Designing better futures

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.

Noise outside of the box and quant models

An interesting paper has been published showing that many extinction events in nature happen due to correlated exogenous phenomenon which looks like colored environmental noise.  This is interesting as most biological models had assumed that environmental (outside the species) variables were random, ie. white noise.  The evidence indicated extinctions were accelerated due to correlated exogenous (environmental) noise.  See link here. 

This is important to econometric and finance models for the following reasons.  Most quant risk models which are used to justify extra leverage or risk allocations for banks and traders are limited to historical market or nearby associated econometric data.  The inherent assumption being that all prior data reflects enough prior noise and events to accurately reflect a noise profile for an instrument or portfolio of related risk. 

Think about it like this.  If I have a simple one variable non-interacting system that ranges from 1 to 100 over a given amount of time like a spring, I can be pretty sure that with enough samplings say X, I have seen the 1-100 range of likely events and their distribution.  If I have a 2 variable system with loosely interacting components I may need a Y times X samplings to see the extreme ranges of possibilities. 

One can see that  extending the context of the system drivers increases dramatically the amount of data required to understand the range of outcomes.  What isn't taken into account is the role exogenous harmonics might induce in the model.  Even weakly interacting system can sync up with dangerous effects.  For example, two grand father clocks set a few feet apart will eventually sync up.  Consider that for a moment.  Two systems could be set up to be non-correlated and yet over time will become exactly correlated, just like that portfolio last year experiencing a 25th standard deviation event.  The event wasn't 25th standard deviation, just the way it was measured in the first place.

An environmental system rapidly becomes complex enough to by impossible to model relative to the drivers and historical data available.  Even a simple closed predator prey network has vast complexity.

Economic systems or markets are the same way, in that the external variables make the pursuit of a true model challenging.  We are currently undergoing a rapid economic shift that is a result of a credit bubble which is impacting markets far removed from the perceived point source. 

The economic environment is going through a period of rapid accelerated extinctions due to these events.  Yet many still operate under the assumption we can model our way through risk control and portfolio construction going forward using the same tools that got us here with a few tweaks.

Finance models fail due to an inability to capture context both at the physical and at the agent behavioral level.  I have yet to see a risk model incorporate the risk that everyone else starts using the same model inducing over weighting and run away prices in an asset class or instrument. 

After all most modern finance risk models are mostly trend following band wagon style into whatever was hot over the last year, decade etc. with a little tweak for co-variance, CAPM, Mean variance approaches at portfolio construction etc.  Bandwagon finance as such isn't a thinking persons approach to risk or portfolio construction.  Most applied risk allocation models are clever but useless tools.  Perfect in a vacuum, but down right dangerous in the real world if you don't look at the assets underneath or the environment driving their price.

I remember studying Artificial Intelligence for fun in college and the professor discussed context in regards to a chess playing computer.  The computer might be able to analyze deeply the possibilities of the chess board, but wouldn't know that it was time to leave the game if the room caught on fire.  The context was limited and always would be by definition of a closed model.  The inability of most risk models to incorporate variable context, regime shift or environmental state shifts,"such as NINJA loans" coupled with our over-reliance on them is a dangerous proposition and sadly one we haven't learned yet.

My suggestion would be to drop any mean variance portfolio or bank risk model which assumes to show via historical data the risk relationship between economic instruments.  I advocate a return to simple leverage metrics and the elimination of VaR. 

Basic rule of thumb leverage is in-efficient but stable, a trade off many would be happy to make right now.  If it can't be understood within a traditional banking context don't touch it.  Ignorance admitted isn't a bad thing, false pride is.

Electric stimulus crunch? Generating utilities could see 10% revenue declines

A lot of renewable energy make sense when one looks at the 20 year levelized cost of energy. All one has to do is make assumptions about Coal and Natural gas inflation over the next few years and wind and some forms of solar start looking good  from a risk management perspective in a power generating portfolio.  Here is a spreadsheet tool for comparing renewable energy costs with fossil based equivalents. 

Please note oil generate only 1.6% of electricity in the US. So please ignore any pundit comparing electricity generation via wind and solar with oil prices at $X price, it is a red herring and something for the CNBC crowd.

Crunch Time!

The new stimulus package is going to include $54billion for efficiency.  If one assumes the efficiency investments have a 6 year payback period or IRR of 16.6%, then we might see $8.6 billion less spent on fuel for heating and electricity due to efficiency savings.  If we assume a 50-50% split for electric and fuel based energy, there could be a $4.3billion contraction going forward in the US electricity market due to the stimulus.

This isn't large for a +$320b industry, but look out if a green smart grid gets put in for transmission &distribution, then it could be a 10% upstream demand side hit going forward.  I am for the green smart grid as it makes things more efficient, safe and reliable over time, but it is important to realize change has ripples.

The real crunch: The larger impact comes from national HVDC transmission installation and efficiencies in distribution. You can read about the US grid here.

ABB thinks upstream demand could be reduced over 10% with an HVDC grid and smart distribution.  See report here: read with care, this is a vendor speaking. 

That could mean a +10% decline in demand for power generation in a few years if implemented, a big decline in upstream generating requirements.  From a value investors perspective, some power generators moats are going to get smaller as the effective grids get larger and more efficient.  Depending on the wholesale power to retail mark-up, it could be $10billion in lost upstream generating revenues. 

Utility experts feel free to submit your estimates in the comments.

On top of the decline in revenue for power generators, a national HVDC grid and/or making the current multiple US grids more efficient would mean the retirement of many Peaker plants and other fixed assets held by generating utilities.  

Current grid congestion means peaker plants and other capex intensive resources are required to be available 24/7.  Depending on the regulatory regime, firms receive compensation for a fair ROI on these assets.  In light of efficient grids a lot of these assets will become redundant shrinking the rate base.  Regulatory regimes vary from state to state and region to region.  EnerNoc and other smart load management systems could see their addressable market shrink dramatically.

A smart green grid makes sense.  Some wind farms recently had negative electric rates due to poor grid infrastructure.  The generating utilities and FERC have been far to cozy for too long. 

Upstream electricity could shrink as will coal related rail traffic and gas deliveries via pipeline. That might not be a bad thing on the macro time scale.  Some utilities with generating assets instead of just T&D (transmission & distribution) may face a bit of a challenge as the assumption of 1%/year growth reverses quickly and they are fixed with usage assumptions and fixed debt loads.  Earnings could suffer. 

Ever wonder why those smart meters never made into your home or why you never had variable rate electricity pricing?  The technology has been around for 15 years.  The problem is no utility wants to pay for the privilege of shrinking the consumption rate of their product.  Variable rate pricing and in-house meters cause behavior shifts in domestic users leading to 3-10% declines in consumption.  Makes a person think a little about what will happen if these become standard. 

I am not advocating an energy or policy position, just saying, it pays to look around and see who the winners and losers are from inevitble change that is happening, in the long run efficient, sustainable use of resources benefits everyone.  Decisions made in energy typically run for 20-30 years, so before you jump to a decision think about your children's world.

Hedge fund industry reality 2009

The tide has gone out for hedge funds.  Many have indicated that they "feel"  X% of the hedge fund industry will go under.  Perhaps a little data and a crude stab at a model will improve a rather dire debating point.  The term hedge fund basically means anyone who believes they can manage money and get away with justifying a 1-2% management fee and 15-44% performance fee, it has become more compensation class than asset class and has been for years for many.  True sustainable alpha is rare.  Here are some rough estimates and stats, please inform me of changes or errors:

• Estimated 9-11,000 funds as of 2008
• Estimated average annual return for 2008 -18%
• Median AUM (assets under management) of $80m

 Using these crude figures and a little back of the envelope calculating we can come to the conclusion that there will be >20% hedge fund closures in the next year. My own guess is closer to 40% or 6,600 left standing close of business 2009.

It is mostly the little guys who are going to be squeezed out.  Many funds large and small shouldn't have been there in the first place. Although, I haven't seen the data, my guess is that Fund size in terms of AUM follows a simple power law distribution. 

If we assume the median fund has $80m under management we can model the situation going forward.  Please note that $100m AUM is the magic level where pension funds etc. used to have a sniff at you about doling out an allocation and other such heady acts of a bygone era.

Most fund companies are small cottage funds with an average staff of 4 professionals, that equates to more than 20%x11,000x4 roughly 8,800 hedgies looking for jobs soon, not including support staff.  If we assume the average fund in the past had 5 professionals x 11,000 funds, then the industry supported 55,000 Sr. roles.

Assuming a 5% natural turnover within fund companies that equates to 2,750 open position in a non-growth year.  With shrinkage of 20% that means maybe 2,200 open positions available in 2009.  Assuming no external talent enters the competition for a role, a laid off hedgie has a 1 in 4 chance of finding a new role back in the business.

A simple operating model for a small hedge fund with $80m AUM may look like the table to the left.  Spreadsheet available Download Hedgefundblowupometer
Of course each fund is a different animal with lower or higher costs, staff count etc.  Please note the base salary for the median hedge fund trader is far from stratospheric in a poor year.  I shall refrain from comment in regards to Sr. bankers and other socialist ilk who receive bailout bonuses or collect checks from weak kneed compensation committees at the board bored level. 
The important lesson here is that during a recession, a positive feedback mechanism makes it more difficult to find a job.  The number of available jobs shrinks at the same time those with experience and skills on the market expands.  This multiplier effect in a single industry is dangerous especially one that was falsely puffed up on 5-10% credit and ridiculous leverage in some instances.  Spread across an economy this feedback is downright debilitating.  GD 2.0.

Simple Graham & Dodd growth valuation tool

Graham and Dodd had a simple formula for valuing growth companies based on estimated earnings growth and the triple AAA bond yield. 

I am not passing judgment on its efficacy, but thought I would make a spreadsheet widget for people to use if interested.  utor procul vestri own periculum.

Even Warren Buffet is characterised as a "growth" investor using principal component analysis of his portfolio holdings.

Buffet argues there is no difference between growth and value, it is all value to him.  Here is the tool, let me know your thoughts. Download the Graham & Dodd tool here.

This model is a simple tool which really says nothing about the market a company operates in, it competition or management, it is a single tool which may or may not be useful.  Should you hear the Graham and Dodd model quoted often as a useful tool, you can be certain its useful life will be limited for the near future.

Post Model Manifesto

Paul Wilmott, eminence grise of the quant world has posted a manifesto which among other things points to the wrongs of the past, but neglects the fundamental truth of models and risk. 

In applied systems the rules determine the outcome. 

The very concept of a "truth" in modeling or risk is flawed.  I am ashamed to use the term, but will.  Markets are post modern.  In that I mean that sustainable markets rely on multiple truths: perceptions of risk, reward and timing.  If everyone has the same truth, the system fails as it teeters toward an extreme.  A uni-model system is less stable than multi modeled one.  When everyone looks over the edge of the boat on the same side at the same time it tips over.  Everyone working in a Basel 2 or VaR type of framework will share very similar opinions.  We need radical dissent and it to hereticaly ignore the pursuit of collective models of risk.

If all groups pursue or use the similar models with slight tweaks they will reach similar conclusions, at the same time and like everyone on a life boat looking over the same side, tip it over.  The unified presentation of "correct" or "true" models and unified risk is what caused the problem with CDOs etc. in the first place.  The ratings monopoly on Truth combined with similar models and trader laziness among other things tipped the mortgage boat over. AAA all the way.

Bubbles and manias are caused by everyone sharing similar models and horizons of risk.  Whether it is gut feeling "mental model" that says Tulip Bulbs can only go up or a CDO valuation cranked through the latest hot quant model, shared truths become false truths.  Lets stop chasing them or forcing banks to pursue them.  Wilmott's proclamation is nothing but a request for the red queen to run faster, individually that is fine, but a collective pursuit is dangerous.

Basel 2 with its self similar approaches to modeling is crying out for reformation if not outright elimination.  Forced use of mean variance type models will induce nasty problems as everyone seeks to game the systems capital weighting in the same way.  SIV 2.0 will beget Credit Crunch 2.0, in a bigger more sophisticated way.  Our faith will be emboldened by faster computers and brighter quants. "modern" finance always comes up with new ways to blow up. Speed and greed translate into leverage in the financial world.  It may take 10 years, but we will get there and we will be sorry.  The first group to use a model will make money and the next few less, by the time it becomes a "risk" management tool, the boat tips over.  The model bears the seeds of its own destruction in a Popperian sense.

I prefer my inevitable banking failures small if you please.  Lets minimize systemic risk, by decoupling the system.  De-coupling means getting everyone to form their own opinion of risk and reward.  Naive inter bank trading "caveat beware" should return.  Yes this will lead to a less efficient system, but it will be a more stable one.  It is a Tobin type tax if you will, that acknowledges the unknown and the unknowable inherent in any economic transaction.  Get me there slower but safer at the over all system level. 

In every other engineering business the rule is safety first!  Safety should come before optimization or transactional efficiency.  A few basis points for the bankers bonus isn't worth the collective risk. Slow down.

When the models and regulations rule, actor's risk horizons and opinions converge.  This convergence on a truth put the system in a dire and unstable state.  Does anyone find it interesting that the only entity apparently capable of bailing out the current crisis is the FED which doesn't have a Tier 1 capital requirement? God help us if/when the bond market wakes up to the shenanigans going on at the FED.

As Churchill said," where all think alike, none think much."  Dissent and ignorance of counter party risk counter intuitively provides stability as actors tread lightly upon the stage of finance.
 

My credo is lets all use models mental or mathematical, but let none be beholden to a collective "truth" of them a la Basel 2 or regulatory regimes.