Meradia investment performance_systems

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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
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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.

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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.

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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
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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.

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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.
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FIGURE 2: Account Classification Hierarchy

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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
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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.

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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.
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FIGURE 3: Return Data Elements

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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%

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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:
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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.

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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:
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FIGURE 5: Macro Attribution

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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.
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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
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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.
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FIGURE 6: Reporting Architecture

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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.
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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.
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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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