Avalanches, share markets and the feedback loops that lead to an inevitable collapse.

Welcome to Finance and Fury - Listen to last Monday episode on Share as a complex system – today diving in to discuss the application of a complex system and a financial collapse – or pre collapse – talk about phase transitions – build up and slowdown – pre-collapse

1. Last ep - Complex is non-linear - more in the relationship of inputs and outputs than direct relationships of cause and effect
   1. Also Adaptive – so markets are evolving, dynamical as people will change their behaviour
2. Shares have three characteristics of complex dynamical systems
   1. highly unpredictable - due to their non-linear relationships / interactions
   2. contagion effects – panic or bubbles are things that spread very quickly – through being interconnected
   3. modularity – while the whole system is well connected parts of the system are more connected within than between, which may help its resilience, or the ability for the system to return to equilibrium after turbulence
      1. broad inter-connectedness of global markets - only increased with globalization, the vast network of factors at play, the flipping correlation between assets over time
      2. External crises create a sudden shock experienced by financial markets over history – creates a far-from-equilibrium phenomena which is common in complex systems.
   4. financial markets are typical VUCA - Volatility, Uncertainty, Complexity, Ambiguity
      1. they are interconnected, interdependent, non-linear and a structurally volatile complex system

Metaphor -

3. Avalanche – an avalanche is a complex system – Snowflakes and avalanches – avalanche is a good metaphor for financial collapse – systems analysis for avalanche is the exact analysis for the collapse of one bank, collapsing into another –
   1. As an example – say one Snowflake is one mortgage, or derivative position within a bank collapsing – how many does it take before one bank collapses – triggering more collapses in the system-
4. both avalanche and bank collapse are complex systems – going through phase transition – something used in physics
   1. Phase is a state of being – steady, collapse, rebounding – so a transition is going from Steady to collapse to new paradigm –
   2. Helps o study collapse of avalanche – complexity offers insights into how financial markets behave –
5. Important to distinguish between something that is complex and complexity –
   1. Something complex is linear – like a clock – or motor – constrained, but not complex –
   2. Complexity is different – parts are interactive and adaptive – which branch into infinity of outcomes
   3. You might know what may occur – but not why or truly how – has a massive computation problem – limits risk management
6. Example - back to the Avalanche – people at risk never really know when it will happen – but know that conditions are different and likely to affect chances – snow pack sides – systemic scale – larger = larger avalanche on expotential scale –
   1. Locate villages away =or stay above ridge line – or explode/descale and incur avalanche – cant predict but can try to stay safe
   2. Regulators help increase danger –
      1. allow Banks and derivatives are like snow packing up
      2. JP morgan to grow larger in size -like telling villages to build in path of avalanche

• Allowing value at risk as a regulation tool is like – building ski lift in path of avalanche

7. WS execs know models unsound – but like it due to higher leverage – use anyway – bigger profits and higher buonus
   1. Regulations know as much as they want to land a job In the banks after
   2. The everyday man is in the path of the avalanche while bankers higher on the ridge
8. A tipping point and subsequent inflection in any of those endogenous or exogenous subsets would clearly impact other subsets, and their own tipping points.

Phase transitions -

1. There is a space between order and chaos – this is called a phase transition zone where a system reaches criticality
   1. criticality – part of the self-organisation (characteristic in the last episode) – where investors have what's called a critical point as an attractor – in English: how many people need to sell shares versus buy for the market point
      1. Example – Say sellers are represented by S – and there are 100 people in a market –
         1. S=1 – little change – if B = 99 – market likely to go up further – but this isn't linear
         2. There is a threshold of S when reached that triggers more sellers
         3. Say S=10, this might trigger the next 20 to sell based around adaption and self organisation
         4. Now S=30, which might trigger the next 40 to sell = S=70 – now the market is in panic
      2. There is a threshold once reached triggers a phase transition – until the adaption and self organisation occurs to reverse the trend

• Probability of a collapse increases when the resilience of a system gets weaker

1. Resilience in a system is the ability to absorb shocks and to retain the same structure functions and feedback as before. It implies persistence, adaptability, transformability of the system. It requires a wide basin of attraction, a good balance between order and disorder. Essential to resilience is the presence of negative feedback loops.
   1. Resilience falls and the system nears a critical transition zone. The system stands at an unstable equilibrium, from where it can flip at any time on closing up to the tipping point.
2. Approaching criticality has tell tale signs and can suddenly and abruptly morph into a whole new contrasting system
   1. is a place where its resilience may get weakened to the point where disorder and randomness prevail, and lead into a totally different environment – i.e. going through a phase transition
   2. As the system approaches the peak in criticality - it can then drift away from an ordered predictable states into a chaotic unpredictable state
      1. Metaphor - where snowflakes suddenly accrete to form avalanches at some critical tipping point

Phase Transitions occur around Changes In Feedback Loops

1. How does the system degrade or reach criticality? How is resilience lost? How does it happen? – most common way is a change in feedbacks
2. Feedback loops are essential forces in the build-up of any system
   1. Have mutual causal interactions between the elements of the system. The natural world is full of it. Negative feedback loops are the internal stabilizing forces of a system, as they bring the system back into balance early on after small perturbations.
3. happens when self-correcting negative feedback loops weaken, and self-amplifying positive feedback loops arise.
   1. Self-amplifying positive feedback arise - $15trn printed in public funds from Central Banks - $10bn into passive index funds and similar investment products – feedback is 1) knowing this is happening and 2) continuing to invest for expected returns
   2. Self-correcting negative feedback weaken – the behaviours of the market adapt – eventually the 'free ride' probability lowers and people run for the hills and sell their shares – makes the markets fragile
4. Years of monumental Quantitative Easing / Negative Interest Rate monetary policies affected the behavioural patterns of investors and changed the structure itself of the market, in what accounts as self-amplifying positive feedbacks.
   1. This has been going on since 2009 – we are yet to see the unintended consequence of extreme monetary policymaking play out – but it created a system that through feedback loops has a far-from-equilibrium status
   2. We are entering a system where the resilience weakens and market fragility approaches critical tipping points
      1. Years of printing money and pumping it into hard assets like property or shares hasn't saved the economy
      2. Has created a zombie states in the economy making it even more fragile – leverage is up with little real growth

• A small disturbance is then able to provoke a large adjustment, pushing into another basin of attraction, where a whole new equilibrium is found.

1. So while it is impossible to determine the threshold for such critical transitioning - very probable that we are already in such phase transition zone, where markets got inherently fragile, poised at criticality for small disturbances, and where it is increasingly probable to see severe regime shifts.

Markets have entered into what complexity analysists call = the edge of chaos, a shift in feedback loops provokes a proximity to one or more critical tipping points. This is the zone where rare events become typical.

1. Becoming evident that there is a decreasing rate of recovery and a critical slowdown in the effects of feedback loops –
   1. a general property of complex dynamical systems is that as they come close to a tipping point – which leads to a major critical transformation – or phase transition - a relatively minor disturbance can be the first slow flake that triggers an avalanche
   2. Most likely cause at this stage is the news of if we have a recession and the gig is up – people will see that the monetary policy on steroids approach has failed and the market will adapt – it will have reasons to suspect the possibility of a critical transition and the early-warning signals may enough to cause a panic

The build up – helped with feedback loops - Financial Markets Are 'Complex Adaptive Systems'

2. The more the market gets feedback in information - the tipping points get nearer
3. markets are in an uncomfortable spot as the method of escape via lending cant
4. An exogenous or endogenous trigger can easily push the equilibrium out of its small basin of attraction – enter into a phase transition and enter a new equilibrium