Noise is one of the most striking and ubiquitous properties of the human brain. Each individual brain cell, or neuron, produces a series of spikes of electrical activity the detailed properties seem largely to be random. If we measure the electrical activity of networks in the entire brain, the overall patterns, it seems, are also rather random. Indeed, excessively synchronised brain activity is a sign of something unusual, such as deep sleep or, more ominously, an epileptic seizure.
People’s choices, even between simple options, in the best-controlled lab conditions, are astonishingly variable: in a typical lab experiment, if we slip in repetitions of the same choices between, say, £5 for certain or a 50% chance of £12, it is not unusual to find that people are inconsistent about a quarter of the time, within no more than a period of minutes.
Financial markets, too, are notably noisy and unstable. Markets are, after all, determined by real-world decisions made by the brains of investors and fund managers (although, of course, this picture is increasingly complicated by the rise of algorithmic trading). Could the noisiness and instability of markets somehow be connected to the noisiness and instability of the brain?
In traditional economic and finance theory, it has been assumed that the answer must be “No.” One line of thinking is that the noisiness of all those individual nervous systems will surely cancel out, just as the noisiness of trillions of individual molecules cancels out when chemists model how heating a gas causes it to expand.
Another line of thinking points out that stock market patterns generated systematic errors made by participants in a market should lead to profitable opportunities for sophisticated traders, who will soon exploit and eliminate such patterns. Extending this line of thinking, it might seem that the noisiness of the stock market is inevitable, irrespective of the noisiness of the brains of market participants. The market just has to be noisy, because a predictable market would offer ‘free money’ to the wily investor; and free money seems to be in short supply.
But perhaps such arguments are too hasty. First, note that statistical analysis has shown that the nature of the noisiness of the stock market is rather special – to a fair approximation, stock market prices exhibit a ‘fractal’ structure – the patterns of short-term and long-term market fluctuations look strangely similar. This is especially interesting when we noticed that the very same type of fractal structure is ubiquitous whether we look at the timing of brain activity or simple behaviours (as observed in a wide range of experiments, from tapping tasks to memory search). Not only that, but markets and human brains and behaviour seem to exhibit the characteristic “fat tails” that have long interested stock-market watchers – large shifts in both markets and minds seem more likely than simple random models would predict.
Second, behavioural finance has observed systematic biases in market behaviour that has not been eliminated by ‘wily traders.’ For example, our collaborator at Harvard, Andrei Shliefer, and his colleagues have observed that forecasters systematically overestimate the impact of market-relevant news, whether good or bad. They explain this over-reaction using the concept of ‘representativeness’ from cognitive psychology (roughly, people focus excessively on stereotypical models of a situation). Not only that, our recent work has shown that simple computational models of how people deal with uncertainty (may, potentially, map quite neatly into some recent models of brain function) may give rise to just this type of bias.
Over the coming months, we will be applying our existing models of individual decision making to understand collective market behaviour. By the end, we hope to provide some insight into whether or not it may ultimately be possible to create a rigorous link between noisy brains and noisy markets.