Yesterday's note on Amazon and the intangibles sent me back into the world (and work) of Michael Mauboussin.

Specifically the approach to forecasting (or predicting the future)

This line was the culprit πŸ‘‡

Research shows that a thoughtful combination of the inside and outside views leads to more accurate forecasts.

I checked the citations (all the cool kids do it), and the research was authored by none other than....

Michael Mauboussin!

Referencing your own research like a boss.

The Base Rate Book: Integrating the Past to Better Anticipate the Future

He builds on work by Kahneman & Tversky: β€œOn the Psychology of Prediction.”

The paper, published in Psychological Review in 1973, argues that there are three types of information relevant to a statistical prediction: the base rate (outside view), the specifics about the case (inside view), and the relative weights you should assign to each.

How do you determine the importance of each?

The luck-skill continuum is one method offered...

One way to determine the relative weighting of the outside and inside views is based on where the activity lies on the luck-skill continuum.
Imagine a continuum where luck alone determines results on one end and where skill solely defines outcomes on the other end.
A blend of luck and skill reflects the results of most activities, and the relative contributions of luck and skill provide insight into the weighting of the outside versus the inside view.

As a framework for trading, it isn't robust enough.

There is a big difference between forecasting company outcomes, and forecasting the impact those outcomes will have on the valuation of the company.

Dipping back in to the 'intangibles' research, we can get to the heart of it.

Storytelling Vs Statistics

There are broadly two ways to make a forecast, which is really a judgement about the future. Β 
The first method is to think causally, which is called taking the inside view.
You gather lots of information about what is of interest, combine it with your own input and experience, and project into the future.
Analyst models are a good example of this approach. The analyst studies a company’s businesses and projects sales and operating profit margins based on a combination of macroeconomic factors, industry trends, and the company’s competitive position.
Causal thinking is a form of storytelling that comes naturally.
It is a compelling way to anticipate the future and a convincing way to explain the past. Our minds are great at creating facile narratives to explain what happens in the world around us.

This is how we are naturally wired.

Stories > Everything else.

Stories & 'truth' have no real relationship most of the time. Β 

Even truth is subjective and flexible, especially between people discussing cause and effect relationships.

Markets can stay irrational etc..

If 'your story' matches the story of lots of other people, then the truth doesn't really matter.

Number goes up or number goes down. It's all based on beliefs of the crowd.

But the truth shouldn't be ignored entirely...

The closest thing to truth in markets?

Statistics πŸ‘‡

The second method is to think statistically, commonly referred to as the outside view.
Rather than weaving a story based on causal links, the statistical approach examines what happened to an appropriate reference class of cases in the past. The results of the reference class are called base rates.
Now the analyst builds their model not by seeking causal links but rather by asking, β€œhow did other companies perform that were in a similar position to the one I am studying?”
Instead of relying on your own experience, you tap the experience of others.
This type of thinking is unnatural because it features statistics rather than stories.
Further, base rates may not be readily available.

Nikola was/is a textbook example.

The story was strong.


Hydrogen powered semi-trucks came first, and the Badger (to compete with Tesla's cybertruck) was announced later.

The day Nikola announced that they would accept pre-orders for the Badger was the day the story died... πŸ‘‡πŸ‘‡πŸ‘‡

Distorted the scale to fit the tweet, sorry!

The truth took over.

Nikola had no manufacturing capacity whatsoever.

As soon as the narrative shifted from hype to real-world commitment, it was done.

They still had a very interesting hydrogen fuel cell design. Maybe that could keep the story going!

Until the day Hindenburg released their short sellers report...

That was the day that story ended πŸ‘‡

This is a consistently recurring theme in markets, especially among growth and innovation companies.

The growth story is all that matters... until reality/truth/statistics come along and smash that story to pieces.

For companies that are selling a story and nothing else, it's black and white.

The story of 'the next big thing' continues or it dies.

For companies that are actually productive, there are more shades of grey.

Whatever the forecast, it is exceptionally hard to be precise and accurate.

Tetlock's Superforecasting provides some guidelines to lean on πŸ‘‡πŸ‘‡πŸ‘‡

Ten Commandments for Aspiring Superforecasters
Philip Tetlock can teach you how to boost accuracy in predicting the real world with Ten Commandments for Aspiring Superforecasters.

But there are no hard and fast rules that apply to everything...

β€œIt is impossible to lay down binding rules,” Helmuth von Moltke warned, β€œbecause two cases will never be exactly the same.”
Guidelines (or maps) are the best we can do in a world where nothing represents the whole.
As George Box said: β€œAll models are false. Some are useful.”