Industry evolution : Why asset managers need to become more systematic : Lynn Strongin Dodds

Investors want to see simplified risk models and lower costs, creating the need for change.

Over the past few years, active asset managers have been under intense scrutiny for charging high fees but delivering disappointing performance. Passive funds have been the beneficiaries of inflows which has forced these buyside firms to up their game. They are not only having to re-evaluate their pricing structures and strategies but also talent pool and technology in order to enhance their investment decision making and operational processes.

There has been a spate of studies showing active managers are missing the mark. One of the most recent reports from Scope Analysis revealed that fewer than a quarter of the 2,000 global equity funds it analysed beat benchmarks in 2018, a dramatic drop from the 53% in 2017. Adopting the longer view, S&P Dow Jones found that between 2006 and 2016, 99% of actively managed US equity funds sold in Europe failed to outshine the S&P 500 in the ten-year timeframe. Only two in every 100 global equity funds outperformed the S&P Global 1200 since 2006 while almost 97% of emerging market funds failed to deliver.

In the past, investors may have been willing to give managers the benefit of the doubt but in the current prolonged low interest rate environment, patience has worn very thin. This explains EPFR figures which demonstrate the migration to passive investing. Last year alone global passive equity funds garnered $472bn, while active ones lost $488bn. In Europe, Moody’s Investors Services predicts they could account for 25% to 27% of the overall market by 2025 from the roughly 14% share they currently have. 

This shift is undoubtedly having an impact on the collective bottom line. A recent report from Oliver Wyman and Morgan Stanley, ‘Searching for Growth in an Age of Disruption’ predicts that over the next five years, the revenue pool of core active management in developed markets is set to shrink by over a third and will no longer be the largest contributor to industry revenues. 

“The general dynamic in the industry has been an overall tilt to passive investing such as exchange traded and smart beta funds as investors focus more on value for money,” says Dr Anthony Kirby, head of regulatory intelligence, FS for EY UK. “This has put pressure on fees and has meant that many money large money managers who run active strategies are having to justify that the fees they are charging are consistently adding value. We are also seeing a regulatory uptick in these areas.”

For example, the Financial Conduct Authority which has been banging the drum for years about index hugging expensive active managers has just enacted new laws that require asset managers to provide more detailed information on their strategies, benchmarks and performance fees.

Upping the game

Against this backdrop it is obvious that active managers have to change their modus operandi in order to stay in the game. A new study by Allianz Global Investors showed that 61% of the 500 global institutional investors with $15 trillion AUM believe active managers are better able to capture market opportunities by taking advantage of artificial intelligence and big data. However, they have been slow in adopting the tools of Silicon Valley as evidenced by a separate survey by Element22 and UBS Asset Management. It found that 55% of the 20 asset management firms polled with a combined $14 trillion in assets under management, are still in the early stages of adoption.

Around 10% have just started their journey while at the other end of the spectrum, 10% are breaking new ground. One reason for their reluctance is the expense of integrating nonstandard and unstructured alternative data into a portfolio manager’s systems and workflows. Unsurprisingly, it is the largest mainstream managers who are and have been ahead of the curve in developing robust alternative data and advanced analytics capabilities. The latter mainly includes machine learning (ML) and natural language processing (NLP), while smart robotic process automation (SRPA) is largely in trials.

In general, Toby Pittaway, partner at Oliver Wyman believes that the industry needs to redefine its value proposition into something that investors are willing to pay for and that managers have to deliver their services at a materially lower price point. He cites its report which states that the asset management industry has the potential to cut up to 30% of its current cost base through a combination of improved automation, data driven strategies, outsourcing and rationalisation.

“Quantitative strategies have used alternative data for a number of years but we are now seeing more traditional asset managers tap into these sources,” he adds. “It does not change fundamental analysis on a balance sheet but helps to improve the research process and validate the hypothesis.”

Javier Rodriguez-Alarcon, European head of quantitative investment strategies at Goldman Sachs Asset Management echoes these sentiments. “These tools need to be systematically embedded in the investment process and used to substantiate for example fundamental criteria” he adds. “The drivers are less from a cost savings perspective but more about using alternative data sets and techniques into the investment decision making process to maximise the outputs.” 

The range of alternative data sets that can help paint a more rounded picture of a company and its prospects seems bottomless. However, the most popular to date include satellite imagery, social media sentiment, consumer transactions, geolocation, online reviews, and web-crawled data. Using the retail sector as an example, active managers are increasingly employing satellite imagery to count the number of cars in car parks as a metric for sales as well as geospatial analysis to identify the geographical proximity of competitors, or in which neighbourhood new stores have been opened. There are also tools that can analyse and track changes in a brand’s health, as well as how it is faring against competitors. 

Hand in hand with the technology is the need for data scientists to analyse, interpret and parse the data. “One of the issues is that due to the increase in computational power, there has been a huge increase in data particularly unstructured (images, text, etc) and it is difficult for humans to consume and analyse it,” says Rodriguez-Alarcon. “However, there is a lot of noise in the data and it is important that you not only have the right tools but also people with the right skills (market practitioners) to evaluate information. This is different from a traditional asset manager approach as not everyone has the right skillset and resources to build the infrastructure to do this.”

Mark Ainsworth, head of data insights and analytics at Schroders also stresses the importance in leveraging both people and machine to create value and produce optimal outcomes. He adds that the data unit’s team which has over 20 data scientists sits on the same floor with the credit and equities portfolio managers and uses what it calls Intelligence Augmentation, a tool that uses AI to help humans make decisions, rather than making the decisions for them.

He notes that fund managers have access to many useful pieces of information about a company such as its financial state, revenues and the stated plans of its management. However, there are other important snippets of information that can’t be found through traditional channels. These include customer’s views of a brand, their demographic target group and whether growth can be achieved by organic growth or via acquisition. 

“The investors’ proficiency is to understand the core accounts and markets while the data scientists’ skills are in computers and maths,” says Ainsworth. “The key is to find the overlap between the two and to give portfolio another lens and greater clarity of their decisions. It is important though that they ask the right questions so we can build the right frameworks and data sets to fill in the gaps.” 

The operational challenge

While data driven strategies play a crucial part, they are only one component of the overall equation. According to the Oliver Wyman report more work needs to be done to improve the modular technology architecture. This is particularly the case with data integration and application orchestration layer. The former can enable asset managers to adopt a more customer-centric data model designed to generate client insight and increase process automation while the latter offers institutions the flexibility to “plug in” and experiment with service providers. It not only helps to drive process automation through use of third-parties but also supports new operating standards as well as builds new levels of customer understanding. 

Akbar Sheriff, Head of UK Global Services at State Street, also believes that active managers should look more closely at the operational efficiencies and business benefits that can be gained by outsourcing ‘front to back’ functions including data management so that they can focus more on their main business of investments .“The aim is to simplify the risks and reduce the cost,” he adds. “They need to ask whether for example managing the data is core to our business and if it is not, who and how should it be efficiently managed.”

©TheScienceOfInvestment 2019


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