"Towards A Low-Diversity Trap": Visualizing The DNA Of A Market Crash

Submitted by Alessandro Balata and Francesco Filia of Fasanara Capital

Analysis of Market Structure: Towards A Low-Diversity Trap

Ever since early-2017, our theory has been that multiple years of monumental Quantitative Easing / Negative Interest Rate monetary policies affected the behavioral patterns of investors and changed the structure itself of the market, in what accounts as self-amplifying positive feedback loops.

Fake markets, where artificial money flows killed data dependency, affected market functioning and changed the structure itself of the market (May 2017).

The positive feedback loop between fake markets and investors created system instability, and divergence from equilibrium (July 2017). That is the under-explored, unintended consequence of extreme monetary policymaking. A jammed-up, stuffed-turkey market system, where it is easy to detect heavy concentration risks, all the while as its size (i.e. valuations across both equities and bonds) got ginormous:

  • Concentration of size on few top players: top 8 AM shops account today for $22trn, from $8trn in 2006
  • Size of ‘passive’ or ‘quasi passive’: considering leverage and turnover, ca. 90% of daily flows in equity today are passive
  • Correlation of risks across investment strategies: ca. 90% of strategies today are either TREND-linked or VOLATILITY-linked


As we try to substantiate the view with hard data, we now further analyze the market structure across the two dimensions which may well represent its fault lines:

  • Concentration of size on few top players:   we use as proxy the top 22 asset managers globally
  • Size of ‘passive’ or ‘quasi passive’: we use as proxy the top 2000 ETFs, as represented by their largest 350 since 2007

We focus on largest ETFs and largest Asset Managers as we believe them to be the cracks in the financial system, the fault lines that lead to market fragility, hence our focus on them as a meaningful proxy for the broader financial market.


The analysis that follows is powered by our Fasanara Analytics team, a proud addition to the Fasanara family of late. It is not intended to be a finished product, but rather a work-in-progress, along the way of truth-seeking data mining. Any feedback/critique, please reach out, happy to collaborate and incorporate.

Our analysis framework borrows from complexity theory and network modelling, we investigate phase transition from one state of the market to another by applying ideas from earthquakes prediction, information theory and pure mathematics.

We model the market as a network of agents (the nodes of the figure below) whose strength of interaction (edges, distance) is computed using a non-linear transformation of the pairwise correlations; for details on the network construction please see Onnela et al. “Dynamics of market correlations: Taxonomy and portfolio analysis”.

We provide a visualisation of the market structure as modelled by a graph where each node represents an ETF, and the length of the edge represents the strength of interaction (inversely proportional). Please note the density/crowding of the nodes (market concentration) in September 2008, and how it looks after the pressure is released, in the healthier conditions of 2010. The stiffness of the market increases again after 2015, leading to a current situation of high density and potential danger as the market is no longer able to absorb shocks.

All in all, we observe signals that a phase transition in the passive investment industry might be approaching, as shown by our analysis of the Asset Management and ETFs segments of the industry, which give similar results. When coupled with their size, and the tight ties with financial markets at large, we believe systemic risk are at or close to the cliff, ready to transition.

Similar levels of fragility, as defined and measured in this paper, were visible in the most recent proper crash of 2007/2008.


How does a crashing market looks like in terms of market structure?

Here below we pit the healthy faces of the market, in peaceful blue-sky environments, against the ugly faces revealed during periods of stress.

One big annotation: no truly meaningful crash occurred ever since the Lehman-moment. Here we only see timid, tepid, shallow, fleeting market sell-offs. None of them lasted, if anything they got more and more irrelevant over the years as the buy-the-dip mentality compounded. Most importantly, none of them look even remotely like the one we expect in the not-so-distant future for markets. Still, they can be analyzed as ‘small-scale rehearsals’ for the Big One approaching, and certain general properties of their structure can be learned.


Where does the current market structure belong? It may belong to the list on the right, the ugly faces of the market in the midst of a stress period.

With one notable difference: there is no crash today. Today’s market structure looks like the market structures visible during flash crashes, without being in one.

It may be yet another signpost, in a long list of early warning signals, that the market system is full, stationing on paper-thin ice, ready to transition.


As background material of our ‘Critical Transformation Hypothesis’ for global markets, this note further analyses the structure of the market, and how it weakened under the force of positive feedback loops between public flows and the private investment community. We looked at largest asset managers and largest ETFs globally as a meaningful proxy for the broader financial system, as we think they represent the weakest links in the market lithosphere. We find that, over recent years, measures of market diversity fell in lockstep with measures of entropy, all the while as concentration rose to record levels. Entropy in the ETFs market decayed at an average rate of 4.5% per year in the last ten years, and its trend-line has almost reached 2008 levels. Measured as ‘’average closeness centrality’’, concentration in the ETF market increased by a striking 12.1% year-on-year since 2008, and its trend-line reached levels only seen in 2008.

Looking at systemic risk through the lens of complexity theory, we attempt a visualization of how the market structure on passive ETFs evolved over time. We visualized how the market structure weakened progressively over the last ten years, becoming more concentrated, entropic-fragile, and ready to snap. We analyzed the structure of the market network during good and bad times, trying to identify the DNA of a market crash. The current market exhibits the typical structure visible during flash crashes, yet despite not being in one. We conclude that the market system is full, stationing on paper-thin ice, ready to transition.

The analysis is powered by our Fasanara Analytics team, a proud addition to the Fasanara family of late. It is not intended to be a finished product, but rather a work-in-progress, a live project on systemic risk as a complexity problem, which forms the conceptual framework around our ‘Fat Tail Risk Hedging Programs’. Looking forward to any feedback/support in taking this analysis further ahead.

To read the full piece, and download the PDF, please use this LINK


itstippy Thu, 07/12/2018 - 20:05 Permalink

I've read this article three times and I'm still not clear on the methodology.

"Our analysis framework borrows from complexity theory and network modelling, we investigate phase transition from one state of the market to another by applying ideas from earthquakes prediction, information theory and pure mathematics."


The chart porn is bizarre; they look like illustrations of nerve bundles lifted from a medical textbook.  Am I getting senile or are others struggling with this piece?

TheEndIsNear itstippy Thu, 07/12/2018 - 21:02 Permalink

Yes, the charts weren't very helpful, but the gist of the piece is that the instabilities present in any dynamic system with positive (reinforcing) feedback and complex control loops is inherently unstable when certain boundary conditions are exceeded, and we are near the point where the system becomes unstable and undergoes a phase change or discontinuity, such as when the denominator of a differential equation goes to zero.

"itstippy" mentioned the similarity of the charts to bundles of neurons, which is actually a good analogy. In the case of the human brain, which does have both positive (reinforcing) and negative (inhibitory) circuitry, an instability would for example be an when an epileptic seizure occurs because of too much positive feedback throughout the entire brain, causing all neurons to fire at once. .


In reply to by itstippy

lew1024 itstippy Thu, 07/12/2018 - 22:27 Permalink

Standard network thinking, standard use of entropy as measures of variability. The 'nonlinear transforms' could be anything, but the fact that their networks make sense means those don't produce bogosity, so they were chosen well.

Complexity theory is too big a subject to know what they use it for without reading the paper, and who has time,

But, all in all, anyone who reads evolving-in-time analyses network structures will find it familiar.

Keeping in mind that models are maps, not the territory, it is at least interesting and suggestive.

But nobody in their right mind would be in this market, this is just another reason why not. This is  time when great fortunes are lost, not made.


In reply to by itstippy

Maj Thu, 07/12/2018 - 20:33 Permalink

That is some really impressive chart porn.  I actually looked and referenced each one just out of mother lovin curiosity.  From bar charts to blob charts to pie charts, that's some of the weirdest chart porn I've ever seen.

ZIRPdiggler Fri, 07/13/2018 - 01:46 Permalink

This is a bunch of ridiculous nonsense. I can sum it up in one sentence: QE causes people and banks to hoard money and creates deflation. Those stupid Austrians and neoclassicals think it causes inflation. The QE experiment hopefully proved otherwise so we dont ever need to do this again.

Money_for_Nothing Fri, 07/13/2018 - 09:01 Permalink

QE caused inflation in financial assets. Current US policies are causing inflation in real assets but in a way that works for most US Citizens. World being forced to adjust.