Quantifying the Unquantifiable

I found this neat article on 13 tipping points by Market Watch’s Paul Farrell.  I managed to read it in between multi-napping and watching the Dow go down in response to the the State of the Union Address and bad  numbers coming out of housing.  Then, I watched the market return to a more neutral position following Ben Bernanke’s second day of congressional testimony .  I decided it might be a good idea to talk about how information comes into markets and how markets react to that information using his article.

The article is subtitled why “Obamanomics may backfire, triggering the next Great Depression”.  It’s actually less about Obamanomics than it is about the number of unquantifiable ‘shocks’ to the macroeconomy that may lurk out there and panic or entice Wall Street.  These shocks (called so because they shock the economy and frequently appear unforeseeable) represent a huge amount of risk but can’t be easily written into the mathematical models.  They wait out there like a cat ready to pounce on an unsuspecting mouse.  Farrell’s basic point is that we don’t know right now if the stimulus package will work because there is too much unquantifiable risk out there.  However, just because the mouse is unsuspecting, I always think that there must be ways of detecting that big old cat. Hence, I research.

Usually the chance of the shock can be added into a model using Bayesian ‘dummy’ variables.  They take on either a 1 or 0 value and are ‘weighted’ by the probability of realizing the event.  So, the 13 variables that Farrell lists could be used to cause a negative shock (using a negative one for the occurrence of the event) times its Bayesian probability (say something like a 20 % chance of occuring vs a 80% chance of not occuring).   Shocks can also positive impact by using a positive 1 for the occurrence of the event say like a technological advance that just suddenly happens.

Farrell identifies these 13 things that are possibilities that haven’t been “quantified” by most Wall Street Risk models because these models focus on getting at the risk and pricing of an asset using asset-related events, rather than macroeconomic-related events.  Here’s his list.  It’s a basic what’s what of tinfoil hat scenarios.

  1. Massive debt: government, private; Fed printing money, tax increases
  2. Population: exploding demand, resources depleting, conflict, rebellion
  3. Lobbyists: feeding frenzy, 40,000 run Washington, sabotaging democracy
  4. Derivatives: $683 trillion hiding in shaky global “shadow banking system”
  5. Petro czars: Exxon, Saudis, Chavez, Iran — all vulnerable, unstable, risk
  6. Universal health care: 46 million uninsured; costs inflating debt
  7. War on drugs: massive global failure: Afghan, Mexico, Latin America.
  8. Deflation? Inflation? Stagflation? 1970’s sideways market ahead?
  9. Entitlements: Social Security, Medicare, drug benefits may soon sink us
  10. Politics: “Grand Obstructionist Party” Or “New Contract with America?”
  11. Savings: sabotaging consumer spending, the engine driving the economy
  12. Climate change: Pentagon sees increasing tension, triggering new wars
  13. Socialism and nationalization: will free markets return, or sink us?

tippingpointUsing Malcom Gladwell’s idea from the Tipping Point (a book club selection that Readers of the Confluence will recognize,),  Farrell isn’t sure if any of these dicey situations will actually reach that critical place where the event impacts everything it touches.  Hence, becoming one of Gladwell’s Tipping points.

More significant, although invariably left out of Wall Street’s equations, true economic tipping points will grow to a “moment of critical mass, the threshold, the boiling point,” according to author Malcolm Gladwell, where “change” (whether positive or negative) is “unstoppable.” And although left out, these macroeconomic variables can account for over 90% of the risk in an economic equation or derivatives contract, as we’ve discovered so painfully this past year.

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