REVITALIZING ACTIVE MANAGEMENT - Oliver Wyman
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REVITALIZING ACTIVE MANAGEMENT
The raft of headlines heralding the death of active management
is overblown. It is old news that flows are going into passive, and
that relentless fee pressures are crimping margins across the entire
industry, but we believe there is hope for active managers. While
Strategy 101 would dictate not to focus on shrinking markets with
compressing margins, many active managers don’t have a choice;
either they can capture a larger share of a shrinking pie, or they can
slowly bleed assets and revenue until there is nothing left. Some
will invariably succumb to this fate, waiting idly by in the futile hope
that the good old days will return. Others will embark on ambitions
cost-cutting or operational improvement campaigns. Still others
will double down on new products, seeking the mystical “magic
bullet” that quickly garners new flows at attractive margins. These
strategies will certainly help in delaying the inevitable, and any
asset manager serious about its own survival needs to be pursuing
all of these, but none addresses the most fundamental issue of all:
the need to deliver sustainable alpha. At the end of the day, it is
the ability to consistently generate alpha – the core competency
of active management – that represents the most fundamental
and sustainable competitive advantage. It is the path not just for
surviving, but for thriving.
In this paper, we offer concrete suggestions for how asset managers
can revitalize their active management business by reimagining
those specific mechanisms that underpin sustainable alpha
generation. As we hope will become clear, not only is this the
single greatest strategic opportunity for active managers, but it is
also eminently possible. Importantly, however, it will require that
active managers abandon a number of their traditional beliefs
and biases, and be willing to disrupt the long-cossetted halls of
portfolio management.
3Exhibit 1: Flows between funds THE INCREDIBLY
PERCENTAGE INDUSTRY AUM,2016 SHRINKING MARKET
5%
The growth in passive strategies relative to active has
1% been astounding, and only seems to be accelerating:
outflows from active funds were over $340BN in 2016,
up from $230Bn in 2015, while passive hauled in a record
2% $504BN in 2016 and over $400BN in 2015.1 Continuation
of this trend implies that to grow, active managers must
capture a larger piece of a shrinking pie. One source
2% of growth that is often overlooked is the flows between
Multi-asset
1% active funds. As described in a recent Oliver Wyman
2%
Fixed income report prepared in conjunction with Morgan Stanley, The
1% World Turned Upside Down, this opportunity is significant:
Equities as a percent of industry AUM, flows between core active
Inflows into Flows between
passive funds core active funds1 funds (includes traditional actively managed funds and
excludes hedge funds and alternatives) are two and a half
Source: Oliver Wyman
times greater than inflows to passive funds (Exhibit 1).
Flows between active funds imply a significant amount
of “money-in-motion”, i.e., flows that are actually “up-
for-grabs”. Given the relatively high fee levels compared
to passive (even if compressed relative to history) and
the sheer size of the category in terms of AuM makes
capturing this opportunity the single largest revenue
opportunity for asset managers over the next five
years (Exhibit 2). Note the active equities bubble on
the upper right of the figure: not only does it have the
highest revenue associated with it (i.e., it is farthest to
the right on the graph), it also is the asset class with an
extremely high amount of money-in-motion (i.e., it is
high on the graph). Managers that can capture even a
slice of that flow stand to benefit significantly, not just in
terms of AuM, but also in terms of revenue.
1 Source: Morningstar
Copyright © 2017 Oliver WymanUnlike the handful of at-scale passive managers that are benefitting from the secular flows
into passive, succeeding as an active manager in a shrinking market is all about winning share
from others. And performance is a huge part of determining who will win and who will lose.
We wouldn’t suggest that performance is the only consideration in selecting an active
manager, but as much as investors claim that it is only a part of their evaluation process,
the truth is that it carries huge weight in the decision, especially in those mandates where
significant alpha generation is the primary part of the value proposition. For example, our
own proprietary analysis shows that an active US equity small cap manager that performed
in the top 20% of their peer group over a three-year period saw a greater than 10% increase
in revenues over the subsequent two years, while those that performed in the bottom 20% of
their peer group saw revenues fall by 10%.
The stakes are high: those that underperform face existential threats, but for those that can
outperform, the rewards can be significant.
Exhibit 2: “Money in Motion” forecast (2016-2021)
NET US AM 2016 -2021E TOTAL MIM
2016-2021E, $BN
4,000
Bubble size:
Active FI Corporates 2016 assets under
management
Passive Domestic Equities
3,000 2 Active Hybrid Active Domestic Equities
1 6 Other
Hedge Funds
2,000 Passive Intl Equities 3 Hybrid
Active FI Rates
& Agencies Cash
Passive Alternative
Hybrid Active FI Munis 6 Active International Equities
1,000
Annuities
Passive
Other 4 Real Estate
5 Private Equity
0 Other Alternative Assets Active
0 5 10 15 20 25 30
Passive Active FI Passive
Intl FI Securitized Domestic FI REVENUE PER YEAR
$BN
Large net new flow opportunity but prices keep After full recovery, overall demand slows but opportunities
1 the revenue opportunity limited 4 remain in select sectors such as opportunistic RE
Fastest growing retail opportunity as a result of
Limited new opportunity as the industry dry powder is at
2 continued adoption of QDIAs and changing 5 an all time high particularly in traditional buyout strategies
investment orientation from product to outcomes
Continued demand as the market volatility is Active equity still continues to represent the largest
3 expected to persist 6 opportunity primarily turnover driven
Source: Oliver Wyman MiM model
5IN SEARCH OF ALPHA
Where does alpha come from? Simply put, alpha comes from three sources: getting better
information, processing it faster, or processing it more intelligently. As we explain below, in
our view, it is only through building a capability to consistently process it more intelligently
that most asset managers will be able to build a sustainable competitive advantage, as
highlighted in Exhibit 3.
GET BETTER INFORMATION
Alpha generation requires information. However, obtaining information that is “better” by
virtue of not being available elsewhere is challenging. Regulation largely prevents public
companies from selectively disclosing material nonpublic information to analysts and, while
there are other ways to get an information edge (such as employing satellite imagery to
predict agricultural yields), many of the insights obtained in this manner are incremental
in nature. More fundamentally, unless an asset manager can maintain access to unique
information that no one else has, this is not going to be a sustainable source of alpha—what
was a novel source of information yesterday becomes widely known tomorrow.
PROCESS INFORMATION FASTER
Asset managers that can process and trade on information more rapidly (e.g., by using
natural language processing technology to read research reports and company filings,
clever routing algorithms, or through co-locating their servers nearer the exchanges, etc.)
will enjoy a performance advantage, all else equal. The problem is that the more these
approaches rely on easily replicable strategies and/or application of brute-force technology,
the more quickly the pace of obsolescence from cutting edge to commonplace becomes.
The clever use of technology certainly has an important role to play in revitalizing active
management, as we will get onto shortly, but utilizing technology where the primary goal
is to simply process information faster or more cheaply quickly becomes an arms race.
For the vast majority of asset managers, they need to look elsewhere to build sustainable
competitive advantages.
Copyright © 2017 Oliver WymanIn short, while getting better information or processing information faster are important to
any firm’s investment process, those that focus exclusively on these methods eventually find
themselves in an unsustainable arms race. For sure, some will win this race, but given the
costs involved to maintain advantage and the fact that there are likely diminishing returns to
those investments, it will quickly become prohibitive for all but a select few. That leaves one
other lever.
Exhibit 3: Alpha generation levers
NOT A SUSTAINABLE
COMPETITIVE ADVANTAGE
• All investors have access to
similar information
• For most, trying to build
information advantage
becomes unwinnable arms race
Get
better
information
SUSTAINABLE COMPETITIVE NOT A SUSTAINABLE
ADVANTAGE COMPETITIVE ADVANTAGE
• Ability to generate unique • Raw information processing
insights and then technology commoditized
successfully translate them
• High-frequency trading
into portfolio positions Process Process constant battle of
cannot be fully commoditized information information
better faster one-upmanship
or competed away
7PROCESS INFORMATION BETTER
In contrast to the other two, the primary path toward sustainable competitive advantage for
most asset managers is through building the ability to process information more intelligently,
and using that to generate consistently unique insights, and to translate those insights into
winning portfolio positions. That is not to say that enhancing an asset manager’s ability to
process information more intelligently is a “once and done” initiative; continual improvement
is always necessary. Nor is it to say that it in itself is sufficient for generating consistent alpha—
sourcing better information (or at least as good as everyone else sources) and processing it as
quickly as possible are still important elements of the overall approach. But finding a way to
process information more intelligently is a necessary element. Why? Because unlike the other
two levers, it doesn’t naturally devolve into an arms race, where the incremental benefit of
sourcing more information or processing it more quickly decreases while the incremental cost
of securing that information or processing it faster increases.
Firms that are committed to processing information more intelligently will seek
improvements in three fundamental areas: people, organization, and technology (Exhibit 4).
In the sections that follow, we explore the steps managers need to take on each of these
dimensions to revitalize active management and build a sustainable competitive advantage
in the years to come. For those that can do this successfully, there is no larger opportunity
out there.
Exhibit 4: Elements of building a sustainable competitive advantage
SUSTAINABLE COMPETITIVE ADVANTAGE
=
PEOPLE + ORGANIZATION + TECHNOLOGY
• Train portfolio managers and • Transform the organization to •Utilize emerging machine
analysts to become ensure the best insights get to learning/artificial intelligence
significantly better forecasters the right hands/portfolios capabilities to not only
through systematic at the right times augment the ability of
application of cutting-edge “humans” to generate unique
• (Re)-structure incentives and
techniques, i.e., make them investment insights, but to
develop tools/processes that
“super forecasters” generate insights
foster information sharing
independently
mechanisms to maximize
value of information • Reengineer investment
process to explicitly reflect
relative strengths/
weaknesses of “humans” and
“machines”
Copyright © 2017 Oliver WymanIT’S A BIRD! IT’S A PLANE! IT’S A SUPERFORECASTER!
As a group, investment analysts make horrible forecasters. And yet they are among some of
the most highly paid professionals in financial services. Consider the chart below which shows
over 30,000 returns forecasts from over 400 analysts from 2011-2016 and compares them to
the actual returns achieved. Taken as a group, the quality of analysts’ forecasts are no better
than randomly throwing darts—there is no statistically significant difference! (Exhibit 5).
There are few things as important to generating alpha than being able to forecast key
variables accurately, whether it be earnings for a company, direction of interest rates or
credit spreads, or the demand and supply for commodities. The analysis above suggests that
a typical analyst is not delivering forecasts of any meaningful value – but that doesn’t mean
all analysts are poor forecasters (buried in this cloud of data are some very good forecasters),
nor does it mean that any typical analyst couldn’t improve his/her accuracy. In other words,
not only are some human beings naturally better forecasters but forecasting is also a skill
that can be cultivated and improved. This has been demonstrated and documented in recent
years through “forecasting tournaments” held in fields outside of investment management,
such as the Intelligence Advanced Research Projects Activity (IARPA), a group within the
Exhibit 5: Buy-side Analysts1: actual return vs. expected return over one year periods
ACTUAL RETURN
PERCENTAGE
450
300
150
0 R2 = 0.0079
-150
-100 0 100 200 300 400 500
EXPECTED RETURN
PERCENTAGE
1 n=30,000; 400 Analysts, 70 firms, 2011 – 2016
Source: Alpha Theory, Inc.
9Office of the Director of US National Intelligence, and further developed by researchers
Philip Tetlock and Dan Gardner, whose book Superforecasters: The Art and Science of
Prediction2, has prompted cross-disciplinary interest. Originally applied to geo-political and
geo-economic forecasts, the techniques show great promise for investment forecasting.
The basic premise of “superforecasting” is that well-disciplined habits of thought – e.g.,
ways of thinking, how we gather information, our ability to challenge and update our beliefs
in the face of new information – can be applied systematically to provide the foundation for
better predictions. Such habits and behaviors are considered to be of greater importance
to improved forecasting than traits such as above-average intelligence or numeracy and,
therefore, cannot be identified by traditional means such as I.Q. or personality tests,
or for that matter, from looking at the seniority of a given portfolio manager. Rather,
“superforecasters” are identified by the results of their efforts.
For an asset manager, this insight is ground-breaking, and likely hugely disruptive,
especially in the more senior echelons of the investment organization. What if the new
analyst is actually a better forecaster than the senior PM? But the data is incontrovertible:
superforecasters are up to 40% more accurate than regular forecasters3. Organizational
disruption and ego issues aside, asset management firms that are serious about revitalizing
active management owe it to themselves to try to identify who these people are.
THE MAGIC FORMULA THAT ISN’T SO MAGICAL
We have found the key to identifying the best forecasters and improving the forecasting
capabilities of all analysts is discipline. The vast majority of firms with whom we work do not
systematically measure, track and provide feedback on investment professional’s forecasts.
Sure, they look at the performance and cut the return and risk data in a number of ways, but
we’ve observed they stop well short of systematically tracking and storing all the various
forecasts that are made. Perhaps no one wants their predictions to be tracked, stored and
then evaluated—what incentive do they have, especially for the most senior and experienced
professionals? However, this raw data is among the most valuable an investment
organization can collect because it allows for systematic assessment of the accuracy of
predictions. In particular, if the forecasts are properly structured and the data properly
tracked, it supports the calculation of “Brier scores” which allows the accuracy of each
forecaster (analyst) to be rigorously measured on an apples-to-apples basis with his/her
peers. Moreover, by calculating Brier scores, it can provide a robust feedback mechanism to
help analysts “keep score” over time and help their managers identify areas where additional
training can help improve their capabilities.
2 Philip Tetlock, Superforecasting: The Art and Science of Prediction (Crown, 2015)
3 Source: Good Judgment Project
Copyright © 2017 Oliver WymanIdentifying the best forecasters in the organization and providing them with some additional
training can yield about a 50% improvement (as measured by Brier scores). Additional gains
can be made by introducing teaming and information sharing mechanisms and utilizing
smarter forecast aggregation algorithms (e.g., “extremizing”, non-linear forecast weighting)
that more effectively captures the wisdom of crowds. Collectively these techniques can yield
close to a 100% improvement in forecasting accuracy4. Imagine what this might mean in
terms of an organization’s ability to generate alpha.
(RE)DESIGNING THE ORGANIZATION
Maximizing the value that improved forecasting capabilities can yield will require firms
to rethink how to best organize their investment functions and take a hard look at how
their current culture, operating norms and incentives might be impeding adoption of the
optimal model.
Let’s first consider the various ways in which a firm could organize its superforecasters to
obtain the most value from their capabilities. Should they be centralized in a “center of
forecasting excellence”? Kept in the individual investment teams or split time between
their investment teams and a centralized research group? A few different potential
organizational models are highlighted in Exhibit 6. Each of these schematics depicts three
investment teams comprised of seven or eight analysts/PMs. The blue circles represent
superforecasters and the gold circles are centralized research team members.
Exhibit 6: Different investment and research organizational models
OPTION C
OPTION A OPTION B TOP FORECASTERS
TOP FORECASTERS TOP FORECASTERS PROVIDE INPUT RESEARCH GROUP,
IN PM TEAMS IN SEPARATE RESEARCH GROUP BUT REMAIN IN PM TEAMS
US Small Cap Emerging Markets
Asia-PAC
4 As measured by the increase in Brier scores.
11Each model has its advantages and disadvantages. For example, in Option A, the best
forecasters have significant influence into investment processes even if they are in more
junior roles, but this also creates tension within teams based on seniority levels and may limit
the benefits that a more centralized teaming approach might bring in terms of diversity of
views (which would be a benefit of Option B).
In our experience, however, making organizational adjustments is just one element of the
change required. Firms also need to examine their culture, operating norms, and incentive
structures, which will prompt difficult questions around whether current practices are fully
conducive to achieving the best investment results.
To be clear, there is no one optimal investment model—some firms have been successful
employing a siloed, multi-boutique, star PM model; others have had success by adopting
more team-based approaches. However, one needs to know what his/her starting
point is in order to identify the best way to incorporate superforecasting concepts into
the organization.
TECHNOLOGY AND THE BATTLE OF MAN VS. MACHINE
There is a lot being said surrounding artificial intelligence/machine learning (AI/ML)
and topics like Big Data. It seems like every day industry pundits and asset management
executives are weighing in on the debate of whether AI spells the end of days for humans
or whether the expectations for AI are better described as “Artificially Inflated”. Our view is
that there is real substance behind these trends and that while AI/ML may be transformative
in many ways, it won’t usher in the end of all PMs or analysts any time soon. Instead, it has
the potential to change the respective roles of man and machines. For those unwilling to
change, who hold fast onto their traditional ways of doing research, constructing portfolios
and trading, they are likely to be left behind. Waiting a quarter for an earnings report to come
out when others are processing real time data from news feeds and tweets, satellite imagery,
auction prices, etc. and then trading on these data instantaneously … well, it’s like pitting a
bee-bee gun vs. a bazooka.
While many of the advanced statistical techniques behind ML have been around for some
time, it was not until recently that suitable amounts of machine readable data and enough
processing power to actually do something valuable with it have become widely available.
In our view, AI/ML is likely to change how a large portion of asset managers generate alpha
over the coming years. PMs and analysts are going to have to become familiar with concepts
underlying AI/ML and big data and related trading strategies, and data scientists and
researchers are going to have to become familiar with the world outside of a classroom or lab
and learn how to translate their skills into ones that can generate viable trading strategies,
not just academic citations.
Copyright © 2017 Oliver WymanBut before firms rush to hire data scientists, download ML libraries or spend millions getting
access to huge data sets, however, it is important to understand the intrinsic shortcomings
of machine-based investment management, which are laid on in Exhibit 7.
THE BIONIC INVESTOR
The key is optimizing the respective strengths of humans and machines, i.e., creating a truly
bionic investment processes. Humans excel at specifying the overall investment framework,
finding valuable sources of data and how to combine them, ensuring that trading strategies
pass reasonability tests, thinking about future states of the world and regime changes,
focusing on distinctly human variables and on working with the data scientists to determine
what models work best for what type of data and market and continually finding way to
improve the algorithms and trading strategies.
Exhibit 7: Shortcomings of applying Machine Learning to an Investment Process
As more and more processing power is thrown at every conceivable relationship, the very act
REFLEXITY of those computers trading on that information will change those relationships that existed in
history and eliminate alpha in the future
Systems that are designed to emulate human thinking, but to just do it faster and apply
it more comprehensively may end up simply adopting all of the worst characterictics of
HUMAN ANCHORING human investors
“Some people get rich studying artificial, intelligence. Me, I make money studying natural
stupidity” – Carl Icahn
EXCLUSION OF QUALITATIVE FACTORS Computers struggle to inform judgments on intrinsically “human” traits like management’s
AND NUANCE character, firm culture or durability of a brand
Computers’ processing power allows them to excel precisely in those areas for which the
relationships are more tenuous, i.e., those between macroeconomic data and the supposed
MACRO-MICRO FALLACY impacts on individual companies
“Forming macro opinions or listening to the macro or market predictions of others is a waste
of thime” – Warren Buffett
AI/ML can only “uncover” relationships that can be identified in historical data; it cannot
PERFECT HINDSIGHT, NO FORESIGHT
postulate about future relationships. Nor, can it recognize when there has been a structural
change in a market that has not occured before which would cause the historical relationship
to break down
13Machines, in contrast, should initially be focused on automating all the repetitive tasks
that humans once did—any area of information processing, strategy testing, risk reporting,
etc. is something that is best left to the machines. They simply accept the data and run
algorithms to figure out viable trading strategies, and they do it leveraging a bewildering
array of structured and unstructured data. It’s the thoughtful combination of inputs provided
by superforecasters who can see further into a future that does not necessarily resemble the past
with those from machines that can tease out shorter-term relationships that the promise of more
consistent alpha can be realized.
LARGE SLICE VS. A SLIVER
Revitalizing active management is the single most impactful initiative asset managers can
pursue. While the broader secular flows toward passive is leading to a shrinking pie, those
managers that can capture a larger share of that pie stand to enjoy significant economic
benefits. While there are many factors that go into “winning” the active game, the ability to
consistently generate alpha is indispensable.
Generating alpha more consistently is a sustainable competitive advantage, but it can only
come from finding ways to process information more intelligently. This requires:
1. building the systems and instituting the discipline to measure and identify the firm’s best
forecasters as well as make all forecasters better
2. (re)structuring the organization and incentives to ensure the best trading insights
consistently make it into client portfolios
3. embedding technology into the core idea generation and trading processes that
optimizes the respective strengths of man and machine
This efforts shouldn’t be pursued to the exclusion of efforts to source better or more
complete data, or ensuring that it is processed as efficiently as possible, but relying on those
strategies is ultimately a losing proposition as the incremental costs grow more quickly than
the incremental return benefits. They can help firms maintain parity, but alone, they are not a
winning solution.
Firms that are ready to begin on this journey must start with an unvarnished view of their
capabilities and be ready to face some potentially uncomfortable truths. But it is the
willingness to stare down these truths and make some hard changes that will differentiate
those firms that will be savoring a much larger slice of the active management pie vs. those
left with the slivers.
Copyright © 2017 Oliver WymanOliver Wyman is a global leader in management consulting that combines deep industry knowledge with specialized expertise in strategy, operations, risk management, and organization transformation. For more information please contact the marketing department by email at info-FS@oliverwyman.com or by phone at one of the following locations: AMERICAS +1 212 541 8100 EMEA +44 20 7333 8333 ASIA PACIFIC +65 6510 9700 AUTHORS Michael Hanus, Partner Joshua Zwick, Partner www.oliverwyman.com Copyright © 2017 Oliver Wyman All rights reserved. This report may not be reproduced or redistributed, in whole or in part, without the written permission of Oliver Wyman and Oliver Wyman accepts no liability whatsoever for the actions of third parties in this respect. The information and opinions in this report were prepared by Oliver Wyman. This report is not investment advice and should not be relied on for such advice or as a substitute for consultation with professional accountants, tax, legal or financial advisors. Oliver Wyman has made every effort to use reliable, up-to-date and comprehensive information and analysis, but all information is provided without warranty of any kind, express or implied. Oliver Wyman disclaims any responsibility to update the information or conclusions in this report. Oliver Wyman accepts no liability for any loss arising from any action taken or refrained from as a result of information contained in this report or any reports or sources of information referred to herein, or for any consequential, special or similar damages even if advised of the possibility of such damages. The report is not an offer to buy or sell securities or a solicitation of an offer to buy or sell securities. This report may not be sold without the written consent of Oliver Wyman.
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