15 September 2017: Resignation

At the moment of writing this first part of today’s blog post, US stock futures were hardly moved by another North Korean missile test. Is ‘resignation’ a word that comes to mind? Or will we see a (late) reaction of markets during trading hours? Although political tension is on the rise with respect to North Korea, investors are shocked the most by unexpected things. In this case, another missile test was definitely not something unexpected. Maybe one should not be surprised if markets will go down in the case of the absence of yet another North Korean missile test in the near future…

white-chapel-logo-small

The Dow closed at a record for a third session in a row on Thursday, but the S&P 500 and NASDAQ suffered losses. The Dow gained 0.20%, the S&P 500 lost 0.11% and the NASDAQ closed 0.48% lower. Volatility was not really going somewhere. The VIX closed 0.57% lower, whereas UVXY ETFs recorded a small gain: +2.29%. XIV ETNs had to take a step back: -1.31%. For a change, Danny Daredevil was yesterday’s winner. (A positive effect of the get well soon cards he received?). His RSS rose to 51%. Adventurous Anny is still holding cash. Solid Suzy and Lazy Larry saw their RSS drop to 32%. Their AAR remains at a healthy 69%.
None of our models gave a trading signal at the end of yesterday’s session.

Model  Holds Start date

RSS

YTD

QTD

AAR

Danny Daredevil UVXY 1 January 2016

51.03%

-62.53%

-65.30%

27%

Adventurous Anny Cash 6 March 2017

3.84%

3.84%

-25.11%

7%

Solid Suzy XIV 6 March 2017

31.85%

31.85%

5.34%

69%

Lazy Larry
XIV 6 March 2017

31.85%

31.85%

5.34%

69%

RSS = Return Since Start | YTD = Year-To-Date | QTD = Quarter-To-Date | AAR = Average Annual Return

 

RS_v05-smallRené’s Reflections @ Friday: One snowflake too many

To accurately predict when a market crash will occur, is the Holy Grail of investing. Why is it so important? Because money makes the world go round. Investors who are caught off guard by market crashes, risk losing an awful lot of it — often more than they can bear. While on the other hand, those who ‘saw’ it coming and took the right precautionary measures, typically make their fortunes under such circumstances. Why is it so hard to accurately predict when the next market crash will occur? The stock market is an enormously complex system with its own inner dynamics. It is a system which maintains a certain steady state until a so called ‘tipping point’ is reached. This is when a perturbation occurs that causes tolerances to be exceeded, the system to be destabilized, and pushed from one dominant dynamical pattern to another.
This all sounds very scientific, you might think. And if it is scientific, it should be possible to predict when such a tipping point will occur, right? Well, the difficult part is: ‘when a perturbation occurs that causes tolerances to be exceeded’.
Research has shown that there are similarities between economical crisis and natural phenomena, like earthquakes, hurricanes, volcanic eruptions, and epidemics. My favorite metaphor to describe the dynamics of market crashes, is that of the avalanche. The way they slowly build up before they finally suddenly hit, has a lot in common with the way the stock market works its way toward the next crash. We know that it starts with the first snowflake. Then many more snowflakes follow. Innumerable snowflakes accumulate into a snowpack. Over time, the amount of snow becomes so much that the weight of the snow becomes too high for the strength of the snowpack. At a certain point, the tolerance of the snowpack will be exceeded and as a result, tons of snow suddenly come thundering down. But how do we know which snowflake sets it all in motion?

Literature:
Dynamical analogy between economical crisis and earthquake dynamics within the nonextensive statistical mechanics framework: Complex system dynamics
Stelios M. Potirakis · Pavlos I. Zitis · Konstantinos Eftaxias
Market dynamics immediately before and after financial shocks: quantifying the Omori, productivity and Bath laws
Alexander M. Petersen · Fengzhong Wang · Shlomo Havlin · H. Eugene Stanley