🔔 The Illusion Of Knowledge

"No amount of sophistication is going to allay the fact that all of your knowledge is about the past and all your decisions are about the future" – Ian H. Wilson (former GE executive)

I attended a lunch with a number of experienced investors, plus a few people from outside the investment industry...

At one point, the host posed a series of questions: What’s your expectation regarding inflation? Will there be a recession, and if so, how bad? How will the war in Ukraine end? What do you think is going to happen in Taiwan? What’s likely to be the impact of the 2022 and ’24 U.S. elections?

I listened as a variety of opinions were expressed.

Regular readers of my memos can imagine what went through my mind:

“Not one person in this room is an expert on foreign affairs or politics. No one present has particular knowledge of these topics, and certainly not more than the average intelligent person who read this morning’s news.”

None of the thoughts expressed, even on economic matters, seemed much more persuasive than the others, and I was absolutely convinced that none were capable of improving investment results. And that’s the point.

It was that lunch that started me thinking about writing yet another memo on the futility of macro forecasting.

Forecasters have no choice but to base their judgements on models, be they complex or informal, mathematical or intuitive. Models, by definition, consist of assumptions: “If A happens, then B will happen.”

In other words, relationships and responses. But for us to willingly employ a model’s output, we have to believe the model is reliable. When I think about modelling an economy, my first reaction is to think about how incredibly complicated it is.

The U.S., for example, has a population of around 330 million. All but the very youngest and perhaps the very oldest are participants in the economy. Thus, there are hundreds of millions of consumers, plus millions of workers, producers, and intermediaries (many people fall into more than one category).

To predict the path of the economy, you have to forecast the behaviour of these people – if not for every participant, then at least for group aggregates.

A real simulation of the U.S. economy would have to deal with billions of interactions or nodes, including interactions with suppliers, customers, and other market participants around the globe. 

Is it possible to do this? Is it possible, for example, to predict how consumers will behave (a) if they receive an additional dollar of income (what will be the “marginal propensity to consume”?); (b) if energy prices rise, squeezing other household budget categories; (c) if the price for one good rises relative to others (will there be a “substitution effect”?); or (d) if the geopolitical arena is roiled by events continents away?

Clearly, this level of complexity necessitates the frequent use of simplifying assumptions. For example, it would make modelling easier to be able to assume that consumers won’t buy B in place of A if B isn’t either better or cheaper (or both).  

And it would help to assume that producers won’t price X below Y if it doesn’t cost less to produce X than Y. But what if consumers are attracted to the prestige of B despite (or even because of) its higher price? And what if X has been developed by an entrepreneur who’s willing to lose money for a few years to gain market share?

Is it possible for a model to anticipate the consumer’s decision to pay up and the entrepreneur’s decision to make less (or even lose) money?

 Just checked the dates on the FedEx chart. Got to be one of the worst calls ever (happens to us all)

Published on 20th Feb 2007. Seven days later $FDX tops out and begins a two year downtrend, losing over 70%...👇 https://t.co/oTMFYuksRE pic.twitter.com/XkzJDhcmP5— Tim (@VolaTim) September 16, 2022