Investment decision making in face of uncertainty

Investment process

It ought to just kind of scream at you

We all want to make really good investment decisions. The problem is that the world is an uncertain place. That’s the topic today.

We can start with a quote from Peter Lynch: “It’s also important to be able to make decisions without complete or perfect information. Things are almost never clear on Wall Street, or when they are, then it’s too late to profit from them. The scientific mind that needs to know all the data will be thwarted here.” (Lynch, One Up on Wall Street, 1989,1990) p.69. (Emphasis added)

Two sides

A wannabe Jim Simons might disagree that the data can’t get you there.

At risk of oversimplify, let me suggest there are two contrasting approaches on how to grapple with uncertainty in investing. One would be that the use of reams of data and emotionless computers can spell success. The other is that you can get there based on a wealth of experience and your gut. To some degree Warren Buffett would exemplify this latter approach and perhaps Jim Simons the first.

I don’t think that either of these simplifications is true for either Simons or Buffett. To examine this further we need to talk about models.

I can disclose my own bias. Years ago, I decided I wanted to learn how Warren Buffett does it, rather than how Jim Simons does. This, in spite of the fact that I have a university degree in math and physics. Or, perhaps I should say, because I have a degree in math and physics, I recognize the limits of math.

Discounted cash flow (DCF) models

Let’s enter model land. We can use DCF to get us started. It’s a land where mathematical formulas, estimates of future cash flows and assumptions about how the world will unfold, go into a computer to produce estimates of the fair value of a company or stock.

At a meeting with University of Maryland MBA Students on November 15, 2013, Warren Buffett explained the concept in the simplest possible terms. This is from notes taken by Professor David Kass.

“We generally think the value of a company is the PV [present value] of cash flows until judgment day.”

How Warren Buffett uses DCF models

In the Value Investing World blog on Thursday, March 29, 2018 it was reported that at the 1996 Berkshire Hathaway Annual Meeting the following exchange took place:

Munger: We have such a fingers and toes-style around here. Warren often talks about these discounted cash flows, but I’ve never seen him do one.

Buffett: Some things you only do in private, Charlie.

Munger: Yeah. If it isn’t perfectly obvious that it’s going to work out well if you do the calculation, then he tends to go on to the next idea.

Buffett: That’s true. It’s sort of automatic. If you have to actually do it with pencil and paper, it’s too close to think about. It ought to just kind of scream at you that you’ve got this huge margin of safety.

A step back

What is a model? In investing we are surrounded by models. For example, financial statements are models. All charts are models. Economic data like Gross National Product or Consumer Price Inflation are, or come from, models. All quantitative stock analysis is the use of models.

A model is nothing more than a framework we use to interpret data.

We are told: “In finance, economics and business, models never describe ‘the world as it really is’. Informed judgement will always be required in understanding and interpreting the output of a model and in using it in any large-world situation.” (Kay and King, Radical Uncertainty – Decision-Making Beyond the Numbers 2020) p.261 (Emphasis added) King is a former governor of the Bank of England (comparable to the U.S. chair of the Federal Reserve) and a professor at New York University and emeritus professor at the London School of Economics. Kay is a Fellow of St John’s College and Oxford University.

What Warren Buffett actually does

We could easily be misled by the apparent simplicity of what Warren Buffett says he does. In reality, there is much more to it.

When he’s looking at a potential investment and thinking about cash flows, free cash flows and owner earnings, he is working with models. He says in the quote above that it’s ‘sort of automatic’. What he is actually doing is analyzing the business.

He has written elsewhere: “When investing, we view ourselves as business analysts – not as market analysts, not as macroeconomic analysts, and not even as security analysts.” (Buffett W. E., The Essays of Warren Buffett: Lessons for Corporate America. Lawrence A. Cunningham. 1998) p.63.

So, what he’s actually doing is scanning the numbers in a financial statement or report to develop a sense of whether the company’s earnings are virtually certain to be materially higher in five, ten and twenty years. And he looking at whether his ‘sort of automatic’ DCF shouts out a margin of safety in the price to be paid.

He’s clearly using a model. He is using a framework to interpret the financial data. It is not seat of the pants. It is not straight from the gut. He does his homework. It’s just that he has a lot of experience and can focus right away on what is important and understand what the model is telling him.

Proper use of models

The interpretation of a model’s output is not a mechanical thing. Let’s dig a bit further.

The most stimulating writing on uncertainty I have read recently is the 2022 book by Erica Thomson titled Escape from Model Land – How mathematical models can lead us astray and what we can do about it. She writes: “Those who make a career facing day-to-day decisions up close to this uncertainty have generally learned strategies for coping with it. Some of that involves humility, remaining skeptical of any optimization and adding margins for error.” (Thomson, 2022) p.134 (Emphasis added) Thomson is a senior policy fellow at the London School of Economics and has a PhD from Imperial College.

Warren Buffett no doubt would be skeptical of a fancy spreadsheet with a DCF assessment of fair value expressed as a single dollar figure. Adding ‘margins for error’ fits with Buffett’s ‘huge margin of safety’.  He’s humble enough to limit himself to investments within his circle of competence.

Thomson’s conclusion is that the best strategy is a smart synthesis of human judgement with mathematical support. She advocates robust decision-making. She quotes Rob Lempert, an author on the Intergovernmental Panel on Climate Change:

“Rather than relying on improved point forecasts or probabilistic predictions, robust decision-making embraces many plausible futures, then helps analysts and decision makers identify near-term actions that are robust across a very wide range of futures…” (Thomson, 2022) p.213

Balance and diversification

A robust portfolio is also one that is suitably balanced and diversified. These are a separate topic but they clearly are important to address uncertainty.


Peter Lynch is right that investors need to be able to make decision without complete or perfect information. Even interpreting historical data is uncertain and the future even more so. What is going to happen in the economy over the next five years is anyone’s guess. What new technologies will come along is unknowable. Will the U.S. be at war with China in the next ten years? How will this particular company fare in future? We can make various educated guesses. The first message of this post is that it is best is to make investment decisions that are robust across ‘many plausible futures’.

Second, various tools are available. Most of them involve models. The other message of this post is that models can help but they need to be used with educated human judgement.

The comfort for the individual investor is that professional investors operate under the same debilitating conditions. The playing field is more level than one might expect. Professionals do not have the welters of precise information and high-level analysis many individuals think. In fact, their fancy models that spit out precise answers are dangerous because they create a false sense of certainty.


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