A group of tourists was visiting a dinosaur museum. A guide was entertaining them with interesting trivia about various dinosaur species. Just when they were passing by a huge skeleton of an ancient carnivore, an inquisitive member of the tourist group asked the guide, “How old is this skeleton?”
“Oh, that big T-rex skeleton? It’s about 100 million and 5 years old.” quipped the guide.
“That’s quite an odd figure. I understand the 100 million part but how are you so sure about the last 5 years?”
With all earnestness, the guide replied, “Well, that’s the most accurate part of the figure because exactly 5 years ago a world famous expert on dinosaurs told me that the skeleton is 100 million years old.”
The guide was honest in his attempt to provide an accurate information but he confused accuracy with precision. His answer was precise but was it really accurate? In fact, a better question to ask would be – did the guide make expert’s answer anymore useful by making it more precise? I think no.
Sir John Maynard Keynes said, “Better roughly right than precisely wrong.”
When it comes to investing, precision has much less practical application than a new investor would think. This tendency to look for precision where none exists is a human bias. Charlie Munger calls it Physics Envy .
In 2003, in his lecture on Academic Economics, Munger said –
It’s my view that economics could avoid a lot of this trouble that comes from physics envy. I want economics to pick up the basic ethos of hard science, the full attribution habit, but not the craving for an unattainable precision that comes from physics envy. The sort of precise, reliable formula that includes Boltzmann’s constant is not going to happen, by and large, in economics. Economics involves too complex a system. And the craving for that physics-style precision does little but get you in terrible trouble…economics should emulate physics’ basic ethos, but its search for precision in physics-like formulas is almost always wrong in economics.
Our mind is wired in such a way that it hates ambiguity and anything that can’t be measured by assigning a precise number to it is ambiguous to a human brain.
In Poor Charlie’s Almanack , Peter Kaufman writes –
Charlie strives to reduce complex situations to their most basic, unemotional fundamentals. Yet, within this pursuit of rationality and simplicity, he is careful to avoid what he calls “physics envy,” the common human craving to reduce enormously complex systems (such as those in economics) to one-size-fits-all Newtonian formulas. Instead, he faithfully honors Albert Einstein’s admonition, “A scientific theory should be as simple as possible, but no simpler.” Or in his own words, “What I’m against is being very confident and feeling that you know, for sure, that your particular action will do more good than harm. You’re dealing with highly complex systems wherein everything is interacting with everything else.”
Paul Graham, a very successful venture capitalist and founder of Y-Combinator, in his wonderful book Hackers & Painters , writes –
Everyone in the sciences secretly believes that mathematicians are smarter than they are. I think mathematicians also believe this. At any rate, the result is that scientists tend to make their work look as mathematical as possible. In a field like physics this probably doesn’t do much harm, but the further you get from the natural sciences, the more of a problem it becomes. A page of formulas just looks so impressive. (Tip: for extra impressiveness, use Greek variables.) And so there is a great temptation to work on problems you can treat formally, rather than problems that are, say, important.
Excessive quantification is the norm in physics and mathematics, but dangerous in investing. When looking at numbers in investing, always ask as to what do they mean and in what context were they arrived at.
Making investing decisions involves dealing with a lot of moving parts, including but not restricted to human behaviour, market conditions, competition, future prospects, and industry dynamics. Which means it’s nearly impossible to predict the final outcome accurately. Trying to put a lot of false precision into a complex system like the stock market is the source of severe errors.
The legendary investor John Bogle wrote in his book The Clash of the Cultures –
When applied to the physical world, scientific techniques have been successfully used to determine cause and effect, helping us to predict and control our environment. This success has encouraged the idea that scientific techniques can be productively applied to all human endeavors, including investing. But investing is not a science. It is a human activity that involves both emotional as well as rational behaviour. Financial markets are far too complex to isolate any single variable with ease, as if conducting a scientific experiment.
There’s a famous saying in the value investing community – more fiction has been created using Excel than Word.
Excel, or any spreadsheet software for that matter, is a dangerous tool. Relying too much on Excel driven models (like DCF etc.) can divert your attention away from things that really matter. There’s a lot of wisdom in the adage – Everything that can be counted doesn’t necessarily count.
Price is what you pay and value is what you get, instructed Benjamin Graham. So as a value investor, the first thing I learned is to ensure that I don’t pay more than the intrinsic value of a company. Now, this poses a challenge. We are being asked to compare the price, which can be measured precisely, with the value which is largely an estimate i.e., inherently imprecise. But most new investors attempt to do that i.e., try to arrive at a precise number for intrinsic value. It’s a classic case of Physics Envy in action.
Calculating the intrinsic value of Berkshire Hathaway is a challenge, says Warren Buffett, “Present that task to Charlie and me separately, and you will get two different answers.” In his 2006 letter to shareholder, Buffett wrote –
…calculations of intrinsic value, though all-important, are necessarily imprecise and often seriously wrong. The more uncertain the future of a business, the more possibility there is that the calculation will be wildly off-base.
If you are a long term investor, then price target is a misleading number to follow because the preciseness of target price builds a false sense of confidence. And this false confidence makes you vulnerable to serious mistakes.
The majority of the small investors are not mentally wired to handle this counterintuitive aspect of investing. That’s why most people never make money in stock market. And that’s why the best strategy for most of the small investors is to invest through mutual funds. But there lies another paradox. To be able to select a good mutual fund, you need to learn the fundamentals of long-term investing. And once someone learns the nuts and bolts of investing, it’s almost impossible to resist the urge to pick stocks directly.
So dear investor, if you’re going to get your hands dirty in the stock market, in spite of all the warnings, beware of the pitfalls of Physics Envy.
I have an alternate hypothesis that they are not all different. While we have precise formulae that apply universally, many of these are theoretical. In the real-world scenario, Physics has to take in a lot of assumptions for the precise formulae to hold correct. For example, take the gravitational acceleration constant, 9.8 m/second squared. This holds true for all objects – in vacuum. No matter what the mass of the body, each object will take the same time to fall a certain distance. When you bring this scenario into the real world, however, a lot of factors come into play. Air resistance, for instance. I would say it is the same with Investing as well. Models such as the CAPM, or Black-Scholes would work well provided all other external factors stay the same. This will almost never happen. Hence, financial models often go wrong in the real world.
Anshul Khare says
I agree with you partially.
When Physics meets real world, you get engineering. When investing meets Physics envy, you get financial engineering.
The real world factors like air resistance can be measured and accounted for while building the space ship and it works beautifully but in economics, the real world factors are governed by theory of reflexivity.
Imagine the air being aware that its resistance has been accounted for while designing an airplane. So just when the flight is about to take off, air decides to change its properties (density, viscosity, etc.). That’s what happens in economics.
The idea is that preciseness works beautifully in real world of physics and engineering but not necessarily in other areas. It particularly fails to work where two way complex interactions exist between agents.
In the end, Physics laws are made useful by engineering because engineering creates predictable and reliable results. Financial engineering hardly does.