Since the emergence of “Modern Portfolio Theory” and the “Capital Asset Pricing Model” in the late 1960s, institutional investors have taken a quantitatively driven approach to portfolio construction, looking to create portfolio diversification and obtain better risk-adjusted returns by balancing their asset-class exposures. This journey has seen several important advancements in thinking about how to optimally achieve desired results.
Funds that want to take the total portfolio approach first need to get the total portfolio view. To do that they not only need data – and lots of it – but a rock-solid understanding of exactly how they’re going to use it.
More talk than action on data usage
Fund managers show a keen willingness to embrace data science and systems such as centralised data platforms for decision making when it comes to formal surveys. But the reality is still something different.
According to a survey of 300 global asset management firms sponsored by the asset servicing arm of Northern Trust, nearly half (48 per cent) admitted that their organisations were still measuring the investment skill-level of their investment team by using a “qualitative measurement, which mainly relied on anecdotal evidence of proper decision-making”.
This is even though the survey results show a total of 98 per cent of respondents are already using, planning to pursue, or are interested in incorporating data science/decision-support tools into their investment process in the next one to two years.
In a white paper resulting from the survey, ‘The Art of Alpha: It’s All About Investment Data Science‘, Northern Trust says this incongruence was evident in other responses to survey questions.
Nearly half have a system to measure decision-making and inputs, but only 12 per cent use a formal research management platform that is not based on spreadsheets.
Only a quarter of respondents (24 per cent) use a decision-support platform to identify drivers of performance and behavioural root causes at a more granular and individualized level, the paper says.
The survey data presents a view of current data science capabilities and the ambitions of global asset managers in the next two years, as well as some of their key strategic priorities. Other findings include:
- 66 per cent of respondents say they currently leverage five to eight sources of investment data, with ESG data (59 per cent) and traditional factor data (55 per cent) prioritised but alternative, consumer and sentiment are also increasingly used in the search for new sources of alpha
- 52 per cent say their organisations are still using spreadsheets to aggregate internal and fundamental data; other data sources are accessed manually (such as email, PDFs and so on) and integrated to make investment decisions, and
- 52 per cent say “making their best investment ideas repeatable” is the investment process that could most benefit from data analytics.
Gary Paulin, head of global strategic solutions at Northern Trust, said: “There is growing evidence that incorporating investment data science helps managers better meet their obligations to regulators, owners and investors.
“Asset managers need to become more digitally conversant, not only because it will lead to improved investment outcomes, but because it’s being demanded more by their stakeholders, who are leveraging data science tools to do analysis of their own.”
Conducted in the June quarter, the survey captures responses from chief executives, CIOs, chief data and information officers and other executives of asset management firms with funds under management of between US$1 billion and US$750 billion and hedge fund firms with funds between US$250 million and US$10 billion across North America, EMEA and Asia-Pacific.
