Authors
The analysis that follows leverages on the collaboration of Fasanara’s open network of friends and partners; this time it is Andrea Marcello Mambuca, PhD student in Applied Mathematics, King’s College London. The analysis is also again powered by our Fasanara Analytics team, a proud addition to the Fasanara family this year. Within it, Alessandro Balata was instrumental. Mirco Lamperti, developer at Fasanara Capital, was then helpful in integrating new findings and techniques to our previous studies on quant warning signals. The analysis today is not intended to be a finished product, but rather a work-in-progress, along the way of truth-seeking within heavily manipulated markets (‘Fake Markets’), aiming at framing systemic risks in new inventive ways, more in tune with the transformational times we live in. Any feedback/critique, please reach out, happy to collaborate and expand the nodes in our open network!
How Close Is The Cliff?
How do you assess the vicinity of a major market crash? Do we have the right conceptual framework and tools to monitor the build-up of systemic risks and the approaching of a seismic shift? How can we go about quantifying market fragility and inherent vulnerability of the financial system?
In institutional markets and policymaking inner circles, we still think we know how and have it all figured out. In a show of hubris, despite the Great Financial Crisis of 2007-2008, the models used for the task are more of the same, variations of what used to be there before the crisis. The Efficient Market Hypothesis (‘EMH’) still holds, and volatility is still the basic ingredient for most risk management models, considered to be the best available measurement of risk. This runs counter to ever-recurring empirical evidence: the EMH has been proven to fall short of explaining real world dynamics, while volatility is a bad predictor of impending chaos on systemic-scale (if anything, as warned by Hyman Minsky, it is its bedrock!). Behavioral finance helped ameliorate the shortages of the EMH, while agent-based modeling / stress testing / expected shortfalls helped fill the deficiencies of vol-based models of risk: however, both of them failed to produce any new-age broad-ranging framework of analysis, nor did they produce much in terms of practical toolkit for investors to use.
The dominant narrative still holds it true that a ‘bubble can be known only in retrospect’ and timing is impossible to determine’ for a crash and such and such, therefore not demanding additional speculative search. This may be too easy an excuse, a case of astonishing plausible deniability and drop of responsibility, at a time when heavy interventionism in public markets is otherwise perpetuated without remorse, for the better part of a decade now. So, as a Central Banker, I can get markets to move the way I want by outright asset purchase programs on bonds and/or equities, but I cannot be held to blame if there is short-circuit, nor do I have a responsibility to monitor for it. Bizarre? Not really, as neither for asset managers nor central bankers there is much ‘skin in the game’ (we discuss it on pages 70-73 at this link). When we let human nature run wild, led and misled by perverse incentives, it will do as it did invariably across history, stopping at nothing until it crashes.
Transformational Times: For Markets, But Also For Systemic Risk
Systemic risks are morphing in nature from the far and recent past: they relate more and more to the structure of the market itself and less so to traditional sources such as banks and leverage/lending (
market risk becomes systemic risk
). This happens as
the industry of institutionalized asset management goes through transformational times and is heavily impacted by a confluence of global macro trends:
- new technologies: the rise of passive asset management, ArtificiaI Intelligence / Machine Learning, the 4th Industrial Revolution;
- a range of ETFs giving direct market access to retail-type investors,
- emerging patterns of consumer behavior for millennials when it comes to financial products;
- new-age of coordinated global central banks’ manipulation of price discovery for mainstream risk assets: world-scale central planning through the drug cartel of QE, QQE, NIRP, ZIRP, TARP, TLTRO, APP, OPERATION TWIST, RATE SETTINGS;
- related-problems of extreme income inequality and rising political and geo-political risks: nationalism, populism, de-globalisation of global commerce, end of Pax Americana, the rise of China.
When it comes to systemic ricks, macro-prudential policymakers have all eyes on the banking sector as the historical culprit of potential faults, but chances are that next time around the largest risks will instead materialize out of the financial market itself (we discuss it in this e-Book on pag 40).
In present times, systemic risks are emerging properties of a complex dynamic financial network that is going through a secular transformation, and the framework of analysis around them needs a proper updating too. At Fasanara, we try to wrap our heads around it reaching out to the disciplines of Complexity Theory, Systems Theory for clues.
Complexity Markets: A Conceptual Framework
The financial network is no different than other complex dynamic systems in physics, ecosystems, geology and social sciences. In so doing, they relate closely to the dynamics of criticality for energy, epidemics, epileptic seizures, extinctions, glaciations, earthquakes, the human brain, the climate. As such, we can learn a great deal from universal properties valid for all of them. Common concepts in Complexity Theory parlance apply to financial markets as much as they do to any of those fields and disciplines: critical transitions, tipping points and critical thresholds, positive feedback loops and basin of attractions, non-linearity and far-from-equilibrium dynamics, stochastic stressors and outbursts into chaos, phase transition zones and unstable equilibria. As such, we frame the analysis around ‘Complexity Markets’, and attempt at embedding the dynamics of criticality for complex systems by using the conceptual framework here exemplified:
- Analysis of Tipping Points (‘TPA’): break-down and independent valuation of the key dimensions of potential expansion of the financial system (which is to understand its basin of attraction). This is where we determine that the system has become inherently fragile, vulnerable to small shocks and unable to recover from perturbations, ready to transition.
- Analysis of Early Warning Signals (‘EWSA’): if we have reasons to believe the system has become inherently fragile, we look for confirmation signals to help assess a probability and time-scale of transition. What are the crash hallmarks and how can they help determine a probability of critical transition, the crisis signposts that indicate that system degradation has gone on long enough and a severe rupture approaches in time. General properties of complex systems in transition offer clues: critical slowing down, flickering/bi-modality, variance, autocorrelation/memory, skewness of swings, correlation/spatial patterns, pockets of stress.
- Analysis of Butterflies Effects (‘BEA’): what are the small stressors that can provide the final push off the cliff – for example political risk, populism / tariffs / de-globalisation, China-US’s Thucydides trap, idiosyncratic stories (Turkey, Italy, Brazil), interest rates reborn after a 40 years decline