A deep dive on data
30 April 2024
Performance measurement is a crucial component of securities lending programmes. Sophie Downes explores why
Image: stock.adobe/Natali Snailcat
Performance measurement.
For many of us, it might prompt fearful recollections of exam halls and school grades; in the securities lending sphere, it has become a vital component of the way the market operates.
Its nature has also transformed irrevocably. With the advent of the digital age came the shift from human number crunching to computer programming. Data is now collated more quickly, and with greater breadth; results are aggregated at the click of a button, and the human counterpart has more insight than ever before.
By measuring performance, market participants can use their financial resources to more efficiently benefit both their clients and the broader financial system.
So whether it involves the launching of a new product, or adjusting pricing and risk strategies, performance measurement is a business imperative.
The players
Data measurement is a necessary component of the securities finance space. It is also profitable. Dominated by a handful of industry giants, a competitive spirit marks the discourse between data intelligence firms.
S&P Global Market Intelligence is a veteran in this arena. Covering approximately US$36 trillion in global securities, from more than 20,000 institutional funds, the firm holds 17 years worth of daily historical data. It also provides insights into the repo market through its Repo Data Analytics product. For Matt Chessum, director of securities finance at S&P Global Market Intelligence, both data sets are essential tools for market participants 鈥渢o make informed decisions about lending activities, pricing strategies and risk management鈥�.
Another contender on the performance measurement stage is DataLend, the securities finance data division of parent company EquiLend. Similar to S&P Global Market Intelligence, DataLend sources its data directly from industry practitioners, including prime brokers, custodians, asset managers and hedge funds, with the advantage of pulling insights from EquiLend鈥檚 own trading platform.
While S&P Global publishes blog posts and its Insight weekly newsletter, DataLend publishes a quarterly research publication called 鈥楾he Purple鈥�. Aptly named given 鈥渋t provides an editorial view of industry trends and market colour鈥�, says DataLend鈥檚 global head of Data & Analytics Solutions, Nancy Allen.
鈥淐ombined, DataLend and The Purple have democratised transparency in the securities lending market,鈥� declares Allen.
In context
Regardless of which firm you might choose to provide your insights, the role of data is invaluable. Within the context of securities lending, performance measurement covers a number of areas, including revenue generation, risk adjusted returns, and borrower demand and dynamics, to name a few.
Data is fundamental to this process as it allows market participants to directly compare their programmes with those of their peers 鈥� an exercise that Chessum believes participants should be engaging in on a regular basis. 鈥淧erformance measurement remains a critical exercise for beneficial owners,鈥� he argues. 鈥淚t enables them to generate the optimum level of returns within an understood risk framework.鈥�
As to what analytics are of most interest to firms, Chessum responds measuredly. For the securities lending market, the greatest focus is on borrower demand, market trends, lending fees and income.
On a more granular level, he notes that different market participants focus their attention on a number of more specific metrics. A hedge fund may focus on borrowing fees, short squeeze scores and stability of supply, while agent lenders may focus on days to cover, market share, and changes to fees that are being captured through intraday feeds.
鈥淓ach market participant will use the data in a different way, to both capture additional revenues and monitor their market risk and exposure,鈥� Chessum explains.
An industry standard
With this amount of data, naturally a need arises for standardisation and a set of best practices. Here, the 网爆吃瓜 Lending Performance Measurement (SLPM) guidelines represent the industry standard.
Introduced by the International 网爆吃瓜 Lending Association (ISLA) in 2020, agent lenders can choose to follow the guidelines to ensure that their data is provided in the same manner and format.
Farrah Mahmood, director of regulatory affairs at ISLA, discusses why it was necessary to implement an industry standard: 鈥淭here was a mutual feeling from beneficial owners that the data being provided to them by aggregators was inconsistent.鈥� She explains that, due to the variations in the way data was being presented, it was harder for beneficial owners to effectively assess their performance against one another.
Enter the SLPM guidelines.
Besides improving transparency and enabling beneficial owners to make more informed decisions about strategy, Mahmood highlights how these standards create a level playing field, particularly as inconsistent reporting can distort performance results.
鈥淚n short,鈥� Mahmood summarises, 鈥渕easuring agent lender performance empowers beneficial owners to be proactive managers of their securities lending activity, ensuring it delivers optimal value.鈥�
The guidelines are divided into three main areas: inventory data, transaction data and static data. As Chessum notes with pride: 鈥淪&P Global Market Intelligence is the only data provider across the industry to both meet, and exceed these industry standards.鈥�
Data evolution
The mechanisms of measuring performance have evolved considerably over time.
Historically, beneficial owners relied on their agents to provide data. Performance measurement would be focused on general market trends or comparative year-over-year performance, and was usually conducted on a quarterly or semi-annual basis. This meant that collecting data, and producing insights from it, was a largely manual process for the agents.
Today, Allen tells me, clients want direct access to more detailed and standardised reporting that can be accessed ad-hoc, and without the need for significant manual analysis. Emphasising the impact of this evolution, she suggests that the demand for performance insights has grown 鈥渕ore than ever before鈥�.
Indeed, the improvements in technology have resulted in an increased appetite for independent data among beneficial owners. Gone are the days of outsourcing data to custodians or external investment managers. Rather, Allen believes, owners are preferring to take a more 鈥渉ands-on approach鈥� with their investment teams.
鈥淭he majority of our clients not only leverage our standard product offering, but also work with our product specialists to design more bespoke analytics to monitor their lending programmes and help add value,鈥� she explains.
Chessum also acknowledges the impact of access as performance measurement evolves. 鈥淗aving the ability to access the data through either a portal, API, or data aggregator, has increased the ability of end users to use the data in different ways.鈥� The result is a more 鈥渢ailored to鈥� situation, where owners can individualise their data needs.
The tools
As the use of data has grown, securities lending markets have become more sophisticated. 网爆吃瓜 lenders can now manage risk in real time. Alerts can be triggered and actions can be corrected, all at a much quicker pace. 鈥淎utomation has created more streamlined processes, faster decision making and improved client service levels,鈥� reports Chessum.
This can be seen through the vast array of S&P Global Market Intelligence鈥檚 product solutions, all of which depend heavily on access to data. One clear example is the firm鈥檚 exchange traded fund (ETF) collateral lists, where the underlying components of ETFs can be identified to match admissible assets and markets within an individual collateral schedule. The firm also provides an onboarding accelerator tool, which analyses inventory to review the quality and level of demand of assets in a lending programme.
鈥淏oth examples use data to generate efficiencies in either the optimisation of asset pools or the unlocking of liquidity within the market, improving the allocation of financial resources throughout the lending chain,鈥� explains Chessum.
He continues to describe the 鈥榟olistic perspective鈥� provided by aggregated data, highlighting how it can identify broader trends that might not otherwise be apparent if looking at individual data points. But, ultimately, the strength of data lies in its simplicity 鈥� 鈥淚t reduces noise by providing clearer underlying trends and patterns.鈥�
Like S&P, DataLend also uses programmatic tools to offer a near real-time view into lending programme performance. Allen describes the firm鈥檚 CPR and Portfolio offerings as 鈥渞elative to a like-for-like peer group鈥�. 鈥淭hese tools offer a multitude of ways to slice and dice your data, all without the need for any manual input,鈥� she enthuses.
The opportunities posed by these tools are significant.
DataLend Portfolio, for example, automates traditionally complex revenue attribution analysis. Aimed at beneficial owners, it allows them to view the data within the context of liquidity, credit, market and structuring risk.
鈥淚n addition to helping them understand what happened with their programme during a given period, access to data also helps them to understand what could have happened, in order to gauge the performance of their portfolio and its managers,鈥� Allen explains. This insight is unparalleled, and it is changing the way investors engage with their portfolios.
AI in action
Having already undergone dramatic change, how much more can we expect performance measurement programmes to develop?
One obvious factor is the use of artificial intelligence (AI). While the term gets thrown around in industry discussions rather casually, Allen predicts the implementation of AI is imminent, if not incorporated already.
鈥淭he feasibility of deploying such a technology is much more realistic today than even five years ago,鈥� she observes. 鈥淣ot just because of the advances in AI, but because the securities lending industry has collectively moved towards transaction automation and data standardisation.
鈥淭hat is the ethos by which EquiLend was formed, so it鈥檚 natural to consider the potential for AI as a further extension of our mission and vision.鈥�
Meanwhile, S&P Global Market intelligence has already started to implement AI into a number of its metrics. One notable example is a model that uses machine learning to calculate the likelihood of a short squeeze taking place.
As machines adapt, these processes are set to become more sophisticated. Chessum sees this firsthand in his company鈥檚 use of AI: 鈥淎s the model consumes the data, it continues to learn 鈥� increasing its level of accuracy as the data consumption grows.鈥� It is clear from talking to both Chessum and Allen, that the exponential growth of technology and data generation is rapidly transforming the way in which businesses operate across the industry.
With effective tools in place, beneficial owners are best placed to deploy their assets within an ever-evolving lending market. Evidently, the potential is limitless.
For many of us, it might prompt fearful recollections of exam halls and school grades; in the securities lending sphere, it has become a vital component of the way the market operates.
Its nature has also transformed irrevocably. With the advent of the digital age came the shift from human number crunching to computer programming. Data is now collated more quickly, and with greater breadth; results are aggregated at the click of a button, and the human counterpart has more insight than ever before.
By measuring performance, market participants can use their financial resources to more efficiently benefit both their clients and the broader financial system.
So whether it involves the launching of a new product, or adjusting pricing and risk strategies, performance measurement is a business imperative.
The players
Data measurement is a necessary component of the securities finance space. It is also profitable. Dominated by a handful of industry giants, a competitive spirit marks the discourse between data intelligence firms.
S&P Global Market Intelligence is a veteran in this arena. Covering approximately US$36 trillion in global securities, from more than 20,000 institutional funds, the firm holds 17 years worth of daily historical data. It also provides insights into the repo market through its Repo Data Analytics product. For Matt Chessum, director of securities finance at S&P Global Market Intelligence, both data sets are essential tools for market participants 鈥渢o make informed decisions about lending activities, pricing strategies and risk management鈥�.
Another contender on the performance measurement stage is DataLend, the securities finance data division of parent company EquiLend. Similar to S&P Global Market Intelligence, DataLend sources its data directly from industry practitioners, including prime brokers, custodians, asset managers and hedge funds, with the advantage of pulling insights from EquiLend鈥檚 own trading platform.
While S&P Global publishes blog posts and its Insight weekly newsletter, DataLend publishes a quarterly research publication called 鈥楾he Purple鈥�. Aptly named given 鈥渋t provides an editorial view of industry trends and market colour鈥�, says DataLend鈥檚 global head of Data & Analytics Solutions, Nancy Allen.
鈥淐ombined, DataLend and The Purple have democratised transparency in the securities lending market,鈥� declares Allen.
In context
Regardless of which firm you might choose to provide your insights, the role of data is invaluable. Within the context of securities lending, performance measurement covers a number of areas, including revenue generation, risk adjusted returns, and borrower demand and dynamics, to name a few.
Data is fundamental to this process as it allows market participants to directly compare their programmes with those of their peers 鈥� an exercise that Chessum believes participants should be engaging in on a regular basis. 鈥淧erformance measurement remains a critical exercise for beneficial owners,鈥� he argues. 鈥淚t enables them to generate the optimum level of returns within an understood risk framework.鈥�
As to what analytics are of most interest to firms, Chessum responds measuredly. For the securities lending market, the greatest focus is on borrower demand, market trends, lending fees and income.
On a more granular level, he notes that different market participants focus their attention on a number of more specific metrics. A hedge fund may focus on borrowing fees, short squeeze scores and stability of supply, while agent lenders may focus on days to cover, market share, and changes to fees that are being captured through intraday feeds.
鈥淓ach market participant will use the data in a different way, to both capture additional revenues and monitor their market risk and exposure,鈥� Chessum explains.
An industry standard
With this amount of data, naturally a need arises for standardisation and a set of best practices. Here, the 网爆吃瓜 Lending Performance Measurement (SLPM) guidelines represent the industry standard.
Introduced by the International 网爆吃瓜 Lending Association (ISLA) in 2020, agent lenders can choose to follow the guidelines to ensure that their data is provided in the same manner and format.
Farrah Mahmood, director of regulatory affairs at ISLA, discusses why it was necessary to implement an industry standard: 鈥淭here was a mutual feeling from beneficial owners that the data being provided to them by aggregators was inconsistent.鈥� She explains that, due to the variations in the way data was being presented, it was harder for beneficial owners to effectively assess their performance against one another.
Enter the SLPM guidelines.
Besides improving transparency and enabling beneficial owners to make more informed decisions about strategy, Mahmood highlights how these standards create a level playing field, particularly as inconsistent reporting can distort performance results.
鈥淚n short,鈥� Mahmood summarises, 鈥渕easuring agent lender performance empowers beneficial owners to be proactive managers of their securities lending activity, ensuring it delivers optimal value.鈥�
The guidelines are divided into three main areas: inventory data, transaction data and static data. As Chessum notes with pride: 鈥淪&P Global Market Intelligence is the only data provider across the industry to both meet, and exceed these industry standards.鈥�
Data evolution
The mechanisms of measuring performance have evolved considerably over time.
Historically, beneficial owners relied on their agents to provide data. Performance measurement would be focused on general market trends or comparative year-over-year performance, and was usually conducted on a quarterly or semi-annual basis. This meant that collecting data, and producing insights from it, was a largely manual process for the agents.
Today, Allen tells me, clients want direct access to more detailed and standardised reporting that can be accessed ad-hoc, and without the need for significant manual analysis. Emphasising the impact of this evolution, she suggests that the demand for performance insights has grown 鈥渕ore than ever before鈥�.
Indeed, the improvements in technology have resulted in an increased appetite for independent data among beneficial owners. Gone are the days of outsourcing data to custodians or external investment managers. Rather, Allen believes, owners are preferring to take a more 鈥渉ands-on approach鈥� with their investment teams.
鈥淭he majority of our clients not only leverage our standard product offering, but also work with our product specialists to design more bespoke analytics to monitor their lending programmes and help add value,鈥� she explains.
Chessum also acknowledges the impact of access as performance measurement evolves. 鈥淗aving the ability to access the data through either a portal, API, or data aggregator, has increased the ability of end users to use the data in different ways.鈥� The result is a more 鈥渢ailored to鈥� situation, where owners can individualise their data needs.
The tools
As the use of data has grown, securities lending markets have become more sophisticated. 网爆吃瓜 lenders can now manage risk in real time. Alerts can be triggered and actions can be corrected, all at a much quicker pace. 鈥淎utomation has created more streamlined processes, faster decision making and improved client service levels,鈥� reports Chessum.
This can be seen through the vast array of S&P Global Market Intelligence鈥檚 product solutions, all of which depend heavily on access to data. One clear example is the firm鈥檚 exchange traded fund (ETF) collateral lists, where the underlying components of ETFs can be identified to match admissible assets and markets within an individual collateral schedule. The firm also provides an onboarding accelerator tool, which analyses inventory to review the quality and level of demand of assets in a lending programme.
鈥淏oth examples use data to generate efficiencies in either the optimisation of asset pools or the unlocking of liquidity within the market, improving the allocation of financial resources throughout the lending chain,鈥� explains Chessum.
He continues to describe the 鈥榟olistic perspective鈥� provided by aggregated data, highlighting how it can identify broader trends that might not otherwise be apparent if looking at individual data points. But, ultimately, the strength of data lies in its simplicity 鈥� 鈥淚t reduces noise by providing clearer underlying trends and patterns.鈥�
Like S&P, DataLend also uses programmatic tools to offer a near real-time view into lending programme performance. Allen describes the firm鈥檚 CPR and Portfolio offerings as 鈥渞elative to a like-for-like peer group鈥�. 鈥淭hese tools offer a multitude of ways to slice and dice your data, all without the need for any manual input,鈥� she enthuses.
The opportunities posed by these tools are significant.
DataLend Portfolio, for example, automates traditionally complex revenue attribution analysis. Aimed at beneficial owners, it allows them to view the data within the context of liquidity, credit, market and structuring risk.
鈥淚n addition to helping them understand what happened with their programme during a given period, access to data also helps them to understand what could have happened, in order to gauge the performance of their portfolio and its managers,鈥� Allen explains. This insight is unparalleled, and it is changing the way investors engage with their portfolios.
AI in action
Having already undergone dramatic change, how much more can we expect performance measurement programmes to develop?
One obvious factor is the use of artificial intelligence (AI). While the term gets thrown around in industry discussions rather casually, Allen predicts the implementation of AI is imminent, if not incorporated already.
鈥淭he feasibility of deploying such a technology is much more realistic today than even five years ago,鈥� she observes. 鈥淣ot just because of the advances in AI, but because the securities lending industry has collectively moved towards transaction automation and data standardisation.
鈥淭hat is the ethos by which EquiLend was formed, so it鈥檚 natural to consider the potential for AI as a further extension of our mission and vision.鈥�
Meanwhile, S&P Global Market intelligence has already started to implement AI into a number of its metrics. One notable example is a model that uses machine learning to calculate the likelihood of a short squeeze taking place.
As machines adapt, these processes are set to become more sophisticated. Chessum sees this firsthand in his company鈥檚 use of AI: 鈥淎s the model consumes the data, it continues to learn 鈥� increasing its level of accuracy as the data consumption grows.鈥� It is clear from talking to both Chessum and Allen, that the exponential growth of technology and data generation is rapidly transforming the way in which businesses operate across the industry.
With effective tools in place, beneficial owners are best placed to deploy their assets within an ever-evolving lending market. Evidently, the potential is limitless.
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