“So you will have retail investors who will write a lot about Asian markets and especially China. If you trade in those markets there is a rich source of data. Bulletin boards seem to drive a lot of hype and excess volume tends to get associated to reversal, so we can construct some sort of reversal insight in those markets.” But all this can overlook the fact that noisy data can be misleading. A good example, says Champonnois, was when Google declared that it was better at predicting flu epidemics than local health authorities in the US, based on searches for flu-related items, rather than using data from health authorities. However, in 2013 media coverage of the flu, which got people searching the topic, led to Google erroneously predicting an outbreak. “Our response is you need to have a scientific process - you can't use this stuff as a pure black box,” says Champonnois. He illustrates the point. “I manage a model that's related to developed market firms. Every morning I get a sort of spreadsheet: each line is a company in that universe, and each column is an insight, which is a sort of component of returns. We bunch those insights into certain categories so some will be related to fundamentals, which is very traditional quant (analysis). But we will have another set of columns that's related to sentiment. Then you also have some insight into macro. “So for each of the stocks in the universe I know my view in terms of fundamentals, my view in terms of sentiment, my view in terms of macro-themes. At some point you combine those different insights into what is your best return forecast for each of those stocks.”
AlgoDynamix is working with a number of buy-side firms using artificial intelligence (AI) to aid portfolio construction and risk management. The UK-based group uses deep data algorithms to scan the real-time behaviour of buyers and seller to identify endogenous portfolio risk, rather than looking at external one-off black swan events such as when the Swiss central bank removed its currency peg. According to the CEO and co-founder of the firm, Jeremy Sosabowski, the AI focuses on identifying internally generated risks that can blow up a portfolio, by spotting clusters of uncharacteristic buying and selling that typically occur 20 or 25 times a year,