Why do equity markets crash? Can we predict them before they happen?
Hold your breath. The answer to this mind boggling question, maybe just one beach trip away!
Let us find out how..
Beach trip and sand mountains..
Do you remember going to the beach as a kid and making a sandpile – that small mountain which you constructed using sand?
Imagine, you kept adding sand bit by bit to the top of the sand pile, trying to increase the height. You notice that, sometimes, when you add new sand to the sand pile, there is an avalanche (slippage of sand) down the sides of the mountain. Most of the time, it is a small one, but once in a while, it seems like one whole side of the sand mountain slides down to the bottom.
This seemingly unimportant observation, has some amazing insights for us.
The size and frequency of those avalanches, mathematically speaking, bear a notable resemblance to the size and frequency of natural phenomena such as earthquakes, river floods, forest fires and stock market returns.
So if we can predict the timing of the avalanche in the sand pile, we may get some clues on how to predict a market crash.
That leaves us with an interesting question:
If you drop sand grains onto the same spot continuously, which specific grain would eventually cause the sand mountain to collapse?
Now while I obviously don’t expect you to derive a mathematical formula for this, don’t you think someone would have already found the answer.
The three weird physicists and their sandpile experiment..
Well, thankfully for us in 1987 three physicists, named Per Bak, Chao Tang, and Kurt Weisenfeld had the same question in mind and set out to do an experiment to find the answer.
Now before, you wonder on how jobless they must have been, their actual intent, just like us was to check, if they could apply the learning from the sand mountains to the behavior of complex systems such as weather patterns, ecological systems, global markets, ocean currents, etc.
Since doing this experiment manually would take them a hell a lot of time, they developed a computer program that would build each sand pile, adding one grain at a time and then observing the results. They ran thousands of trials.
What do you think they found out?
I thought that across all the trials, there would be a particular threshold size (or height) for the pile and the moment it reaches there, any addition of new sand grain would trigger the avalanche or collapse.
If not, there should atleast be some sort of a pattern such as – higher the size or number of sand grains in the pile, higher the likelihood of a collapse in a given sand pile.
Here comes the shocker!
I was outrageously wrong.
Each time the experiment was run the results on when the avalanche would happen were completely unpredictable.
Yes you read that right – COMPLETELY UNPREDICTABLE!
After thousands of repeated experiments, with millions of grains of sand, they observed no patterns, no typical number of grains required to trigger an avalanche.
Sometimes it was a single grain, sometimes 10, sometimes 100, or sometimes even 5,000 grains!
In other words, the avalanche could occur any time irrespective of the sand pile size. It was impossible to predict how large or how often they occur.
This leads us to the next interesting question..
Why in the world is a simple sand pile so unpredictable?
Just like us, our 3 physicists were also dumbfounded by the same question. So they pushed the experiment further.
They wanted to check if there were any particular vulnerable areas in the sand pile which could help them predict the collapse.
They looked at the virtual sand mountain from above, and they color coded its regions according to steepness which they used as a proxy to indicate the stability of the area. The relatively flat areas were shaded green (relatively more stable areas) and steeper sections were shaded red (relatively more prone to fall).
In the beginning, the sand mountain was mostly green (though it still would collapse periodically), but as the experiment progressed, some of the piles grew and more red areas would infiltrate. For the largest piles, a dense skeleton of random red danger spots spread through the sand.
The avalanches mostly tended to occur when a new grain of sand hit a red area. The grain falling on red spot often set-off a domino like action, causing further slides in the nearby red spots.
If the red network was sparse, and well separated from each other, then a single grain could have only limited repercussions.
But when the number of interconnected red spots increased in the pile, the possible consequences of the next grain became very unpredictable. Sometimes it fell innocently and did nothing to the pile, sometimes a few grains tumbled, and occasionally it set off a large avalanche sending walls of sand cascading down the entire pile.
They had an interesting conclusion:
Greater the number of interconnected red spots in the pile, greater was the possibility of an avalanche.
But here is the catch.
While they found that the possibility of an avalanche was high, if the red spots were dense and high in number, they still weren’t able to predict the exact sand grain that would cause the avalanche and its intensity.
The reason was simple.
The timing of an avalanche was not a function of the size of the sand mountain or the number of grains of sand added, but instead was dependent on the interactions between those individual grains of sand.
Unfortunately, each and every time a sand grain falls on the pile the interactions between the sand grain on which it falls and the adjacent sand grains in terms of how they shift and slide relative to each other were completely random. No pattern emerged as each and every interaction was different.
The more grains of sand in the pile, the more interactions that occur between the individual grains and the more difficult it is to predict the next avalanche.
So while we can get an approximate sense on the possibility of an upcoming avalanche by looking at the steepness and the inter-connectedness (via the red area), we still cannot predict exactly as to which grain of sand will trigger the next avalanche.
This deceptively simple sand mountain is a great example of a complex nonlinear system that does not produce the same result every time even though the inputs and conditions are the same.
Equity Markets and the domino effect
Most of us think of events impacting equity markets as a linear domino effect where one event triggers a chain of ripple-off effects.
The unfortunate problem with this mental model, is that each and every times post the event, there seems to be a clear cause and effect narrative for the rally or the decline.
In 2000 it was the dot com bust..
In 2008 it was the Sub Prime lending..
In 2011, it was the Euro crisis..
In 2013, it was the currency crisis..
In 2014, it was the elections..
In 2016, there was a China crisis..
In 2017, it was the after effects of demonetization..
This post event narrative and the mental model of “domino effect” provides us with a deceptive conviction that these events and most importantly the response of millions of market participants (read as emotions) to these events could have been predicted.
And hence the drama begins..
The moment someone says monsoons are going to be weak, some media firm organizes a special interaction with SKYMET top officials.
The moment one of our soldier gets captured by Pakistani army, there is a special interaction with a ex-army chief on what would be the possibility of a war..
If there is a currency crisis, then all the economists are interviewed for their views on how it would play out.
Subprime, Euro debt, Donald Trump, Demonetisation etc. Name it and our financial media is ever ready with its share of market “experts” who clearly explain the specific causes that led to specific market movements – all after the fact.
This is a trap which unfortunately my industry has made you believe –
That maybe with special access to information and experts you would know the future.
Equity markets as non-linear systems
Instead of thinking of stock markets as a linear domino effect based system, thinking of it as a non linear system such as a sand mountain model is better approach.
Think about the irony..
We can’t predict when an avalanche is going to occur in a simple sand mountain because the interaction amongst the brainless, emotionless sand grains can’t be predicted.
In stock markets, the interactions between the intelligent, emotion filled participants are exponentially more complex than those found within a sand pile.
And yet we are made to believe stock markets, which involve millions of participants with emotions and a vast array of objectives, strategies, time frames and amounts of leverage can be predicted.
The sandpile model allows us to have a better perspective on various events.
Think of each and every event as a grain of new sand added from top.
Euro crisis, Currency crisis (Fragile 5), China issue, Demonetization, Trump becoming US President, Brexit etc are all new grains of sand added on top of the stock market. Any of them could have caused the next avalanche (crash) as the interactions amongst millions of investors is impossible to predict. What we saw was one version of history – which had its neat cause and effect narrative spun by experts, of course, post the event.
If you were to go back and repeat all these events, the results could be extremely different.
Predicting these events in the first place is extremely difficult if not impossible. On top of it, you will also need to predict how millions of investors will emotionally react to these events!
Now if you still think experts can predict, let us listen to what someone who does this for a living has to say about predicting markets..
Straight from the horse’s mouth
What All This Means for You as an Investor
There are 4 important takeaways:
1.Ignore predictions from EXPERTS
Don’t listen to experts and doomsayers who are predicting the next bear market. They don’t know. Neither does anyone.
“There’s no way to know what markets will do next” is the most accurate market outlook.
2.Large avalanches (large market crashes) are infrequent.
The logical course of action is to assume the next grain of sand (negative event) will NOT cause an avalanche (bear market). Stop assuming that each and every event will lead to a large crash and focus on things under control – Savings, Asset Allocation, Cost, Security selection, Diversification and your BEHAVIOR
3.Large avalanches (though rare) will eventually occur.
It is important you have a what-if-things-go-wrong plan in place. Refer here for a detailed explanation.
4.While you can’t PREDICT, you can PREPARE
While it is impossible to predict when a market downturn will occur, it is possible to PREPARE by evaluating when the conditions are indicating high possibility of a financial crisis (similar to the dense red spots in the sand pile). The market can be split into Boom, Bubble, Bust, Best phase. You can use the following framework here to evaluate the conditions and prepare accordingly.
Next time somebody predicts the markets and tries to lure you into the trap, it’s time to remember the beach and the sand mountain!
Happy investing folks.
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Disclaimer: All blog posts are my personal views and do not reflect the views of my organization. I do not provide any investment advisory service via this blog. No content on this blog should be construed to be investment advice. You should consult a qualified financial advisor prior to making any actual investment or trading decisions. All information is a point of view, and is for educational and informational use only. The author accepts no liability for any interpretation of articles or comments on this blog being used for actual investments.