Computer Simulation Suggests That The Best Investment Strategy Is A Random One

This doesn’t come from nowhere. The authors of the study have been probing the strength of random decisions over planned ones in dealing with a variety of leadership situations. They’re inspired, they write in the paper, by the fact that “[i]n physics, both at the classical and quantum level, many real systems work fine and more efficiently due to the useful role of a random weak noise.”

The basic idea behind their hypothesis is the fact that knowledge of markets – no matter how much of an expert you are – is going to be pretty limited. That, they think, will cause people to start to see patterns that aren’t there. Which leads to misleading strategies and poor investment decisions. So they wanted know if  ”a trader assumes the lack of complete information through all the market… would an ex-ante random trading strategy perform, on average, as good as well-known trading strategies?”

To determine the best trading strategy, they looked at data from four different stock indices: the UK FTSE, the MIB FTSE (Italian stocks), the DAX (German) and the S&P 500. They used about 10 years of data from the two FTSEs and about 15 years of DAX and S&P Data. They then compared their random investment strategy to four different traditional investment strategies: momentum investing, where stocks are bought and sold based on past performance; investment based on the relative strength indicator of stocks; up and down persistency, where investment one day is the opposite of market direciton the day prior; and investing based on the moving average convergence/divergence of the stocks.

The primary result they determined was that over the long periods of time, all of the different strategies performed about the same. Yes, over smaller time windows, some of the strategies showed better performance than a random investment, but over the long run those differences mostly went away. What’s more, although some of the “traditional”‘ strategies showed better performance in a particular stock indiex, none performed better over the four indicies. Consequently, the authors concluded that where a strategy “won” a particular index, “the advantage of a strategy seems purely coincidental.”

But the authors did find one big advantage of a random strategy. Counterintuitively, the random investing strategy was much less volatile than the others. In other words, while on a given day, the strategy might not gain as much as another, it wouldn’t lose as much, either. The swings were much more manageable. That means, they conclude, “the random strategy is less risky than the considered standard trading strategies, while the average performance is almost identical.”

In other words, a random strategy may not give you better returns than a traditional investment strategy, but it will turn out to be a one where your portfolio shows a lot fewer ups and downs – without adversely impacting your returns. As a consequence, the authors suggest that one way that markets could be stabilized in the long run would be for central banks to adopt random investment stategies to reduce volatility.

The benefit of that central bank strategy, the authors state, might be two-fold: “From an individual point of view, agents would suffer less for asymmetric or insider information, due to the consciousness of a ‘fog of uncertainty’ created by the random investments. From a systemic point of view, again the herding behavior would be consequently reduced and eventual bubbles would burst when they are still small and are less dangerous; thus, the entire financial system would be less prone to the speculative behaviour of credible ‘guru’ traders.”

The authors admit that they still have quite a bit of work to do in establishing a clear central banking policy utilizing a random investment scheme. And the strategy still hasn’t been tried with real money. Still, it’s an interesting result that held true over a lot of different data points. It’s worth thinking about.