Retooling the investment management industry’s performance yardstick
18 Nov 2020
Kate Maryniak, Lance Gardiner and Richard Mailhos discuss how new technologies can help performance teams address and overcome their challenges while positioning themselves for long-term success
Image: destina/stock.adobe.com
Performance has always been the industry’s yardstick for measuring investment effectiveness
Whether for a single security, a fund or portfolio, an asset class, a strategy, or for a firm-wide look, asset managers need to capture the right data and then accurately calculate, analyse and report on their performance and risk exposure.
This reality won’t change any time soon. What has changed; however, is the size, scope and complexity of the performance measurement task, and what it takes to manage performance effectively. Many performance teams are straining under the weight of this ever-growing responsibility. In the past, these teams had fewer sources and far lower volumes of data.
They could manage with siloed views of performance, had more time to produce reports, and fewer people to whom to send them. Today performance teams are facing an entirely different task. To meet increasing front office demands, they need to provide near real-time reports that include performance and risk exposure analyses for all asset classes and across all customer types.
They need to produce reports for various internal audiences and external constituencies, such as regulators. Then there is the need to do all of it quickly, accurately and cost-effectively – even though many teams are still using legacy systems and outdated tools. In this paper, we highlight the growing criticality of performance reporting to an investment management firm’s operations and the changing demands on performance teams.
We spotlight some of the more important and pressing challenges these teams are facing in today’s complex, global and rapidly evolving investment business landscape. Lastly, we outline how new technologies can help performance teams address and overcome their challenges while positioning themselves for long-term success.
Increasing data, complexity and stakeholders, with less time: how technology can help meet these challenges
The overarching problem faced by many firms and their performance teams is a persistent lack of enterprise-wide visibility into asset performance and risk exposure. Front office teams expect their middle office performance teams to generate and validate rate-of-return performance numbers and risk analyses that they can trust and rely on. Market data shows that consistently achieving this goal remains an ongoing challenge for many performance teams.
During a recent Financial Technologies Forum webinar entitled ‘Performance Teams Under Pressure’, we asked participants to name the top operational challenge that performance teams need to navigate today.
More than 40 percent of respondents cited “timely delivery of accurate performance data to various stakeholders” as their top challenge. What clients, front office staff, regulators and other stakeholders want today are single, ‘clean’ datasets that cover all asset types and classes. They want that clean and normalised data to fuel the robust return calculation and risk assessment engine, and for the results to be made available to sophisticated analytics systems. They also want performance and risk workflows and processes to encompass the entire investment lifecycle.
Lastly, and most importantly, they want the results delivered quickly and cost-effectively so they can use the resulting business intelligence for maximum effect. That includes populating reports, informing client conversations, validating investment strategy adjustments, justifying new fund ideas, and more.
As the gatekeepers of this information, performance teams strive to meet these arduous requirements. For many teams, it’s a tall order and sometimes they miss the mark. Let’s examine some of the main reasons why this situation exists and persists.
Enterprise data management challenges abound
High-quality and high-capacity data management capabilities are must-haves for all investment firms today. Many firms claim to have these capabilities, and that these capabilities underpin their data-driven investment strategies and decisions.
Too often, those claims are merely aspirational. A look behind the curtain at many firms shows that they are still struggling to address and overcome their enterprise data management challenges. Contributing factors include:
- More volume according to estimates from the World Economic Forum, by 2025 humans will be generating 463 exabytes of data each day worldwide: For those unfamiliar with the term, an exabyte is equal to one quintillion bytes – or one billion gigabytes. The financial services industry is one of, if not the largest contributor. Handling the sheer volume of data is a challenge in and of itself.
- More sources: Stakeholders want full views that include the basics as well as integrated performance results and risk exposure analysis covering all lines of business and all asset classes. That requires a unified and cohesive approach, but in many firms, data ‘ownership’ and management still reside at the departmental level.
- More slices: Another major challenge is handling performance calculations for many clients, in different operating regions – each with its own measurement requirements – and for a growing and changing roster of investment vehicles, fund types and asset classes. It is a difficult task when firms are managing diversified asset pools that include alternatives and other nonmarketable investments, like derivatives, private equity, real estate and structured products.
Even more complexity is introduced when firms try to synch up the varied methodologies they use to meet the unique requirements of each asset class.
How technology can help
To get a better handle on their data and the operational challenges posed, most have implemented data warehouses, and more recently, data lakes. Data warehouses are central repositories of disparate historical and current data from different sources that has been transformed, normalised and highly structured. Relational database technologies are used as the foundation of many data warehouses because they excel at running high-speed queries against structured data. But due to all the structure applied, data warehouses tend to offer up ‘packaged’ views of an organisation’s data that may not be easily applicable in all cases in which it is needed.
Data lakes are different in that they are repositories of data stored in its natural or raw format. They typically include raw copies of the data from source systems, including unstructured material such as emails, PDFs and other documents. They also can include semi-structured, structured and binary data. Big data technologies usually underpin data lakes because they are highly adaptable and can handle any data type or structure. They also can scale easily to handle very large volumes of data.
Data warehouses and data lakes are both useful – even essential – tools. They can be deployed side-by-side to give performance teams a ‘best of both worlds’ approach to performance and risk analytics. In addition, investment firms tend to have highly skilled data professionals on staff, so building new or enhancing existing data warehouses and lakes are well within reach. There also are numerous software vendors that presently provide high-quality, commercially available enterprise data management solutions. It is also worth mentioning that to stretch beyond the confines of ‘packaged’ views of an organisation’s data, there are a plethora of self-service business intelligence (SSBI) platforms available, offering capabilities such as visual exploration of extensive time series data sets, interactive dashboarding and innovations such as AI-driven explanation of data points and natural language query processing.
We see a trend for financial technology vendors to partner with such SSBI platforms and this is starting to open new vistas for clients looking for deep insight into their data.
Multiple books of record: ABORs, IBORs and Now PBORs
Investment management firms have long relied on the accounting book of record (ABOR) to keep track of their activities and results. However, due to the limitations of ABORs, most firms have added an investment book of record (IBOR).
Now, with enormous increases in the complexity of capital markets, and the pressing demands of clients, regulators and internal stakeholders for more performance, risk and business intelligence to be delivered faster, lots of firms are adding yet another book – the performance book of record (PBOR). It’s important to understand these books, the differences between them, and why firms need all of them today.
The ABOR is a baseline of sorts. It supports basic back- and middle-office functions, such as generating daily net asset value data, and day-to-day fund administration, transfer agency, and custodial services, as well as client and regulatory reporting. An ABOR is focused on costs, on total net asset value, and on charts of accounts. It’s critical for determining cash positions, conducting reconciliation and for closing periods. Generally, ABORs are processed on a T+1 basis, meaning that trades are recognised as a part of their funds and net asset values (NAVs) are calculated one business day after trades are executed. An ABOR supports performance returns at the total fund or portfolio level, and updates are applied to current holdings and open periods.
The IBOR goes further, providing users with broader, more granular and more real-time views of performance and risk data. IBORs are focused on market price and on start-of-day and even intra-day positions. They support performance returns at the individual position level, with updates applied to historical holdings or open periods. IBORs help teams overcome problems like tool proliferation. That happens when a firm has one system for handling the active management of equities, another for fixed income, and still others for over-the-counters, derivatives, alternatives, foreign exchange and structured products. Then there’s the data from outsourcing partners like sub-advisors. IBORs roll up all of this disparate data in a centralised storehouse of all the information required to produce firm-wide reports on risk exposure, performance, attribution, and reports for regulators and other stakeholders.
The PBOR is focused on fair market valuations and non-held benchmark constituents. PBORs typically use trade dates and are benchmark-aligned. They support the generation of performance return calculations and risk assessments at the sub-position and underlying exposure levels. They also offer compositing and highly advanced analytics and modelling. As for the datasets included, PBORs form a superset of all relevant data, including investment information, integrated performance results, risk exposure analysis, reference data and adjusted data, (for example, notional economic exposures and alternative valuations), plus a variety of external benchmark and peer data. PBORs can stretch to cover all lines of business and all asset classes – including alternatives and other non-marketable investments, such as derivatives, private equity, real estate and infrastructure funds.
Essentially, PBORs provide transparency and visibility into all investments covering all facets of performance and risk analysis. They are designed to be single, cohesive and efficient systems providing unified views of data lineage, data processing and calculation methods across a firm’s unique array of investment vehicles, strategies and asset allocations, as well as its roster of investment managers.
Unfortunately, given the pace with which markets are evolving, both the ABOR and IBOR are falling short of expectations. The ABOR serves its purpose in generating official return calculations, and the IBOR enables in-depth performance, attribution and risk analysis. Today’s front-office teams expect more, so middle office teams need to deliver more; hence the growing popularity of the performance book of record or PBOR.
How technology can help
Generally, ABORs lack the speed, context and accessibility necessary to support decision making for the front office, and decision-making support for the back and middle offices. Today, top vendors provide modernised accounting and investment systems. Some are labelled as PBORs. More carry labels such as ‘Performance and Risk Analytics’ solutions. Furthermore, in the quest to achieve high levels of data quality as the foundation for investment decision-making, modern systems are often reinforced by effective data management tools and services which assist in maintaining the quality of the data and closing data gaps without disruption to daily operational workflows. Whatever they are called, these systems offer real- or near real-time performance, sophisticated enterprise data management capabilities, fast and accurate performance calculations, advanced analytics for risk exposure and other metrics, data enrichment functionality and powerful yet intuitive report-generation features. They also can aggregate and normalise data from external sources. To that end, the right accounting system can serve as an effective IBOR solution for mid- or smaller-size firms without the significant resources to implement and support a complex, dedicated solution
Conclusion
The investment management industry is at an inflection point. More and more data is being generated, and that data is being enriched, analysed and reported on with more speed, granularity and breadth than ever before. While performance is still performance, and risk is still risk, the yardsticks the industry uses to capture, measure and understand all of this data are fundamentally changing. In performance and analytics, the new must-haves include not only faster, more accurate, and more comprehensive data management and analysis, and intuitive report generation, but also transparency and visibility into all relevant workflows and processes. This moment has arrived because many firms have taken their existing investment infrastructures as far as they can go.
As their performance requirements continue to grow and change, gaps are widening between what legacy systems can handle and what performance teams need to deliver. To remain competitive, many firms need to modernise key parts of their investment operations, including their performance and analytics systems and processes. Whether they develop their new resources in-house or leverage commercially available solutions from top vendors, the key goal is digital transformation. The time has come for firms to start moving away from their legacy systems and toward flexible, scalable and automated cloud-based systems. It’s the most effective way for firms to position themselves for long-term success and growth. While these are major changes, the good news for investment firms is that there are market-tested and proven ways now available to help them make the transition. The first step is recognising the need for a new yardstick.
Whether for a single security, a fund or portfolio, an asset class, a strategy, or for a firm-wide look, asset managers need to capture the right data and then accurately calculate, analyse and report on their performance and risk exposure.
This reality won’t change any time soon. What has changed; however, is the size, scope and complexity of the performance measurement task, and what it takes to manage performance effectively. Many performance teams are straining under the weight of this ever-growing responsibility. In the past, these teams had fewer sources and far lower volumes of data.
They could manage with siloed views of performance, had more time to produce reports, and fewer people to whom to send them. Today performance teams are facing an entirely different task. To meet increasing front office demands, they need to provide near real-time reports that include performance and risk exposure analyses for all asset classes and across all customer types.
They need to produce reports for various internal audiences and external constituencies, such as regulators. Then there is the need to do all of it quickly, accurately and cost-effectively – even though many teams are still using legacy systems and outdated tools. In this paper, we highlight the growing criticality of performance reporting to an investment management firm’s operations and the changing demands on performance teams.
We spotlight some of the more important and pressing challenges these teams are facing in today’s complex, global and rapidly evolving investment business landscape. Lastly, we outline how new technologies can help performance teams address and overcome their challenges while positioning themselves for long-term success.
Increasing data, complexity and stakeholders, with less time: how technology can help meet these challenges
The overarching problem faced by many firms and their performance teams is a persistent lack of enterprise-wide visibility into asset performance and risk exposure. Front office teams expect their middle office performance teams to generate and validate rate-of-return performance numbers and risk analyses that they can trust and rely on. Market data shows that consistently achieving this goal remains an ongoing challenge for many performance teams.
During a recent Financial Technologies Forum webinar entitled ‘Performance Teams Under Pressure’, we asked participants to name the top operational challenge that performance teams need to navigate today.
More than 40 percent of respondents cited “timely delivery of accurate performance data to various stakeholders” as their top challenge. What clients, front office staff, regulators and other stakeholders want today are single, ‘clean’ datasets that cover all asset types and classes. They want that clean and normalised data to fuel the robust return calculation and risk assessment engine, and for the results to be made available to sophisticated analytics systems. They also want performance and risk workflows and processes to encompass the entire investment lifecycle.
Lastly, and most importantly, they want the results delivered quickly and cost-effectively so they can use the resulting business intelligence for maximum effect. That includes populating reports, informing client conversations, validating investment strategy adjustments, justifying new fund ideas, and more.
As the gatekeepers of this information, performance teams strive to meet these arduous requirements. For many teams, it’s a tall order and sometimes they miss the mark. Let’s examine some of the main reasons why this situation exists and persists.
Enterprise data management challenges abound
High-quality and high-capacity data management capabilities are must-haves for all investment firms today. Many firms claim to have these capabilities, and that these capabilities underpin their data-driven investment strategies and decisions.
Too often, those claims are merely aspirational. A look behind the curtain at many firms shows that they are still struggling to address and overcome their enterprise data management challenges. Contributing factors include:
- More volume according to estimates from the World Economic Forum, by 2025 humans will be generating 463 exabytes of data each day worldwide: For those unfamiliar with the term, an exabyte is equal to one quintillion bytes – or one billion gigabytes. The financial services industry is one of, if not the largest contributor. Handling the sheer volume of data is a challenge in and of itself.
- More sources: Stakeholders want full views that include the basics as well as integrated performance results and risk exposure analysis covering all lines of business and all asset classes. That requires a unified and cohesive approach, but in many firms, data ‘ownership’ and management still reside at the departmental level.
- More slices: Another major challenge is handling performance calculations for many clients, in different operating regions – each with its own measurement requirements – and for a growing and changing roster of investment vehicles, fund types and asset classes. It is a difficult task when firms are managing diversified asset pools that include alternatives and other nonmarketable investments, like derivatives, private equity, real estate and structured products.
Even more complexity is introduced when firms try to synch up the varied methodologies they use to meet the unique requirements of each asset class.
How technology can help
To get a better handle on their data and the operational challenges posed, most have implemented data warehouses, and more recently, data lakes. Data warehouses are central repositories of disparate historical and current data from different sources that has been transformed, normalised and highly structured. Relational database technologies are used as the foundation of many data warehouses because they excel at running high-speed queries against structured data. But due to all the structure applied, data warehouses tend to offer up ‘packaged’ views of an organisation’s data that may not be easily applicable in all cases in which it is needed.
Data lakes are different in that they are repositories of data stored in its natural or raw format. They typically include raw copies of the data from source systems, including unstructured material such as emails, PDFs and other documents. They also can include semi-structured, structured and binary data. Big data technologies usually underpin data lakes because they are highly adaptable and can handle any data type or structure. They also can scale easily to handle very large volumes of data.
Data warehouses and data lakes are both useful – even essential – tools. They can be deployed side-by-side to give performance teams a ‘best of both worlds’ approach to performance and risk analytics. In addition, investment firms tend to have highly skilled data professionals on staff, so building new or enhancing existing data warehouses and lakes are well within reach. There also are numerous software vendors that presently provide high-quality, commercially available enterprise data management solutions. It is also worth mentioning that to stretch beyond the confines of ‘packaged’ views of an organisation’s data, there are a plethora of self-service business intelligence (SSBI) platforms available, offering capabilities such as visual exploration of extensive time series data sets, interactive dashboarding and innovations such as AI-driven explanation of data points and natural language query processing.
We see a trend for financial technology vendors to partner with such SSBI platforms and this is starting to open new vistas for clients looking for deep insight into their data.
Multiple books of record: ABORs, IBORs and Now PBORs
Investment management firms have long relied on the accounting book of record (ABOR) to keep track of their activities and results. However, due to the limitations of ABORs, most firms have added an investment book of record (IBOR).
Now, with enormous increases in the complexity of capital markets, and the pressing demands of clients, regulators and internal stakeholders for more performance, risk and business intelligence to be delivered faster, lots of firms are adding yet another book – the performance book of record (PBOR). It’s important to understand these books, the differences between them, and why firms need all of them today.
The ABOR is a baseline of sorts. It supports basic back- and middle-office functions, such as generating daily net asset value data, and day-to-day fund administration, transfer agency, and custodial services, as well as client and regulatory reporting. An ABOR is focused on costs, on total net asset value, and on charts of accounts. It’s critical for determining cash positions, conducting reconciliation and for closing periods. Generally, ABORs are processed on a T+1 basis, meaning that trades are recognised as a part of their funds and net asset values (NAVs) are calculated one business day after trades are executed. An ABOR supports performance returns at the total fund or portfolio level, and updates are applied to current holdings and open periods.
The IBOR goes further, providing users with broader, more granular and more real-time views of performance and risk data. IBORs are focused on market price and on start-of-day and even intra-day positions. They support performance returns at the individual position level, with updates applied to historical holdings or open periods. IBORs help teams overcome problems like tool proliferation. That happens when a firm has one system for handling the active management of equities, another for fixed income, and still others for over-the-counters, derivatives, alternatives, foreign exchange and structured products. Then there’s the data from outsourcing partners like sub-advisors. IBORs roll up all of this disparate data in a centralised storehouse of all the information required to produce firm-wide reports on risk exposure, performance, attribution, and reports for regulators and other stakeholders.
The PBOR is focused on fair market valuations and non-held benchmark constituents. PBORs typically use trade dates and are benchmark-aligned. They support the generation of performance return calculations and risk assessments at the sub-position and underlying exposure levels. They also offer compositing and highly advanced analytics and modelling. As for the datasets included, PBORs form a superset of all relevant data, including investment information, integrated performance results, risk exposure analysis, reference data and adjusted data, (for example, notional economic exposures and alternative valuations), plus a variety of external benchmark and peer data. PBORs can stretch to cover all lines of business and all asset classes – including alternatives and other non-marketable investments, such as derivatives, private equity, real estate and infrastructure funds.
Essentially, PBORs provide transparency and visibility into all investments covering all facets of performance and risk analysis. They are designed to be single, cohesive and efficient systems providing unified views of data lineage, data processing and calculation methods across a firm’s unique array of investment vehicles, strategies and asset allocations, as well as its roster of investment managers.
Unfortunately, given the pace with which markets are evolving, both the ABOR and IBOR are falling short of expectations. The ABOR serves its purpose in generating official return calculations, and the IBOR enables in-depth performance, attribution and risk analysis. Today’s front-office teams expect more, so middle office teams need to deliver more; hence the growing popularity of the performance book of record or PBOR.
How technology can help
Generally, ABORs lack the speed, context and accessibility necessary to support decision making for the front office, and decision-making support for the back and middle offices. Today, top vendors provide modernised accounting and investment systems. Some are labelled as PBORs. More carry labels such as ‘Performance and Risk Analytics’ solutions. Furthermore, in the quest to achieve high levels of data quality as the foundation for investment decision-making, modern systems are often reinforced by effective data management tools and services which assist in maintaining the quality of the data and closing data gaps without disruption to daily operational workflows. Whatever they are called, these systems offer real- or near real-time performance, sophisticated enterprise data management capabilities, fast and accurate performance calculations, advanced analytics for risk exposure and other metrics, data enrichment functionality and powerful yet intuitive report-generation features. They also can aggregate and normalise data from external sources. To that end, the right accounting system can serve as an effective IBOR solution for mid- or smaller-size firms without the significant resources to implement and support a complex, dedicated solution
Conclusion
The investment management industry is at an inflection point. More and more data is being generated, and that data is being enriched, analysed and reported on with more speed, granularity and breadth than ever before. While performance is still performance, and risk is still risk, the yardsticks the industry uses to capture, measure and understand all of this data are fundamentally changing. In performance and analytics, the new must-haves include not only faster, more accurate, and more comprehensive data management and analysis, and intuitive report generation, but also transparency and visibility into all relevant workflows and processes. This moment has arrived because many firms have taken their existing investment infrastructures as far as they can go.
As their performance requirements continue to grow and change, gaps are widening between what legacy systems can handle and what performance teams need to deliver. To remain competitive, many firms need to modernise key parts of their investment operations, including their performance and analytics systems and processes. Whether they develop their new resources in-house or leverage commercially available solutions from top vendors, the key goal is digital transformation. The time has come for firms to start moving away from their legacy systems and toward flexible, scalable and automated cloud-based systems. It’s the most effective way for firms to position themselves for long-term success and growth. While these are major changes, the good news for investment firms is that there are market-tested and proven ways now available to help them make the transition. The first step is recognising the need for a new yardstick.
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