How do you know that the share markets are likely to be in for a collapse?

Welcome to Finance and Fury

Last week – The lead up of markets in relation to complexity theory – phase transitions and feedback loops in markets

https://financeandfury.com.au/how-to-analyse-share-markets-by-treating-them-as-a-complex-system/

Today – look at the question – How do we know that we are in for a collapse – or better – what are the early warning signs in of a change in feedback loops triggering a phase transition a complex system

1. There are signals – complexity can pick up on but equilibrium can’t – Uber listing on the market with $1.5bn loss – signal
2. Today’s episode is a conceptual framework expanding on the previous episode – particularly focus on market fragility and what signals point to it increasing
   1. The last episode talked about how after a while the same positive feedback loop can create an increased instability in financial markets – this can occur between public and private investors – but after enough of the same feedback – exposes markets to the risk of a systemic risk escalation.
   2. But what sort of events act as generic early warning signals for chaos? – i.e. phase transition in the markets

To start –

how is the probability of critical transformations assessed? What acts as early warning signals?

1. Preface this – nobody can ever predict the exact point at which the system transforms – going through a phase transition of stable to collapse – financial markets are stochastic system – having a random probability distribution or pattern that may be analysed statistically but may not be predicted precisely
   1. Events that push the market out of equilibrium happen randomly – at any time – but the probability of one event triggering a collapse is low if the markets around a long term equilibrium between buyers and sellers –
      1. No net money entering or leaving the market
   2. But when more net money enters or leaves markets – what we can see is when the system (share market) has become inherently unstable, fragile, vulnerable – see bigger chances of large gains or losses
   3. Lots of money entering markets – through feedback loops – starts to exponentially increase the chances of anyone small perturbed event being the trigger for critical phase transition – right now
   4. All about probability – increasing with the nature of markets and the early warning signs
2. Another preface – expansion of similar policies that have created a bubble are currently continuing –
   1. QE4, printing press with cheap money – may create more of a bull run in the markets over the next 12 months
   2. But these events create more of a fragile market – so while the markets may run for the next few months to years, the collapse will be of a bigger magnitude when it occurs

When markets collapse – it is chaos

– thankfully a school of complexity theory helps with this – that is chaos theory

1. Jeff Goldblum – his character from Jurassic Park is a mathematician who specialises in chaos theory
2. Chaos theory is very useful when the apparent randomness of chaotic complex systems (such as a share market) has an underlying pattern, along with feedback loops creating repetition and self-similarity and self-organization
   1. The metaphor for this behaviour is that a butterfly flapping its wings in China can cause a hurricane in Texas –
   2. The butterfly effect describes how a small change in one state of a deterministic nonlinear system can result in large differences in a later state, meaning there is sensitive dependence on initial conditions.
3. What is chaos theory a branch of mathematics focusing on the behaviour of dynamical systems that are highly sensitive to initial conditions – looking at the theoretical probability of chances happening in non-linear models
   1. Initial conditions – called a seed value, is a value of an evolving variable at some point in time designated as the initial time – in financial markets – the variables change every second the market is open – number of buyers, sellers, currency exchange, employment, costs – many variables – at any given point – say now is time t – where do each of these lie? – then aims to look at the probability of something happening from here
   2. Chaos relates to the Sensitivity to initial conditions – this is a key characteristic of complex deterministic systems
      1. But due to the nature of complex systems – the system may change dramatically without a change to initial conditions, but rather as the result of moving beyond critical tipping points, or points of no return
   3. Why is it almost impossible to time market?
      1. Small differences in initial conditions- even due to rounding errors in numerical computation – so each calculation based around the assumptions can yield widely diverging outcomes – renders long-term prediction of market behaviour impossible – behaviour is known as chaos:
         1. Chaos: When the present determines the future, but the approximate present does not approximately determine the future.
         2. All this means is that markets may collapse at any time for any number of reasons – hence chaotic –

Collapses are well explained by the nature of complex systems – due to the features of complex systems – go through 6 main:

1. Cascading failures – there is a high level of interconnection in complex systems – lots of buyers and sellers following a crowd – creates a strong coupling between components in complex systems – i.e. buyers and sellers and the shares themselves
   1. A failure in one or more components can lead to cascading failures which may have catastrophic consequences on the functioning of the system – cascading is thanks to interconnection
   2. Localised attack may lead to cascading failures and abrupt collapse in spatial networks.
2. Complex systems can be open systems – like the share market which is frequently far from energetic equilibrium:
   1. This is fundamental analysis – what is the share worth based on the FCF models – and what is it trading at
   2. But despite this flux, there may be pattern stability
3. Complex systems may have a memory – The history of a complex system is important due to the cyclical nature of human/investor behaviour – being driven by fundamental human emotions of fear and freed
   1. Because complex systems are dynamical systems they change over time, and prior states may have an influence on present states.
   2. More formally, complex systems often exhibit spontaneous failures and recovery as well as hysteresis. Interacting systems may have complex hysteresis of many transitions – crowd behaviour and panics/hysteria
4. Dynamic network of multiplicity – dynamic network of a complex system – how connected is the system to the environment? The system is the market – the environment is the world – i.e. every part that interacts with the market from wages, confidence, debt level
5. Relationships are non-linear – In practical terms, this means a small perturbation may cause a large effect – i.e. the butterfly effect – a proportional effect, or even no effect at all. In linear systems, effect is always directly proportional to cause
6. Relationships contain feedback loops – Both negative (damping) and positive (amplifying) feedback are always found in complex systems
   1. The effects of an element’s behaviour are fed back to in such a way that the element itself is altered but in the non-linear fashion

Current signs and signals –

1. See a lot of listing on share markets – seen it with tech companies that aren’t making money but are listing on the exchange – payment platforms, tech start-ups, ones like Uber – signs of a peaking of markets
2. Derivative counter party positions are at an all time high – $1,200 Trillion estimate
3. Cost of borrowing all time lows – free money to bankers so no risks for gambling – if you could gamble on CC for 0% interest?
4. Aus banks offering Instalment warrants – geared equity products being issued in the billions – self-funding through dividend
5. ETF bubble – passive inflows and technology – talked about this already – but of the $15trn printed recently by central banks – $10bn in passive ETF/alternative structures
6. One of the biggest ones – Credit/lending in markets all time high in like NYSE – check out graph at website
7. (Thanks to advisor perspectives for putting together)
   1. Back in Dot Com bubble – Negative balance 130bn at peak – went to -45bn while market dropped 47%
   2. Back in Pre-GFC 2007
   3. October 2019 – sitting at $240bn but was at $330bn back in July 2018
8. Stable/slowdown into Chaotic – Phase transition –
   1. The lag between net leverage being withdrawn from markets – Occurs every time – cyclical non-deterministic patterns
   2. Markets go up – then go down – the size of the transitions can differ – it looks like a big one is due –

Pretty heavy episode – leave it there

Next episode – explore the current possible triggers for the markets

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