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  • Chris Mayer

Hot Water, Banks, Sapolsky

The macro stuff seems deafening right now. Trade wars, interest rates, currency shifts, etc., dominate the headlines and seem to drive stock prices.


People often think about these things too narrowly – and therefore get the effects wrong. (Presenting an opportunity for those who know better). Before I show you how, let me ask you a simple question:


What happens to the reading on a candy thermometer if you suddenly plunge it into hot water?


Most everyone would say the reading would rise. Because, they think to themselves, “the reading measures the expansion of mercury, and mercury expands when heated.” Simple.


But, the answer is that the thermometer reading will first decline.


Gerald Weinberg, who wrote a book on systems thinking, and from which I take this example, explains:


“When we plunge the thermometer into hot water, the glass, being on the outside, warms first, and therefore begins to expand first. Since the mercury has not yet begun to warm, the mercury has not expanded and thus begins to fall in the tube…”


What do people miss? They don’t consider the glass. And they don’t consider time.


Now, when we think about companies, industries and economies, we also have to remember they operate as part of a system – nested together and connected in all kinds of ways. They do no operate in a vacuum. And any changes take place over time.


So, you can’t just change one variable X and expect it to instantaneously lead to Y. But people do this all the time.


Let’s look at one example. Take the idea that “a flatter yield curve is bad for bank profits.” As the ten-year Treasury rate falls and spreads narrow, I have heard this more than once. It’s too pat. And it’s wrong anyway.


For one thing, bank net interest margins (NIMs) have gone up every year since 2015 (through 2018) despite a flattening yield curve (a narrowing of the spread between the 10-year treasury and the 2-year). I’m not saying NIMs will continue to rise. But I am saying the relationship between the yield curve and bank profits is not so tight. (Hat tip to my friends at PL Capital for the insight).


The chart may be hard to read, but you can see the blue-shaded area as the spread between the 2- and 10-year Treasury Yield going down. And the yellow lines are NIMs, rising.


There are a lot of other factors in the mix of what drives bank profits. And they change too: loan demand, tax rates, credit quality, cost efficiencies, etc. To just turn one variable like “yield curve” is not likely going to be insightful. Indeed, relying on that alone as an indicator has been dead wrong for the last several years.


The message here is to avoid simple “X causes Y” type thinking. You cannot change one thing in isolation. And any change takes place over time. (Remember the thermometer!) Keep these things in mind should help you as you think about your own businesses and how they will deal with shifting macro variables.


(For more, see Chapter 2, “Distrust Cause and Effect Thinking” in my book How Do You Know?)


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One other point on the macro concerns: If you are a long-term owner of businesses – I’m talking years of ownership – then you probably shouldn’t spend much time thinking about tariffs, interest rates, currency effects, the overall market, etc. (This is probably what you expected me to say in the beginning).


Do you really have an edge in predicting these things?


And even if you did, it doesn’t mean you’d be able to tease out the effects correctly (as the bank NIM example shows). Macro involves categorical type thinking, where you take big abstractions (“interest rates” “the economy” “the market” “stocks” etc.) and try to predict behaviors and prices.


If you want to read an impressive case against categorical thinking from a biological perspective, read Robert Sapolsky’s Behave: The Biology of Humans at Our Best and Worst. It applies to markets as well.


Sapolsky notes how we often break down behavior as attributable to some single factor. So we know that brain chemistry, genes, hormones, prenatal environment, childhood, etc. all impact how you behave today. But if you want to answer the question why you’re so impatient when it comes to doing your taxes or why you can’t wake up on time, people tend to reach for one category for an answer. It’s genetic. Or it’s hormonal. Or whatever.


“The goal of this book is to avoid such categorical thinking,” Sapolsky writes. “Putting facts into nice cleanly demarcated buckets of explanation has its advantages – for example, it can help you remember facts better. But it can wreak havoc on your ability to think about those facts.” As I hope is obvious, this applies not just to biology but to, well, everything.


In short, as Sapolsky says, “it’s complicated.” (Even the categories themselves are not clean-cut. What is “the brain” exactly? Where does it start and end? Reminds me of other categories in life we use, such as “mountain.” We know what it is conceptually, but where the dividing for where a mountain “begins” is hard to say. Or what about "growth stocks" "tech" "value" "small cap" etc.? Vague terms at best. Meaningless at worst).


Consider testosterone. This hormone is long thought to “cause” aggression. But… it’s more complicated than that. Sapolsky shows how testosterone does subtle things to behavior. It can’t generally explain why some individuals are more aggressive than others. It seems associated with aggression in different ways, but the relationship is not causal per se.


For example, if you’re already aggressive, lowering your testosterone doesn’t seem to affect this aggression – which points to aggression as more a learned behavior, or perhaps to other factors. It does seem to increase confidence and optimism. But there is a chicken and egg problem here: When you succeed at something, whether its sports or investing, testosterone levels go up, while decreasing fear and anxiety. But testosterone can also increase anxiety, causing aggression, in certain circumstances.


“It’s a crucial unifying concept that testosterone’s effects are hugely context dependent,” Sapolsky writes. “This context dependency means that rather than causing X, testosterone amplifies the power of something else to cause X.”

This context dependency is a big takeaway from Sapolsky’s book.


All of this stuff – hormones and genes and personal experiences – all impact behavior and do so differently in different contexts. The same is true in markets. How do rising interest rates affect stocks? (It’s complicated.) How does GDP growth affect stock market returns? (Ditto). What about interest rates on valuations? (Complicated!) And on and on for a host of other questions searching for simple cause and effect relationships…


And even after all these biological insights about how different factors influence behavior, we still can predict very little at the individual level. As Sapolsky says, we can explain a lot and predict little.


I think that sums up a lot about finance and investing as well: We can explain a lot (after the fact) and predict little.


Thanks for reading and enjoy your weekend. I’ve been knee-deep in earnings reports this week. But I’ll write up an idea or two soon.


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Published August 9, 2019

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