The main reason I set up this website was to draw more attention to the fact that we are fairly useless at making economic predictions. The graph I use as a logo is just one of many comparisons of economic projections with reality which could be shown to demonstrate just how useless. The problem is that there is then a bit of a void when we are looking for ways to support economic decision making. Nassim Nicholas Taleb and Nate Silver are, in different ways, exploring ways to fill this void.

Taleb, in his book Antifragile, links the fragility of the economic system, amongst other things, to an absence of “skin in the game”, ie something to lose if things do not go to plan. Something is fragile if it is vulnerable to volatility, robust if it is relatively unaffected by it, and antifragile if it profits from volatility. If decision makers gain an upside when things go right, but are effectively bailed out from the downside when they don’t by others who then suffer the downside instead, they will have no real incentive to be sufficiently careful about the decisions they take. This makes the chance of a downside for everyone else consequently greater. Taleb refers to people in this position, championing risky strategies (or, more often, strategies where the risks are not really known) without facing the risks personally, as “fragilistas”. He suggests instead a system of “nonpredictive decision making under uncertainty” on the basis that “it is far easier to figure out if something is fragile than to predict the occurrence of an event that may harm it”.

Silver, in his book The Signal and the Noise, suggests the main reason why economic predictions are routinely so awful, even when the forecasters are trying to be accurate rather than making a splash, is that the events forecasters are considering are often out of sample, ie the historical data they are considering to make their forecasts do not include the circumstances currently being faced, but that the forecasters are confident enough to make predictions despite this. This explains, for instance, the failure in the US to predict the housing crash (there had never been such a boom before), the failure of the ratings agencies to understand how risky mortgage-backed securities were (they were in new more complex forms) and the failure to predict that the housing market would take the rest of the economy down with it (much more trading betting on house price rises that did not materialise than had ever been seen before).

Silver cites the economist Terence Odean of the University of California at Berkeley, whose paper Do Investors Trade Too Much shows that equity traders trade excessively in the sense that their returns are, on average, reduced through trading (due to transaction costs), and suggests that this is partly due to overconfidence in the constant stream of information in the media drawing their attention from one possible model to another. This effect can be modelled to show that markets behave irrationally in the presence of overconfident decision making, even when decision-makers are otherwise completely rational.

However, when we are looking at the calls made by traders there are other forces at work that make things even worse. Silver’s book advances the theory that it is not so much an absence of skin in the game that leads traders to continue betting on rising markets when bubbles have started to develop, but skin in the wrong game (at least from the point of view of the rest of us fragile people). He gives the example of a trader looking a year ahead to make a call on whether the market will crash or not. If he buys and the market rises everyone is happy. If he buys and the market crashes, he will be in the same boat as everyone else and will probably keep his job if not his bonus. If he sells and the market crashes he will be seen as a genius, but the tangible rewards of this beyond his current position might not be life changing. However, if he sells and the market rises he may never work in the industry again.

For a trader who wants to keep his job and remain popular with his colleagues, selling therefore has limited upside and unlimited downside, to analyse it in a Talebian way. Buying on the other hand has potentially unlimited upside while the music is playing and limited downside when it stops. So these traders do have skin in the game, which keeps them buying even when they can see the iceberg approaching, and does not particularly reward accuracy in forecasting. It’s just a different game to the one the rest of us are playing.

For these amongst many other reasons, economic forecasting (Silver differentiates forecasting – meaning planning under conditions of uncertainty – from predicting – meaning calling the future correctly) is unlikely ever to be very accurate. But whereas Taleb believes that planning for the future must be nonpredictive to avoid making suckers of us all, Silver believes there may be some scope for improvement. This brings us to the idea of prediction markets and their ability to introduce some of the right skin in the right game, which I will discuss in my next post.