The Patient is Dead Part II

Previously, here, we broke down what was a tad wobbly with The Glorious Free-Market System in the US pre-2004.  We’re going to continue the autopsy, at least from my viewpoint of what’s going on with our global economic meltdown.

The 12 to 1 market valuation pre-2004 was based on some fascinating calculations of the net worth of the investment houses and the policing done by the Securities and Exchange Commission. 

The policing is easy to report on:  Not much done.  Yes, the SEC was understaffed, had no regulatory teeth and rarely attracted the best and brightest, as Wall Street paid more.  Coming out of MBA/Law/Accounting school, with $50,000 worth of debt, who would you work for?   Government, offering you a starting salary of SFA as a drone so you could pay off your student loans by 2020, or Wall Street where your student loan would be a rounding error after your first years’ bonus? 

Hmmm.  I’ll take Junior Masters of the Universe for $200 please Alex.

Of these Junior Masters of the Universe, some were statisticians and mathematics geniuses called ‘quants’.  Quant is a slang for Quantitative Analyst, the kind of person who can, using very high-end math, reduce a company to a formula that expresses risk and reward numerically.  (I am over-simplifying to the point of crazy-talk here, but bear with me)

Your credit score, as a very simple example, is a Quantitative Analysis of your credit worthiness that a lending institution uses to determine if you get the car loan, or the mortgage.  Assuming you have a job, some debt, some savings and so on, the credit score is a three digit representation of how risky you are to the bank, to default on your car loan. 

The lender doesn’t want to own your car as they’ll lose money on it paying the repo man, selling the car at auction and all the paperwork needed to retire your loan.  They want you paying interest, eventually owning your four year old car. 

To make quick decisions, they need a way to judge your ability to pay for four years and a credit score is a ‘good’ way to do it.  Decisions can be be made automatically, as long as you score over whatever number the lender decides is the lowest threshold they’re willing to risk.

As I said, this is a very simple example, but you get the essential idea.  The granting of credit becomes a mathematical formula. 

The same math was applied to businesses, with several hundred times more variables involved.  Eventually it got to the point where buy or sell decisions were automated.  One could look to the 90’s where brokerages were moving vast lumps of money around by the second, almost completely without human intervention, as the math could be linked to some reasonable programming skills to test for variances from the ‘norm’ and make a buy-sell-hold decision and then execute the trade.

Automagically the buyer and seller generate the orders, commission is calculated and paid, the money is moved, the banks get their service fees and the cycle starts again. 

As the human, you come in at 9 am and see that you’ve bought and sold a bunch of stuff overnight, paid and received commissions, made several thousand in profits and now have a bunch of ‘new’ money sitting in your account, which is being bought and sold to increase your profits during the day while the North American markets are open.  All you did to earn it, was be asleep overnight and let the program do its work.

Multiply times several hundred thousand and that’s what banks, investment houses and governments do every second.  As long as the mathematical assumptions are good, it all works reasonably well.  Notice that caveat:  as long as the mathematical assumptions are good. 

Part of the assumptions is the credit ranking of the debt.  Who does the credit ranking for big corporations and governments?  Standard and Poor’s, Moody’s Fitch and the rest of the rating agencies, like Dominion Bond Rating Service in Canada. 

How do they come up with their judgements?  Quantitative Analysis of the organizations involved, using their own, proprietary, analysis tools designed by quants who scour data looking for minute by minute anomalies in the ‘value’ of the organization they’re looking at, based on a different set of mathematical assumptions, variables and trends, out to the seventeenth decimal place. 

Then they spit out a value:  A, AA, Aa (if you use Moody’s) AAA, B, BBB- and so on, which plugs into the other programs to generate buy-sell-hold decisions in other organizations. 

A change in the rating of a company, as you can see, can have extraordinarily wide-ranging effects, as you’re changing one of the mathematical assumptions being relied on by another number-crunching quant in another company.

Now, track back and see how the 12 to 1 market cap rule is affected by the valuation of the investment company.  A single change, then run through the various interrelated transactions that peg the value of the investment, can jigger the discount used to determine if the investment house is compliant or non-compliant with SEC rules. 

Done well, meaning on a financially sensible basis to protect against the whole investment house going broke, a change means some investments don’t get done as they’re too damn risky, as defined by the Quantitative Analysis.

In 2004 the rules were changed by the SEC, after being nagged into a coma by Wall Street.  If your investment house had more than $5 billion in value, you could exceed the 12 to 1 ratio of debt to cash, regardless of how you calculated it and you kept your ratings high. 

It didn’t matter how second-by-second the calculations were done, using whatever assumptions were thrown into the formula.  Nobody understood them to start with, so the SEC decided to trust the financiers, who trusted the quants, who trusted S&P et al, who trusted their quants, who trusted the research, who trusted the math.

Merrill-Lynch, as an example, worked their debt-to-cash ratio up to 40 to 1.  There was no way in hell if Merrill-Lynch had some debts go bad, could ever have enough cash on hand to keep from going broke.  Even selling all the Merrill employees as meat at $2.10 a pound, there wouldn’t be enough money around. 

(I’m certain there was an analysis done of the actual value of Merrill-Lynch staff as meat, as well as the value of the paper clips in the various offices, both as paperclips and as recycled steel, leading to a decision to sell all the paperclips and to add more potatoes to the staff’s cafeteria menu.  I’m kidding.)

Except the quants, doing their assumptions, could ‘prove’ that Merrill, or Lehman or the others involved, were perfectly sound, deserving of their high credit ratings, and should be left alone as The Glorious Free-Market System will self-police and self-correct.  Selah.

Since everyone was making money hand over fist, it must be true. Right?

Enter Asset-Backed Commercial Paper with a AAA rating.