Subscribe to Asset Servicing & Fintech Insights
Investment Data Science Builds Asset Allocators’ Power
Recent innovations in data science could bring huge payoffs in the investment industry. As sophisticated asset allocators take advantage of greater access to data and better analytical tools, they have the ability to more closely scrutinize their portfolio managers.
By Paul Fahey,
Head of Investment Data Science at Northern Trust
Today's institutional investing industry has encountered a shift in the dynamic between investors and their asset managers, led by the availability of investment options in the market.
Twenty years ago, the demand for funds was high, with many allocators seeking opportunities amongst far fewer asset managers. Now, we see a proliferation of funds resulting in greater selectivity for investors. This new dynamic has shifted the power balance – the days when investors were price takers are gone. Instead, they are price makers, and are demanding greater transparency and more value for their money when assessing asset managers.
What's more, access to data has become democratized, as have data analysis tools. Firms of all sizes can access technology and capabilities that were once only available to industry giants. Thanks to this eased access, investors can now more granularly oversee their managers’ performance and conduct advanced data analysis within their own portfolios.
Given this evolving industry landscape, it’s reasonable for allocators to expect to embrace a modern and quantitative approach to multi-asset portfolio risk management and investment decision making. A key part of this approach includes ensuring their asset managers are also taking advantage of progress in investment data science.
To understand the state of asset managers’ data science embrace, Northern Trust and WBR surveyed 300 CEOs, CIOs and Chief Data Information Officers from asset management firms across the globe during Q2 2021 to find out what their strategies are for maximizing their data, how they incorporate those insights into their investment process and their plans to leverage data science tools to optimize their investment performance.
Overall, the survey found that the asset management industry understands the value of data science and is adopting the emerging tools and technologies, but still needs to make key strides in transforming their systems and embracing new data-centric processes. Some key insights:
- Industry-wide buy in – The asset manager community is largely on top of the need for data insights to support their investment strategies. Ninety-eight percent of managers have adopted or are planning to adopt data science decision support tools.
- Multiplying data sources – The number of data streams that managers need to be on top of is growing, particularly with a stronger emphasis on ESG, but also due to a growth in alternative asset data, consumer data and sentiment data. Two-thirds (66%) of managers say they access five to eight different data streams for their investment data needs.
- Opportunity to learn from success and failure – Just over half (52%) said the area of their investment process that could most benefit from data analytics was making their best ideas repeatable.
- Pressure to trade ‘qualitative’ for ‘quantitative’ – Nearly half (48%) of survey respondents admitted that their organizations are still measuring the investment skill-level of their investment team by using a “qualitative measurement, which mainly relies on anecdotal evidence of proper decision-making.
- Need for centralized investment data platform – The majority of respondents (57%) cited the need for a centralized platform to consolidate their investment data to aid decision-making. As data sources proliferate, asset managers are looking for ways to harmonize their data sets, a need that became more pronounced during the pandemic year.
These insights can give asset allocators an idea of where to expect their managers’ investment data science adoption to land as well as what barriers they may run into as they build data science practices into their own operations. While there are challenges to iron out in managing high volumes of data from an increasing number of sources, the progress that has been made promises encouraging outcomes for both asset allocators and asset managers.
To ensure their portfolios are benefitting from the strides made in investment data science on both the internal decision making and external manager adoption, asset allocators can focus on a few key initiatives to bring confidence to their own decisions as well as trust in the managers they select.
- Asset allocator-specific data analysis – While many asset management applications of data science target the repeatability of investment decisions, there are use cases and tools on the market that specifically target asset allocators and provide enhanced insights and analytics designed to help them make more confident portfolio decisions. For example, an asset allocator-targeted data analysis platform may be able to simulate how your portfolio would perform under market stresses, examine the implications of manager changes to your risk and return, and provide performance estimates for assets and portfolios that may be lacking current data.
- RFP and research management – If asset allocators are to put emphasis on how their asset managers rely on data analysis in their processes, they must dig into these issues from the outset. Asset allocators should track this information in their research management process and include data analysis questions in their request for proposal documentation.
- Due diligence review – When asset allocators partner with due diligence teams to review their new or existing asset managers, they should ensure that the review covers data science strategy, including what data sources the manager relies on, what tools they use to analyze that data and what insights they rely on most. Due diligence teams should also request demos of data science platforms used by the asset manager.
The asset management industry is on the cusp of massive transformational change – with data management and analytics at the center. Asset allocators have a vested interest in this transformation, and amid the current industry balance between managers and allocators, are in a position to be selective. To ensure they’re investing with managers who will be on the right side of the movement, they should look to work with the partners and technologies that will empower them to be active participants in the rise of investment data science.
Head of Investment Data Science, Asset Servicing, Americas