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How To Make Better Predictions
How To Make Better Predictions
By Tis the season of Wall Street predictions. How will US, European and Asian stocks do next year? How many times will the Fed increase interest rates? Where will the 10-year Treasury trade in 2022? All this got us to thinking about the process of making predictions, and that is the subject of this week’s Story Time Thursday.
We’ll start with some wisdom from Daniel Kahneman and Amos Tversky, who coauthored a paper they called “On the Psychology of Prediction” in 1973 (link here). Despite the title, their approach to making useful predictions is math- (not psychologically-) based:
- Any prediction should start with the statistical base rate associated with a given outcome. For example, if we’re trying to predict where the S&P 500 will be at year-end 2022 then the index’s long run annual rate of return should be our starting point. Let’s call that 10 percent.
- Then we ask the question “how representative will 2022 be of the general environment that created that base rate?” In the case of the S&P, we might make a list of plusses and minuses with “strong earnings” in the first camp and “possible Fed policy mistake” in the latter. Let’s say the bias is to the bearish by 2-3 points.
- That would leave us with a predicted return of 7-8 percent for the S&P 500 next year, which is what our DataTrek Look Ahead Survey reported was the plurality of readers’ expectations. Given the answers to several other questions in that survey (notably the coin-flip odds respondents gave to a Fed policy mistake), the “what” and the “why” of the survey’s output lines up very well.
Now, if one has a profoundly bearish outlook, this approach also allows you to consider that view in a statistical framework:
- Let’s say we want to predict that the S&P 500 will be down 10 percent or more next year.
…click on the above link to read the rest of the article…
Breaking The (Supply) Chains
Breaking The (Supply) Chains
“Supply chain disruptions” has become a catch-all phrase to explain product shortages and inflation. But how exactly does that work, and why is this problem taking so long to fix? For Story Time this week, Nick uses his 30 years of experience analyzing the US auto industry to explain what’s going on. It all comes down to “lean manufacturing”, which started in Japan after World War II and caught on worldwide in the 1980s/1990s. The pandemic has created systematic challenges to “lean”, enough that structural inflation is a threat.
Here is a story about how supply chains work and why they are so snarled just now. Yes, a grimy topic compared to some of our others, but important and my personal history as a long-time (30 years) auto industry analyst gives me a unique perspective on the issue.
To understand how global supply chains operate the way they do today, you really need to go back to Japan just after World War II.
The country, defeated and impoverished, desperately needed to restart its industrial base. It did so by producing what it could with a minimum of capital. That meant, for example, no capital-intensive vertical integration; there were parts suppliers, and then there were final assembly companies. It also meant keeping the supply chain tightly integrated to maximize throughput.
This spawned a new production model, now commonly known as lean manufacturing, and Japan’s auto companies led the way in its adoption.
Parts like exterior metal stampings and interior trim like seats and dashboards all arrived at automotive final assembly plants from suppliers within a few hours of when they were installed in a vehicle moving down an assembly line…
…click on the above link to read the rest of the article…
Three Examples Of How Chaos Theory Affects Financial Markets
Three Examples Of How Chaos Theory Affects Financial Markets
Chaos Theory – the idea that a butterfly in Thailand could cause a US hurricane – can actually create positive outcomes as well as mayhem. Consider that European banks, German long-term bunds and the offshore yuan are essentially the butterflies making for pleasant investment conditions just now. All have turned sharply in the last 2 months after previous discounting disaster. And all have more room to run.
Chaos Theory gets a bum wrap, and I think the reason is bad branding. The most common explanation of the phenomenon is the classic “a butterfly flapping its wings in Thailand can cause a hurricane in the Gulf of Mexico”. Initial conditions, in other words, can have outsized effects in complex systems like weather patterns. Fair enough, but one usually associates Chaos Theory with bad outcomes like cyclones and stock market crashes.
What about when initial conditions push their way through to create unexpectedly good outcomes? That’s Chaos Theory as well, but no one talks about the mayhem created by a lovely day… Bad branding, that, or at least misleading packaging…
Turning to the current sunny spell in global risk markets, three examples of why Chaos Theory can work to investors’ benefit as well as harm.
Exhibit #1: European Bank Stocks:
- In early August, the EURO STOXX Banks Index looked like it was about to implode. At 77, it had not been lower since the 1990s. We wrote about it, highlighting that several market bears thought the group was destined to go into chaotic (there’s that word again) free-fall.
- But then the group found its footing as Eurozone long-term interest rates bottomed (more on that in a minute).
- From August 15th to now, the index is up 20%. Disaster averted, at least for now.
