How to analyse share markets by treating them as a complex system

Welcome to Finance and Fury

Series on Share markets as complex systems – a different way of thinking about them –

1. This episode, discuss some basics of shares and introduce complexity theory –
   1. Nonlinear, Emergence, Spontaneous order, Adaption, Feedback loops

Shares – What are they?

1. Ownership in a company – private or public company – shares are the legal title to your ownership in a company
   1. As an owner of a company – you entitled to profits – that is what dividends are – decided by boards of public companies
   2. You also get price gains or losses based around how much people want the shares –
      1. Linked to performance but also irrational exuberance – future expectations
2. Share market – is the collective representation of all the companies listed on an exchange
   1. ASX – Publicly available companies to purchase –

Share Markets are Complex Systems 

1. I Used to break shares down to supply and demand – the equilibrium models – does work as an educational example (what and how)
   1. Failure in explanation of why – shows the after the fact obvious points – people demand less, then prices drop – or never dilute supply and shares go to $320k like Berkshire Hathaway
   2. Great explanatory tool for the mechanics – but essentially useless in practical terms
2. A complex system is a system composed of many components which may interact with each other.
3. What does this have to do with share markets? –
   1. Share market is the collective behaviours in decisions to buy or sell a company –
   2. Financial institutions, individuals, professional advisers like myself, every decision to buy or sell a holding in a share, bond, gold, etc. has characteristics of each – thanks to us and out human behaviours
4. What are Complex systems – chiefly focuses on the behaviours and properties of systems.
   1. A system, broadly defined, is a set of entities that, through their interactions, relationships, or dependencies, form a unified whole – Systems exhibit complexity means that their behaviours cannot be easily implied from the very properties that make them difficult to model
      1. Any modelling approach that ignores such difficulties or characterises them as noise won’t be accurate or useful.
   2. Complex systems are always defined in terms of its boundary, which determines the entities that are or are not part of the system. Entities lying outside the system then become part of the system’s environment.
      1. Share market entities – shares and buyers/sellers are part of the system directly
      2. Most other factors exist outside of the system (share market) – but they do have an effect – GDP, wages, confidence, individual preferences, regulations (SG conts), weather, etc. – probably hundreds of thousand things that can affect the environment of the share market
      3. System-wide or global properties are all characteristics of how the system (share market) interacts with its environment (all factors that influence individual behaviours)
      4. Behaviour of the system (share market) – does it go up or down tomorrow? Depends on the behaviour of those buying and selling shares – which in turn is affected by the environment of each individual doing the buying and selling
5. Systems that are “complex” have certain distinct properties – due to the relationship of the system – five of the most important – in no particular order
   1. Nonlinearity – nonlinear system is a system in which the change of the output is not proportional to the change of the input – Something linear = add $100 to your bank account, have $100 in your account = then earn 2% interest = $2 return
      1. Nonlinear = put $100 into the share market = shares might be worth $60, or $160 plus earning 0% to 7% in income – changes all the time which is not dependent on the inputs
      2. Nonlinear systems may also respond in different ways to the same input depending on their state
         1. News or purchases of shares may yield significantly greater than or less than proportional changes in output of the price changes
      3. Most systems are inherently nonlinear in nature – a change in one variable over time, may appear chaotic, unpredictable, or counterintuitive, contrasting with much simpler linear systems
         1. Think about human behaviours – not linear – so why should share markets perform in the same way
      4. Why share forecasting is difficult to solve – but while chaotic behaviour may resemble random behaviour, it is in fact not random
         1. Example is the weather – can be chaotic, where simple changes in one part of the system produce complex effects throughout – high – and low-pressure systems meeting
   2. Emergence – occurs when an entity is observed to have properties its parts do not have on their own – therefore the behaviours only emerge when the parts interact in a wider whole
      1. Share markets – the parts are the shares themselves and the people buying and selling
         1. A share on its own won’t move in price – look at shares that have no buy/sell orders in a day – the price doesn’t move
         2. Someone then comes along and either sells or buys a lot of the shares – this moves the price
         3. This is the emergence of a price movement from the nature of us interacting with the shares
      2. Emergent states in the markets can occur in bubbles or crashes – neither can exist without investors over purchasing or selling investments
         1. Emergent states are used to refer to the appearance of unplanned organised behaviour in a complex system, emergence can also refer to the breakdown of organisation; it describes any phenomena which are difficult or even impossible to predict from the smaller entities that make up the system.
         2. Share crash or bubbles – if you are buying shares, you aren’t asking every investor in the world if they are buying the same shares directly – you might see news about others actions and take action – but it is often reacting to others actions – so not in cahoots with them and pre-planned a sale
      3. Example – 911 terrorist attacks created massive market sell offs – one event occurs creating the emergence of a selling state
   3. Spontaneous order – also named self-organization = the spontaneous emergence of order out of seeming chaos
      1. Related to emergence – describes the appearance of unplanned order, it is spontaneous order/self-organization 
      2. Spontaneous order in financial markets can be seen in herd behaviour – group of individuals coordinates their actions without centralized planning.
      3. You have emergence from the interaction of investors with shares – but the bull/bear markets are the outcomes from spontaneous order – if everyone is buying due to signals = the markets will go up
      4. Manifestation of self-interested individuals who are not intentionally trying to create order through planning
         1. A free-market economy is an example of systems which evolved through spontaneous order
         2. Started with barter – You have someone with milk who wants to trade it for some wheat
      5. Two levels of the complex system and spontaneous order – spontaneous order is defined as “the result of human actions, not of human design”
         1. Shares are companies created and controlled by humans – human design
         2. The nature of spontaneous order isn’t created, controlled, and controllable by no one – human actions
      6. Spontaneous order is an equilibrium behaviour between self-interested individuals, which is most likely to evolve and survive, obeying the natural selection process “survival of the likeliest”.
   4. Adaptation – A complex adaptive system is a system in which a perfect understanding of the individual parts does not automatically convey a perfect understanding of the whole system’s behaviour.
      1. Fundamental v Technical analysis – Fundamentals (revenues, market share, management) of a company may be great, or horrible – but the prices will act in an unexpected manner
         1. Normally occurs in companies in vouge (next big thing industries like lithium shares, buy now pay later companies)
      2. Adaption relates to complex systems due to the networks of interactions – the behaviour of the market is not predicted by the behaviour of the individual shares – collective versus individual holdings
         1. Markets are adaptive in that the individual and collective behaviour of investors mutates and self-organizes –
         2. There is often a change-initiating event or collection of events – think about the GFC – wasn’t just due to Lehman Brothers collapse – there were many flow-on effects – one after another
         3. Where the Black Swan events (rare share market crashes) could happen at any time – it is the responses to individual events
      3. Example – Recent issues with Deutsche bank – flow-on effects for the counterparties of derivative positions – then China banks are falling over as well – even enough of these occur the market adapts – not just to dumping DBK shares but everything out of fear
      4. Complex adaptive systems are special as they have the capacity to change and learn from experience – share market
         1. Occurs in any human group-based endeavour – inflows and outflows of the market
   5. Feedback loops – Feedback occurs when outputs of a system are routed back as inputs as part of a chain of cause-and-effect that forms a circuit or loop – The system feeds back into itself
      1. The notion of cause-and-effect has to be handled carefully when applied to feedback systems:
         1. Cause – people acting out of self-interest – to make the most money in the markets or to avoid losses
         2. Effect – prices go up, go down, or stay the same – depends on the size/magnitude
      2. Simple causal reasoning about a feedback system is difficult because the first system influences the second and the second system influences the first, leading to a circular argument.
         1. This makes reasoning based upon cause and effect tricky, and it is necessary to analyse the system as a whole.
         2. Chicken or the egg – what one thing causes a share market panic? There isn’t one – it is a feedback of negative or positive information – news, price signals, deleveraging – eventually the feedback compounds and becomes a market panic
      3. ETFs are an example of a feedback loop – people buy index funds – prices go up – so more people buy index funds – driving the prices up
         1. Market trends are their own feedback loop – either a positive or negative reward from a decision will reinforce the behaviours
         2. Happens in everyday life – not touching a hot pan is a feedback loop – pan is hot, touch it, get burnt, learn not to do it again

Summary – Share markets have a tendency of a complex system – due to characteristics – which are all connected

1. Nonlinear – nonlinear system is a system in which the change of the output is not proportional to the change of the input – Something linear = add $100 to your bank account, have $100 in your account = then earn 2% interest = $2 return
2. Emergence – occurs when an entity is observed to have properties its parts do not have on their own – therefore the behaviours only emerge when the parts interact in a wider whole
3. Spontaneous order – Spontaneous order in financial markets can be seen in herd behaviour – group of individuals coordinates their actions without centralised planning – everyone moving in the same direction
4. Adaption – Adaption relates to complex systems due to the networks of interactions – the behaviour of the market is not predicted by the behaviour of the individual shares – collective versus individual holdings
5. Feedback loops – Feedback occurs when outputs of a system are routed back as inputs as part of a chain of cause-and-effect that forms a circuit or loop

This is the reason why modelling and forecasting the short-term behaviours of share markets are pretty hard if not impossible

Now that the basics of complex systems are out of the way = Next ep – talk about the application to financial markets

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