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Meradia investment performance_systems

While the prime objective of active asset management
firms is the search for alpha, important questions arise:
How does one measure alpha, identify levers that
contribute to alpha and define a consistent process?
In simpler terms, these could be viewed as return
computation, performance attribution and systemic
automation of the investment performance process
respectively. A deeper insight into the process reveals
nuances and behavior required from an investment
performance management solution/product.

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Meradia investment performance_systems

  1. 1. 1meradia.com This paper attempts to develop a systemic framework to view and understand existing investment performance products. This systemic framework will enable the reader to understand the different components of an investment performance system and logically visualize how the sub-components orchestrate together to form a holistic solution. Broadly, the framework detailed attempts to answer the following questions regarding an investment performance product: WHAT are the necessary functionalities / features? HOW do the different features interplay and fit together to form a cohesive solution? WHAT are the important technological aspects to be considered? Alternatively, this framework can also be used as a starting point to commence business architecture oriented discussions while attempting to develop an investment performance system. The intended audience of this paper are those who perform duties at the intersection of business and technology; i.e. ranging from Business Unit Managers and IT Delivery Heads at the decision-making level to Consultants, Business Analysts, Architecture Designers and Operations Personnel involved at the System design and implementation level. This paper would also be appreciated by investment performance personnel who are working in a specified performance area (Risk Analytics or GIPS reporting) and want to get a birds-eye view of the entire investment performance system functionality. Investment Performance Systems - Demystified b a c Introduction While the prime objective of active asset management firms is the search for alpha, important questions arise: How does one measure alpha, identify levers that contribute to alpha and define a consistent process? In simpler terms, these could be viewed as return computation, performance attribution and systemic automation of the investment performance process respectively. A deeper insight into the process reveals nuances and behavior required from an investment performance management solution/product. Jose R. Michaelraj, CIPM With more than ten years of progressive, varied investment services experience, Jose brings a fresh perspective to each engagement. His strong functional expertise is equally matched by his technical and domain talents. Fluent in SQL, and expert in translating business needs, Jose is more than up to the task with respect to helping clients design, test and implement practical long-term solutions. Jose comes to Meradia with deep experience in performance measurement, data management, systems implementations, risk management and project leadership at firms including Victory Capital Management, T Rowe Price and Bank of New York.
  2. 2. Investment Performance Systems - Demystified 2meradia.com Investment Performance Management System – Characteristics ‘As humans have traits, so do systems have characteristics’ To answer the specific questions posed in the previous section, we develop three important characteristics. Although the characteristics specified in this paper are detailed from an investment performance perspective, the authors believe they could be used to dissect other systems / solutions that cater to any section of the business (Front, Middle or Back office) in the Investment Management space. FUNCTIONALITY - This is at the core of any system and answers the question “What business processes and features are present?” The nature of increasing asset types in the market and regulatory rules play an important role in evolving the functionality of the products available in the market. FLEXIBILITY - Flexibility relates to specificity. There is always an exception to the rule in most business needs. “What are the systemic provisions available to meet this unique necessity?” is answered by flexibility. Flexibility is directly correlated with customization as well. The ability of a product to expand, limit or augment existing functionality or support new functionality is referred to as customization. SCALABILITY - This is a technology focused characteristic. While the above two focus on ‘what is available’, scalability relates to ‘how quickly’ the available functionality can be completed or achieved. Time is of the essence in achieving scalability. Recent advancements in technology architecture (such as parallel processing & cloud computing) have led to rapid increases in processing efficiency. These characteristics could vary in priority for different functional domain systems. For example, throughput (a function of Scalability) would carry a higher weight in Order Management / Execution Management systems but will have relatively lower significance in fee billing systems or monthly accounting systems. Accordingly, an asset manager with $5 billion in AUM and 1,000 transactions per month has a very different perspective on scalability than an asset manager with $900 billion in AUM and 1,000,000 transactions per month. The fact that these three tend to constitute the basic building blocks of a suitable product or solution cannot be an overstatement. We attempt to mention some of the challenges in the investment performance management space in the next section and then proceed to arrive at a common set of parameters. Investment Performance System – Challenges Investment performance systems have strived to keep pace with the changing market demands. The progression in research methods (i.e., advanced Fixed Income attribution models) take some time to find their way into system features due to a couple of reasons: first, the plethora of implementation methods must be ascertained to develop a generic solution. Secondly, integrating the new suite to an existing product architecture brings its own set of challenges. Consequently, we find that there is a heavy reliance on manual processes including Excel spreadsheets until a solution is available in the marketplace. The ability of investment performance systems to constantly adapt to the market trends would be a continuous process. However, such changes do not preclude us from developing a framework within which the broad contours of the system must be defined. This framework can be used as a starting point to understand existing systems and build new platforms in the future. Research on existing products available in the market, experience based on extensive interactions with Performance Operations personnel and available literature have led to the parameters mentioned here.
  3. 3. Investment Performance Systems - Demystified 3meradia.com Investment Performance System – Parameters Parameters serve as building blocks to understand the characteristics. They represent an aspect that enables us to view them as stand-alone features / functionalities. The inter-dependency between parameters is an important consideration while trying to understand investment performance systems. Hence the readers of this paper might encounter frequent references to other sections. This should not be construed as information overlap but rather required to obtain complete comprehension. The parameters dealt in this paper have been listed below: Account Management Security Management Benchmark Management Data Augmentation FIGURE 1: Investment Performance Framework PERFORMANCE SYSTEM OUTPUTS PERFORMANCE INPUTS ACCOUNTS SECURITY MASTER BENCHMARKS ACCOUNTING DATA MARKET DATA/ PORTFOLIO CHARCTERISTICS DATA AUGMENTATION DATA VALIDATION DATA INGESTION RETURN COMPUTATION RETURN ATTRIBUTION RISK METRICS OTHER ANALYTICS REPORTING DELIVERY SCALABILITY BUSINESS PROCESS AUTOMATION Return Computation Attribution Methods Risk Metrics Reporting Options Process Automation Throughput Service Delivery a e i c g k b f j d h
  4. 4. Investment Performance Systems - Demystified 4meradia.com Before we further detail on each of these parameters, we comment on Meradia’s perspective to understand the investment performance systems in the market. Keeping this perspective in mind along with the parameters listed on the previous page will help to better understand the relative positioning of each product in the market. Depth, Breadth and Market-data oriented systems – A Perspective Each investment performance product / system with its unique value selling proposition will cater to a specific need of the market and do an extremely good job at it. When viewed from a different need perspective, it might not be a perfect fit. In such a case, completely acceptance of one perspective or total rejection of the other perspective might not be a wise idea. It would be prudent to plot the product on a continuum scale in multiple perspectives (let’s call it dimension). We attempt to provide a holistic classification criteria that would enable to view the various products on a 3-dimension scale – Depth, Breadth & Market Data oriented. Vendor products in the investment performance space are attempting to serve several segments of the market. They could be classified as either ‘depth-oriented’, ‘breadth- oriented’ or ‘market data-oriented’. A ‘depth-oriented’ product would dig into detail on several functionalities that exist within the investment performance product. Some of them include Risk Metrics, Portfolio Analytics and Attribution methods. Typically, such systems provide multiple solution options for a functionality. Taking attribution for example, the system could offer many attribution methods out of the box such as Brinson Fachler, Karnosky Singer, Manager Level and Duration based. Depth oriented is not about the system being flexible to develop a new option. It is related to a readily available functionality in the product that can be quickly taken to market. A ‘breadth-oriented’ product attempts to vertically integrate several functionalities that are involved prior/ post to computing return and risk numbers. In other words, they cater to the functionality bedrock upon which investment performance resides. Prior-functionality integration is termed as ‘Upstream integration’. Some examples are Data Management and Portfolio Accounting. Post-performance integration is referred to as ‘Downstream integration’. An example of integrating downstream functionality would be ‘Client Reporting’. As the name implies, a ‘market-data oriented’ system contains integration links with several data providers (Bloomberg, Wilshire, RIMES, etc.) and maintains a rich base of security, benchmark and analytics data. They typically offer a plug and play functionality i.e. quickly map the clients (external) portfolio holdings information with the reference & and benchmark data (internal) to produce the relevant portfolio return, risk & analytics numbers. The reader is advised to not fit each product into a specific category i.e. breadth, depth or market data. Rather, the combined interplay of these factors on a relative scale for each vendor product vis-à-vis client needs would determine fitment.
  5. 5. Investment Performance Systems - Demystified 5meradia.com Investment Performance System – Parameter Descriptions ‘Accounts, Securities & benchmarks constitute the majority of reference data required for performance’ ACCOUNT MANAGEMENT The ability to define Accounts at the basic level and have several hierarchies based on classification criteria would be an important feature. The ability to slice the data into various account reporting structures is directly linked to the ability of having several account hierarchical structures. Many firms operate in a business environment that typically spans across multiple geographies and business divisions. It becomes imperative to understand the performance of an account at the lowest level (at which it is instituted or setup) to the highest level (to which it is rolled up). Complexity is further exacerbated when several ‘rolled-up’ views are required. This should be construed as an effort to depict complementary views juxtaposing consolidation hierarchies. A couple of different account classification criteria are shown below. GEOGRAPHY ACCOUNT CLIENT GROUP CLIENT TYPE (SMA, Mutual Funds, etc.) INVESTMENT ADVISOR GEOGRAPHY SECURITY MANAGEMENT Although the same logic can be extended to Securities, an additional factor manifests itself in the form of historical security versions. Processes to receive, modify & store reference historical security versions would be of paramount importance especially when classification categories implement changes, Global Industry Classification System (GICS) being a case in point. Securities Data encompass a wide range of categories including Ratings, Industry Classifications, Analytics, Issuer info, Factors, Schedules & Rates. The ‘time’ property of Securities is extremely important, i.e. certain security attributes become available on a specific date. The investment performance system should have the ability to not only apply changes to the past and store them but also re-calculate risk, return and analytic numbers from the past into the current period. a b FIGURE 2: Account Classification Hierarchy
  6. 6. Investment Performance Systems - Demystified 6meradia.com Replication of past data might also be required when portfolio classifications should be changed in the past with ‘going-forward’ effect. But it is more pronounced in the case of Securities where new characteristics are added or existing characteristics are modified during the life of the security. Since Accounts & Securities are the bedrock upon which other functionalities & capabilities reside on, they need to be closely coupled with transactions & holdings. The ability to support complex fund structures (e.g. Commingled funds) would be a desirable feature. BENCHMARKS Benchmarks or indexes play an important role in investment performance and come in a variety of flavors. The performance return of most portfolios (except for absolute return funds) makes sense only when compared to its benchmark or other comparable portfolios / strategies. Comparable portfolios could either be internal or external to the firm. Morning Star provides standard fund strategy classifications and ranks portfolios within each of the categories. A tightly coupled performance measurement system should have the ability to quickly map funds to their corresponding benchmarks. An additional level of complexity in benchmark management is the need to blend benchmarks in various methods. For example, it might be necessary to blend two different vendor provided benchmarks or combine a vendor provided benchmark with an internal one or adding a flat rate to a custom or vendor provided benchmark. Further it might be required to setup primary, secondary & tertiary benchmarks for a specific fund to obtain several return comparisons. In any case, setting up of benchmarks / indices with its relevant components, processing index values from vendors, rebalancing index and its constituents, computing returns periodically (similar to portfolios) and ability to make changes to past data with going forward effect would be a necessary functionality required from most investment performance systems. Obtaining precise security level benchmark data is critical for classical equity attribution methods (Brinson Fachler, Brinson Hood Beehover) and relative characteristic or risk analysis. The ability of a system to effectively and efficiently handle the vagaries of security level benchmark data and complex benchmark structures cannot be understated. Herein is often the separation between an accounting system with good bottom-up performance calculations and a true performance analytics engine. In addition, both systems and internal workflows should be able to test for common benchmark data errors and be able to effectively process a restatement. DATA AUGMENTATION Data augmentation refers to options available for entering adjustments to transactions, holdings, or cash flows. When data feeds from accounting record keeping systems / source systems are not complete or accurate, adjustment entries are necessary. This might require identification of certain patterns that can be configured as rules or entered manually upon encountering exceptions. User interfaces and the ability to process bulk transactions are important aspects of this parameter. Limitations of custodial or other accounting systems to supply data in a manner required by the investment performance system would require data augmentation. For example, let’s take the case of a quarterly fee paid by the fund and sent by the accounting system at the end of every quarter. From a performance c d
  7. 7. Investment Performance Systems - Demystified 7meradia.com management perspective, it might be required to apportion the fee amount over the entire period, while the accounting system simply books the fee when paid. Both are correct for their specific function but incorrect for each other. Alternatively, a similar scenario might arise when recording income. A data augmentation exercise is required in such cases in the form of a smoothing configuration available in the system to convert quarterly fees to a daily amount (business environment where performance numbers are churned out daily). Data augmentation assumes significance when the normal course of business operations is too complex to achieve a high level of automation. An IT system claiming to have 100% automation would be utopian and the degree to which it is less would define the periodicity of manual intervention and subsequently the need to have intuitive and flexible graphical user interfaces (GUI) in the investment performance product. The necessity to perform Data Augmentation leads to another question – Where should it be done? There are usually 3 options available: 1. Make changes in the Source system (feed enhancements) 2. Design a stand-alone middle level ETL layer between Source system (typically accounting) and performance system 3. Enhance the source feed while loading to the performance management system Though project sponsors and teams involved in the project are factors determining the outcome, it is a best practice to correct the data at the source. If this is not possible (due to project management constraints), the data augmented in ETL layer or performance system loads should be made transparent to users. An important aspect of data augmentation is Data Translation. In many investment performance systems, there a few defined transaction types (Contribution, Withdrawals, Buys & Sells, Dividends, etc.) supported by the product. As performance systems typically receive data from various custodial accounting systems, there needs to be a simple yet intuitive way of mapping these transaction types to the established classifications. The interplay between transaction type, asset type and security master is critical to accurately map the transactions to the performance management system. A rule based interface would be an ideal standard and greatly reduce the time-to-market of a performance system implementation. In some cases, a transaction type (by nomenclature) would create different effects based on the type of security with which it is associated. Hence flexibility within the rules based interface would be an added advantage. Data Augmentation / Data Translation requirements usually bridge the gap between what vendors / data sources usually provide and what is supported by the product. The following categories can serve as broad necessary data element categories required for computing return numbers. A. Transactions (Buys, Sells, Accrued Income, Fees, etc.) B. Holdings/Market Value (Start of period, End of Period, etc.) C. Fund Net Asset Value (Mutual Fund returns) In the case of transaction based systems, computing Beginning of Day (BOD) and End of Day (EOD) market values are required. Product functionality that have in-built features to perform those and other necessary calculations will drive down the project implementation effort and time. We believe that firms that have implemented ‘Investment Book of Record’ (IBOR) would require lesser data enrichment (than those who haven’t) since many accounting period inputs required for performance would reside in IBOR.
  8. 8. Investment Performance Systems - Demystified 8meradia.com The number of return data elements and various other performance data elements required at a portfolio level has been increasing with the advent of new asset types and complex investment strategies i.e., a portfolio manager invested in Total Return Swap might be curious to know the return of the underlying asset in addition to the return of the derivative itself. While it is not feasible to expect an investment performance system to have an ‘infinite’ data model, it would be prudent to expect scalable features that reside on existing data structures or build new ones which can be integrated easily and tightly with existing models. Our interaction with industry experts demonstrates that performance systems require the ability to store the ‘scrubbed’ return (initial calculated return before approval) and finally release the ‘approved’ return for reporting purposes after authorization. RETURN COMPUTATION While the time weighted method (a measure of the portfolio manager’s ability to invest insulated from the timing of client-directed inflows and outflows) has become the industry standard in calculating returns, there is a growing need for money weighted (how the investment did using internal rate of return - IRR) methods as well . Also, with hedge fund managers controlling the cash flows into the fund, Money weighted returns assume significance. Calculation of multi-period returns (in addition to single period returns) would provide better insight into asset management decisions over longer periods of time. Provision of a similar process (both in terms of definition & implementation) to calculate the entire set of return data elements for both portfolios and benchmarks would be a desired feature. A limited set of return data elements required from performance systems is provided in the below figure. While these standard multi-period returns are popular on client reports be wary of systems that have a limited subset to choose from. The most flexible performance systems allow for calculation and configuration from any two data points. e FIGURE 3: Return Data Elements
  9. 9. Investment Performance Systems - Demystified 9meradia.com Though the above simple performance report mentions the returns generated by the securities, it does not throw light on the securities invested within the mutual fund. The ability to ‘look through’ the mutual fund securities and juxtapose their returns within the overall portfolio return report would reveal a deeper understanding of fund performance. One such ‘Drill down’ report is shown below. Presenting individual securities and their corresponding weights within the funds add better value to the portfolio returns report at the total level. The ability of an investment performance system to effectively combine data management and returns computation functionality is critical to yield the results shown in the above report. Again here, the most flexible systems will achieve this look-through functionality by (behind the scenes) creating a new portfolio with the lowest level holdings. Such an approach enables more sophisticated combinations of security level rollups to be achieved. In the data management section, there was a brief reference made to Commingled funds (AKA) Fund of Funds. We will now provide an example how an intelligent investment performance system might help to provide interesting insights. The following is a sample performance return report of a portfolio. The portfolio invests in stocks, bonds as well as other mutual funds. PORTFOLIO RETURNS REPORT (JAN 1 2015 - DEC 31 2015) PORTFOLIO SECURITY GROSS RETURN WEIGHTS Diversified Fund Amazon 2.30% 50% 365 Day T-Bill 1.50% 10% LinkedIn -1.30% 30% Equity Strategy Mutual Fund Capital One General Electric Target -1.48% -12.37% 21.23% -4.35% 10% 60% 30% 10% Total Fund Return 0.76% 100% PORTFOLIO RETURNS REPORT (1 JAN 2015 - 31 DEC 2015) PORTFOLIO SECURITY GROSS RETURN WEIGHTS Diversified Fund Amazon 2.30% 50% 365 Day T-Bill 1.50% 10% LinkedIn -1.30% 30% Equity Strategy Mutual Fund -1.48% 10% Capital One -12.37% 60% General Electric 21.23% 30% Target -4.35% 10% Total Fund Return 0.76% 100%
  10. 10. Investment Performance Systems - Demystified 10meradia.com ATTRIBUTION METHODS “Doing the right thing is more important than doing things right” - Peter F. Drucker Attribution deals with the critical question in Portfolio management – How is the portfolio manager performing against the expectation set? Each client would have listed the mandate / objective to the portfolio manager or plan sponsor. The attribution process helps to decompose the total return generated at the fund level into smaller components that throw light on the ideas implemented by the portfolio manager. The smaller components combined define the attribution model. It would be wise to understand the investment management process to appropriately map the relevant attribution model and identify the relevant ‘effects’. Otherwise the attribution process would produce an exhaustive set of fanciful numbers that is equally meaningless. Based on our industry experience, ‘attribution’ is the kernel of investment performance measurement systems. The robust nature of different attribution methods supported would lend credibility and might otherwise single out a specific product as superior within a peer group of similarly ranked platforms. A quick summary of the different equity attribution methods is mentioned below: f FIGURE 4: Equity Attribution Methods Many equity portfolio managers first identify the sectors and the amount to be invested in them. This is known as allocation. Then, securities within those sectors are picked and invested according to the relative attractiveness. This process is known as Selection. The Currency effect is used to identify the quantum of superior / lagging performance due to FX rate movements. Interaction is not really a process but a mathematical residual of the attribution model. It would be prudent to not lay much emphasis on this effect.
  11. 11. Investment Performance Systems - Demystified 11meradia.com Fixed Income Attribution Fixed Income and exotic product investment strategies are quite complex and have a multitude of factors that go into investment decision making. Standard attribution models do not exist for these asset types. As there is a continuous body of literature arising out of research in this area, performance vendors face a significant challenge to build a generic model. Vendors have evolved in this space such that you can use their tools to add the building blocks of fixed income attribution to customize attribution to your specific needs. Be wary of tools that offer prescribed approaches. It is critical that fixed income Portfolio Managers be included in the selection of a fixed income attribution calculation engine. Though some of the decisions pertaining to Equity portfolios such as Stock Selection and Currency management are applicable for Fixed Income portfolios, there are specific hedges done based on factors such as the magnitude of interest rate changes, the direction of interest rate movements and credit spreads that are relevant for Fixed Income portfolios. Accordingly, the following outputs would serve as fundamental requirements from a fixed income attribution model. DURATION EFFECT - Sensitivity to interest rate changes SPREAD EFFECT – Sensitivity to the magnitude of difference over the Treasury return CURRENCY EFFECT – Relevant for multi-currency portfolios SELECTION EFFECT - Individual Stock selection Manager Attribution Apart from standard attribution methods mentioned above, there has been considerable interest in the market for Manager Level attribution also referred to as Macro Level attribution. Let’s take the case of a pension fund manager who selects various fund managers (asset type based) to allocate different portions of the fund. The fund managers in turn might make ‘Allocation’ and ‘Selection’ decisions. While the performance of the fund managers can be inferred from the standard attribution methods mentioned, there is also a decision to be evaluated about the percentage allocation of funds made to different fund managers by the pension fund manager. This is achieved by performing Manager level attribution. An investment performance system should have capabilities and in-built features to aid manager level attribution along with standard equity / fixed income methods. A pictorial representation depicting macro level attribution is mentioned below: a c b d FIGURE 5: Macro Attribution
  12. 12. Investment Performance Systems - Demystified 12meradia.com While most investment performance products would provide Level 1 attribution effects, it would be a significant benefit to provide Level 2 i.e. macro attribution effects. In the above figure, Level 2 attribution effect would help to evaluate the 50%, 30%, 20% allocation decision made to Equity, Bonds and Option manager respectively. Strategy Attribution Another interesting attribution method used by some hedge funds is ‘Strategy Attribution’. Traditional attribution methods are heavily based on security characteristics. This method rather relies on the investment idea (strategy) that was the cause for performing the trade in the first place. To illustrate, assume the investment ideas (strategies) as follows: IDEA 1: Buy a security in the IT sector that has a market cap of more than $100 million (70 securities) IDEA 2: Buy a security present in the MSCI index which lost 5% in the previous week (30 securities) Let us also assume that both strategies result in buying 100 shares of IBM stock. Also, closure of one strategy position will result in reduction of IBM positions. If we need to find how much return each strategy produced, traditional methods (based on Security characteristics) would not yield the desired results. The solution to this lies in identifying each idea / strategy and tagging it to the transactions / positions . Since several strategies could result in actual one transaction or trade (as in the example above) in the market place, a mechanism that apportions or splits a single trade or position into multiple parts is required. Though not attribution in the strictest sense, the ‘Contribution by Strategy’ return decomposes the portfolio level returns into meaningful parts and reveals an important aspect i.e. how is the strategy working. Strategy Attribution, unlike the other methods does not relay on security level attributes. This is a significant difference for functional capabilities and not all performance systems can do it. Depending on trade volume and the time range over which attribution is to be calculated this can be a significant scalability determinant. It is entirely plausible that a separate data store design be required for implementing strategy attribution. Capabilities to support the various attribution methods mentioned would result in a robust investment performance system. Since attribution is at the center of investment performance systems, breadth of this functionality would provide a superior edge to other products or platforms. a b
  13. 13. Investment Performance Systems - Demystified 13meradia.com RISK METRICS Return numbers without risk metrics is an incomplete measure of manager skill. A wide variety of risk metrics is used to measure the performance of investment managers. Most risk metrics or risk analytics can be derived from the underlying data used for performance return computation. Hence a separate data management exercise should not be required in most cases. A performance measurement & attribution system should have the provision to configure risk analytic parameters at different account levels. The ability to calculate risk analytics at the basic account level and thereafter consolidating to multiple hierarchies (as mentioned in Data management section) should be available. Risk metrics can be classified into different types i.e. based on absolute measures, relative measures, drawdown measures, etc. While typical investment managers rely on the grandfather of all risk measures i.e. Sharpe ratio, hedge funds are increasingly drawn towards drawdown measures. Once the relevant risk metrics have been identified, the investment performance system should have the provision to calculate and store risk metrics for funds as well as its corresponding benchmarks. Certain hedge funds could require Value-at-Risk related metrics such as VaR, MVaR, Loss Ratio, etc. In recent years, there has been a growing interest towards frameworks that decompose a portfolio’s return to components (factors) of risk that the asset is exposed to. Risk factor analysis provides insight into the underlying risk factors that build up an asset. It goes without saying that each asset type would have unique underlying risk factors to which it is exposed and a one-size fit all approach to all investments in a portfolio wouldn’t work. To arrive at a parsimonious set of risk factors for each asset type is important. It would be prudent to arrive at the factors at a detailed asset sub type level (Inflation bonds, Sovereign bonds, High Yield bonds, etc.) to obtain meaningful results. There also exist scenarios where reliance on traditional risk metrics (such as volatility and VaR) for shorter time periods could result in greater estimation errors and risk factors would be a better alternative. An extension of risk factor modelling is Scenario modelling. Scenario modelling requires defining the hypothetical scenarios that the portfolio needs to be subjected to and recalculating the revised portfolio returns / metrics under those assumptions. For example, an Investment manager could swap in an asset class or a specific security to understand what would have been the ‘prophesized’ return had the security / asset class been included in the first place. In investment performance systems, risk metrics manifest in a couple of other dimensions as well i.e. periodicity and Global Investment Performance Standards (GIPS) related. Periodicity gives rise to ex-ante and ex-post risk measures. Ex-post measures can be considered as a supported need and available in many investment performance products in the market place. On the other hand, ex-ante measures are beginning to scratch the surface and would be required by many asset management firms in the future. If a product already supports ex-post measures, it would be curious to embark on an analytic study to determine the necessary add-ons required to make it ex-ante. With respect to GIPS standards, there are specific metrics required for specific fund types, for example, Real Estate closed-end funds. Some of them are Investment Multiple, Realization Multiple, Unrealized Multiple, etc. A mature investment performance system needs to contain the following aspects for calculating these metrics: RELEVANT DATA MODEL to store the underlying data elements CONFIGURATION OPTIONS to design the formulas g a b
  14. 14. Investment Performance Systems - Demystified 14meradia.com REPORTING OPTIONS A very important and often overlooked aspect is the nature of investment performance systems to quickly assimilate the information stored in data stores /tables and present it in a wide variety of formats for consumption purposes. It would not be an exaggeration to state that data delivery needs should occupy equal emphasis along with functionality and scalability during initial exploration discussions of system design. A typical reporting architecture: A thorough analysis should be performed to determine what information is available in the product screens versus those available via canned reports. Many products in the market provide canned reports in the form of Excel files. If complex formatting and additional data layering is required as in the case of external stakeholder requirements, a separate reporting database might be a reporting requirement. Though return and attribution methods have one single requirement definition, reporting requirements vary since multiple stakeholder groups might be the end consumers of the data. In our experience, we have found that while calculated numbers within the performance system are sacrosanct, data delivery mechanism and format isn’t. Aggregation and display structure requirements in the industry are different for Portfolio managers, clients & regulators. The investment performance system should be robust & flexible to support the needs of different groups. This cannot be understated – a performance system that lacks flexibility and ease with regards to data delivery should give significant pause in the examination of a solution. An important aspect of an investment performance system in the reporting function would be its nature to provide off-the-shelf reports that are GIPS compliant. Once the relevant performance information has been made available in the data stores, there should be little or minimal effort to produce GIPS compliant reports. Given the market adoption of this gold performance standard by many clients across the globe, it is no surprise that many vendors are offering GIPS compliance package as a separate service. h FIGURE 6: Reporting Architecture
  15. 15. Investment Performance Systems - Demystified 15meradia.com PROCESS AUTOMATION ‘Straight through processing is the buzzword’ Several business processes such as return calculation, attribution computation, risk metric computation and finally report generation are involved in a typical investment performance process. These processes should be automated in a seamless fashion with the correct interdependencies and timings. The complexity becomes further exacerbated when there is information received from different accounting system data providers and external vendors (for example, benchmark indices) throughout the day that needs to be consolidated and processed to arrive at the holistic portfolio snapshot. With performance numbers being churned out daily, there is a consequent demand of daily Service Level Agreements in the market. The investment performance product should have the capability to couple and decouple the processes as the need arises. Provision to define several automation ‘assembly lines’ and execute a single process when exception arises would be a desired feature of an investment performance system. An important feature while considering automation is the ability to support ‘back-dated’ transactions. Once a trade / contribution done in the past has been entered, the system should have the ability to automatically update holdings, recalculate cash flows, re-compute returns and regenerate attribution effects. THROUGHPUT Throughput in simple terms is defined as the speed of processing transactions and the ability to support more transactions / accounts / securities / return elements. The higher the number of transactions processed per second, the more efficient the system. This is a critical parameter and can decisively determine the Go / No-Go decision for a firm with huge number of portfolios / composites or one with a few portfolios but large number of holdings or transactions. A boutique asset management firm might consider this parameter trivial while it could be extremely important for a global custodian. High degree (and flexibility) of Process automation coupled with greater efficiency can indicate increased levels of technical robustness of an investment performance product. Firms that have tight SLAs should lay greater emphasis to this parameter. The data architecture of the investment performance system plays an important role in Scalability. If there is a change to one data element, data warehousing architecture should have the provision to update the related replication data stores seamlessly and quickly. This is extremely important where information is duplicated across several data stores for information delivery purposes. A delay in processing could result in stale data being sent to downstream systems. i j
  16. 16. Investment Performance Systems - Demystified 16meradia.com SERVICE DELIVERY While the above sections have dealt with several functional and technical aspects necessary to provide a high quality of service to market demands, this section deals how that service can be delivered. Rapid transformational changes in the past decade have given rise to many different service delivery models. A couple of models that are prevalent in the market place are shown below: 1. On-Premise Package The entire performance management software is installed on the client premises. Typically, this results in increased software & hardware costs but provides greater security and flexibility in terms of client data requirements. There would also be increased upgrade costs since the client would have to constantly keep track of product upgrade versions and perform the upgrade as well. An in-house IT team with good knowledge of the product does play a significant role in performing the upgrade efficiently. 2. Hosted Systems In this delivery method, the investment performance system is hosted by the vendor and the client need not purchase upfront hardware or incur high maintenance costs. This results in overall lower cost to asset management firms but additional controls would have to be in place to ensure data security. With the advent of advanced security mechanisms, remote systems provide a compelling value proposition and the trend to adopt them is on the rise. This model to a Pay-as-you-Go one (in the future) where asset managers would be paying based on the number of transactions processed on the platform / day rather than a fixed monthly price. Meradia foresees the business case of a transaction based utility pricing to evolve henceforth. Another implication with hosted systems arise with the use of sharing a security master or asset class definition by many clients. Though this results in reduced implementation time, there is a limitation to modify standard security classifications. Some limitations are overcome by building a layer on top of security master. But this adds another layer of complexity and impacts scalability. The fine line between standardization and customization needs to be closely weighed in by clients opting for this model. Conclusion We hope that this attempt of developing formal literature to understand investment performance systems has helped the reader obtain a glimpse of the varied functional components and systemic touchpoints. This paper can be considered to have done its job if the reader has been able to appreciate the nuances and best practice recommendations mentioned in several sections. Backed by decades of industry knowledge and technology know-how, the framework for investment performance systems has been developed. Viewed differently, these can be the defining characteristics of an optimal performance product. We believe that the characteristics outlined would serve as a starting point to detail the functional requirements of a Performance system. This could channelize the requirements and help firms pick the right product suited to their needs. k
  17. 17. Investment Performance Systems - Demystified 17meradia.com References 1. Journal of Performance Measurement – Spring 2015 2. White Paper: ‘A Flexible Benchmark‐Relative Method of Attributing Returns for Fixed Income Portfolios’ - Stanley J. Kwasniewski, FactSet Research Systems 3. White Paper: ‘Characteristics of a Performance Book of Record (PBOR)’ – Eagle Investment Systems 4. Webinar: ‘Investment Performance Analysis & Trends’ – BI-SAM Inc 5. White Paper: ‘Investment Performance Reporting’ – Greycourt Investments 6. ‘Annualizing Daily Returns – A Twist and a Solution’ by Arun S Muralidhar 7. ‘Bespoke Attribution: Illustrating the Manager’s Process’ – Mark R David 8. ‘Risk Factor Portfolio Management’ - Milliman Research Report, January 2015 9. ‘Money Weighted Rate of Return: A calculation to Consider’ – State Street Global Services, September 2015. 10. ‘Better Risk and Performance Estimates with Factor Model Monte Carlo’ by Yindeng Jiang and R. Douglas Martiny.

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