Earlier this week I wrote about James Montier, global equity strategist for Dresdner Kleinwort, and his contention that purely quantitative models outperform independent human judgment for a wide array of decision problems across many fields of expertise. You can read his whole article here: Painting By Numbers: An Ode To Quant. He gives examples of quantitative models outperforming experts in medical diagnosis, university admissions, predicting criminal recidivism, and even judging the quality of wines. When there is so much evidence of quantitative models outperforming exports, why are the former used relatively rarely?

The most likely answer is overconfidence. We all think that we know better than simple models. My own confession at the start of this note is a prime example of such hubris. The key to the quant model’s performance is that it has a known error rate, whereas our error rates are unknown.

And furthermore:

Grove and Meehl suggest many possible reasons for ignoring the evidence presented in this note; two in particular stand out as relevant to the discussion here. Firstly, the fear of technological unemployment. This is obviously an example of a self serving bias. If, say, 18 out of every 20 analysts and fund managers could be replaced by a computer, the results are unlikely to be welcomed by the industry at large.

Secondly, the industry has a large dose of inertia contained within it. It is pretty inconceivable for a large fund management house to turn around and say they are scrapping most of the processes they had used for the last 20 years, in order to implement a quant model instead.

Another consideration may be the ease of selling. 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.

Read more:  Painting By Numbers: An Ode To Quant

Earlier:

• James Montier: Quantitative Strategies Rule
• All posts tagged James Montier

Shhh! Whisper it quietly, but what if the whole active fund management business is a con? What if the multi-billion pound investment game, employing thousands of highly-paid money managers in the City, is based on a myth? What if the Emperor’s got no clothes?

Maybe that’s overstating it, but there is compelling evidence that all but the luckiest fund managers are doomed to underperform not just the averages but simple quantitative approaches to stock selection. If machines can really do it better, why do we accept an expensive, inefficient system that makes us pay through the nose for mediocrity?

You are probably aware that most active fund managers underperform their benchmark. You may even be aware that 90pc of investment returns are nothing to do with stock selection but a product of being in the right or wrong market or asset class each year. You may not have considered that there might be something hard-wired into the human brain that makes active investment a mug’s game.

That is the implication of an interesting piece of research by Dresdner Kleinwort’s behavioural strategist James Montier, in which he questions why the City offers so few funds based on simple quantitative approaches when the data suggest these models significantly outperform human judgment.

His work is based on a study of 136 different decision-making situations in which mechanistic models were compared with approaches relying on an assessment of the facts by supposed experts. Just eight of the 136 found in favour of human judgment and in each of these cases the people had extra information that the quantitative models did not. On average the experts made accurate or successful judgments in 66.5pc of situations, while the quantitative models had a hit rate of 73.2pc.

Read more: The unsaid truth: machines are better stock pickers