Distinguishing between skill and luck in investment - with Michael Mauboussin, Part 3

Often, when we reference the work of market strategist and author Michael Mauboussin, here on The Value Perspective, it is in the context of the roles luck and skill can play in investment. That being so, regular visitors will not have needed much in the way of either to guess we would broach the topic when Mauboussin, one of our favourite investment thinkers, was recently a guest on The Value Perspective podcast.

As the main theme of these podcasts is ‘decision-making at times of uncertainty’, we were interested to know how he thought investors could best determine how much of the outcome of any decision made as part of their process was down to skill and how much to luck. Mauboussin acknowledges this is not the easiest of questions to answer directly so suggests thinking in terms of a spectrum of different activities.

At one extreme, you could, for example, have a lottery, which is all luck and no skill and, at the other, perhaps a sprint or a chess match, which are – to all intents and purposes – down to skill rather than luck. “An important thing to bear in mind is that, on the ‘all-skill’ side, like chess or a running race, outcomes are extremely indicative of skill,” Mauboussin observes.

“In those cases, you do not need to worry about process at all – you just know the fastest person is going to win the race or the best chess player will win the match. It is only when you slide from the ‘skill’ side to the ‘luck’ side, that process becomes increasingly important – and, because the noise-to-signal ratio in investment is relatively high, we need to focus on process to understand the likelihood of success.”

As such, the answer we were after is embedded in our original question: it all comes back to process and Mauboussin is kind enough to reference our own investment approach, here on The Value Perspective, by way of illustration. “I think you guys are doing a good job of this already, which is to be very overt about laying out your thesis,” he notes – and we honestly are just quoting him here.

“And, in your thesis, there are going to be certain elements that will distinguish your view from that of the market – and you’ve tried to call those out very specifically.” What such an approach enables is the creation of a two-by-two matrix – originally promoted by the behavioural scientists J Edward Russo and Paul Shoemaker – that allows you to analyse process (good or bad) versus outcome (good or bad).

“If the stock is up – so it’s a good outcome – and your process was good – in other words, your thesis unfolded – then you give yourself credit and you pat yourself on the back,” says Mauboussin. “And if your process is good and the outcome is bad – in other words, one of the low-probability events shows up … well, that happens.

“But if your process is well-calibrated – meaning it recognises the event happens the ‘right’ percentage of the time – then you just dust yourself off and do it again tomorrow, right? Because even when you are playing a hand in poker, say, or decision-making within a sports match, there can be things that don’t have good outcomes that were still right to do and you would want to do them over and over.”

If it is a given that bad process merits bad outcomes, Mauboussin continues, the most challenging of the matrix’s four boxes is thus ‘good outcome, bad process’ – that is, when you end up being right for the wrong reasons. “That should stand out if you have laid out your thesis and talked about this,” he adds. “And it is where you say, in effect, we got bailed out – and then you really try to draw lessons.”

This then is how Mauboussin suggests investors think about – and continually audit – their decision-making. That said, he quickly acknowledges a significant challenge for long-term investors: “The stockmarket is a very bad short-term indicator of your process because it is much too noisy,” he says. “So we need to compensate by being very explicit in our theses for different securities.

“So it involves saying, here is what we expect to happen that is different than what the market believes and here are the probabilities we attach to those outcomes. This allows you to create a ‘side track’ record to help measure your own calibration and the quality of your decisions. Then you can be brutally honest in giving yourself short-term feedback – with some faith and belief that will ultimately feed into long term outcomes.”