Where can quant analysis identify fund manager skill?

Broomer Jason square mile

Square Mile believes that fund analysis requires a qualitative approach. Quantitative analysis has its place but we feel that it can only ever have an ancillary role. Intuitively this may seem odd, although the regulator’s semantic saturation warning us that ‘past performance is no guide to future performance’ offers some clue to its value.

Zealous passive promoters have accused active managers of resorting to “smoke and mirrors” to justify their existence and refer to the lack of empirical proof to support the idea that active managers reliably add value. Certainly there is a weight of academic study to support this, however, the vast body of this work is focused on US funds. This focus on funds creates problems for analysis. Short term analysis is too beholden to the temporary trends in markets, long term analysis stumbles as funds change their spots and managers move onto pastures new. To use a metaphor, analysing the wind area and weight of a bumblebee can drive us to the conclusion that bumblebees cannot fly.

If we consider what sort of outperformance is required (assuming a standard equity market volatility) to be sure that a manager is truly adding value over a 10-15-year-period, we get a 5-6 per cent per annum outperformance hurdle rate. This would necessitate an outstandingly strong performance record but the likes of Anthony Bolton and Nigel Thomas demonstrate that bumblebees can fly. By definition, exceptional managers such as these are rare. Sometimes, we can have little more than faith in a manager’s abilities.

Can we use quants at a more granular level to help identify a manager’s skills? One such approach might be to perform transactional analysis and consider factors such as hit rates, win/loss ratios and vacation reports. Such factors may help, but the information that they contain has its limitations.


In February, Square Mile switched out of River & Mercantile UK Smaller Companies into Jupiter UK Special Situations as I felt small caps were vulnerable to a potential Brexit result and the Jupiter fund held companies with greater levels of international earnings. This turned out to be a successful trade and the Jupiter fund went on to outperform by around 10 per cent during the period following the vote. On a 5 per cent position, this trade contributed 0.5 per cent to our relative performance. This sounds great and no doubt you have seen many managers produce charts such as this.

However, we should be wary of drawing conclusions from outcomes. The result of the referendum was not the most likely outcome; in fact it was probably only a 25 per cent shot. Perhaps we should only be attributing 25 per cent of the relative contribution as skill.

To calculate the expected value of the trade, we also need to consider what the outcome would have been if the vote had gone to the remain side. Perhaps the market trends that we saw over 2015 would have reoccurred and small caps outperformed by say 6 per cent. The 5 per cent position multiplied by the 75 per cent chance and the 6 per cent underperformance equals -0.23 per cent. Although I got a good outcome, perhaps I had just got lucky in a trade that was offering negative expected value.

Here lies the nub of the issue. We don’t know what the odds were nor do we know the outcome of the counterfactual. Our precise quantitative approach crumbles in the assumptions that we need to measure skill. Square Mile does not believe there is a quantitative approach that will help unravel this conundrum, hence, our focus on more qualitative factors. A qualitative approach is more laborious but we believe it is the most appropriate way to analyse funds.