Next Gen Mathematics

“Do not worry about your difficulties in Mathematics. I can assure you mine are still greater”

-Albert Einstein


The good news about the difficulties associated with applying chaos theory to financial markets is that it’s getting less difficult as access to information expands. That said, everything is relative. These are still very early days in terms of how governments, institutions, and individuals apply Next Gen Mathematics to managing risk in a globally interconnected marketplace.

One of the main roadblocks to successfully applying chaos theory to asset allocation models and cross-country analysis is that many fixed income and derivatives markets do not trade transparently. That is, unlike most natural systems where chaos theory is applied (like the weather), only a select group of buyers and sellers of derivative securities know whether it is sunny out or raining.

I have heard many people argue over the course of the last 72 hours that allowing derivatives to trade over-the-counter is going to “hurt liquidity.” In the short run, for the narrow marketplace of players interacting, that may very well be the case. In the long run, arguing against an expansion of markets and the subsequent reduction in costs to transact in those markets is basically arguing against transparency. In principle, and in science, I don’t buy it.

Having lost the odd toe attempting to trade everything from currencies in swap to making short sales in illiquid Chinese shell companies, I can at least tell you that one of the main reasons why a buy-sider might argue for not regulating markets is that you can make yourself a ton of money by picking off the monkeys out there who not only don’t know what they don’t know, but they can’t see someone who is supported by a multi-billion dollar cost structure.

I have worked with some hedge fund portfolio managers who are not mathematically oriented,  but by trading markets have a form of chaos theory embedded in their practitioner’s experience. They learned by doing. This is good, for them. But not for our economic system. If an asset manager finds an edge, the last thing she is going to do with that is share it with a world of wannabes looking to evolve.

Let’s consider this as a practical matter and look at how different people I have worked with consider “earnings season”:

  1. You start with a market narrative that “earnings season is going to be great”
  2. Stocks start to discount this positive expectation (SP500 up 78.6% since last year’s low)
  3. Earnings season hits, and there is price action that follows the “news”

So, a really good risk manager will consider the driving part of the Street’s story line (“earnings are going to be great”) and score that, quantitatively, relative to the price action born out of the “news.” I have seen some managers build a base of “hot” inventory that measures price moves on a 1-3 day basis into/out of news. I have seen others overlay that with volume and volatility studies. I have seen some people ignore all of the aggregated data and only focus on the stock they own.

The upshot of how all of these different personalities measure different kinds of data is the definition of chaos theory – “studying the behavior of dynamical systems that are highly sensitive to initial conditions” (Wikipedia). As opposed to waking up thinking you know exactly what is going to happen, you should wake up accepting that everything is grounded in uncertainty.

Back to the real-time example…

  1. The SP500 has rallied +15% since the initial February Freakout about Greece.
  2. The Street’s narrative is “earnings season is going to be great”
  3. Coke and IBM report earnings before yesterday’s open, and both stocks close down almost -2% on the day

So now what? What if you only follow AAPL and you are right convinced that the company is going to be the largest on the planet and that yours is a differentiated thesis that you should be paid 5 and 50 for? What if you didn’t see GS have a huge intraday reversal to the downside on big volume after a monster earnings report? What if you did see all of this and you are waking up to the following price reactions to earnings reports from last night?

A)    Trading up on earnings: TPX +10%, AAPL +5%, ALTR +5%, TUP +4%, STX +3%, TCK +3%, TSS +3%

B)    Trading down on earnings: VITC -31%, TSFG -29%, CSIQ -9%, SNV -8.%, JNPR -8%, CREE -6%, NUVA -6%, YHOO -4%, GILD -3%, PLXS -3%.

Now everything, of course, should be scored relative to the last trading price. No, that’s not the calculus the US Government uses when inflating your property taxes relative to marked-to-market trading prices of homes. It’s certainly not the ideology that Tricky Dick Fuld upheld as the written gospel of marked-to-model at Lehman Brothers. It’s just real-time math, and you need the Next Gen of Mathematics to solve for what to do.

How you interpret these real-time prices in your mathematical models relative to the one-factor we have briefly addressed (earnings season) is up to you. To me, chasing a market that has responded this poorly to earnings news because AAPL was “great” is plain reckless.

When we overlay the degradation in the earnings narrative with global risk factors like sovereign debt (Greece 10-year bond yields blasting to all time highs this morning) or the rise of short term US Treasury yields to 1.02%, this really starts to get interesting.

Don’t worry about the difficulties associated with evolving. There is much work to be done out there on the proverbial ice of market transparency, and I can assure you that my team’s issues are still greater. We know what we don’t know.

My immediate term support and resistance levels for the SP500 are now 1199 and 1214, respectively.

Best of luck out there today,


Next Gen Mathematics - AAPL