9 March 2016
The World Economic Forum at Davos this year was full of talk about AI - Artificial intelligence will spur economic growth and create new wealth. Machines that “think” like humans will help solve huge problems, from curing cancer to climate change. Yet millions of human workers will need to retrain, as robots make their existing jobs redundant.
Inevitably this will, and already has, impacted the world of trading and investing. Our clients are thinking long and hard about ways of harnessing technology to drive the search for alpha and create returns that appear increasingly hard to find in global investment markets. But will human fund managers go the way of the dodo? Perhaps not quite, but the future looks more challenging.
Major advancements in computing power are transforming every facet of modern life, and financial markets are no exception. The investment skills most in demand now are often found in computer scientists rather than economists and investment bankers with MBAs. Whilst this has been the case for a number of years in the “quant” investing world, even larger more diversified asset managers such as Blackrock, Schroders and Fidelity have been building capability to use “quantitative” techniques enabled by modern computing and complex mathematical models. A constant theme with our clients is the search for talent in this space – which more often than not means targeting individuals outside of the investment world – F1 Motor racing Data Analysts, IBM Computer Scientists and so on. .
Advances in “Machine Learning” are of increasing interest in the industry, where some believe a thinking, learning and trading computer will make even today’s superfast, ultra-complex investment algorithms look archaic — and possibly render human fund managers redundant.
A machine learning algorithm is a dynamic programme that scans through large data sets — such as stock prices, weather patterns, earnings call transcripts, Facebook posts or Google searches — seeking themes from the noise. The technique is not new, but more powerful computers mean that it can now be applied to financial markets. But that is not the only advantage of machine learning. When markets undergo “black swan” events and trusted strategies no longer apply, one of the classic challenges for quants is that their models can often prove useless — or worse. Algorithmic trading strategies that are successful one day can blow up the next.
A machine-learning algorithm can evolve and search for new patterns, adjusting to what works in markets that day, week or year. That means asset managers can use them as a tool to enhance their investment process, perhaps by screening for patterns undetectable by humans, or even to develop strategies and trade by itself.
Nonetheless, machine learning has pitfalls.
Even if a model works well in testing it can collapse when confronted by real markets. And lets not forget some of the attributes of being human! A recent FT article on the subject notes that Brad Betts, a former Nasa computer scientist now working at BlackRock’s “scientific active equity” arm, highlighted the 2009 emergency plane landing on the Hudson river by Chesley Sullenberger as an example of when man trumps machine.
We are moving in a certain direction, though. Investment Management is about converting information into data, analysing it rationally, and doing so with some speed. That plays to a machine’s strengths. But clearly, human asset managers will adapt rather than disappear. Most probably, the future of investment management will involve a synthesis of human and artificial intelligence that harnesses the power of both.