conditions actually create safety from moment to moment.

(System participants need to look out for changes, be they over or under performance of a system normal behaviour.  Keeping an eye out for "innovation" in finance is highly recommended.)

18. Failure free operations require experience with failure. Recognizing hazard and successfully manipulating system operations to remain inside the tolerable performance boundaries requires intimate contact with failure. More robust system performance is likely to arise in systems where operators can discern the “edge of the envelope”. It also depends on providing calibration about how their actions move system performance towards or away from the edge of the envelope.

(More work needs to be done discussing multiple points of failure in a system, including the hubris or collective myopia that lead to the failure.  My own belief is that a culture or group which reflects hubris, is obsessed with over optimizing or believes that today's profit means they are "right", are the things to watch for. For quants out there: beware of geeks baring gifts.)

Don't trust your senses they can make things disappear: the Troxler effect and change blindness.

Stare at the red dot in the center of the figure for a minute or two. Before long, the green ring will disappear--it simply seems to fade into the white background. There are no tricks: This is a simple, static image file. The effect has been known for more than two centuries and is named for its discoverer, Ignaz Paul Vital Troxler (1780-1866), a Swiss physician and philosopher. "Troxler fading" is actually related to what you experience when you get "dizzy": You become so habituated to a phenomenon (spinning in a circle or seeing a green ring in your peripheral vision) that you stop noticing it's there.

Or, rather, you don't realize that your perceptual system has begun actively ignoring it. It's only when your circumstances change that you see what the phenomenon has done to your perceptual system. When you stop spinning, the world seems to continue, in reverse. When you look away from the green ring, you see a red ring in the same part of your visual field.  From the excellent blog Cognitive Daily.

Something similar to this happens in Media, if we don't hear about it is assumed to have gone away.  In many areas of risk, there are risks, I refer to as Oxygen risks, ie things that are so common and pervasive in the environment that we only notice their absence.  Liquidity and the efficacy of a Triple AAA rating used to be like that.  Flawed or un-considered assumptions due to unchanging environmental factors.

If that green circle doesn't mess with you check out the Change Blindness video. again Courtesy of Cognitive Daily.

Good Senate Risk Commentary you may have missed

Nassim in some more focused presesntations before congress. Comments on Stimulus and free markets: Ht: Rocky Chen

Here is Rick Bookstabers Testimony on VaR, which is a very good introductory read.

My own opinion is that the best way to measure financial risk is to assume it is a function of concentration of opinion or belief. The more people, models, institutions etc. all believe the same thing to be true the greater the risk.

There is no "truth" in finance it is a social phenomenon, not a physical reality like physics, the laws of economics are plastic. Homogeneity in opinion leads to homogeneity in action and reaction.

It isn't the models, ratings agencies, regulators, traders or banks that cause financial catastrophes it is the fact that sometimes their opinions through mania, legislation or fate all converge leading to mania, panics and crashes.

Concentrations of opinion/belief are a form of collective myopia that is part of the human condition. We are social, we group, agree, act and propagate risk. The bubbles, actors and rationalizations change, but the ultimate causes of risk don't, we are all too human.

My own opinion is that the greatest current systemic risks outstanding are:

The dollar as safe currency, example last years lesson via the flight to quality leads to the maginot line behavior of today as the USD is now the darling of the carry trade.

Basel II implementations leading to a "unified" framework for global banking risk.

Alphaclone: +33% on your portfolio YTD at half the cost of a fund manager

One of the more interesting things about investing is that the race doesn't always go to the swiftest.  Assuming you don't live in the sub-microsecond world of ultra high frequency trading then a more measured pace may be for you.

 If you can't beat them (hedge fund managers), join them.  Mebane Faber and team have created Alphaclone for just this purpose. 

Why pay 2 & 20 when you can get by with a far more modest fee of $600/year and a far more transparent portfolio.

AlphaClone follows the publicly filed 13-d statements of various funds allowing you to track some of the portfolio holdings of some of the most successful managers.  Yes you will be a little late to the party as 13-d's are filed after the fact, but these are typically positions held by funds for 2-5 or more years.  

Even if you don't follow the recommendations of AlphaClone, you may use it as a filter or a "what are they thinking" tool.

For a small price At AlphaClone you get to see the investments of the smart money.  Caveat, you won't get to see the thinking behind the selections.

The most recent update from AlphaClone indicates performance of +33% YTD which may not seem like much in the context of an S&P melt-up, but the hedge funds by and large funds held their own during the crisis.   AlphaClone can probably help a person in any of 3 ways:

1. Filter (for good ideas you have screen it through the list of clones)
2. Pointer (Use it to point you in the direction of what may be interesting)
3. Portfolio allocator(Should you wish allocate like a clone)

On a $100k Portfolio @ $600 per year it works out to %0.6 which is far cheaper than your active mutual fund manager and AlphaClone provides more transparency and control for your portfolio.

To the team at AlphaClone: How about some embeddable instructional videos on the value and how to use the tool, it would be great education for everyone.

Full disclosure: I have met Mebane Faber a few times for lunch in New York and he seems like a bright guy who wants to do the right thing. Nothing wrong with that.

Using the Iron Prediction Triangle to pillory pundits

Acme Rocket Motor's shares are going to the moon because Wily E. Coyote uses them all the time to chase the Road Runner....

A lot of financial writing and prognostication is poorly done.  I am not merely speaking of the outcomes, but rather the rigor of the presentation and thought behind the processes.   Few escape the sobriquet, of bad punditry, be it Joe Random Blogger or the once august periodicals such as the Wall Street Journal and the increasingly banal Barron's which serves as an over priced blog in paper form with increasingly dubious editorial quality and declining relevance. 

Forget John Galt, where is the Tyler Durden of Print? It probably isn't Matt Taibbi.

The pundit who says X will go up and Y will go down is noise.   A prediction or opinion to be useful and actionable needs 3 components.  

1. Entry (A time, price, fair value or point of entry)
   

2. Exit (A time, price, fair value or point of exit)
   

3. Risk : Reward (Sharpe ratio, Loss limits, Stop losses, Probability of success, risk of ruin etc.)

Please note that a useful prediction does not mean a correct one.  A useful prediction is one which can be acted upon with some measured degree of confidence and then assessed for effectiveness in hindsight.  Not all predictions are correct, but some are useful. 

In project management there is a standard triangle model of framing the boundaries or constraints of a project.  Using these constraints, communicating and managing a project and its participants becomes easier as the expectations are understood as bounded. 

The graphic shows, the traditional project manager's model.  If you are a project manager you tell the respective parties that they can move any 2 points on the project triangle, thus keeping the potential quality area realistically fixed.

The equivalent in the prediction sphere is that all three components need to be explicitly stated or implicitly understood for an effective prediction to be potentially considered much less acted upon.

If an individual suggests you buy this or that stock, your next questions should be for how long and at what risk in addition to why?  Without the other 2 legs to the prediction triangle you have pretty much nothing. 

It is tough enough with all 3 legs to choose a prediction or model, but without 3 legs, it is impossible in retrospect to assess the value of a prediction or selection process.

Entry can be explained as buy now or this asset is worth price $ or value.  An exit can be expressed as hold for 10 years or until price/ value Y is reached.

The trickiest and most often missing component is the Risk:Reward qualifier or quantifier.  If one is investing/trading/betting, it is better to know whether to bet the farm or merely a farthing. 

Predictions are acted on in the context of alternative choices in a portfolio of options, so it is important to understand the risk reward scope of a particular choice.   Risk can be expressed as a stop loss, likelihood of major loss or potential probability weighted outcome etc. even the dreaded VaR.  This doesn't mean the risk assessment will be correct, but it is better to discuss risk in some context leave it as the unmentionable issue blighting the brilliant predictor.

So there you have it.  If some joker shows up on television, print or digital form lacking a robust prediction triangle with an Entry, Exit and Risk Reward stated, please discount said blather, ink or bits accordingly.

Chinese Math, Cascading failures and Dragon Kings

Didier Sornette is a mathematician, but don't hold it against him. He works in the same genre as Mandelbrot in being a gifted polymath considering epiphenomenon. Sornette is one of the most interesting practitioners in the field because he publishes and makes a few predictions along with being a fairly lucid writer with some interesting ideas on many concepts. 

Sornette stated on July the 10th, 2009 that the Chinese Equity market would face serious challenges and peak between the 16th and 27th of July 2009.  So far his prediction has worked out pretty well.  Only time will tell if the prediction was effective on a risk: reward basis.  There is a chart below.  And yes, I am extremely loath to put up funny pictures with pointless lines and other forms of tripe on them as a way of divining the future for the lottery ticket crowd.  

Figure A indicates the point at which the prediction and its associated paper,entitled Chinese Equity Bubble About to Burst was made public. Figure C represents the max risk if you had shorted on the earliest date of the prediction and held.  You would just recently have crossed the risk experienced versus reward ratio of 1:1, so there is still time to see if this plays out to some interesting risk reward degree.  So far so good, one could have Value Weighted into the position over the prediction period and be showing a paper profit.  The question now is, when to exit?

This paper, The Chinese Equity Bubble Ready to burst is a subset of work by Sornette on the topic. 

One of his more interesting papers puts forth the statistical concept of Dragon Kings, this is a more empirically robust construct than the Black Swan in my mind and more interesting as it approaches causal agents.  Dragon-Kings as near as I can tell are extreme outliers caused by cascading co-incident system failures.  They aren't surprising empirically in the right statistical framework, but rather somewhat predictable, which may make them more useful. 

The known unknowns are more interesting to study than unknown-unkowns if you will. Dragon-Kings may be more useful than Black Swans.

After all warning people about Black Swans and then telling them to fear what they can't know isn't so helpful. It produces anxiety, but not so much understanding or potential for action.

Dragon-Kings are functionally assumed to be derived or rather driven from multiple discrete similar or self similar processes co-inciding in time, like a perfect storm.  A fuller explanation is available in this Sornette Paper: Dragon-Kings, Black Swans and the Prediction of Crises.

Sornette has put these ideas forth before in a book entitled Why Stock Markets Crash and a more academic book entitled Critical Phenomena in Natural Sciences: Chaos, Fractals, Selforganization and Disorder: Concepts and Tools (Springer Series in Synergetics). 

The key thesis in Why Stock Markets Crash is that co-incident processes create feedback loops in a network dynamic which manifests itself outwardly in a time-series as a double exponential moving growth curve. 

Basically a few things co-incide: cheap credit, securitization, housing policy, 1031 transfers etc. and out pops an unsustainable double exponential growth curve of debt growth for example.

I like the idea because rarely is an interesting system change driven by a single variable.  Most complex systems tend toward stability, even in the face of a single vector shift.  It is the co-incident shifts that can create in the words of Charlie Munger the Lalapalooza effect.

Buffet and Munger have stated that they look for positive co-incident factors in the firms in which they invest in.

Forget the silver bullet, bring an automatic to the party loaded with silver bullets.

The concept of coupled networks or states exhibiting phase change behaviour is intuitive after one reads a few example in the literature of network dynamics such as Barbasi.

Economically of course the question is begged, if participants in the collective became aware of such a state wouldn't they mitigate it thereby preventing the bubble?  Read the book and or papers and see what you think.  I have  a link on my site entitled books which may provide some tips.

On the prediction front, Sornette's prediction still has to play out.  The prediction was made Publicly at point A in the chart above with a range of dates, but no end objective or termination.  So far the risk reward looks good, but the value in any prediction is its ability to be utilized on a risk reward basis with a degree of confidence, statistical and otherwise.

In that vein I offer up a critique of a sad blogger's past China prediction for analysis and comparison.  This blogger boldly stated in June 2007 that China Shares will Bust by Sept 1 with a 40% decline.  Here is a graph of the outcome.

Figure A was his prediction date, Figure B was his end state and Figure C was when a target of 40% from peak was reached.  The prediction wasn't bad, but probably exposed the investor to 200-1,500 points of risk depending on the entry point.  The blogger in question was me and I was using Sornette's approach. 

It was a sloppy and stupid thing to post in retrospect as it was really only more econo pundit junk in the blogosphere without context and proper actionable rigor.  In addition I published another piece indicating a 60% likelihood for the potential macro change of Chinese politics entitled the Rice Bowl Cracks.  The range for that prediction is running out in the next few weeks, but again it is fairly useless unless someone finds interest in the Gott's Theorem tool. I still personally see political problems ahead for China, but that is an opinion and therefore has limited value to others.

All in all, this blogger has learned his lesson, predictions without proper measurements and rigor are only cheap opinions and as such, I will dial them back in the blog as the opinionsphere offers little in the way of education or insight for others.  China may face severe political crises in the next few months, but that doesn't make my earlier prognostications right or useful for others.

I still recommend the Sornette books and papers for their analytical rigour and explanations of complex dynamics, coupled states and phase changes.  I think Sornette is on to something interesting in the broader area of complex system dynamics.  From a predication perspective it just needs to be phrased in a more actionable context in such a way that probabilities and risk limits are assigned and outcomes can be measured, an empirical falsifiable set of repeatable experiments.

If  Sornette is onto something, maybe mega bubbles can be minimized, but I am not sure even perfect knowledge can get in the way of profoundly recurring human economic foibles.  Read Kindleberger to get a healthy does of bubble reality history and how the shows always goes on or better yet read Shakespeare, only the actors names have changed in the ongoing human drama.

Flaw of Averages a useful book and tool

Sam Savage's latest offering Flaw of Averages is a useful book and set of thought tools he calls mindles.    If you have a PHd in statistics or mathematics, your job or role rarely begins and ends with you.  Often your models will need to be explained to people.   More importantly the places where the models could or would go wrong need to get explained.

The Flaw of Averages provides a great read and introduction to basic concepts in probability and real life situations where things behave in un-intended ways, such as the hidden stories in the "average". 

The book's core strength is the author's fun and helpful voice .  He is the stats teacher you wish you, your kids or co-workers wish you had.

Sam Savage has spent a long time in industry explaining and teaching how statistics and models work across sectors.  His chief skills is as a creator of simple stories and mental models that let managers and regular people understand the value of statistics applied.  Dr. Savage sells powerful "light bulb goes on in the head moments" for a living and sells the tools that let people have those light bulb moments in their own respective domains.

If you want a way to understand something from a new angle or some interesting new thought tools, take a look at the Flaw of Averages.  Quite a few useful examples and free simulations are available at his website. 

Topics covered include:

• Flaw of averages in finance and real estate
• Flaw of averages in supply chain management
• Examples of more successful product launches
• Petroleum (peak oil is hinted at)
• Many other real world applications and examples that would allow managers to share powerful concepts.  If you can't afford a $2k/day stats manager to assess some of your business issues, try a few hours of reading this book.

Disclosure: 10 years ago, I bought one of Sam's modeling tools for excel and over the course of 4 years proceeded to lose it somewhere between living in Dubai, Denmark and Brussels.  I called Sam up explained the situation and he let me pick up an upgrade for minimal cost.  He is not only a great teacher/practitioner but a nice fellow as well.

Free resource for learning about finance, statistics, physics etc.

Education is one of the most important ways we have of passing "progress" on.  The Khan academy is a great free video resource for learning.

The available subjects are very cool: including the current economic crisis, the Geihtner plan etc.  Also tutorials on great stuff in math.  If you hated math or some subject in college or high school, give it a shot here, you may find a new way to look at and understand the world.

Calculus  |  Precalculus  |  Trigonometry  |  Algebra  |  Finance  |  Pre-algebra  |  Arithmetic  |  Geometry  |  Physics  |  SAT Preparation  |  Probability  |  Linear Algebra  |  Differential Equations  |  Credit Crisis  |  Banking and Money  |  Paulson Bailout  |  California Standards Test: Algebra II  |  California Standards Test: Algebra I  |  California Standards Test: Geometry  |  Venture Capital and Capital Markets  |  Statistics  |  Geithner Plan  |  Current Economics

Here is a video about the housing bubble.  You may not agree with it, but the explanation is clear and lucid.

Housing Happiness, median incomes relative to house value 1987-09

Just a little video of the housing prices using Case Shiller Data and annual median incomes.

Household income in a catchment area is the ultimate arbiter of aggregate house values.  It works like a carrying capacity argument for a species occupying an ecological niche.  Homes need peoples incomes to support their values.  I know it is a weird way of expressing it.

I wrote about the current issue back in 2007 and a potential loss of value estimated at $6.6 trillion. It was outlandish then, but rather pedestrian now.  For more detail on the thought processes in 2007 look here. 

A book that has been getting rave reviews and is on my to read list is Barry Ritholtz's  Bailout Nation.  Barry usually has a pretty solid view on things.

The source data for the movie is hereDownload ShillermovieV1.0.  If you want a regular XLS file without the whizbang overpirced office 2007 graphics let me know and I can send you a copy.  Music by De-Phazz.  Video capture with Camtasia and MS excel VBA For Loop.

Retro finance: Lo-Fi manifesto

"High finance", used to be a phrase exemplifying large amounts of value and high degrees of acumen.  It turns out in retrospect that High finance, meant bankers and mortgage brokers gone wild on CDO & CLO crack.

Finance is a shared cultural belief system and set of behaviours, not a physical reality.  In finance, the most well  known sub-culture from a western finance perspective is Sharia finance which seeks to adhere to various culture practices of lending and interest etc.  Many cultures practice alternative forms of lending, saving and allocating capital.

Through various western eras, lending has been allowed or not for various groups with various constraints based on the fashions of the times, the label usury is a culturally subjective term that springs up in times of fiscal crisis.. 

In music Lo-Fi refers to a style that is purposely sub-optimal from an acoustic fidelity perspective but aesthetically more pleasing.  Lower fidelity doesn't always mean lesser quality as quality in music is a subjective value.

I would argue that a culture's use of finance relative to the human biases inherent in any group means that some form of Lo-fi finance may be longer term optimal.  Lo-Fi finance could actually increase fidelity (trust) in the system long term. For the econo geeks out there (the groups long term utility function may be optimized using sub-optimal local or short term temporal constraints)

Lo fidelity finance could be an interesting point of research for the behavourial economists out there.

Glass Steagall was an excellent example of Lo-Fi finance in action. Glass Steagall's repeal in 1999 was a call for optimization, speed and ironically financial strength.  Woops.

Another great example of Lo-Fi finance is the Credit union.  Credit unions are the unwashed little hippies of the finance world.  They aren't "power suits" etc., but have done well all things considered.  I don't mean to get all "Amish" on you, but Lo-Fi has some serious potential.  The Lo-Fi label in finance could serve the same function as the Organic label does in stores.  People could choose a brand with known qualities.

The manifesto for Lo-Fi finance has some basic ground rules. 

1. Seperation of investment banking from deposit taking
2. Seperation of any high gamma (or convexity exposures) unless fully hedged with the undelrying.  This is about CDS and other forms of asymmetric leveraged low probability high impact risk taking.
3.  A predetermined threshold of market domination in any geography or market segment to avoid concentration of risk in any entity
4. Elimination of the Basel 11 capital accords
5. Elimination of the Ratings agencies as a systemic threat
6. Lo-Fi participants would not be allowed exposures to non-lo-fi participants
7. Others, please suggest them....

 The participants would  agree to them and most likely any Lo-fi participant would be barred from engaging with a Hi-Fi operator in all but limited forms of risk exposure.

Lo-Fi would involve separating many risk activities and preclude certain firms from engaging in them.  Yes it would be short term sub-optimal, but long term stable. 

Eventually deep cultural norms are forgotten or thrown off, only to be rediscovered when something bad happens.  The Repeal of Glass Steagall is just such an example of a codified norm that may be relearned in some new fashion.

Plus ça change, plus c'est la même chose.

Here is a little Lo-Fi Music to ponder the issue:

Watch your money at work www.usaspending.gov

The CIO of the USA has put an interesting tool for looking at spending in the US at www.usaspending.gov.  It looks pretty whizbang.  I haven't checked the data available etc. and what is under the hood, hopefully it has a good signal to noise ratio.  I put a little chill Moby into the soundtrack.  Watching the govt spend is anxiety inducing enough. The video doesn't show much of a story, just the widgets at work.

For those interested in making videos, windows movie maker is probably on your PC via Microsoft and camstudio video screen capture software is available free. Here are the steps to animate an excel series

1.  Load a data series and chart into excel
2. Open up the developer section and put in a For loop to increment the appropriate variable
3. In the For loop tell excel to "activate the chart worksheet" which will recalculate it. you can play with recording you actions to see the code for this.
4. Run the loop while record the chart region of the screen with Cam studio running
5. Excel is usually so slow updated charts that you can capture the motion
6. Import the CamStudio AVI file into windows moviemaker
7. Create your relevant information slides in powerpoint as graphic stills( titles pictures etc.).
8. In powerpoint use the saveas:picture setting to export the still frames
9. import the pictures into movie maker
10. For extra speed adjustments in the excel video clip use the half speed, or double speed effect in Windows MovieMaker
11. Season with music to taste by importing MP3 etc into windows movie maker
12. Upload to video sharing site of your choice

This sounds complicated, but it takes 7-15 minutes once you get the hang of it and can bring data to life for many people.  It is a great way to understand data as relationships or time unfold. 

VaR explained & the BS model (not Black Scholes)

Value at Risk is pretty simple.  Someone is making a guess about what they expect to happen 95% or 99% of the time.  i.e. 99% of the time you will lose less than $1000 a day. etc.

What many people forget to focus on is the fact that what happens the other 1-5% of the time is what counts.

What many people don't know is that there are 3 types of VaR.  The Risk manager presenting the report usually just mentions the number and that is all, but scratch below the surface and you can learn what your risk manager is really thinking.  Next time ask them what type of VaR they are using and keep this handy explanatory list nearby to decipher what they are saying.

1. "Well, basically we use Historical Var compiled from the data."  (I am going to let history bullshit you.)

2. "We use our mighty computer resources to calculate a Monte Carlo simulation for VaR."  (I take history, blow it into a million little pieces and then each piece will bullshit you ever so slightly.)

3. "We have a sophisticated financial model creating a robust Parametric VaR."  (I am going to personally bullshit you with a tweaked Garch model or overly sensitive Levy Distribution)

Not knowing much you will nod and agree that, "the number looks about right."  and sign off on the risk limits, now you are bullshitting yourself and the whole team.

I am not Against VaR or any other standard risk tool or view.  I am against any tool which becomes institutionalized.  Once a risk tool or approach becomes a wink and a nod, sign off or a CYA calculation it loses value.  Risk management is meant for curbing human behavior, otherwise individual or collective greed wells up and overwhelms an organizing system of faith, which is what finance and money are.

Examples of institutionalized risk management beliefs that have or will become rubber stamps:

• Ratings Agencies (NRSRO ministry of truth)
• Securitisation magic (of course we all know that now)
• Basel II Capital Accords
• Probably the next financial innovation which will yet again revolutionize access to credit
• Any company wide policy which creates a ...Yeah but it passed the X test. Or well they signed off on it...type of mentality.  See Dilbertian risk management.

The most dangerous risk manager or system is the one that becomes the de facto agreed standard we can all agree will stand the test of time.  Once a unified risk belief is shared in aggregate all participants will start gaming or trading it the same way.  When all participants share a similar view, the system is susceptible to harmonic dynamics.  See the video below.

US Yield Curve Mambo 3 month to the 30 year Dance

I am looking at taking a role as a prop trader/PM with RCG (Rosenthal Collins Group) in Chicago.  They do a bit of Yield curve trading among other things, so I thought maybe I could liven the data up a bit.

The typical yield curve looks boring and doesn't show relationships over time or the whole yield matrix.
Being interested in getting a feel for the data means understanding all the US swaps spreads. So I put them into excel and created a nice scrollable  matrix.
 
But hey even that isn't fully helpful and discovering anomalies in data (which is fun for me) means getting a feel for the relationships.  So I ended up with something like this video.

Trading the curve still means ferreting out the anomalies, but I have already found some things in currency swaps. I am hopeful the US  Mambo will yield some alpha (insert bad pun groan).  One caveat, the data matrix didn't have data for the full duration, for example 7 yr. issuance is fairly new etc., and the curves aren't interpolating the null data.  This is more for fun than information value.  The flattening of the curve and then the spread gaps are interesting to watch from 05-09.

If you want the yield curve mambo get it here from on the Godsdog album from De Phazz

If you are an educator you may use a simple video editing program to illustrate the flattening of 2005 and the risk expansion during the crisis.  Here is the Raw AVI file for editing and educational purposes. Enjoy.Download Testfilm3 (50MB).

Widget for Converting Sharpe to Daily VaR (value at risk)

Here is a an Excel widget I built for converting a Sharpe Ratio to a Daily VaR.  This is helpful for a trader or asset allocator who thinks in terms of Sharpe's which are annual units of risk to reward and having to provide a daily Value at Risk measure.

To use this widget, after downloading the excel file:

1. Move the scroll bar to reflect your Annual expected/historical return - Risk Free Rate
2. Move the Scroll bar to reflect your annual standard deviation. In Excel= Standard Deviation of Daily returns * (Number of Trading days/year ^.5)
3. Move the VaR Daily limit to reflect the VAR threshold you are comfortable with
4. Move the VAR threshold to reflect the confidence or (rate of occurance) you would be comfortable with violating that threshold.

You will see the #of days annually that the threshold will be violated and the annual returns expected.

All the usual VaR caveats apply.  The assumptions being a normal distribution in the generating process which never holds true and the fact that VaR tells you how often you will fall off a $ cliff, but not how far you are going to fall. Caveat emptor.  I am not a big fan of VaR as people tend to get hung up on it. For true buy and hold investing it just plain doesn't make any sense to use at all. 

For a quant trading approaches VaR is an OK starting point but for a buy and hold or value based portfolio, I put Beta and VaR in the same bucket. Both are silly and dangerous.  VaR of course does little if you have a major gamma risk or high convexity low event type of risk embedded in a portfolio or approach.  It can be argued that a pure Stat-arb has a gamma approach similar to selling premium, but that is another topic for another day.

Book Recommendation of the Day. Excel Hacks

Negative options lead to Black Swans

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.

UK & US Govt 5 Year CDS spreads still in extreme territory

I have been using a trial of a nifty credit service called Advantage Data to look at credit spreads and risks.  Here is a look at the last 3 months of the CDS prices for US and UK 5 years.
Looking back over the last 3 months, it looks like things are getting better.  This chart shows  a CDS bid price in bps, referencing the likelihood of default of the UK and US government debt over the next 5 years. 

Below is the chart with a time horizon going back a year or so ago. The message is that the credit crisis while apparently getting better is still posing extreme systemic risk according to CDS spreads.  I posted this data help people think about what is going on in the credit world's view of things. 

The equity markets are important to peoples perception of wealth and confidence etc. in the near term. It is the perception of risk and liquidity in conjunction with demand that actually determines how we get through this.  The current plans have been greeted optimistically, but are not priced in as a full resolution to these national crises.  Let hope spreads keep tightening.  The Q1 US GDP could be an eye opener.

As indicators of perceived risk, the equity markets don't tell a person a lot.  The CDS spreads offer a lot more information and are exhibiting strong signals that appear directionally promising, but still chronically high.  Tarp V 3.5 anyone?

Here is a the same information for the G-10 from Bloomberg. The fat lady isn't singing quite yet.

Naive trading strategies & young carbon markets

This week I am off to the wall street green trading summit on behalf of the seeking alphasters. Thanks lads.  I thought I would take a look at how the emissions (carbon) market is evolving from a traders and economic efficiency perspective.   Here is a snapshot of carbon prices or the right to emit 1 tonne of carbon about 11 euros:

Truly efficient markets have few opportunities for naive trading strategies to work long term.  A naive trading strategy is something so blindingly obvious only your brother in-law or crazy uncle thinks it will work.  Much to your and those pesky economists frustration it often does work.

I spent +20 years doing quant stuff and think about numbers and culture in my spare time, so cranking out naive spreadsheets is simple. caveat emptor, slippage, liquidity and real concerns haven't been addressed here, so your mileage may vary significantly.  This article is released to provoke thought not trades per se.

Here is the data and the trading system related to this article in Excel 2007.Download MOZ9 naive strategy

The original Naive strategy for equities is buy and hold

Naive trading or allocation strategies are the baseline by which all alpha or performance should be measured.  OK, I know the Beta puritans are screaming EMH, frontiers etc., but bear with me.

A naive strategy is used as a test or null hypothesis of other approaches or processes you are going to employ. If you can't beat the naive approach then just join it. Yes, value investing is a trading process which can be compared to by and hold.   HT: for the fundamentalists out there.

Markets are not stable they typically evolve or "regime shift" over time, just like an ecology.  These regime shifts typically involve participants eliminating or diminishing naive strategies for periods of time as their behavior (rent seeking) leads to shifts.  Most likely some physics student quant guy will eat your naive lunch with a souped up server looking at Lyapunov exponents and slicing VWAP into ever tinier pieces net hops and milliseconds for execution. 

I took a look at an Emission market traded in Europe to see how a Naive strategy would perform.  Here is the market and instrument description.

Below is the outcome, a 1.96 Sharpe ratio* with a strategy using the cunning logic: if today was up, buy tomorrow, else short tomorrow.  The Sharpe or risk reward ratio of index buy and hold equities is usually around 0.50.  So this naive strategy at first glance appears to yield roughly 4X better risk return in what is probably a low-correlated (as traded) asset class.

There are major caveats here, the logic implies, you can tell moments before the close which trade to make and there are no transaction costs or taxes removed.  Also please note this is a futures contract, so the volume is anemic to non existent for 2-3rds of the contract life. you need to research rolled contracts to really see what is under the hood here.  Look at the data yourself, I included the volume in the spreadsheet.
 
The interesting thing is that a naive strategy appears to provide interesting returns.  For those unfamiliar with a sharpe ratio, if one were to de-leverage the position so that the risk profile had a volatility of 15% per annum which is less than the historical returns of the stock market, the returns would be greater than 29% per year.

This is an interesting example of a young market where the naive strategy still appears to offer significant alpha returns. My thesis is, because there isn't liquidity and more sophisticated participants playing yet, the various Naive niches, like and ecology are unexploited.  Of course lucrative naive approaches will eventually attract participants and liquidity, leading to a regime shifts.  Strategies or market process are chaotically unstable based on simple agent behaviours and opportunity seeking.

I am not advocating a trading strategy or process in this article, merely highlighting an interesting young market and the use of studying naive approaches to assess trading allocation processes and their viabilities.  If you run a fund and want to talk quant carve out, shoot me an e-mail, getting portfolio level Sharpes north of 2.0 isn't really that tough.

*m sharpe didn't have an annual risk free rate removed, to simplify leverage assumptions etc.

Changing metrics in the Hedge Fund industry

The dog  illustration shows metrics, that may be important to wiener dog specialists. 
I look at a dog, smile and walk on. A specialist sees a whole set of parameters I don't.  I usually look at the tail.  Dogs smile by wagging their tails, so if the dog is happy, I am happy.

Hedge fund metrics.

The business of running a hedge fund is getting tougher on the performance side and the day to day office routine. Due diligence requests are up, redemptions are coming in and the staff is upset that the high water mark is receding faster than AIG's popularity.  It can feel a bit like being the captain of the nautilus in 20,000 leagues under the sea. 

Something else to look out for if you are a hedge fund manager. Inflation.

Many hedge funds don't think much about inflation at the business level and assume the quant guys will come up with a strategy or the trend following commodity approach you just started applying will cover it.

The problem is that if inflation comes back you are going to get hit with a metric shock at the business level and the market level.  Many funds promised to deliver whatever would get people to buy them.  Fund allocators, family offices, pension funds, seeders etc.  would ask for relative returns or absolute returns.  The benchmarks would be set and everyone would be on their merry way until the quarter or year end review.

 The metric shifts with the fashions of finance.  Those who thought relative performance to equities would keep them high and dry are surprised that 1,500 bps of alpha over a -30% S&P index is met with redemption calls instead of a pat on the back. 

Because most investors in hedge funds bought the feel good story and didn't understand the process driving the returns, they are heading for the hills. 

The metric of absolute positive returns has now come into fashion.  Alpha shmalfa, liquidity, absolute returns and transparency are the watch words of the day. 

Inflation metric surprise.

So if you run a hedge fund here is something to think about from a business perspective, Inflation.  Rarely will your allocators or investors mention real vs. nominal returns in today's conversations.  That could change, yesterday's 15% absolute return with 1.0 Sharpe may not make such a bold statement if inflation spikes in the US greater than 10%.   Historical Sharpe ratios could be viewed through the lens of CPI instead of t-bills if inflation returns.  You may want to run the figures now to see how others could be looking at your returns when they fire up the Barra type tools for their annual CYA process reviews.  Co-variance using CPI or another inflation metric could become an important factor (pun intended) in your business.

From a fund business perspective the smart FoF's or hedge fund manager may wish to consider an inflation linked exposure today before things get crowded.  If inflation rears its head, the returns of hedge funds may start to look less appealing in a relative context.  A stable 15% return looks pretty risky in a high inflation environment if the process driving returns isn't understood and that 15% is invariant with respect to inflation. 

A smart thing to do right now is to run the frontiers and performance expectations against various inflation scenarios and metrics, then choose strategies and exposures accordingly.  The only fund, I have heard of publicly that does this is Bridgewater.  Ray Dalio seems to have the 2 key assets required for long term wealth maximization, imagination and discipline.  I have been trying to get a meeting with him to discuss culture, behavior and economics.  If anyone knows how get some of his scarce time, let me know.

 

2 years on: Govt & AA risk & yield curve perspectives

I believe that behaviour is economics.  The path taken and its choice points usually say  more than the destination.  Although 10 years of flat equity markets make Treasuries seem sensible in retrospect, few allocation committees would have had the behavioral discipline (stones) to miss the equity party in the late 90's. 

Here are 2 charts showing the yield curves for govt debt and AA Sr. CDS from today and 2 years ago.  The gap in Sr. Corporates is amazing, but what's 2-3 orders of magnitude risk change between friends.

People reviewing historical graphs always assume a fixed frame of mind exists along the X axis when in reality allocations and perceptions change along that curve.  Having a disciplined process in place for the long term is key.

Most models would not have seen the corporate curve shift like this, as the credit crisis started in the real estate finance related sector. 

Garbage in garbage out is rarely the problem with quant models.  It is usually a failure in context, ie. the context of large events so broad that no particular model can effectively encompass them into the inputs.  Swans show up in the blind spots by definition. 

A good allocator watches many sectors.  My own belief is that any sector variance, seen as positive or negative is important.  Extreme positive variance is probably more important to watch than any other.  Our innate bias when seeing positive variance is too cheer lead instead of getting nervous, this behaviour is anti-thetical to good risk management. 

Hence my apparently elliptical reference to muni-credits lack of response to the Geithner plan yesterday.  The argument appears a non-sequitor, linking an equity rally with a flat muni-risk response.  I view it is a lack of confirmation in expected economic growth delivering taxable revenues at the state level. 

The woods are all around us, we aren't out yet, so stop looking at the equity market trees, this is a credit problem becoming a serious consumer demand problem.

The municipal credit market is un-impressed

Ok, we are all in the economic mother ship together. The other day the equity market got very excited by the Geithner plan.  It is kind of interesting to note that the State Municipal CDS markets didn't really care.
 If we are all in the ole economic boat together should the spreads have narrowed?  So who is right? 

Either the CDS market gets on board and starts singing Kumbya or the equity market may have gotten a little ahead of itself in the shell game that passes for economic policy these days.

Bailout nation update: Where did your $1,000 go?

Here is compiled data from the government bailout programs which equate to about $1,000/citizen so far. I farmed the work to Mechanical Turk for $5.   Here is the spreadsheet dataDownload TARP summary.xls .

If it is useful for you, make a donation to journalists without borders.

Bailout nation has a few moving pieces which are grow like acronym kudzu:

• TARP=Treasury Asset Relief Program
• AIFG=Automotive industry finance program
• TIP=Targeted investment program
• AGP=Asset Guarantee program
• CABLIIP=Consumer & business lending initiative investment program

If you have been missing your tax dollars and wondering what they are up to have a look.  Your tax dollars have been visiting the following institution and hitting the high life in various states. 

I have just banged together the data.  There are over 505 institutions who received a little Bailout love, but I only graphed those receiving over $1 billion.  After all anything less is pocket change these days. Rolfe over at Optionarmageddon will do a better deep dive on this stuff. 

All info and graphs on the site are under a CC licence, use them as you wish just provide attribution.

And for some serious coverage on the issue there is always the Onion. Yet another news network with better analysis than Cramer or was that Jon Stewart:

Monty Hall: information & door #1 investing

One of the most famous problems in popular statistics is the monty hall 3 door problem.  Monty presents you with 3 doors.  Behind one random door is a new car and behind the other 2 doors are goats.

Being averse to goats you select a door hoping for a car.  Monty selects a door with a goat.  You are given the option of changing doors or staying with the door you choose first. 

You should change doors as it shifts your odds from 1:3 to 2:3.  Wikipedia solution.

Being an excel geek, I created a spreadsheet tool to simulate the game 1,000 times.  Warning it does have a macro.  Download MontyHall.xls

One of the things I learned in writing up the visual basic was that Monty does 2 layers of information processing before revealing his answer.

This has a lot to do with investing, as perceptions and behaviours while new information reveals itself are some of the biggest factors to success.  

Many firms operate in mature markets with known market shares and similar inputs to their competitors.  So maybe you are looking at 6-10 doors.

The dynamics of earnings margins are more about the competitive environment and or some structural edge to the respective firm.

The largest piece of information missed by "value" investors is competitive dynamics and the direction of the moat relative to them over time. Munger hints at this in his excellent book Poor Charlie's Almanack.

A firm that has a geographic, regulatory, behavioural or structural moats may grow or shrink that moat over time.  As an allocator, having a process to invest and allocate risk/positions is more important than each individual choice. 

The Moat=the Margin=Earnings edge=extra ROE=Equity price appreciation relative to peers.

Take a look at that relationship above and think about it.  This only works for firms in stable competitive dynamics i.e. mature stable industries.  Most technology lead firms have black swans flying at them out of garages in silicon valley everyday and may not be suitable as classic value investments.  It is my belief that Buffett understood microsofts and many technology businesses. I think he just didn't know how they could keep an edge over time.

You have 25 years and can make 1 investment among 3 choices: Coke, Apple or Google?

So how do you figure out what Monty Hall is telling you, skip the goat and get the car?

The car is a high ROE at fair price over time with a moat that expands as the firm grows leading to share price appreciation assuming you bought it at a reasonable value.

This is the proverbial snowball rolling downhill.  Coke in the 1950-90's was like that with a single simple product, and ever better distribution and purchasing power.  Eventually the behavioral moat "taste"triggered by marketing and lemming consumers watching each other guzzle the stuff meant a widening moat.  Also recognize the Mungerian (sic) lalapalooza effect of multiple feedback loops.

• increasing sales lead to better input margins
• increasing sales lead to more efficient advertising buys
• increasing sales lead to more points of presence
• increasing sales lead to more cultural behavioral visual cues triggers other to consume

Monty Hall is the historical margins earned by a firm relative to its peers gleaned from 10Q's and 10K's.  Watch firms with better operational margins or efficiencies.  Understand their moats and substitute products.  As a hedgie, anthropologist with a deep technology background it is fairly easy for me to see behavioral moats and the limited lives of most technology moats.  

Coke as a product will most likely be here with stable margins in 20 years, Abercrombie & Fitch less likely.  The half life of a fashion firm is limited and in its death throes, it will most likely spend any retained earnings away on marketing and other new forms of "coolness" that allowed it capture large margins in the first place.

The "problem" of asset allocation in mature business sectors isn't purely closed form like Buffett & Munger's pari-mutual betting analogies, but in some industries it is close enough that trends seen in expanding ROE over time may be indicative of sustainable advantage.

If one treats the sustainable comparable ROEs as indicator of competitive edge and longevity then there might be an investing edge assuming the price is right. 

It is critical to understand what drives the edge.  If it is a one off or something that doesn't scale with the business then think again.  Monty can be tricky and most likely 2008 was goatish enough.

Where did value investing go wrong in 2008?

One could argue value investing didn't go wrong, the economy did.  Value investing just tagged along with the market.  Value investing isn't market timing or macro analysis per se, it occurs at the security level.  Portfolio exposures at the economic level are another matter.

The diagram above explains why.  The grey circle represents a firm at some fair value operating in a mature competitive dynamic. 

The investor is looking for the car behind door number one over time.  It is important to realize that relative to its peers the firm may well have delivered #1 results, but occasionally the larger economic environment overwhelms that success.  The background represents the ups and downs of the larger economy green is positive and red is negative.  The GDP and credit, normally limited factors in returns loomed large as the acute credit crunch became chronic.

The loose credit from 2004 on created a false economy probably 20% larger than the "real sustainable one" due to the wealth effect of perceived house values and easy credit. 

When the macro economy credit crunch hit a "value" investment, the investment went down like everything else.  Equity appreciation will most likely return faster to those value firms that truly possessed the attributes of value.

A few weeks back a colleague in my office asked, "When do you think the economy will get back to normal", I  replied, "I think it's on its way there now." 

I wasn't trying to be funny. The statement was meant to indicate that spending levels based on false credit meant an economy as measured by GDP that was 10-15% ahead of itself in terms of spending was reverting to its longer term growth rate. 

A contracting real GDP may overshoot the long term growth rate on the downside but will eventually find equilibrium with a better current account balance and savings rate in the years to come.  Yes it will take years.  Fear doesn't dissipate quickly and the greed built up for a long time as shown in the savings rate. 

The major issues now are how the economy ebbs and flows relative to the latest acronym dreamed up in DC.  The new "normal" economy looks more like a big spending program perched dangerously on top of a massive t-bond bubble trading at a fear premium of 3% for 10 yr government bonds. Dollars anyone?

Drinking & Learning about Quantitative Analysis = Quafafew

Quantitative analysis is a puzzle without a solution.  There is no exact quantitative solution to investment as it is a social behavioral problem.  Even approximations can fail spectacularly a la LTCM. 

Soros refers to the socio behavioural dynamic in markets as reflexivity.

Investing, trading, allocating risk etc. are all processes with the same desired end state, maximum terminal wealth.  As Charlie Munger says it is important to understand things from multiple frameworks.  Quantitative analysis can be one of those frameworks. 

Many cringe at the phrase quant as its conjures images of Nobel prize winning economist types creating belief systems (models) that cost tax payers money...cough cough (securitization ratings).  Quantitative approaches should be taken with a large pinch of salt. If they appear merely descriptive as opposed to explanatory run away. 

If a quant approach doesn't have a causal driver that is behavioral it is probably flawed and never ever believe the "with enough leverage" it works lie.

If you aren't familiar with quantitative approaches then try reading Chan's Book first. It is one of the few books sitting usefully between the "pick the right moving average and win the lottery crowd" and the "if these 5 impossible assumptions about execution, the risk free rate etc. hold true then X....academics". 

With time a practitioner can learn something from both camps even about purely fundamental investing or enough to stay away from quants at parties.

Being able to quantify and qualify investment processes is important to understanding them.  The trick is not to believe one has total understanding.  Humility is key whether fundamental value/growth or quantitative asset allocator/trader.  Humility in applying the process aids the outcome.

Qwaf a few

In New York, Boston, Chicago and Connecticut there is a group of people who meet monthly in bars to discuss quantitative findings in finance called Qwafafew. say it out loud and it makes sense.  Say it on the way to a bar and it makes even more sense.

The meetings are held monthly involving bar food, a few drinks, some bad jokes and a presentation or two from a practicing quant professional.  One of the best things about the meetings is the atmosphere.  Give and take, thrust and parry of ideas in a bar setting.  The pretensions of academia are relaxed enough so that the critical practitioner can throw or yell a few barbs and the presenter can rebut.  This makes for bad theater but great potential learning. 

I highly recommend joining the linked in group or e-mailing Herb.  Attend a meeting, best case you learn something or meet some one who knows something. Worst case you just paid $50 for some hot wings and bad jokes about co-variance stability failure.

Here are some power point slides from a recent New York meeting presented by O'Shaugnessy Asset Managment's  ($4b under management) Founder

The first slide shows an optimistic view of equities based on rolling 20 year returns.  If equities return 6% going forward, the rolling returns will equate to the same level seen after the great depression.  I don't agree or disagree with the data, just wanted to give an example of a Quafafew presentation.

The second slide highlights the returns from purchasing the cheapest 10% Price to Sales shares vs the top 10% price to sales shares.  I believe the data were controlled for survivorship bias, but contact O'Shaugnessy to be sure as this kind of study has huge potential for survivorship bias skew.

Make Interactive Excel Models in under 2 minutes

Making a model that lets people interact with variables via scroll bars is one of the most powerful and underused components in Excel.

If you enjoy making models for people to "walk around" or feel the sensitivity assumptions, this is for you.

A warning about the tutorial below, it pops open another window and autoplays. It is a little loud.

Value investing combined with Kelly betting smarts: The Dhandho Investor by Mhonish Pabrai

Mohnish Pabrai puts forth a quick read for novice and expert value investors in The Dhandho Investor.  Value investing is more than just buying something cheaply, it involves understanding the nature of a business and the markets in which that business operates as well as the intelligent allocation of risk capital at the portfolio level.

Pabrai is a successful investor who understands the major lessons for long term terminal wealth maximization.  He is quick to credit those whom he has learned from and comes across as a helpful and supportive teacher.

Pabrai clearly articulates the keys to value investing for maximum terminal wealth

1. Understand a business and its operating market dynamics, cycles and position
2.  Think about moats as margins that contract or expand overtime or due to catalysts
3. Understand intrinsic value and work back from this figure
4. Come up with a few outcome scenarios and probability weight their likelihoods
5. At the portfolio level focus your portfolio bets according to a fractional Kelly betting/allocation mentality. Only risk a little to gain a lot.  Understanding how and why the odds are stacked against you is the first lesson to overcoming them.
6. Exhibit patience

Many value investors stop at step 4 and never contemplate the nature of change inherent in item2 or the dynamics and importance of item 5 about bet allocation.  Pabrai presents his thoughts clearly on these items.

A few interesting notes in the book include reference to company longevity.  For example companies have finite lives something glossed over in CAPM etc.  Pabrai mentions , the book  The living company by  Arie de Guess and how the average major corporation is shrinking with age as Schumpterian innovation goes into overdrive.  Innovative competitive environments are anti-thetical to value investing as the represent moat uncertainty.

With regards to patience, Pabrai offers an extract from a table of major events seen below. 
It speaks highly to the virtues of patience in light of responding to acute news events which may not be economically chronic. As Portfolio manager and not a trader, he discusses waiting for 1-3 years for ideas to work. Again the anti-thesis of having an emotional Cramer moment a la CNBC.

DJIA declines and subsequent performance after crisis events paper here.  You may be surprised at the resilience in markets after an acute news shock.

 
Pabrai also gives acknowledgment to Joel Greenblatt’s useful book and approach from The little book that beats the market.  Greenblatt's book offers a helpful approach to portfolio design via a basic ranking system.

The Dhandho approach is to risk little in order to gain a lot over a period of time and focus on long term wealth maximization.  Pabrai comes across as level headed and as a person with a significant future ahead of him creating and contributing value in many areas.

Discipline and patience are vital. 

I have found in my research of Mutual and Hedge funds that most investors under perform both the mutual and hedge funds they invest in.  Here is a paper on the phenomenon.  Think about that for a moment, even after the work of selecting a great vehicle, the average investor manages to extract below average returns from that investment vehicle. 

If the average actively managed fund under performs the index by 2% and the investor under performs the fund by 2% and equities return 8% over the long run in real terms.  An individual is left with 4% before taxes which in an active portfolio may be short term capital gains weighted.  If the individual switches often "less than a year" to the next HOT thing they are in trouble.

The stream is swift and you are going up and into it, plan carefully and thoroughly. 

Side note: reading & investing your most important asset (Time):

I haven't owned a TV in 20 years even though I was a former chief analyst for Europe's equivelant to the MIT media lab called STARLAB.  My suggestion, sell or turn off the TV, read a book, excercise or spend time with your friends and  family, these are the sources of real health and wealth.

Liquidity, solvency and acute vs chronic banking system states

I learned in my medical anthropology class, there are two broad types of illnesses in medicine, acute and chronic.  Acute illnesses are typified by rapid onset and short course.  Chronic illnesses are longer lasting more prolonged and assumed to be multi-symptomal and or multi-causal. Chronic illness could be a state of multiple regression in subsystems.  Both terminate in recovery limited capacity or termination.  Lehman is a case in point of an acute case leading to termination.

For those of you quants out there, think of an acute illness as an extreme jump diffusion process and chronic onset as drift or diffusion, both are or can lead to regime changes.

Here is a snapshot of various big money banks CDS (credit default swap) spreads, presented as risk condition proxies. 

The analysis shows the acute crisis of Lehman brothers considered a liquidity crisis in sept 2008 and the current more persistent chronic crisis reflecting fundamental solvency issues. 

It appears that the acute attack was mitigated with liquidity, but the chronic condition persists.  Sadly I see know way out of the chronic process other than inflation or termination of the current banking infrastructure and nationalization is only an ownership change not a radical restructuring.  Neither "solution" is near term (3-7 years) appealing, but please look at the alternative of 28 years of stale growth in Japan as measured by equity returns.

My choice between the Scylla and Charibdis of inflation vs banking Ctrl+Alt+Del is actually Ctrl+Alt+Del for the banking sector.

We are currently distorting the banking market by crowding out the institutions (if there) are any that were prudent.  Keeping the current structures and systems in place isn't so great.  Lets have the prairie fire and restart fresh and fast.

The chart indicates a sharp return to the levels of the acute phase of the problem seen earlier.  The actions taken today will determine the next 15-20 years economically.

I offer the chronic vs. acute discussion, because we are trapped by language in sharing our understanding of things.  The language of economic phenomonon is limited and the 7th inning stretch and uncharted waters metaphors popular last summer do little to help the discovery process or debate.  Medicine has a robust  lexicon to decribe complex sytemic issues.  Lets hope economics can advance to more nuanced concepts.

28 lost years in Japan as equities revisit 1981

Forget the lost decade. If you can't imagine 28 years ago.  Here are the top 10 songs from 1981 to put it in perspective. Ouch.

Quantitative Trading, Ernest P. Chan’s New book is extremely useful

Quantitative Trading, Ernest P. Chan’s New book on trading is extremely useful for Asset allocators, portfolio managers and traders.  They should be doing the same thing, maximizing terminal wealth.

The path and tools choosen varies among the participants. The important thing is to understand what role a person plays and what the processes and tools are for being most effective in that role.

In his book Quantitative trading Ernest P. Chan offers techniques and recipes in Matlab and Excel VBA for the trader wishing to learn more about strategies such as Co-integration, money management, back testing, mean reverting and momentum strategies etc.  Chan has broad experience with tools and techniques for various strategies and explains things clearly with the voice of an experienced practitioner. 

Some of the most important work Chan offers in his book has to do with tying the trade or position level and its potential role in a long term portfolio context.

Many traders and asset managers think in terms of $’s or % of portfolio at risk. Many trade with rules of thumb but may not have studied how to maximize the terminal wealth of their strategies and approaches. 

 
The Sharpe ratio is the standard risk metric that most allocators and analysts use to assess a manager or trading systems performance. The Sharpe ratio is typically shown as the annual rate of return minus the risk free rate divided by the annualized standard deviation. This metric is a simple way to measure risk vs. return.

Many gamblers are familiar with card counting and betting strategies based on the odds of the game they are playing. Years ago a formula was derived called the Kelly formula for optimizing the return on a given strategy over the long run. 

For example, lets say you play a coin toss game that pays you 101% of your bet each times heads comes up and costs you 100% each time tails come up. How much of your “portfolio” should you wager each time to maximize the returns with this game over the long run? The Kelly formula answers that question.  

The transaction pyramid to the right shows the activity levels of various practitioners.  All of them can learn a little thinking about the Kelly and Sharpe Ratio relationship.  Whether gambler or student of high finance, it is one of the keys to terminal wealth maximization.  I recently created a free spreadsheet tool for exploring this relationship based on Chan's work.

Chan comes up with a simple solution for assets allocators wishing to maximize terminal wealth using Sharpe ratios. With any trading system or allocation approach caveat emptor. Investing in fundamentals with a margin of safety that skews the odds in your favor may be a better approach for portfolio managers. 

For the asset allocator converting the Sharpe ratio into a single asset allocation % may be an interesting starting point in allocations, but macroeconomic issues are probably more important than variance assumptions given the time horizons and principal return factors involved. 

The correct application of the Sharpe to Kelly relations is usually to apply a “sub-optimal” allocation to arrive at a margin of safety. Sub-optimal allocations are chosen as portfolio level assumptions of correlation and co-variance are unstable, this is referred to as a fractional Kelly implementation. These days sub-optimal means an increased Cash or a high cash proxy. Please choose your currency carefully, risk free isn't risk free anymore. My bias is for the Norwegian Krone for the next 2-3 years. 

I would highly recommend Chan’s book for the fundamental, technical or quant trader. Chan is concise and clear in teaching the basic methods and tools for a quant. You can visit Chan's excellent blog to get an idea of his thought processes.

For the fundamental portfolio manager, simple factor based analysis may help provide a piece to the puzzle about cyclical influences.  My personal interests are studying inter-market company level competitive dynamics and likely moat variance over time.  This is probably due to my anthropology background.

I consider economics and markets as nothing more than simple group behavior pursuing individual interests.  These groups and individuals are capable of collective myopia and flawed beliefs due to ritualized knowledge and codified social structures.  Watching an economy from the outside like a field researcher can be fascinating and lucrative. 

One just sits and waits for knowledge to ritualized to the point where it isn't questioned and then take the opposite position.  Ritualized knowledge can be in the form of structures such as flawed systems like ratings agencies or Mean Variance risk tools.  False ritualized knowledge can also be in the form of aggregate memes such as house prices can't go down, we are in the 7th inning of the economic storm etc. these narrative become group myopic beliefs leading to blindness and flawed group behaviours.  The chain of logic that generated the meme is collapsed into an oft repeated truism which ultimately proves false.

CNBC is the meme machine of American casino capitalism.  When it goes off air in 6-8 quarters, it  may be time to buy equities again.  The silence will be golden. The ratio of octoboxes and decaboxes to viewers must be sliding. The more CNBC screams and rants the fewer people care to listen.  There is some good stuff on CNBC, but the needles are getting lost in ever more haystack.

Both fundamental and technical trading and allocation approaches have merit when used as part of a long term process. For those interested in applying technical tools to fundamental allocators, check out AlphaClone.

The opportunity to learn yet another approach to asset allocation such as the trading techniques illuminated by Chan, even if not pursued help one think in yet another frame. Cognitive code swithing in linguistics is related to intelligence.  I should like to think multiple analytical framework application is the tool for maximizing terminal wealth at the individual and collective levels.

Grand Unification Theory of equities: a little something to upset everyone.

OK it's not quite a GUT for equities, but a neat trick involving Munger, Buffet, Graham, Sharpe, Claude Shannon, Thorpe, Kelly, Chan, Mr. Market and Douglas Adams.  Thus even if it isn't the GUT for Finance, this blog post is highly buzzword compliant.

Charlie Munger always says, "invert" which means look at the end or out come first.  I think he is right. So lets look at the end goal first.

The goal of any trader, investor or asset allocator should be in the words of Warren Buffet, "to maximize terminal wealth", the long run in Keynesian terms.  Maximizing terminal wealth is done be most efficiently allocating our risk or bets over time.

The brilliant Claude Shannon created information theory which lead to eventually to the Kelly formula used for optimal bet allocation and card counting. Ed Thorpe, card counter, hedge fund manager and mathematical bon vivant among others has made a lot of money using the Kelly formula to allocate risk in the hedge fund world.  You can read about gamblers, mafioso, card counting and information theory in William Poundstone's truly entertaining read Fortune's Formula.

Recently Ernest Chan published Quantitative trading.  For initiates to quant trading this book rules.  The introduction to basic strategies is well done, clear and concise.

One section in the book ties the Sharpe ratio and the Kelly formula together giving the optimal leverage size for maximizing terminal wealth based on Sharpe ratio and thus the expected optimal annualized growth rate for any stable system.   Please note implicit in the relationship is an assumed normal or guassian distribution of returns.  As we all know Guass exits stage left when the real world of finance enters and one of those rascally characters like Levy or Mandelbrot enters.  I met Mandelbrot while acting as Chief Analyst for Starlab, Europe's equivalent to the MIT media lab  a few years ago, nice fellow.  We researched Artificial intelligence, materials science, genetics, nano-tech, even time travel...no kidding.

Mr. Market at the end of the universe.

Mr. Market is Ben Graham's personified embodiment of the stock market, even though he is irrational sometimes, he should be working towards maximum terminal wealth so that he can eat at the Restaurant at the end of the universe with the late great Douglas Adams. 

In order to do this Mr. Market needs to use optimal leverage in the equities market in order to maximize his terminal wealth and pay for dinner.

The thesis is that if Mr. Market is truly an optimal allocator of capital then the relationship for equities between the Kelly Formula and the equity market Sharpe ratio should approximate a leverage ratio of 1.00 for optimal growth.  A 1.00 optimal leverage ratio shows Mr. Market is an efficient gambler and leaves nothing on the table. Given the standard equity assumptions below it looks like the old boy is pretty good at the tables.

 Neat trick huh? Here is the spreadsheet. Download KellySharpeChan

Fortunately, this appears to be the case given the assumptions shown.  Of course it isn't exact as the assumption all shift around, but it is close enough to interesting. 

Thus information theory, Mr. Market and parts of Modern finance all seem to co-exist happily ever after until dinner time.  From time to time the market gets a little ahead or behind itself, but in the long run optimal allocations for maximum terminal wealth are being made.  Now the message is please stay away from leverage and assume that the co-variance matrix isn't stable.  Efficient market theory individual is bogus, but in aggregate may be semi correct. Not Nobel Prize correct but okish.  Scholes & Markowitz should still give back their prizes and Nobel winners like Dr. Amartya Sen should get more daylight as they ultimately help the human condition more.

Please feel free to weigh in with thoughts, critiques and considerations.  The nice thing is that the equity premium puzzle gets resolved by using variance as a risk proxy.

Even if you don't like the thesis above, the tool is helpful in identifying leverage for a "Stable Asset Allocation System" regardless of horizon.

If anyone is looking for a global macro L/S PM with hints of quant talk to this guy, he returned +19.8% last year with +50% cash (no leverage) and a 1.0 Sharpe ratio, has worked in 6 countries and has a sense of humour. CV on request.

The architecture of Capital Allocation

The architecture of allocating capital occurs at many levels with different styles and tools.  The time horizons and tools vary based on what level one wishes to gain expertise in.  This year was rough for many traders as the macro-environment which usually plays a slower and thus perceptually smaller role for those allocating at the company and sector level reared its head in a step function manner.

When choosing to allocate capital, it is important to consider many things and many levels.  The day trader who makes 50% a year in a hyperinflation country isn't going to be ahead for example. 

I haven't seen a good graphic depicting the various hedge funds, mutual funds etc. on a capital investment horizon ranging from global macro to ultra high frequency, but it would be interesting to see the plot, vs the expertise required.  Of course expertise at all levels yields optimum returns but is rarely found. Even Buffett claims to not time markets well.

Each level also has its biases, and opportunities.  Each level operates within the context of the larger level.  The rising US debt (2008 deficit potentially at $5.1 trillion) may not concern the day trader, but the macro trader should surely be looking about for stable environments.

 Any suggestions welcomed in regards to the rules and operational scales of the architecture of allocating capital.

One thing to be wary of at the macro architectural level in is pictured below.

The Post Earnings Economy. Time for the PY valuation ratio

  
According to data from Shiller we have never had an S&P 500 with negative earnings. Of course his data only go back to 1871. 

Estimates are now out for negative earnings on the S&P 500.  So I guess we stop talking PE or yield and start looking around at the e-bay resale value of firms assets less loss rate. 

Every era creates it own novel corporate valuation metric. The new metric for company valuation is probably the PY ratio which is the Price to Yard sale ratio of a firms assets.

Please note the yard sale below is supported by a handy TARP.  I knew that thing had some uses.

Brittle systems: when things fall apart

A tightly coupled supply chain may have many co-products and dependencies that rely on its inherent economies of scope or scale.  Efficiencies gained from scale or stability create dependencies based on positive externalities.  The more stable the underlying source of the dependency, the more dangerous its failing can be.

Did you know that a slow down in automobile manufacturing can impact the delivery of various life saving drugs?  Neither did I.  You can read about it here at the always interesting Blown mortgage blog.

This is a derivative form of risk that can't be modeled as the causal agents and feedback potential are to tough to model.  It is a qualitative NP hard problem if you will.

Black swans are are caused by cascading failures between what are believed extremely non-linked systems.  We usually think of derivatives in the first order.  

An S&P 500 option or future derives its value from the underlying value of the S&P 500 equity values which of course derive their value from the longer term expected prospects of their earnings.  Black swans are caused by 2 or more loosely coupled and viewed as independent events that link up causing extreme outcomes.

Current earnings estimates point to $30 worth of earnings for the S&P 500 which is currently priced at $835.  That equates to a PE ratio of 27.8 or an effective yield of 3.59%.  That yield sounds a little low to me.  The historical average PE ratio implies an S&P fair value at 490.   For those of you who think in Dow points that equates to a 4,400 DJIA.

The free PHaT tool may be helpful when thinking yield and trading ranges over the last 90 years.  For those interested, the low PE during the depression was roughly 5.6 in June 1932.  That equates to  167 for todays S&P 500 and 1,590 on the DJIA based on current earnings estimates.  I am not saying these are my opinions or fair values for the equities markets, merely pointing out that they are within the realm of the historically possible.  As a PM I think 98% of the time about risk.

Quant finance humour for a small audience

OK not that funny unless your into topological conjectures, Sharpe ratios and private jets. :)

Perspectives on this years Banker's Bonus situation

There is an old British Colonial Joke that goes like the following:

Jr. Officer rushing in concerned and afraid :"Sir! Sir! The natives are revolting!"

Sr. Commanding officer drinking cup of tea calmly replies: "I quite agree."

In that vein the following diagram and phrase is offered.

As a tax paying hedgie who returned +19.80% w/ 1.02 Sharpe in 2008 and got $0 bonus due to the team nature of our firm, I stand firmly in Joe Public's circle. 

Most risk failings and opportunities for designing better futures deal with imagination.

Being able to draw it on a cocktail napkin like this book teaches or sum it up in 1-2 sentences, is a test for good ideas.  Jessica Hagy is one of the more funny picture thinkers.  Her site is here she also has a  book.  XKCD is funny technical picture thinker.  Gaping Void by Hugh McLeod is also a brilliant picture thinker.  I garuntee you won't find answers to technical finance problems at these sites, but you may find inspiration or at least a smile. Both are important and rare assets these days.  Share them.

FYI: I am creating a new section of the blog called thinking in pictures in order to sharpen my own thinking.

PHaT PE historical analysis says 15% chance Dow could see below 4,400

Charlie Munger says, "always invert."  Warren Buffet asks "..and then what?"  Profoundly powerful tools.  It could be argued one of the things leading to the current crisis was a lack of imagination in risk control and value.  Unfortunately in banking imagination typically shows up around compensation package structures or fraud. 

While the quants at banks and ratings agencies built correlation models, added their lemmas and tweaked their Garches, the hustlers of the world ran like wild with the free money that the models said wasn't free.  Read Janet Tavakoli's excellent book to learn more about that.

In march 2008 when the dow was at 12,000 I posted an article hinting that US equities seemed high and fair value was closer to 8,800.  The argument was put forward in terms of Price to Earnings ratios and required a bit of work to understand for many.  In 2008 as an associate portfolio manager I returned 19.8% with a 1.02 Sharpe ratio and am now looking for a larger PM role. 

The market now looks more fairly priced if one assumes earnings estimates today reflect the future.  But, a downward estimate of earnings indicates that S&P 500 below 500 and DJIA below 5,000 are within the normal range of trading possibilities sometime in the next 2-3 years. 

PHaT(Priced w/Historical Attributes)charts

Due to over familiarity with recent market levels, people's anchoring bias means that thinking in terms of history objectively becomes challenging.  This problem is due to 2 psychological flaws.

1. Price relevance Anchoring.  Because we are used to seeing price in a relative way, ie. DJIA at 8,000 etc. it is difficult for us to internalize relative values such as 30% mispricing based on historical yield.  The human mind is poor at absolute values, but great at relative values.  Think about when you travel abroad, you always think in terms of home currency as the relative price.  If you live in the US the difficulties in the metric system mean you are constantly converting back to "understand/internalize" the metric values put forward.  In the English Imperial system, many are challenged to think about their own weight in anything but the Stone system even though everything else is in pounds for them.  Internalizing another relative value metric system takes time.  Few think about equity yields, it a price game.
2. Recency bias.  Because we just experienced something or have experienced it multiple times in the recent past we believe it to be the appropriate metric and to reflect truth.  This is Taleb's fed turkey model, or the concept of thinking your house is worth close to what you just paid for it.  Value investors work at overcoming this by constantly trying to separate price and intrinsic value.

To overcome theses 2 biases and provide a useful tool for equity valuation at the market level, one can create a chart of historical price adjusted backwards by another attribute such as the historical PE ratio what I call a PHaT chart.  The 2 charts above are for the S&P 500 and DJIA using a recent earnings forecast of $50 for the forward S&P 500 and then adjusting the price back in time based on the variance from historical PE ratio.  The show the median and 1 standard deviation from current price based on historical yield. Typical variance assuming a static earnings environment could see trading down to 4,400 for the dow and below 500 for the S&P 500.  If the longer term earnings outlook declines, the price range shift down as well. You can download the tool and play with your own assumptions by moving the sliding bar. and putting in the most recent price for the Dow and S&P 500.

Data visualization often uses the reverse technique.  Think of 2 variables scaled to 100 at some fixed point in the past and then plotted to the current point to show divergence from a scaled growth perspective.  The PHaT (Price w/Historical Attribute) graph plots historical price using the most recent price as the scale point.  This can be done with any variable set and helps understand relative value, beyond price.  If you are an engineer think of it as similar to regressive transform. 

Once the end users wrap their imaginations around the PHaT chart it becomes easier to use their inbuilt recency bias and Price anchoring to understand what things look like from a historical perspective.

Feel free to use the excel data and tool here, derived from Robert Shiller's work on a CC Non-commercial with Attributions basis.  Use it for free and tell them Nick Gogerty built it.

PHaT graph by Nick Gogerty is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Permissions beyond the scope of this license may be available at http://www.gogerty.com.