The rationale for a quantitative approach to investing was first described by James Montier in his 2006 research report Painting By Numbers: An Ode To Quant:
- Simple statistical models outperform the judgements of the best experts
- Simple statistical models outperform the judgements of the best experts, even when those experts are given access to the simple statistical model.
In my experience, the immediate response to this statement in the investing context is always two-fold:
- What am I paying you for if I can build the model portfolio myself?
- Isn’t this what Long-Term Capital Management did?
Or, as Montier has it:
We find it ‘easy’ to understand the idea of analysts searching for value, and fund managers rooting out hidden opportunities. However, selling a quant model will be much harder. The term ‘black box’ will be bandied around in a highly pejorative way. Consultants may question why they are employing you at all, if ‘all’ you do is turn up and run the model and then walk away again.
It is for reasons like these that quant investing is likely to remain a fringe activity, no matter how successful it may be.
The response to these questions is as follows:
- It takes some discipline and faith in the model not to meddle with it. You’re paying the manager to keep his grubbly little paws off the portfolio. This is no small feat for a human being filled with powerful limbic system drives, testosterone (significant in ~50% of cases), dopamine and dopamine receptors and various other indicators interesting to someone possessing the DSM-IV-TR, all of which potentially lead to overconfidence and then to interference. You’re paying for the absence of interference, or the suppression of instinct. More on this in a moment.
- I’m talking about a simple model with a known error rate (momentarily leaving aside the Talebian argument about the limits of knowledge). My understanding is that LTCM’s problems were a combination of an excessively complicated, but insufficiently robust (in the Talebian sense) model, and, in any case, an inability to faithfully follow that model, which is failure of the first point above.
We humans are clearly possessed of a powerful drive to allow our instincts to override our models. Andrew McAfee at Harvard Business Review has a recent post, The Future of Decision Making: Less Intuition, More Evidence, which essentially recapitulates Montier’s findings in relation to expertise, but McAfee frames it in the context of human intuition. McAfee discusses many examples demonstrating that intuition is flawed, and then asks how we can improve on intuition. His response? Statistical models, with a nod to the limits of the models.
Do we have an alternative to relying on human intuition, especially in complicated situations where there are a lot of factors at play? Sure. We have a large toolkit of statistical techniques designed to find patterns in masses of data (even big masses of messy data), and to deliver best guesses about cause-and-effect relationships. No responsible statistician would say that these techniques are perfect or guaranteed to work, but they’re pretty good.
And I love this story, which neatly captures the point at issue:
The arsenal of statistical techniques can be applied to almost any setting, including wine evaluation. Princeton economist Orley Ashenfleter predicts Bordeaux wine quality (and hence eventual price) using a model he developed that takes into account winter and harvest rainfall and growing season temperature. Massively influential wine critic Robert Parker has called Ashenfleter an “absolute total sham” and his approach “so absurd as to be laughable.” But as Ian Ayres recounts in his great book Supercrunchers, Ashenfelter was right and Parker wrong about the ’86 vintage, and the way-out-on-a-limb predictions Ashenfelter made about the sublime quality of the ’89 and ’90 wines turned out to be spot on.
Overall, we get inferior decisions and outcomes in crucial situations when we rely on human judgment and intuition instead of on hard, cold, boring data and math. This may be an uncomfortable conclusion, especially for today’s intuitive experts, but so what? I can’t think of a good reason for putting their interests over the interests of patients, customers, shareholders, and others affected by their judgments.
How do we proceed? McAfee has some thoughts:
So do we just dispense with the human experts altogether, or take away all their discretion and tell them to do whatever the computer says? In a few situations, this is exactly what’s been done. For most of us, our credit scores are an excellent predictor of whether we’ll pay back a loan, and banks have long relied on them to make automated yes/no decisions about offering credit. (The sub-prime mortgage meltdown stemmed in part from the fact that lenders started ignoring or downplaying credit scores in their desire to keep the money flowing. This wasn’t intuition as much as rank greed, but it shows another important aspect of relying on algorithms: They’re not greedy, either).
In most cases, though, it’s not feasible or smart to take people out of the decision-making loop entirely. When this is the case, a wise move is to follow the trail being blazed by practitioners of evidence-based medicine , and to place human decision makers in the middle of a computer-mediated process that presents an initial answer or decision generated from the best available data and knowledge. In many cases, this answer will be computer generated and statistically based. It gives the expert involved the opportunity to override the default decision. It monitors how often overrides occur, and why. it feeds back data on override frequency to both the experts and their bosses. It monitors outcomes/results of the decision (if possible) so that both algorithms and intuition can be improved.
Over time, we’ll get more data, more powerful computers, and better predictive algorithms. We’ll also do better at helping group-level (as opposed to individual) decision making, since many organizations require consensus for important decisions. This means that the ‘market share’ of computer automated or mediated decisions should go up, and intuition’s market share should go down. We can feel sorry for the human experts whose roles will be diminished as this happens. I’m more inclined, however, to feel sorry for the people on the receiving end of today’s intuitive decisions and judgments.
The quantitative value investor
To apply this quantitative approach to value investing, we would need to find simple quantitative value-based models that have outperformed the market. That is not a difficult process. We need go no further than the methodologies outlined in Oppenheimer’s Ben Graham’s Net Current Asset Values: A Performance Update or Lakonishok, Shleifer, and Vishny’s Contrarian Investment, Extrapolation and Risk. I believe that a quantitative application of either of those methodologies can lead to exceptional long-term investment returns in a fund. The challenge is making the sample mean (the portfolio return) match the population mean (the screen). As we will see, the real world application of the quantitative approach is not as straight-forward as we might initially expect because the act of buying (selling) interferes with the model.