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How can we assess fund manager skill? A manager’s view

Stuart Fowler, one of the most lucid commentators on the investment industry, clearly explains the problems faced by all investors looking for a manager to run their money:

“Assume [the investor] believes that there are [skilled managers]…Now she needs either to select the managers herself or to select an agent who is skilled at selecting skilled managers. That makes three skills that are not the same…”

If academics are debating the very notion of whether skill in active management can be possible, how can we begin to identify who has it? And if an individual is looking for an intermediary (a financial advisor or a consultant) to pick managers for them, then assessing the ability of the intermediary is a separate consideration again.

The industry has long sought to tackle these issues. But as technology has caught up in replicating decision-making previously done by managers, identifying skill in order to justify active fees has become ever more important. The below sets out how we as fund managers think the task can be approached.

Let a computer do it?

Like fund managers themselves, fund selectors have designed a range of statistical tools to try to aid decision making. These can range from simple metrics like the Sharpe ratio to more complex regressions.

The problem with all quantitative models is that they will be backward looking (or else rest upon the subjective assumptions that they are trying to avoid) and they cannot differentiate between luck and skill.

If someone buys a lottery ticket and wins, it does not mean that it was a sensible investment. Similarly, a manager who bought inflation-linked gilts in 2013 believing that QE would cause inflation and that the UK was due a recession would have earned excess returns even though neither actually occurred. In either case the returns earned may be similar or even better than a manager who correctly assessed the state of the market.

One way to think about these different scenarios is through the diagram below. Making a return “for the wrong reason” would fall into the top left quadrant, genuine “skill” in the top right.

luck-v-skill1

Purely quantitative models cannot distinguish between the top two boxes (or the bottom two for that matter). Moreover, it is likely that truly successful managers will combine both elements, constructing a portfolio which maximises gains and minimises losses from events which they would not have expected.

Understanding and believing in the manager’s edge

It seems clear then, that no matter how tempting, we cannot delegate the assessment of manager skill to a model. Instead it is critical that a fund selector understands and buys into what it is that the manager believes they can do better than the rest of the market. Doing this provides the context which is essential to analysing a track record.

We feel that there are four key questions to try to develop this understanding.

luck-v-skill2

Sometimes the answers to these questions will be straightforward; the manager may operate in a niche, under-analysed asset class, or be in a position to make specific deals that others can’t e.g. in property or lending markets.

In many, probably most, cases however these questions are very hard to answer. This does not mean that they should be ignored; simply accepting that a manager is very smart is a hugely dangerous leap of faith. Without understanding why a manager makes the decisions they do, it is impossible to distinguish between luck and skill.

What is a fund manager’s time horizon?

Of course, if a manager has a longer track record of success one can be more confident that there is more skill than luck involved. But very few managers have what could be considered a ‘statistically significant’ history behind them. And, with so many managers out there, it will still be possible to find enough people who have been able to get lucky on a large number of occasions.

Understanding the manager’s edge again becomes critical. Is it a philosophy that seeks to generate a little bit of outperformance each month, or will it come in short bursts of large gains? Does the manager’s approach need a particular environment to work best, and has the track record been generated in such an environment?

Without such considerations it is very hard to make the distinction between a manager who is “wrong” from one who is “right, but not yet.”

Motivations and “the behaviour gap”

Understanding time horizons is important in another sense, that of understanding a manager’s motivations. A couple of days ago, the CFA and State Street published a report in which they identify a new ‘hidden variable of performance’ which they call ‘Phi.’ Phi is the nature of the motivation behind the decisions we make and the authors of the report argue that finding those with the right motivations will help identify those who are more likely to be successful.

The report noted how human beings are motivated in two main ways: by the negative consequences of failure (the stick) and by achieving positive outcomes (the carrot). The report suggested that, despite high levels of pay, most in the investment industry are pressured by the former: 36% of asset and wealth managers suggested that acting in the best interest of their client implied taking on career risk.

luck-v-skill3

The short term focus in the industry tallies with evidence of the ‘behaviour gap.’ This is the phenomenon whereby the average investor underperforms the average investment because they chase the managers with the best returns and to sell underperformers at the wrong time. Analysis from Morningstar published last week showed that this behaviour gap has been evident in all asset classes in Europe over the last five years. One piece of analysis earlier this year suggested that even an equity manager with perfect foresight of the best equity performers over five years would have to suffer such deep drawdowns that they would have likely been fired.

Evidence suggests that investment consultants (at least in the US) are less likely to make recommendations based on past performance. However, it is easy to imagine that a consultant/advisor hired after the predecessor has been sacked will find it very difficult to say to a client: “your portfolio was correctly positioned; you just need to be patient.”

A successful manager will be one who is motivated by achieving positive returns for the client. We refer to this as the “care level.” Does the manager see themselves as a custodian of client assets or are they more worried about their own job? Are they prepared (and able) to suffer short term underperformance? Are they likely to stay in the industry if the rise of passive products and robo-advisors challenge the status and trapping of the ‘superstar fund manager?’

Conclusions

Many of the observations above are necessarily hard to quantify. No one ever said that investing is easy and selecting fund managers is equally difficult. But we should resist the temptation to delegate the task to quantitative methods. In our opinion it is essential to believe in the manager’s thesis about what it is that they are exploiting, and to understand how they are motivated (and this goes beyond the nature of their pay). These are the lenses through which any past performance must be analysed.


The value of investments will fluctuate, which will cause prices to fall as well as rise and you may not get back the original amount you invested. Past performance is not a guide to future performance.