An altogether bigger prize would be to put these data to work before the fact, identifying the next victim pre-autopsy. That could mean using transactions data to help detect early warnings of systemic fault-lines and stresses. This is a potentially massive analytical and technical challenge. The technical challenge is certainly surmountable. Advances in computer power over the past decade mean that storing and processing huge volumes of data poses no technological barrier. The answer lies in the clouds – conveniently enough, since they too exhibit fractal properties. If we can search and track the world wide web in close to real time, we can certainly do the same for its financial sub-component. The analytical challenge is altogether greater. In essence, it is to find summary measures of billions of transactions data which are informative about impending market stress. There is serious needle-in-haystack risk with such an endeavour. And experience in economics and finance of finding robust early warning indicators is mixed. But here again, the market microstructure literature offers some tantalising clues. For example, Easley et al (2011b) have suggested that measures of “order imbalance” may provide early warning signs of liquidity voids and price dislocations. Their measure of imbalance follows closely in Mandelbrot’s footprints. It uses a volume-based metric of the proportion or orders from informed traders. Any imbalance towards informed traders causes potential liquidity problems down the line as a result of adverse selection risk.

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