Will AI take over active fund management?

The invasion of artificial intelligence algorithms in many different fields from finance and autonomous cars to diagnosing and recommending treatment plans have dominated the public media and academic literature (see The Economist Special Report, 25 June 2016[1]). The number of conference papers or journals on artificial intelligence or machine learning has increased exponentially[2] and computer science academic papers and studies in artificial intelligence increased nine times between 1996 and 2015[3]. Furthermore, the entire academic database, Scopus, owned by the publishing house Elsevier, contained over 200,000 papers in the field of computer science that was indexed with the key term “artificial intelligence”. Despite this increased interest in artificial intelligence by industry and academic experts over the past few years, some are beginning to question whether this trend is just another hype that will end in a whimper or whether this trend will have the stamina to transform certain industries entirely. Andrew McAfee and Eric Brynjolfsson from MIT share the latter view. In their book published in June 2017, Machine, Platform, Crowd: harnessing Our Digital Future[4], they present an optimistic view of how emergent technologies, including AI, are having a huge impact on our daily lives and careers. However, in a recently published article in the Harvard Business Review (Artificial Intelligence for the Real World[5]), Davenport and Ronanki surveyed 152 organizations regarding their artificial intelligence projects and showed that the projects started out with extremely ambitious goals but ended with cost over-runs and significantly less ambitious outcomes. The authors categorized the completed projects within these organizations into 3 types: automating business processes, gaining insight through data analytics and engaging with customers and employees. These were far from the lofty goals they started with. Furthermore, there are reports asking whether the gap between the theoretical underpinnings and implementation of AI is just too big for AI to make a further meaningful impact in the real-world. For example, in an article (Is AI Riding a One-Trick Pony?[6]) published in MIT Technology Review, the author questioned whether current AI applications are relying on three-decade old theory and whether this will limit the scope of innovation in multiple applications going forward. At a recent conference (Are We Ready For The Next Financial Crisis?) co-hosted by the Rotman School of Management and the Global Risk Institute in Financial Services, there were mixed views among professionals and academics on how technology, from cryptocurrency to artificial intelligence, will transform financial services.

The financial services industry, including investment management, have also experienced a dramatic increase in the use of artificial intelligence. The applications range from the development of robo-advisors, that attempts to individualize the asset allocation decision, to the use of AI in portfolio construction and stock picking. There are mixed results at this early stage, however, a more cautious approach is suggested following the performance of a sample of these funds during the market correction of February. The Eurekahedge® index of funds, created in 2011, that use AI in investment management showed that these funds came horribly short during the first major equity market correction in 2 years. The AI index fell 7.3% in February compared to a 2.4% decline for the broader Hedge Fund Research Index in the US. This is a far cry from media headlines in 2017, for example, “Blackrock’s Fink looks to Invest ”better than humans””. To be fair to Blackrock’s CEO, Larry Fink, he did say that pure AI-driven investing “is more of a myth than a eality”. This illustrates the media’s obsession with talking-up the role of AI in investing, which is far from the practical realities of the performance of these funds during the market correction. Any good investment analyst will profess that one of the biggest challenges when estimating the intrinsic value of a company (or other asset) is understanding the difference between what the market perceives the company to be worth (e.g., its stock price) and what its fundamental/ intrinsic value (present value of future free cash flows) actually is. This difference is referred to as the margin of safety by Warren Buffett, which may be viewed as the risk of an investment: if the intrinsic value is below the stock price then the risk increases as the stock price moves closer to the intrinsic value (for a buy-and-hold strategy). The same can be said about the practical realities vs. the market’s perceived ability for technology that use AI.

What I will do in the next section is to explain why this large difference between perception and reality exists: the last decade saw the shift from individual, sophisticated AI algorithms to ensembles of different algorithms. Although this shift has been significant and should not be underestimated, I will argue that AI only solves particular problem-types, which does not include true active fund management. A similar sentiment was echoed in a report[7] published in 2011 that attempted to represent the relevant financial network for systemic events and risk. The report argued that political and social networks may emerge to play a larger role in liquidity transactions and/or in the spread of rumours, which can ultimately influence market fear and greed and hence consensus valuation of markets.