Connecting the dots between technology and insight

By A Paris – Asset managers are capitalising on the power of technology across their whole organisation – from the investment perspective, where artificial intelligence and alternative data can support trading decisions, to the operational dimension to improve efficiency, transparency and consistency in monitoring, processing and reporting.

“To improve the client experience, asset managers must place technology at the centre of their distribution strategy,” a whitepaper by Deloitte stresses. The consulting firm finds that 34 percent of distribution leaders label technology investments as their top priority.

The whitepaper also argues that asset management firms which place technology at the centre of their distribution strategy can enjoy dramatic improvements in distribution efficiency across multiple metrics. The definition of a technology centred firm was measured by them having made above-average investments in data, analytics, and client experience applications.

AI assisting analysis

According to F Norrestad at Statista, in 2020, more than 50 percent of hedge fund managers classified as alternative data market leaders. This means they use seven or more alternative data sets globally. However, the majority of this group, 85 percent, make use of two or more alternative data sets. The Alternative Investment Management Association (AIMA) on the other hand defines market leaders in the space as those managers which have been using alternative data for more than five years. In a study called Casting the Net: How Hedge Funds are Using Alternative Data, AIMA states 13 percent of respondents could be classified as market leaders, by the association’s definition.

Winton Capital Management is a firm which has embraced the use of alternative data and artificial intelligence. In an insight piece debating research methods in relation to different trading strategies, the firm writes: “The rapid recent increase in the amount of data available in just about every sphere has created new possibilities for predictive modelling. For example, a traditional equity analyst might read every report produced by or about the companies they cover and may in the past have known every relevant fact or figure about a specific company when making an earnings forecast. The data used in an earnings forecast today, however, could include satellite imagery, credit card spending information, logistical details of every product on every truck, and much more besides.”

There is no doubting there has been a sharp increase in the amount of data analysts need to contend with. Although the use of machine learning and AI is often linked to high frequency trading strategies, Winton has also found these methods useful in strategies trading at a slower pace: “Our data requirements can often be significant, particularly if we want to perform a lengthy backtest. By way of example, consider a trading strategy that analyses the text of quarterly company reports. 

“To perform a 40-year backtest on the largest 1,000 US companies, we would need to analyse 160,000 reports. And any change to algorithms using the data would require all the reports to be analysed afresh. This task is beyond a group of individuals. Machine learning methods are appropriate instead.”

Social media networks

The growing power of social media networks also means these platforms can provide a source of data for investment managers, with the caveat of taking an informed and discerning approach. Lasse Heje Pedersen, a Danish financial economist and principal at AR Capital Management details the growing trend which sees financial market participants using social media as a source of information.

Following the infamous GameStop surge, the data generated and collected via these networks has, understandably, come under scrutiny. In a working paper entitled Game On: Social Networks and Markets, Pedersen says: “an investment idea can propagate through a social network and generate a trading frenzy with high turnover, a bubble in the price, and high price volatility.” 

His paper posits this environment results in four types of investors trading securities – naive investors who learn via a social network, “fanatics” possibly spreading fake news, rational short-term investors, and long-term investors.

Pedersen concludes: “The GameStop event reinforced some old lessons: demand moves prices and can be irrational; shorting stocks can be risky, and predatory trading can be a price-destabilising event. It also taught us some new lessons regarding the power of social media and innovations in information technology. We hoped that more information sharing might lead to better decision-making and improved outcomes. However, the influx of information could also cause more confusion, which could cloud decisions and more negatively impact outcomes.”

Elsewhere, startup firms like Quiver Quantitative are also making this information available to retail investors. Founded with the goal of bridging the gap between Wall Street and non-professional investors, Quiver’s raison d’etre is to, “allow retail investors to tap into the power of big data, and have access to actionable, easy to interpret data that hasn’t already been dissected by Wall Street.”

Embedding technology

For asset managers themselves, technology has broader applications beyond just investment decisions. The adopting of data and technology needs to be fully embedded within a firm. According to Accenture, to compete and win in the future, asset management leaders need to commit to a strategic data-driven culture. “It’s not enough to have the right tools and technologies. Firms should ‘connect the dots’ between insights and technologies, have a broader vision on how to apply them and ensure end-to-end integration,” note Mike Kerrigan, Darrin Williams and Keri Smith, all managing directors at Accenture.

Deloitte also underscores that deploying the necessary technology, “only works in concert with enterprise-wide initiatives designed to transform the entire distribution organisation, including a new distribution talent model, processes that support more rapid innovation and deployment, and a change management programme that builds confidence and attracts clients.”

The relevance of this has been thrown into sharper relief against the background of a global pandemic. Joseph El Gharib, head of business development at Amundi Technology, outlines: “The Covid crisis is certainly a catalyst for switching to robust and low operational risk technologies and this is a trend continued because asset managers cannot afford anymore large investments on pure commoditised platforms.”

El Gharib also considers the use of technology in the context of the growing appetite for ESG investments: “The crisis, combined with the green wave already engaged, has accelerated the move to solutions which enable impact investment, mainly through ESG capabilities. Investors are looking closely to comply with this ESG structural evolution through strategic allocation solutions and active control of tactical portfolio management.”

Elaborating on the role of AI in ESG investing, S&P Global highlights: “AI can help sustainable investors process mountains of data that hold essential information for ESG investing. Investment managers are coming under increasing pressure to measure ESG criteria in their portfolios. However, a lack of data is making it hard for banks to assess long-term risks and rewards. Here, AI is the answer: technologies will filter essential data that investors currently lack, acting as the catalyst for sustainable investing at scale.”

Looking ahead, El Gharib anticipates asset managers deploying more intelligence in their decision-making tools and looking for scalability to support the pressure on margins and costs: “In finding the balance between customisation and industrialisation, open and robust technologies play a key role to propose flexible and modular operating models.” n

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