The value of probabilistic thinking [plus 3 examples for investors]
Another fantastic tool to improve the accuracy of your decisions…
This mental model involves trying to estimate, using maths and logic, the likelihood of a specific outcome.
In other words, the most likely outcomes.
Making your decisions are better.
People trying to predict the future is a big problem in financial services
The future is far from known (despite what Wall Street analysts say), so you can make life easier by understanding the likelihood of events happening.
Events that impact you.
It’s this lack of knowing that makes probabilistic thinking so useful.
If the past few years have taught us anything, it’s the future is inherently unpredictable.
Not all variables can be known, and predictions can be easily thrown off course.
The best you can do is estimate the future by generating realistic, useful probabilities.
So how do you do that?
Probability is everywhere
Humans have had this mental tool since way before computers, factories, and the stock market.
Back then, life was about survival.
Nowadays, it’s about us wanting to thrive.
You want to compete, and win.
Mostly, you want to make good decisions in complex situations.
The subject of probability is vast.
Broadly, there are three aspects of probability to consider:
- Bayesian thinking
- Fat-tailed curves
Bayesian thinking and the stock market
The core of Bayesian thinking (named after Thomas Bayes, an English minister in the first half of the 18th century) is this: given that we have limited but useful information about the world, and are constantly receiving more, we should consider what we already know when we learn something new.
Take the headline “S&P plummets into a Bear market overnight.”
Without Bayesian thinking, you might become genuinely afraid because your chances of losing much of your investments is greater than it was a few months ago. But a Bayesian approach will have you putting this information into the context of what you already know:
- the stock market is a powerful, information-processing machine
- bear markets don’t last and how many there have been in the last 100 years.
- you invest for the long term, and single years don’t matter in the end.
Prior information here is key.
When you factor it in, you realise your investments have not really been compromised (though it’s not pleasant in the short-term).
So ask yourself: What might I already know that I can use to better understand the reality of the situation?
Fat-tailed curves and diversification
Many of us are familiar with the bell curve, or “normal distribution.” Within this, we can quickly identify our parameters and plan for the most likely outcomes (exam scores, for example).
Fat-tailed curves are different.
At first glance they seem similar enough.
The difference is in the tails.
In a bell curve, the extremes are predictable.
There can only be so much deviation from the mean (if we talk about height of the population for example, you’ll never meet a man who is ten times taller than the average).
In a fat-tailed curve there is no real cap on extreme events.
The more extreme events that are possible, the longer the tails get.
You may regularly meet people who are 10, 100, or 10,000 times wealthier than the average person.
Now, imagine a portfolio of stocks.
Most returns within a portfolio will be average when compared to their sector and geographical location.
Some companies may go out of business, some may have massively outsized returns - beyond a “normal” distribution curve.
Portfolio outperformance is almost always determined by “fat tails”, i.e., those companies that grow exponentially:
Over 40% of the returns of the S&P 500 since 2015 have been caused by five companies: Facebook, Amazon, Apple, Microsoft, and Google.
Of course, no one knows the "fat tails" of the future. The only way to capture the positive effects of them and balance overall risk, is to diversify.
To position our portfolios to survive.
Asymmetries and active fund managers
Here we think about “metaprobability” —the probability that our probability estimates themselves are any good.
Take active fund managers.
They look their clients in the eye and believe they can achieve returns of 20% to 40% per annum, if not higher.
Yet barely any of them ever do, and it’s not because they don’t have any winners.
It’s because they get so many wrong.
They consistently overestimate their confidence in their probabilistic estimates (the S&P 500 has returned around 7% to 8% per annum over a long period, before fees).
Far more probability estimates under deliver, than over deliver.
You’ll rarely read about a professional investor who aimed for 25% annual return rates, but then earned 40% over a long period of time.
Most who aim for 25% per annum with each investment end up closer to 10%.
Identify what matters
Successfully thinking using probability means identifying what matters, assessing any odds, doing a check on our assumptions, and then deciding.
You can never know the future.
But probabilistic thinking is an extremely useful tool to evaluate how the world will most likely look.
So you can plan effectively.