Analytics versus heuristics

Why I don't use DCF models

One criticism of the investors in Free Capital  which I have heard from more than one expert reader goes something like this: “The interviewees say they are making investment decisions, but none ever actually works out what a company is worth.”  These readers then elaborate on the concept of intrinsic value – the discounted future cashflows (DCF) of the company, as distinct from its book value, liquidation value or market value.  They suggest that “real investors” focus on this concept of intrinsic value.

I seldom write down an estimate of intrinsic value, and I’m not sure I’ve ever  attempted a DCF valuation of a company.  I think mainly in heuristic short-cuts: quick and dirty metrics like P/E ratio, dividend yield, price/sales, price/net current assets, price/net tangible assets, and so on.   Of course, P/E ratios imply rates of capitalisation: if I think a P/E of 12 is ‘fair’, I’m saying intrinsic value is the company’s current earnings capitalised in perpetuity at 8½% pa.  But in general, I don’t find it helpful to make this transformation.

There are several reasons why I find simple heuristics more useful than more rigorous analytics like DCF valuation.

Time is precious There are more than 2,000 shares quoted on the London Stock Exchange and AIM.  Given the scope of the search space and the pace of change, DCF models simply take too long. 

If you need a calculator, it’s too close  A good buying opportunity shouts at you from the market.  The cheapness should be striking enough that you can see it without detailed calculations.  If you need a calculator – let alone a spreadsheet – you should pass, because it’s probably too close.

Robustness matters more than refinement Investment is about finding valid discrepancies in a noisy-information environment.  Finding discrepancies is easy: there are always plenty of companies which appear to have extreme valuations.  But most of these discrepancies are not valid: the company deserves its extreme valuation.  When you think you've found something, searching for further independent insights which confirm or disconfirm the discrepancy is more useful than refining your estimate of its size.  

In other words: when information quality is good, focus on quantifying and ranking your different options; when information quality is poor (as it usually is in investment), focus on raising information quality.  (In a different but analogous context, Givewell give an explicit Bayesian justification for this.)

Non-financial heuristics are quicker Sometimes heuristics such as affinity – the class of people associated with a company – can be a quick and sufficiently accurate route to correct decisions.  For example, John Hempton suggests finding stocks to short based on a company’s association with dodgy people, not dodgy fundamentals. He will short a stock (in very small quantity) based on association with one suspect promoter and one suspect lawyer, without any investigation of the fundamentals.  If the stock rises (ie moves against him), he investigates the fundamentals; if it goes down, he just takes the profits and moves on to the next one.

The heuristic investor may make some mistakes the rigorous analyst does not make.  But the heuristic investor works much faster, and is able to evaluate many more opportunities. This is usually a good trade-off.

Guy Thomas Sunday 01 January 2012 at 2:16 pm | ΒΆ | Default | No comments