Decoding AI’s Role in Investment Management

Artificial intelligence has long been used in financial markets, despite its apparent novelty. Investors stand to benefit – just as they have over the past 30 years.

Decoding AIs Role in Investment Management

What if “artificial intelligence” was instead known as “complex information processing”? This is a historical rather than rhetorical question – and one of significance for the investment management industry, where hopes vested in AI have often run ahead of reality.

The term artificial intelligence was coined at a Dartmouth University workshop in 1956. Two participants took issue with it, however. For years, they insisted instead on the terminology of complex information processing, a less evocative but more exacting description of the discipline, which includes statistics, computational science and machine learning.

The connection between AI and financial services goes back to 19th century computing pioneer Charles Babbage, who viewed London’s Bankers’ Clearing House as an information processing system. Yet the work of English mathematician Alan Turing almost a century later first prompted academics to believe that generalised computer intelligence could be achieved.

In the 1980s, an entrepreneurial group of new investment managers began to consider applications of AI. Renaissance Technologies and D.E. Shaw, employing techniques from statistics and computer science, launched in the United States. In London, the firm Adam, Harding & Lueck Limited, was pioneering the application of computer simulation to systematically trade futures markets. These firms and their progenies – including Winton Group and Two Sigma Investments – are today among the world’s most successful quantitative investment firms.

By the 1990s, fund managers including Fidelity and LBS Capital Management were trying – and failing – to use neural networks, a type of machine learning, to identify investments. Nonetheless, a growing number of institutional investors used statistical and computer science techniques throughout the 1990s to amass data, identify trends, and trade markets.

The current surge of interest in AI has again centred on neural networks, thanks partly to systems developed by Alphabet subsidiary DeepMind to play the Chinese game of Go. But games like Go or chess are what statisticians term “fully observable” – they have defined and constant rules, and a large yet finite number of potential permutations.  By contrast, global financial markets are human institutions with ever-changing characteristics. They present computers with a far tougher challenge.

While caution is thus warranted, investment managers should continue to gain from AI – as they have over the past 30 years. There has been substantial growth in computing power and memory – products of micro-processing efficiency gains described by Moore’s Law. Advances in automatic data capture similarly hold out promise.

One thought on “Decoding AI’s Role in Investment Management”

  • While all true, there are two different branches of artificial intelligence, general purpose and augmented intelligence. The AI available today to investment managers is the latter. Products, like our own, are widely used in banking to automate the grunge work of portfolio managers, wealth managers and analysts. The systems automatically gather and prepare information to help people make better, more informed decisions faster. Today 25-50% of a wealth managers time is spent on the low value efforts of finding and preparing information. Augmenting human intelligence with machines is a complimentary approach that uses machines for tireless mundane work, and upskills people to focus their experience and education on using this information.

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