As humans, we are always trying to quantify the world around us, to put an exact figure on the future. This is a very natural impulse: most people value certainty, in both their personal and professional lives, and thus have trouble dealing with uncertainty or chaos. This urge explains the fondness that investors have for mathematical models that aim to estimate the value of businesses to a high degree of precision.
They rely on publicly available data like book value, earnings, cash flow, debt load and so on to arrive at a single number that attempts to adequately represent the intrinsic value of a given business, and - thanks to modern computing - seem to give very precise answers. However, the certainty such modelling provides is illusory.
Problems with models
For starters, even the most conscientious and diligent company accountants cannot describe exactly what is happening within a company when compiling their quarterly reports. They are guided by a strict set of rules and best practices, but these exist mainly to standardise the language of accounting rather than to enhance the accuracy of financial reporting.
The problem of accurate modelling becomes even more difficult when you try to project what might happen in the future. People aren’t even capable of pinpointing the exact value of their home, so why would they be any better at calculating the exact value of a business?
To make matters even more confusing, the inputs to a model will change from day to day and will include far more factors than are typically considered. If you are calculating the intrinsic value of a bond, it is relatively straightforward to determine the net present value of the cash flows that you would be entitled to as a bondholder, since such spreadsheets and calculators are freely available on the Internet.
Of course, there is always the possibility that your counterparty will default on their payments, which is an additional variable that you must take into account. With stocks, there are no legally guaranteed cash flows, which makes it even harder to calculate intrinsic value, but this does not stop analysts from trying to do so.
Accuracy is not the same thing as precision
The problems with models illustrate the difference between accuracy and precision. Although these words are often used interchangeably, they do in fact mean different things. Accuracy is how close a measurement (or set of measurements) is to the true value, while precision is how close the measurements are to each other. The statement “the population of the U.S. is about 330 million” is accurate, but not very precise (it is only given to the closest million). The statement “the population of the U.S. is 8,812,948,934” is highly precise (it is given to the nearest single digit), but it is obviously inaccurate.
So what does this mean for investors? If it is impossible to arrive at a precise and accurate figure for the value of a business, then why bother with analysis at all?
Well, even though you can’t be precisely right, you can be approximately right. Instead of chasing the illusory certainty of a precise number, I try to find a range of values that could potentiall contain the true answer and work from there. As humans, we are not well-built to handle uncertainty, but this is something that all successful investors train themselves to do. It is better to be approximately right than precisely wrong.
Disclosure: The author owns no stocks mentioned.
Read more here:
- Warren Buffett: Why Long-Term Bonds Are a Bad Investment
- Seth Klarman: The Proper Way to Think About Investment Risk
- Has the Unicorn Bubble Burst?
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