This book review is a little different than the others on this blog. I tend to write up interesting books related to investing but, as you may have surmised from the title, this one is not directly an investing/business book per se. But I hope that you will indulge me and follow along.
It’s been almost a year since I wrote up a book review on the blog. I’ve read some pretty decent books between this and the last write up, but nothing I felt overly compelled to write about. Plus, between portfolio company write ups and busy personal & professional lives, I’ve not had a ton of time for a book review post or a lot of recreational reading, frankly. Luckily, my family and I spent a few days on vacation recently so I had the opportunity to catch up on some reading. I find that when I am laying by a body of water, I prefer to read something that’s not directly business related. So, I took along an old book that I picked up for $1 at a local library liquidation – Chaos: Making a New Science by James Gleick. I can’t say exactly why I picked up this book to being with – I don’t know anything about science or physics beyond basic high school/undergrad classes and anecdotal readings – but the price was right and “chaos theory” has always seemed like a seductive sci-fi phrase that peaked my interest.
I would bet if you are reading this post you, like me, suffer from the paradox (affliction?) in which your brain tries to relate anything you read/hear/discuss back to investing/markets/business. I am a big fan of the Value: After Hours podcast (highly suggest it if you have not listened before – the content is pretty unstructured and tends to be more of a conversation than a formal discussion). One of the hosts on the show, Jake Taylor, regularly presents a segment where he discusses observations from a wide range of fields of study (like biology, power generation, gardening, etc.) and relates those to various investing concepts. I suppose this is a nod to the modern “mental models” fascination that seems to be popular with hard core philosopher-investors. Anyway, I bring this up to say that I’m blatantly stealing this schtick for this post. As I read through Chaos, I couldn’t help but see parallels to investing all over the place, so I’ve collected a few of the more memorable passages and put together some quick thoughts around each.
Before we get started, a very brief background on the subject matter: Chaos (as in “Chaos Theory”) is this fascinating intersection of science and mathematics. From this outsider’s perspective it seems most closely akin to physics, but has seen contributions from practitioners and academics across mathematics, meteorology, biology and many other branches of science. The book is quite dated at this point (published 1988) and it seems to me that “dynamical systems” is the more professional and widely accepted term for what began as chaos theory. And to be honest that seems more descriptive of what “chaos” actually is – the study of complex non-linear systems. At it’s core, the work is about finding order in what appears to be randomness. As it turns out, that makes it widely applicable across a great number of disciplines (including some attempts at applying it to economics and financial markets). The book points out that one of the major accomplishments of chaos was the reversal of the trend toward increasing specialization within scientific fields, which seems to be pushing forward innovations faster as a result of more collaboration across disciplines. The book follows the birth and growth of chaos as a legitimate field of study and includes a colorful cast of personalities accompanied by easy to follow explanations of core concepts. Overall, it was a very easy and interesting read for me and if you’ve ever had any sort of inclination toward physics/mathematics I would imagine it would be interesting to you as well.
With no further ado, here are a few of my favorite selections:
Chaos breaks across the lines that separate scientific disciplines. Because it is a science of the global nature of systems, it has brought together thinkers from fields that had been widely separated. “Fifteen years ago, science was heading for a crisis of increasing specialization,” a Navy official in charge of scientific financing remarked to an audience of mathematicians, biologists, physicists, and medical doctors. “Dramatically, that specialization has reversed because of chaos.” Chaos poses problems that defy accepted ways of working in science. It makes strong claims about the universal behavior of complexity. The first chaos theorists, the scientists who set the discipline in motion, shared certain sensibilities. They had an eye for pattern especially pattern that appeared on different scales at the same time. They had a taste for randomness and complexity, for jagged edges and sudden leaps. Believers in chaos – and they sometimes called themselves believers, or converts, or evangelists – speculate about the determinism and free will, about evolution, about the nature of conscious intelligence. They feel that they are turning back a trend in science toward reductionism, the analysts of systems in terms of their constituent parts: quarks, chromosomes, or neurons. They believe that they are looking for the whole.
This excerpt captures the core tenant of much of the book. I look at this and think about how investing has also trended toward specialization over time – sell side coverage on specific industries, hedge fund pods that execute some extremely specific arbitrage, etc. On the other hand I don’t think specialization in the world of investing is nearly as dramatic as it seems to be in academia/sciences. Maybe that’s because in the world of investing even the most niche strategies are impacted by tangential factors and market events. Nonetheless, I think it’s an interesting parallel with the idea that there is some value in being a generalist in markets and having a wider perspective than any one specific niche.
In fluid systems and mechanical systems, the non-linear terms tend to be the features that people want to leave out when they try to get a good, simple understanding. Friction, for example. Without friction a simple linear equation expresses the amount of energy you need to accelerate a hockey puck. With friction the relationship gets complicated, because the amount of energy changes depending on how fast the puck is already moving. Nonlinearity means that the act of playing the game has a way of changing the rules. You cannot assign a constant importance to friction, because its importance depends on speed. Speed, in turn, depends on friction. That twisted changeability makes nonlinearity hard to calculate, but it also creates rich kinds of behavior that never occur in linear systems. In fluid dynamics, everything boils down to one canonical equation, the Navier-Stokes equation. It is a miracle of brevity, relating to fluid’s velocity, pressure, density, and viscosity, but it happens to be nonlinear. So the nature of those relationships often becomes impossible to pin down. Analyzing the behavior of a nonlinear equation like the Navier-Stokes equation is like walking through a maze whose walls rearrange themselves with each step you take. As [world renown mathematician John] Von Neumann himself put it: “The character of the equation […] changes simultaneously in all relevant respects: Both order and degree change. Hence, bad mathematical difficulties must be expected.” The world would be a different place – and science would not need chaos – if only the Navier-Stokes equation did not contain the demon of nonlinearity.
Lot of things to like here. First off, the “demon of nonlinearity” is a great turn of phrase – love it. And I don’t know if I’ve ever heard a better allegory to financial markets than: it’s “like walking through a maze whose walls rearrange themselves with every step you take.” Nonlinearity is probably not so novel for those of us in the investing world given that financial instruments exhibit that sort of behavior all the time but I thought the hockey puck example was illustrative. This is just a glimpse at the broader discussion of nonlinearity that the book covers, but throughout I couldn’t help but think how we try to pave over nonlinear relationships with linear ones in markets. One of the core building blocks of professional portfolio management (CAPM) is a hilariously oversimplified linear model for an unbelievably complex (chaotic?) system, attempting to bypass the “demon of nonlinearity.”
Chaos should be taught, he argued. It was time to recognize that the standard education of a scientist gave the wrong impression. No matter how elaborate linear mathematics could get, with its Fourier transforms, its orthogonal functions, its regression techniques, may argued that it inevitably mislead scientists about their overwhelmingly non-linear world. “The mathematical intuition so developed ill equips the student to confront the bizarre behaviour exhibited by the simplest of discrete nonlinear systems,” he wrote.
Along the same lines as the previous passage, this one reinforces the sentiment more directly. Just replace “scientist” with “investor” in this paragraph and it could be in a modern white paper or Institutional Investor article.
How can we calculate how quickly a cup of coffee will cool? If the coffee is just warm, its heat will dissipate without any hydrodynamic motion at all. The coffee remains in a steady state. But if it is hot enough, a convective overturning will bring hot coffee from the bottom of the cup to the cooler surface. Convection in coffee becomes plainly visible when a little cream is dribbled into the cup. The swirls can be complicated. But the long-term density of such a system is obvious. Because the heat dissipates, and because friction slows a moving fluid, the motion must come to an inevitable stop. Lorenz drily told a gathering of scientists, “We might have trouble forecasting the temperature of the coffee one minute in advance, but we should have little difficulty in forecasting it an hour ahead.
The “Lorenz” referenced here is Edward Lorenz, a highly influential mathematician (and meteorologist) who’s early ground-breaking research established many of the core principles of chaos. This quote from Lorenz reminds me of the old Ben Graham quote: “In the short run, the market is a voting machine but in the long run, it is a weighing machine.” As a value-oriented investor myself, I like to think we can have more confidence about outcomes over an appropriately long time frame while the shorter term is noisey and less predictable.
It was just a vacuum tube, really, investigated in the twenties by a Dutch electrical engineer named Balthasar van der Pol. A modern physics student would explore the behavior of such an oscillator by looking at the line traced on the screen of an oscilloscope. Van der Pol did not have an oscilloscope, so he had to monitor his circuit by listening to changing tones in a telephone handset. He was pleased to discover regularities in the behavior as he changed the current that fed it. The tone would leap from frequency to frequency as if climbing a staircase, leaving one frequency and then locking solidly onto the next. Yet once in awhile van der Pol noted something strange. The behavior sounded irregular, in a way that he could not explain. Under the circumstances he was not worried. “Often an irregular noise is heard in the telephone receivers before the frequency jumps to the next lower value,” he wrote in a letter to Nature. “However, this is a subsidiary phenomenon.” He was one of many scientists who got a glimpse of chaos but had no language to understand it. For people trying to build vacuum tubes, the frequency-locking was important. But for people trying to understand the nature of complexity, the truly interesting behavior would turn out to be the “irregular noise” created by the conflicting pulls of a higher and lower frequency.
This anecdote is followed by some discussion of information theory (which ties into the “irregular noise” phenomenon) but I like this section because I think about how, when it comes to stocks or markets or whatever, we find what we are looking for. That means that a slightly different perspective or frame of reference can lead to dramatically different findings. My mind immediately connected this to Greenblatt’s example of Marriott’s good co/ bad co spinoff in the You Can Be a Stock Market Genius book where most investors were focused on the franchise operations (good co) and ignoring the debt laden, real estate rich bad co. Every investor in good co had to know about bad co, but it wasn’t what they were focused on and wasn’t what they were looking for, which left an opportunity for shrewd investors like Greenblatt.
A few dozen yards upstream from a waterfall, a smooth flowing stream seems to intuit the coming drop. The water begins to speed and shudder. Individual rivulets stand out like coarse, throbbing veins. Mitchell Feigenbaum stands at streamside. He is sweating slightly in sports coat and corduroys and puffing on a cigarette. He has been walking with friends, but they have gone on ahead to the quieter pools upstream. Suddenly, in what might be a demented high-speed parody of a tennis speculator, he starts turning his head from side to side. “You can focus on something, a bit of foam or something. If you move your head fast enough, you can all of a sudden discern the whole structure of the surface, and you can feel it in your stomach.” He draws in more smoke from his cigarette. “But for anyone with a mathematical background, if you look at this stuff, or you see clouds with all their puffs on top of puffs, or you stand at a sea wall in a storm, you know that you really don’t know anything.
I like this passage for two reasons. First, despite this book being largely exposition, I do think Gleick is a pretty solid writer and I think this shows off some of his true writing prowess. Second, I really love the ending quote from Feigenbaum (highly influential physicist in his own right) – particularly the visual of standing at the sea wall in a storm. I like to imagine this metaphor applied to finance undergrads and EMH academics trying to make sense of meme stocks and crypto in 2021.
The question was so deep that almost no one had thought to ask it before: Does a climate exist? That is, does the earth’s weather have a long term average? Most meteorologists, then as now, took the answer for granted. Surely any measurable behavior, no matter how it fluctuates, must have an average. Yet on reflection, it is far from obvious. As Lorenz pointed out, the average weather for the last 12,000 years has been notably different than the average for the previous 12,000, when most of North America was covered by ice. Was there one climate that changed to another for some physical reason? Or is there an even longer-term climate within which those periods were just fluctuations? Or is it possible that a system like the weather may never converge to an average?
I love this paragraph because it is so directly applicable to investing. I feel like I hear this conversation non-stop in the quant and institutional research communities. There’s a mountain of white papers and journal articles that contemplate this exact same problem but reference factors, valuations, econometrics, etc. rather than climates. I also like this because it re-raised for me the idea that maybe averages/medians/(insert your favorite summary statistic here) just don’t matter. If you assume there’s no real equilibrium state (i.e. markets have a “chaotic” quality about them) then you’re just chasing phantoms by trying to time any portfolio decisions based on irrelevant historical data.
That’s all the quotes I’ve got for you. If this peaked your interest at all, I highly recommend reading the book. There’s obviously much more detail than I could cover here in a single blog post, but I found it to be an easy and interesting read – hopefully you will too.
Anyway, if you’ve made it this far thanks so much for reading. If you aren’t already, feel free to follow me on Twitter and/or subscribe to get email updates whenever a new post goes up on the blog (see the top right hand side of this page to enter your email).