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Artificial intelligence in asset management

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Global asset managers are committing more resources to the development of funds using artificial intelligence, writes Richard Newell. How will this affect portfolio management?

The asset management industry has begun to recognise, perhaps belatedly in some cases, that it is likely to be massively disrupted by the introduction of artificial intelligence.

It is estimated that algorithms now account for 90% of financial market trading. Yet there are only a few managed funds that are fully using machine-learning technology. There are plenty of funds using artificial intelligence (AI) to churn out ideas, or refine their trading signals, but the exposure they take and the way they execute trades is still controlled by the human fund manager.

The next wave of funds is set to radically disrupt the industry. Progress with machine-learning algorithms, especially through the development of deep learning techniques, is producing a new wave of managed funds that are robotically controlled in the analysis, security selection and trading processes.

Global fund giants such as BlackRock, and hedge fund specialists including Bridgewater, MAN Group and Two Sigma have had exploratory programmes on machine learning capabilities for some time. BlackRock recently set up an AI laboratory in California and has centralised its data science efforts in a new team called Data Science Core.

Paulo Salomao, managing director at Accenture Asset Management, which tracks the financial industry’s AI evolution, says: “Many institutional investors and pension plans are still in the process of rolling out data lakes and robust data governance frameworks. The most advanced peers have mastered these skills and are looking now at deploying AI and machine learning to improve investment decision making, such as producing investment research, supporting security selection in private assets, driving performance attribution and portfolio construction, and talent management.”

The fund giants are hiring a particular type of professional – the data scientist – to help them develop and optimise algorithms. Analysis by Gaurav Chakravorty, CIO of Qplum, a US-based asset management firm that offers AI-based trading strategies, shows that the asset management industry typically takes in about $1trn (€848bn) in revenue, more than half of which is invested in human capital. In an effort to alleviate fee pressure, Chakravorty expects $100bn of annual investment will go into technology-driven roles in the next two to three years. “A typical asset management firm will soon have more machine learning engineers and data scientists than people with experience in financial markets,” he says.

The evolution of AI funds

The potential use of AI in fund management is broad. It reaches from low-complexity data aggregation and interpretation to highly complex semantic analysis, correlation analysis and predictive modelling that can directly feed into investment decisions.

Another recent development is the use of machine-assisted trading in less liquid asset classes or less efficient markets. These have the potential of higher information asymmetries and AI can be used to efficiently identify small but meaningful signals in the underlying noise.

In its current state, AI portfolio management shows potential, but the consensus seems to be that machine learning has only an augmenting role in the investment process at this stage. While AI funds are capable of independent decision-making, the human interface is still an important, if not crucial, element.

The future looks promising though, to judge from the performance of the next generation of hedge funds – a step on from commodity trading advisors (CTAs) and other funds using AI allied to human portfolio managers. On a five-year annualised basis, the average CTA has returned just 2.63%, while the AI hedge-fund universe has returned 10.71%, according to data provider Eurekahedge.

While results so far for fully AI-managed funds are encouraging, the volatility spike in the first quarter of this year was a rude shock for AI funds, according to Mohammad Hassan, head of research at Eurekahedge. “All of last year the VIX index was down below 10 and suddenly this year it shot up to 50,” he says. Whatever was happening in the AI portfolios was suddenly upset by this volatility swing.”

This recent rocky patch supports the argument that AI portfolio management is not sufficiently robust or adaptable to work autonomously. Despite the availability of historical data, these AI portfolios have not been through sufficient training in different market cycles.

It is early days then for fully-fledged AI funds – even in the US where the AIEQ ETF, which invests in US stocks and REITs and uses an AI model developed using IBM’s Watson, was listed last October. The fund’s methodology is based on taking price predictions on single stocks to build the model portfolio. The ETF has a year-to-date return of 5.54% (to mid-May), outperforming the S&P500 by 4.6% (see AI-powered ETFs vs S&P500).

Another US ETF promoter, Buzz, offers a US Sentiment Leaders fund, with the 75 holdings selected by AI technology, having analysed the most-discussed stocks on different media channels. Over 12 months to May, the ETF returned 21.49%, outperforming the S&P 500, which grew 14.63% in that time.

In March, through its iShares division, BlackRock received regulatory approval in the US for seven sector ETFs powered by machine learning. The funds use a new sector classification system which, using data science techniques, allows single companies to span more than one sector, reflecting changes in their business emphasis – Amazon is the example BlackRock uses – that traditional indices may not.

An example of the AI interaction that could help optimise portfolios is an application that can combat behavioural biases in portfolio management. Fintech company Capital.com says behavioural biases can severely impact an investor’s trading results: “Supposedly rational decisions may stem from mental shortcuts that ignore chunks of information, which can then have a significant impact on traders’ results.” Its AI software claims to identify investor biases and offer approaches to overcome them.

This article appeared in the June 2018 edition of Investment & Pensions Europe. Read the original article here.

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