- Why bother to seek precision about something inherently imprecise?
- Should we all be playing the Keynesian beauty contest?
- It's about the journey, not the destination.
- Minimise noise to maximise results.
- Loads of new ideas generating data.
There is something so precise and comforting about the idea of 'intrinsic value'. Sensibly pricing up what a stock is 'really' worth is an honest-and-wholesome endeavour which should justly earn honest-and-wholesome returns. Right?
Or is trying to calculate a stock’s intrinsic value just an arduous exercise to provide the illusion of precision about something that is inherently imprecise? After all, to know what the fair value of a stock is, investors need to know a lot of unknowable information: all future cash flows of the company the shares have been issued by; return on investment through the company's entire life; and the same for its cost of capital.
In the recently published book Noise, by behavioural-psychology super-group Daniel Kahneman, Cass Sunstein and Olivier Sibony, the authors recount a “noise audit” they undertook for an asset manager. The “noise” being audited can be thought of as the random variability of judgments on a given subject.
For the audit, 42 of the asset manager’s experienced investors were asked to estimate the fair value of a share. They were all given the same one page analysis with simplified accounting data for the past three years and two years of forecast. When the fair value estimates came in, the midway difference between them was a gigantic 41 per cent.
Clearly, despite a shared work culture, identical information and the professional standing of the investors involved, views on fair value were anything but precise. They were very noisy!
Perhaps this means investors are better off simply considering themselves to be partaking in a so-called Keynesian beauty contest?
This is the idea promoted by the great economist John Maynard Keynes that what’s important in investment is to select a “face” that will prove most popular with the other judges rather than the one that’s subjectively most attractive. The essence of the question being whether someone else will pay more for a stock in the future, as opposed to what its intrinsic worth is.
The 41 per cent variance on professional estimates of fair value would suggest a Keynesian game may be easier to play than kidding oneself that precision is possible.
However, attempting to put a fair value on a stock does have benefits, even if investors are unlikely to come up with a very good answer. It requires significant research that develops a deep knowledge of a company. If done well, this research should also anchor expectations in the possible rather than the speculative. And deep knowledge can also make investors stick with the “face” no one else sees beauty in but that has attractions that will ultimately prevail – Amazon during the dot com bust, for example.
As the authors of Noise point out, “judgement is difficult because the world is a complicated, uncertain place”. But that does not mean it isn’t worth attempting to form independent judgments. There’s plenty of value to be gained in the endeavour. But the huge variability in judgments does serve as a reminder that the quality of the process used, including safeguards to reduce the noisiness of results, are a key influence on how much value is gained.