revitalizing active management - oliverwyman.com · times greater than inflows to passive ......

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.

3

THE INCREDIBLY SHRINKING MARKET

The growth in passive strategies relative to active has

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

$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

of growth that is often overlooked is the flows between

active funds. As described in a recent Oliver Wyman

report prepared in conjunction with Morgan Stanley, The

World Turned Upside Down, this opportunity is significant:

as a percent of industry AUM, flows between core active

funds (includes traditional actively managed funds and

excludes hedge funds and alternatives) are two and a half

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

Exhibit 1: Flows between funds

Inflows intopassive funds

Flows betweencore active funds1

PERCENTAGE INDUSTRY AUM,2016

1%

1%

2%

2%

2%

1%

5%

Equities

Fixed income

Multi-asset

Source: Oliver Wyman

Copyright © 2017 Oliver Wyman

Exhibit 2: “Money in Motion” forecast (2016-2021)

5

Other

Cash

Annuities

Active FI Munis

2,000

1,000

4,000

3,000

REVENUE PER YEAR$BN

25201510

NET US AM 2016 -2021E TOTAL MIM2016-2021E, $BN

0

300Active

Passive

Alternative

Hybrid

Other

Bubble size:2016 assets under management

Active Domestic Equities

Active International Equities

Hedge Funds

Private EquityReal Estate

Other Alternative Assets

Active Hybrid

Active FI Corporates

Passive Intl Equities

Passive Hybrid

Passive Domestic Equities

Active FI Rates & Agencies

Passive Intl FI

Passive Domestic FI

Active FI Securitized

1

2

3

4

6

5

1

2

3

4

5

Large net new flow opportunity but prices keep the revenue opportunity limited

Fastest growing retail opportunity as a result of continued adoption of QDIAs and changing investment orientation from product to outcomes

Continued demand as the market volatility is expected to persist

After full recovery, overall demand slows but opportunities remain in select sectors such as opportunistic RE

Limited new opportunity as the industry dry powder is at an all time high particularly in traditional buyout strategies

6 Active equity still continues to represent the largest opportunity primarily turnover driven

6

Source: Oliver Wyman MiM model

Unlike 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.

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IN 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.


Exhibit 3: Alpha generation levers

Getbetter

information

Processinformation

better

Processinformation

faster

SUSTAINABLE COMPETITIVE ADVANTAGE

• Ability to generate unique insights and then successfully translate them into portfolio positions cannot be fully commoditized or competed away

NOT A SUSTAINABLE COMPETITIVE ADVANTAGE

• Raw information processing technology commoditized

• High-frequency trading constant battle of one-upmanship

NOT A SUSTAINABLE COMPETITIVE ADVANTAGE

• All investors have access to similar information

• For most, trying to build information advantage becomes unwinnable arms race

In 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.

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PROCESS 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 analysts to become significantly better forecasters through systematic application of cutting-edge techniques, i.e., make them “super forecasters”

• Transform the organization to ensure the best insights get to the right hands/portfoliosat the right times

• (Re)-structure incentives and develop tools/processes that foster information sharing mechanisms to maximize value of information

•Utilize emerging machine learning/artificial intelligence capabilities to not only augment the ability of “humans” to generate unique investment insights, but to generate insights independently

• Reengineer investment process to explicitly reflect relative strengths/weaknesses of “humans” and “machines”


Exhibit 5: Buy-side Analysts1: actual return vs. expected return over one year periods

150

0

450

300

EXPECTED RETURNPERCENTAGE

4003002001000

ACTUAL RETURNPERCENTAGE

-150

500-100

R2 = 0.0079

1 n=30,000; 400 Analysts, 70 firms, 2011 – 2016

Source: Alpha Theory, Inc.

IT’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

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Office 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


Identifying 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.

4 As measured by the increase in Brier scores.

Exhibit 6: Different investment and research organizational models

OPTION ATOP FORECASTERS

IN PM TEAMS

OPTION BTOP FORECASTERS

IN SEPARATE RESEARCH GROUP

OPTION CTOP FORECASTERS

PROVIDE INPUT RESEARCH GROUP,BUT REMAIN IN PM TEAMS

US Small Cap Emerging Markets

Asia-PAC

11

Each 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.


Exhibit 7: Shortcomings of applying Machine Learning to an Investment Process

REFLEXITYAs more and more processing power is thrown at every conceivable relationship, the very act of those computers trading on that information will change those relationships that existed in history and eliminate alpha in the future

HUMAN ANCHORING

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 investors “Some people get rich studying artificial, intelligence. Me, I make money studying natural stupidity” – Carl Icahn

EXCLUSION OF QUALITATIVE FACTORS AND NUANCE

Computers struggle to inform judgments on intrinsically “human” traits like management’s character, firm culture or durability of a brand

MACRO-MICRO FALLACY

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 impacts on individual companies “Forming macro opinions or listening to the macro or market predictions of others is a waste of thime” – Warren Buffett

PERFECT HINDSIGHT, NO FORESIGHT

AI/ML can only “uncover” relationships that can be identified in historical data; it cannot 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

But 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.

13

Machines, 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.


Oliver 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 [email protected] or by phone at one of the following locations:

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+1 212 541 8100

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+44 20 7333 8333

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+65 6510 9700

AUTHORS

Michael Hanus, Partner

Joshua Zwick, Partner


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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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