Tài liệu Performance - A triannual topical digest for investment management professionals, issue 7, January 2012 pptx

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5
Editorial
Happy New Year 2012, and welcome to this seventh
edition of Performance, Deloitte’s international digest
from, and to, Investment Management professionals.
The entire editorial team is excited to enter the third
year of publication of what has become Deloitte’s main
communication channel for our industry.
Our reader’s base has grown to over 20,000 spread
around more than 30 countries. Looking back at
the beginning of the adventure, we can humbly be
overwhelmed by the growing success and positive
feedback Performance is subject to.
For this first edition of a new and challenging year for
Investment Management, we decided to treat subjects
such as the financial transactions tax, anti-dilution
techniques, analytics, collectible assets, risk management
in UCITS IV, GIPS or corporate governance. Usually, we
try to present our articles from a non-country centric
perspective. For this edition, we thought it would be
interesting to present the asset management trends
for Brazil, one of the world’s most dynamic economy.
As usual, do not hesitate to contact us to exchange
views and ideas on any topic of your choice. I wish you,
on behalf of the editorial team, a pleasant reading of
Performance. Thank you for your support!
Sincerely,
Please contact:
Simon Ramos
Directeur - Advisory & Consulting
Deloitte Luxembourg
560, rue de Neudorf, L-2220 Luxembourg
Grand Duchy of Luxembourg
Tel: +352 451 452 702, mobile: +352 621 240 616
siramos@deloitte.lu, www.deloitte.lu
Simon Ramos
Editorialist
6
Market
buzz
Fund analytics,
regulatory requirement
or business opportunity?
Peter Spenser
Principal
Consulting
Deloitte U.S.
Liliana Robu
Senior Manager
Ne Soe Securities
Deloitte U.S.
David Berners
Analyst
Advisory & Consulting
Deloitte Luxembourg
Xavier Zaegel
Partner
Advisory & Consulting
Deloitte Luxembourg
Benjamin Collette
Partner
Advisory & Consulting
Deloitte Luxembourg
While these factors represent important reasons for the
production of fund analytics, especially as the recent
market turmoil prompted regulators to have a closer
look at financial products such as investment funds,
analytics can be much more than this: they can act as
active revenue drivers throughout the asset servicing
value chain. Whether they are used in profitability
assessments or for marketing purposes, the production
of analytics is shifting from being regulatory-driven
towards a strategic element in business management.
A key driver of this has been the technological
advances achieved in the last few years, which have
led to the development of more complex analytics
capabilities. These include the exponential increase in
raw computing power and data capacity, alongside
the introduction of much more powerful software to
handle data (particularly unstructured data), increasingly
sophisticated techniques such as predictive modelling
and sentiment analyses. The ability to leverage a variable
cost, or 'elastic' capacity, available through cloud
computing, provides opportunities to perform 'big data'
analyses that were inconceivable a few years ago.
In what follows, we will discuss regulatory as well
as business trends in producing analytics. First, we
highlight a highly volatile market environment that
calls for the quick and efficient production of analytics,
and discuss fund analytics under UCITS IV. We then
introduce analytics as a valuable marketing and business
management tool before concluding with recent
business trends in analytics production.
For many years, fund analytics have been perceived as
a necessity of doing business and a cumbersome way
of calculating the metrics required by the regulator
and sought by the investment community in order
to understand performance.
7
A high market volatility environment requires faster
insights into available choices and outcomes
Quick decisions are more important when markets
change direction frequently; a brilliant decision today
could look less than smart tomorrow. Predictive
analytics and scenario generation are critical for asset
managers in decision modelling. Asset managers have
various platforms and processes for sensitivity analysis
and stress testing, but often assets are on highly
specialised, disparate platforms. Moreover, scenario
outcome analysis and stress testing often involve
major efforts in terms of data collection, analysis
and simulations, which can span many weeks and
represent part of a formal reporting process rather than
an element of holistic decision-making. This makes it
difficult to see the overall impact of market swings or
individual key factors across all portfolios, and hampers
dynamic decision-making.
UCITS IV and KIIDs: fund analytics at the service of
the end investor and the regulator
UCITS IV creates the obligation for investment funds to
produce a Key Investor Information Document (KIID).
KIIDs contain a series of fund analytics that are aimed at
informing the investor about different key aspects of the
fund in a concise way. Examples include the Synthetic
Risk and Reward Indicator (SRRI), the past performance
of the fund and the fund’s ongoing charges.
In our experience, what drives success or failure
here is not the size or complexity of an asset
manager or its product range, but the degree
to which various platforms are integrated using
a single analytics framework shared by various
investment groups, such as a scenario generation
tool that includes stress factor models, valuation
models, a factor correlation matrix, a data
warehouse and a reporting platform. This is more
common in asset managers who have evolved
organically and asset managers with a simpler
product range.
8
While the industry argues the shortcomings of the SRRI,
some refer to the ultimate raison d’être of the KIID.
True, the metrics introduced by the KIID seem, to some
extent, to over-simplify a complex reality. For instance,
the SRRI does not take into account liquidity and
counterparty risk. These are important risk dimensions
for the investor, especially in light of the recent market
turmoil. This may lead to a false sense of security for
the investor. For funds with a track record of under
five years, proxies are used to calculate the SRRI.
Inconsistencies in SRRI calculation, and hence, a lack of
comparability are the outcome here. This is more of an
issue when considering the aim of KIIDs: comparability
and standardisation of investor information.
However, the fund analytics used in KIIDs also provide
important benefits. For the first time, they provide a
standardised method of informing investors about the
key elements of an investment fund. The value added of
the KIID for the investor is its simplicity and intuitiveness.
Is it then realistic to expect exhaustiveness from KIIDs
and their analytics?
The production of KIID-related fund analytics can
be challenging. The initial setup of the KIID requires
substantial operational efforts, especially as the proper
distribution of the document to end investors must be
demonstrated. Revising existing distribution contracts to
transfer the responsibility of proper KIID distribution to
the fund distributor is just one step in the distribution
process. Considering fund analytics for instance,
incomplete time series or the lack of track record can
vastly increase the complexity of the SRRI calculation,
as proxies must be used. However, the real challenge
may lay in maintaining the KIID. Substantial changes
in market conditions may trigger modifications of the
SRRI and hence an update of the KIID, meaning a
production-focused approach to creating KIIDs is essential.
In this sense, technology clearly has an important role
to play, as it can enable asset managers to quickly adapt
the KIID and distribute it in an efficient way. A variety of
techniques could be used to remind the end investor of
a KIID update, ranging from electronic alert reminders
that include a link to the new KIID, to the systematic
inclusion of KIIDs in the annual statements of the
fund promoter.
UCITS IV also introduces a series of fund analytics aimed
at informing the regulator about a fund’s various risk-
related aspects. Examples include stress-testing metrics,
Value at Risk (VaR) measures and backtesting reports, as
well as liquidity, currency and counterparty risk metrics.
But although VaR, for example, is a commonly-reported
risk metric, UCITS IV gives no clear indication of how
to calculate it. Different methods, such as Monte Carlo
simulations or historical models can be used, with
the results of the calculations also being different.
This creates inconsistencies in the way the regulator
approaches risk management at the fund level. The
same reasoning applies to stress testing and liquidity
risk measurement.
Besides UCITS IV, the Alternative Investment Fund
Market Directive (AIFMD) creates a new framework
for alternative fund supervision.
While the AIFMD has yet to take its definitive shape
(the grandfathering period is scheduled to end in March
2014), one thing seems clear: the directive introduces
a series of analytics over and above those currently
produced under UCITS IV.
Quick decisions are more
important when markets change
direction frequently; a brilliant
decision today could look less
than smart tomorrow.
9
At the level of investor disclosure, for instance, the
percentage of illiquid assets and the past performance
of the fund must be disclosed, whereas at the regulatory
authority level, relevant supplementary analytics must be
disclosed in relation to a fund’s leverage (e.g. leverage
employed, maximum level of leverage).
As AIFMD introduces an enhanced framework for
fund supervision, we may wonder whether the next
generation of UCITS will reflect this in increased use
of fund analytics.
Marketing and client reporting: fund analytics
as a differentiating element
The emergence of social networks provides a new
medium for attracting and connecting with investors
and customers. Social networks are humming with
unstructured data — valuable information about
customer preferences, behaviours and recommendations
(word of mouth). Making sense of the continuous
flow of data is a daunting task, and while retail asset
managers have not yet made significant investments
in this field, companies in other sectors (e.g. consumer
products) are starting to leverage emerging solutions.
For example, by using Salesforce.com, companies
monitor the limitless supply of customer opinions about
their products, and structure this data into meaningful
metrics (e.g. customer mood and product hype) to
supplement traditional client analytics (e.g. client lifetime
value, segmentation, share of wallet, preferred channels,
service model).
Many asset managers may not have decided on a social
media strategy, but most have established a presence.
While institutional investors have simply created profiles
with general background and company history, most
retail-oriented investors have thousands of followers
and a new, low-cost channel for communications
and marketing.
ETFs and other low-fee products have seen a rapid rise in
investor demand in recent times. The compound annual
growth rate for global ETF AuM over the last 10 years
is 30%. This success can undoubtedly be attributed to
low fees and the ongoing debate over whether passive
investment strategies provide better returns than active
approaches. However, we can see a recent shift towards
higher fee alternatives among high net worth individuals
and institutional investors.
10
Fund analytics can play a role in positioning active
investment strategies against passive ones. Investment
managers can use analytics such as the Sharpe ratio,
alpha and the Treynor measure to show their investors
that a fund is worth its money compared to passive
investment strategies (e.g. through providing investors
with a detailed factsheet).
A series of more or less sophisticated performance
indicators can be used to set an actively managed
fund apart from a passively managed one. Alpha
generation, for example, is one way of demonstrating
a fund manager’s stock-picking capabilities. Actively
communicating this analytic can therefore represent a
valuable marketing tool for fund promoters to position
their funds on the market. Another commonly-used
performance metric is the Sharpe ratio (i.e. a risk-
adjusted performance indicator).
Fund performance metrics are a valuable tool for fund
managers too. This is one of the key metrics used by
investors to benchmark an investment fund against other
funds or benchmarks. However, neither the production
nor the interpretation of this metric is standardised. The
main challenge in producing fund performance analytics
lies in precise position keeping in order to manage
intermediary gains and losses. Moreover, accurate
valuation of the different positions is crucial whenever
performance is calculated.
Besides the overall fund performance, fund managers
are interested in performance attribution. Performance
attribution analysis enables managers, inter alia, to
distinguish performance relating to currency effects
from asset-intrinsic performance.
While currency-induced performance is often only a
by-product of the security selection process, asset-
intrinsic performance is a valuable indicator of the quality
of the security selection process. In addition to the
usual challenges in performance calculation (i.e. data
collection, valuation, position keeping, etc.), the outcome
of the attribution analysis depends on the attribution
methodology used. Although there are a number of
different approaches (e.g. adjusting for deviations from
the portfolio base currency via an equity risk premium),
there is still no clear-cut solution for accurately attributing
performance in a multi-currency portfolio.
Substantial amounts have been invested in performance
attribution systems over the last few years. While these
tools were initially developed for portfolio managers,
they can be equally useful for senior management, client
relationship specialists, risk controllers and marketing
personnel. Senior management, as well as clients, for
instance, are concerned that the rewards received must
be worth the risks taken. This is not only true at total
fund level, but at every step of the decision process. It is
therefore advisable for risk management teams to work
closely with performance measurers, as both elements
should be assessed in a consistent way.
Another good reason for fund managers to adopt a set
of fund analytics is the rating eligibility of the fund. Fund
ratings such as Morningstar or Lipper are established
quality indicators for private as well as institutional
investors. Scoring a high rating with these companies
is therefore an important selling point for investment
funds. The methodology used to establish these ratings
is, to a large extent, based on a set of analytics such as
Morningstar’s Risk-Adjusted Return (MRAR), which uses
a fund’s annualised historical excess return adjusted for
the fund’s historical volatility. Fund managers targeting
good ratings have to constantly monitor the parameters
underlying the ratings.
Social networks are humming with
unstructured data — valuable
information about customer
preferences, behaviours and
recommendations (word of mouth).
11
Business management: fund analytics as
profitability gauges
Product profitability analytics are critical for enabling
asset managers to decide which products to discontinue,
reprice, or bundle, with a view to eliminating products
that have a negative impact on their bottom line and
improving pricing strategy by product (e.g. passing on
the high cost of customisation, setting pricing floors).
Profitability analytics also enable informed decisions to
be made on pricing for new product launches, revenue-
sharing agreements and custom mandate negotiations,
and provide insight into the required scale for each
product. This allows asset managers to develop a set of
criteria and be proactive in pruning products that have
not reached the required scale in the target timeline, or
are simply not profitable in the current cost structure.
In our experience, product profitability is more difficult in
practice than it initially appears. For example, while many
asset managers present fund profitability information to
their board of directors each year, this information is very
detailed but not easily actionable, as asset managers
monitor their performance most often by strategy
and not on a fund-by-fund basis. Most often, product
profitability assessments represent one-off efforts. When
product profitability is not a regular, well-established
process, it is likely that there is no universally-accepted
approach for a product’s P&L, and no mechanisms for
attributing the costs of shared functions. As a result,
significant heroics are required to collect data and obtain
consistency across business lines, often hindered by low
levels of transparency in relation to the unprofitable
businesses or product lines. However, in the asset
management organisations where this process is more
mature and takes place quarterly, repeatable profitability
assessments are in place, leveraging a suite of enterprise
applications in which allocation models are integrated
and reviewed periodically.
Net revenue per assets under management has been in
continued decline, especially for institutional investors,
due to the shift in preferences towards passive strategies
and investors’ flight to quality and therefore lower-
yielding products. This has resulted in significant pricing
pressure and deteriorating margins, and in declining
economies of scale — a trend that has been further
exacerbated by increased regulatory compliance costs.
As a result, asset managers have increased their focus
on cost, and analytics play a key role in providing the
transparency required for effective cost management.
As key success factors and core competencies vary
significantly between providers of alpha or beta, the
relevance of analytics also differs. For providers of beta,
given that operational efficiency is a key success factor,
12
analytic needs to cover execution capabilities and related
issues, e.g. transaction processing metrics, breaks and
errors, volume information and service level agreement
compliance. Collection of these analytics is done weekly
or monthly, often in operational excellence reporting
packages. These analytics are frequently supplemented
with one-off analysis of cost drivers, scalability of
operations and operational risk sensitivity. Asset servicing
institutions share a similar focus on operational efficiency
and process metrics analytics, supplemented by strong
client service analytics, e.g. response times, aggressive
monitoring of service level agreement performance
and root cause analysis for issues, and service costs
by client category. For providers of alpha, portfolio
and performance analytics are the most relevant.
In addition, the advent of cloud services and
virtualisation enables the large amounts of data required
for analytics to be processed on a pay-as-you-use basis,
providing for a lean infrastructure and lower costs while
supplying all the advantages of significant processing
power. In our experience, many large asset servicing
companies are pursuing partnerships with leading data
mining and analytics companies to meet their analytics
needs while keeping infrastructure costs down.
Business trends: fund analytics as a means
of extending the service range
The asset management industry is not the only sector
to have suffered margin erosion; asset servicers have
also been affected. The asset servicing industry is
increasingly moving away from the traditional bundled
service offering model. The ongoing commoditisation
of services favouring plain vanilla products and the
ever-increasing interest in sophisticated alternative
investments are forcing asset servicers to reconsider
their pricing grid, moving towards unbundled à la
carte pricing. Through unbundling, asset servicers can
achieve better margin management by charging greater
margins on highly sophisticated products, and being
flexible enough to react to price pressure on the plain
vanilla side.
Nevertheless, the increased interest in alternative
investments (and hence asset servicing solutions for
alternative investments) seems to be insufficient to offset
the revenue loss on the plain vanilla side. Meanwhile,
there does not appear to be much scope left for
differentiation in investment management core services.
Asset managers are therefore endeavouring to find
alternative revenue sources in asset management.
On the other hand, the current market environment
is pushing asset managers towards an increased
use of fund analytics for better risk and performance
management. Fund analytics can therefore be a valuable
means of extending the service range towards higher
margin services.
We may see a greater tendency among asset servicing
firms to offer value-added services related to the
production of fund analytics. The analytics produced
range from performance measurement and attribution
(e.g. return, portfolio, attribution or risk/return analytics)
to regulatory risk reporting under UCITS IV, and to fairly
sophisticated investment analytics, such as security level
attribution or fixed income analytics.
The production of regular fund industry reports using
a series of fund metrics (e.g. fund returns) is another
example of using analytics to extend a company’s
service offering.
The recent market turmoil and
its effects on end investors have
prompted increased supervision
and regulation of nancial
market instruments by market
authorities.
13
Conclusion
Advances in IT increasingly enable companies to collect
and process massive amounts of often heterogeneous
and unstructured data in a way that supports decision-
making at firm level. Increased computational power,
virtualisation and cloud computing are but three of the
multiple innovations that enable decision-makers to have
quick access to relevant information in a highly volatile
market environment.
Four main drivers are encouraging fund promoters
and service providers to make greater use of fund
analytics: fund sales strategies, regulatory requirements,
management support and the search for new revenue
streams.
Fund analytics can be actively used as a marketing
tool by investment fund promoters: communicating a
comprehensive set of fund analytics can be an effective
way of indicating the strength of an investment fund to
the potential end investor.
The recent market turmoil and its effects on end
investors have prompted increased supervision and
regulation of financial market instruments by market
authorities. Several directives have been put in place
by European market authorities to enhance investor
protection and increase financial product transparency.
Two directives, UCITS IV and AIFMD, have had a
particular impact on the production of a series of
fund analytics. These metrics can either be produced
for the regulator or the end investor.
Fund analytics can be a valuable management support
tool too. For example, they can play an important role
in risk management and profitability analysis. In light of
this, the position of the performance measurer within the
asset management firm should be reconsidered in order
to achieve a closer link to risk management functions.
The production of fund analytics can be a mean of
extending the range of services offered by a service
provider, and can help firms mitigate the increasingly
strong pressures on margins in the fund industry.
In light of the above, our answer to the question posed
in the title of this article is: yes, producing fund analytics
is a worthwhile task.
14
Investing in Château Late,
Picasso or Patek Philippe
The rise of collectible assets
What do Bill Gates, Queen Elizabeth II and
Brad Pitt have in common?
Beyond being worldwide celebrities, each in their
own way, these three people are passionate collectors.
The American business magnate drives a 1999
Porsche 911 convertible, while the movie star has
gathered an impressive contemporary art collection,
and the Queen owns rare stamps.
Pauline-Gaïa Laburte
Analyst
Advisory & Consulting
Deloitte Luxembourg
Thierry Hœltgen
Partner
Advisory & Consulting
Deloitte Luxembourg