A sideways look at economics

Crossing the road in Cairo is a dangerous business: I speak from experience. As someone who grew up with the Green Cross Code, zebra crossings and the rest, standing on a street corner in Cairo with no pavement, with traffic lights honoured more in the breach than the observance, and with traffic moving in all directions irrespective of the road markings, while a bigger and bigger crowd of pedestrians builds up behind you, was decidedly nerve-wracking. We eventually adopted what we called the ‘wildebeest technique’ — the same approach those doughty animals take to crossing a river populated by crocodiles: wait until there’s a really big group of you, try to work your way into the centre of the pack, and then go for it, all together. If you’re near the middle, you’ll probably be ok. Convoys crossing the Atlantic in WWII, threatened by U-boats, took a similar approach. Safety in numbers.

There are interesting dynamics in groups like these, with every individual always trying to get to the middle of the pack. Shoals of small fish move in this way, each seeking to put a buffer of other fish between themselves and the circling medium-sized predators (and, in the process, creating a dense, nutritious bait ball that will eventually summon big predators from the vasty deep); and the beautiful murmurations of starlings do something similar, but more loosely, and apparently for purely aesthetic reasons.

Similar dynamics operate in the world of economic forecasting. Assume, for the sake of argument, that the variables we are all trying to forecast are random processes, perhaps with a drift component and — further — that all forecasters know that. In that world, anything other than ‘naïve’ forecasting (last time’s value plus or minus any drift) is pointless — but let’s leave that aside for now.

A risk-averse forecaster will act like our wildebeest or small fish or starling: always seeking to find the middle of the pack. Whether I’m right or wrong is random but, if I’m wrong I’ll only be as wrong as everyone else, while if I’m right I can argue that I was saying something sensible, plausible, in line with the consensus, and what a wise thing that was to do. We can all pat ourselves on the back and remark to each other, over a drink or two, how valuable we are, and how we all basically agree on the way the economy works. And we can confirm our soi-disant expertise by appearing in newspapers regularly and by hobnobbing with policymakers, fund managers and the like, shaking our heads sadly at the foolish mavericks who think they know something we don’t.

A risk-loving forecaster, by contrast, will position herself (randomly) above or below the pack. If I’m wrong I’ll look foolish as I’ll usually be more wrong than everyone else (and then I’ll emphasise how I’m not afraid to stand out, unlike some), but if I’m right I’ll be the only one who was right. I’ll appear on TV a good deal, and note how most of my peers are unfortunately crippled by groupthink. If I happen to be on the right side of the consensus a few times in a row, I’ll be hailed as a ‘guru’, and might be rewarded with high office or a lucrative consultancy business.

All of that is entirely random.

I don’t think we as a profession are quite as cynical as that: I believe there are systematic components of the processes that we try to forecast that can be revealed by careful analysis (though there are undoubtedly large random elements too). But on the other hand, many forecasters are highly risk averse. I would describe the rule of thumb for most forecasters as something like: get into the pack, but aim to position yourself away from the middle in the direction that economic analysis takes you, to a small degree.

Suppose that is the general rule, and suppose, further that the forecast variables do indeed have a systematic as well as a random component, where the systematic component can be detected with some degree of confidence by forecasters. At any point in time, forecasters don’t know where the ‘middle’ — the consensus – is now, only where it was a minute ago. With that set of assumptions, the population of forecasters will move over time a lot like the murmurations of starlings, folding in on itself with a kind of loose cohesion around a moving target, with one or two risk-loving individuals as lone outriders. The ‘consensus’ will be dragged towards the actual out-turn, but only part of the way — and there will be a degree of inertia around the pre-existing middle of the pack. The movement of the pack towards the actual out-turn will depend on the degree of inertia — or risk aversion on the part of the individual forecasters.

In other words: it will depend on their independence.

These points were demonstrated convincingly in The Wisdom of Crowds.[1] If the crowd is constructed of individuals with a high degree of independence from one another (individuals that do not seek to position themselves close to the middle of the pack) then the average estimate can be shown to do a better job than any individual estimate, over time, since the idiosyncratic biases of the individuals tend to cancel out in the aggregate. This finding can only be true if there is a systematic component to what is being estimated, which is accessible through some kind of analysis.

The problem is that there are many factors that push against independence. Risk aversion (a tendency to hug the consensus) on the part of individual forecasters is one. Another is the hierarchical structure of forecasters: small forecasting houses tend to start by looking at what the official forecasters (central banks, finance ministries, the IMF and World Bank, etc.) said in their most recent forecasts. But the official forecasters, for obvious reasons, are constrained. If, for example, the Bank of England were to forecast a recession in the UK before it started, it’s likely that forecast would be a self-fulfilling prophecy — although the difference between the Bank’s forecasts for the UK post referendum and the actual out-turn suggests perhaps the relationship is rather weak. Official forecasters will generally tend to understate the degree of variance around the steady state, for that reason.

Smaller forecasting houses tend to start by framing their own forecast with a necessarily ‘cautious’ official forecast. And then, in the next round, official forecasters frame their own forecast by looking at the most recent consensus.

There are other factors that undermine independence too. The most important of these, in my experience, is the kind of bias that arises from having assets to sell or clients with particular interests to serve. For example, a sell-side institution that’s holding a large stock of Chinese equities might find it difficult to go to its clients with a bearish view on the outlook for China. No doubt they would argue that, if they had that view, they wouldn’t be holding Chinese equities. But it is, let us say, possible that the causation works the other way around too: that the forecast shifts (perhaps in unconscious ways) to support whatever their asset position might be.

This issue is well known and much discussed. One potential game-changer arises out of part of the MiFID II regulations introduced at the start of this year. These were aimed (in part) at clarifying exactly who pays for precisely what, preventing sell-side institutions from bundling together the services they provide to their clients with the research (and recommended asset positions) that they produce. Post MiFID II, those on the sell side have to unpack their investment services from their research, and price each of those services separately for their clients.

On the face of it, this unpacking ought to provide a market opportunity for independent research providers like Fathom. If we can take on the sell side straightforwardly on price and quality, that’s a game we can win — since the problems of bias, arising from holding assets, that afflict sell-side research are well known and don’t affect us.

That’s not how it has played out, so far. The reason: exceptionally aggressive pricing of research on the part of the major sell-side institutions (the lurking big predators in our world). We’ve been told by a number of clients that the price of sell-side research has collapsed, sometimes to less than 1% of its (implied) pre-MiFID-II level.

There’s no way that a company engaged solely in producing independent research could survive if it were forced to match that price point (Fathom is fortunate in that the bulk of our revenue comes from bespoke consultancy). But there can be a future for independent research for as long as clients continue to put a high price on independence.

What’s it worth, folks? Over to you…

 

 

 

[1] James Surowiecki, The Wisdom of Crowds: Why the Many are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, Doubleday 2004.