An EDHEC-Risk Institute Publication
Mass Customisation versus
Mass Production in
Investment Management
How An Industrial Revolution is about to Take Place in
Investment Management and Why it Involves a Shift from
Investment Products to Investment Solutions
January 2016
Institute
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Printed in France, January 2016. Copyright EDHEC 2016.
The opinions expressed in this study are those of the authors and do not necessarily reflect those of EDHEC Business School.
I would like to thank Sanjiv Das for very useful comments.
A version of this paper is forthcoming in The Journal of Investment Management.
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Table of Contents
Introduction: New Challenges in Institutional and Individual Money
Management.............................................................................................................. 5
The Death of the Policy Portfolio and the Emergence of Liability-Driven
Investing and Factor Investing in Institutional Money Management.............7
The Evolution from Mass Customisation to Mass Production and the
Emergence of Goal-Based Investing in Individual Money Management..... 11
The Relationship between Dynamic Asset Pricing Theory and
Goal-Based Investing.............................................................................................. 15
The True Start of the Industrial Revolution in Investment Management.....19
Conclusion...................................................................................................................23
References...................................................................................................................25
About EDHEC-Risk Institute.................................................................................29
EDHEC-Risk Institute Publications and Position Papers (2013-2016).........33
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About the Author
Lionel Martellini is Professor of Finance at EDHEC Business School and
Director of EDHEC-Risk Institute. He has graduate degrees in
economics, statistics, and mathematics, as well as a PhD in finance from
the University of California at Berkeley. Lionel is a member of the editorial
board of the Journal of Portfolio Management and the Journal of Alternative
Investments. An expert in quantitative asset management and derivatives
valuation, his work has been widely published in academic and practitioner
journals and he has co-authored textbooks on alternative investment
strategies and fixed-income securities.
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Introduction: New Challenges in
Institutional and Individual Money
Management
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Introduction: New Challenges in Institutional and
Individual Money Management
Over the last 15 years or so, the investment
industry has experienced a series of profound
structural changes, and an increasing
number of serious new challenges are being
faced by both institutional and individual
investors as a result of these changes.
1 - Fabozzi, F., L. Martellini
and J. Mulvey. 2014. Proper
Valuation Rules for Pension
Liabilities. Investment and
Pensions Europe, EDHEC-Risk
Institute Research Insights
Winter (4-5).
2 - As an example of this
evolution, the number of
Fortune 500 companies
offering traditional DB
plans to new hires fell from
51% in 1998 to 7% in 2013
(Retirement in Transition
for the Fortune 500: 1998
to 2013, Insider Report,
September 2014, Towers
Watson, available at https://
www.towerswatson.com/
en/Insights/Newsletters/
Americas/Insider/2014/
retirement-in-transitionfor-the-fortune-500-1998to-2013).
3 - Fernandes, D., J. G. Lynch
Jr and R. G. Netemeyer. 2014.
Financial Literacy, Financial
Education and Downstream
Financial Behaviors.
Management Science 60(8):
1861-1883.
beyond standard product-based marketcentred approaches and to start providing
both institutions and individuals with
meaningful investor-centric investment
solutions has become more obvious than
ever.
On the institutional side, pension funds
have been particularly impacted by the shift
in most accounting standards towards the
valuation of pension liabilities at market
rates, instead of fixed discount rates, which
has resulted in increased volatility for
pension liability portfolios (see Fabozzi et
al., 2014, for a discussion on pension liability
discounting rules).1 This new constraint
has been reinforced in parallel by stricter
solvency requirements that followed the
2000-2003 pension fund crisis, while ever
stricter solvency requirements are also
increasingly being imposed on insurance
companies in the US, Europe and Asia.
These evolutions in accounting and
prudential regulations have subsequently
led a large number of corporations to close
their defined-benefit pension schemes so
as to reduce the impact of pension liability
risk on their balance sheet and income
statement. Overall, a massive shift from
defined-benefit to defined-contribution
pension schemes is taking place across
the world.2 Consequently, individuals are
becoming increasingly responsible for
making investment decisions related to their
retirement financing needs, investment
decisions that they are not equipped to
deal with given the low levels of financial
literacy within the general population and
the reported inability of financial education
to significantly improve upon the current
situation (Fernandes et al., 2014).3
In such a fast-changing environment and
an increasingly challenging context, the
need for the investment industry to evolve
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The Death of the Policy Portfolio
and the Emergence of LiabilityDriven Investing and Factor
Investing in Institutional
Money Management
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The Death of the Policy Portfolio and the Emergence
of Liability-Driven Investing and Factor Investing in
Institutional Money Management
To get a better sense for how the investment
process critically needs to evolve, I will
first discuss the standard long-term
investment approach widely adopted
in institutional money management
practice. In this traditional approach, asset
allocation practices are firmly grounded
around one overarching foundational
concept, the policy portfolio – a theoretical
reference portfolio allocated among
asset classes according to a mix deemed
to be most appropriate for the investor.
4 - van Binsbergen, J., M.
Brandt and R. Koijen. 2008.
Optimal decentralized
portfolio management.
Journal of Finance 63(4):
1849-1895.
5 - Martellini, L. 2006. The
Theory of Liability Driven
Investments. Life & Pensions
Magazine 2(5): 39-44.
6 - Tobin, J. 1958. Liquidity
Preferences as Behavior
Towards Risk. Review of
Economic Studies 25: 65-86.
The first step of the investment process
therefore consists in grouping individual
securities into somewhat arbitrary asset
classes or sub-classes according to several
dimensions such as equity or debt, and
then country, sector and/or style within
the equity universe, or country, maturity
and credit rating within the bond universe.
Once a centralised decision maker (e.g. a
pension fund chief investment officer)
has decided how much capital should be
allocated to the different asset classes
and sub-classes, one or more internal or
external asset managers are then expected,
in a second step, to decide how to allocate
the funds made available to the individual
securities within the corresponding asset
class (see van Binsbergen et al., 2008, for
a recent analysis of the efficiency loss
involved in this two-step process).4
In a nutshell, the key sources of added
value in this investment process
according to the old paradigm are (1)
the ability to design a meaningful policy
portfolio, and (2) the ability to select
the right managers, who themselves are
expected to demonstrate an ability to
select the right securities (the case of
active managers) or accurately replicate
an arbitrary index chosen as a benchmark
(the case of passive managers). In the
face of the aforementioned profound
8
structural changes, this old paradigm has
progressively been recognised as a purely
functional and obsolete method for
organising the investment process, which
is somewhat orthogonal to the needs of
investors. Due to its sole focus on market
risks (risks embedded within asset class
benchmarks and associated investment
managers), the traditional approach fails
to account for what is the only relevant
risk for institutional and individual
investors, namely the risk of not achieving
their meaningful goals.
The need to move away from the old
paradigm is progressively surfacing on
many apparently distinct dimensions,
which I argue start to form a whole new
coherent investment framework when
carefully examined.
The first driving force behind the paradigm
change that has taken place in institutional
money management over the last 15 years
has been the progressive recognition that
pension fund investments should not be
framed in terms of one all-encompassing
reference policy portfolio, but instead in
terms of two distinct reference portfolios
(Martellini, 2006).5 These two portfolios
are, respectively, a liability-hedging
portfolio (LHP), the sole purpose of which
is to hedge away as effectively as possible
the impact of unexpected changes in risk
factors affecting liability values (most
notably interest rate and inflation risks),
and a performance-seeking portfolio
(PSP), the focus of which is to provide
investors with an efficient harvesting
of risk premia, without any constraints
related to a possible liability mismatch.
This dual portfolio approach is consistent
with the “fund separation theorems”,
which lie at the core of asset pricing theory
since Tobin (1958)6 and which advocate
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The Death of the Policy Portfolio and the Emergence
of Liability-Driven Investing and Factor Investing in
Institutional Money Management
a separate management of performance
and risk control objectives, extended to an
asset-liability management context. More
generally, and regardless of the exact
form of implementation of what is now
known as liability-driven investing (also
known as liability-directed investing or
LDI), this change has led to an increased
focus on liability risk management, which
is precisely a first step towards properly
accounting for an institutional investor's
meaningful objective, and the risk factors
that impact the probability of said
objective being achieved.
7 - Merton, R. C. 1971.
Optimum Consumption
and Portfolio Rules in a
Continuous-Time Model.
Journal of Economic Theory
3(4): 373-413.
8 - Badaoui, S., R. Deguest,
L. Martellini and V. Milhau.
2014. Dynamic LiabilityDriven Investing Strategies:
The Emergence of a New
Investment Paradigm for
Pension Funds? EDHEC-Risk
Publication (February).
9 - Ang, A. 2014. Asset
Management: A Systematic
Approach to Factor Investing.
Oxford University Press.
10 - Martellini, L. and
V. Milhau. 2015. Factor
Investing: A Welfare
Improving New Investment
Paradigm or Yet Another
Marketing Fad? EDHEC-Risk
Publication (July).
11 - Ang, A., W. Goetzmann
and S. Schaefer. 2009.
Evaluation of Active
Management of the
Norwegian Government
Pension Fund Global.
Available at http://www.
regjeringen.no.
12 - Amenc, N., F. Goltz, A.
Lodh and L. Martellini. 2012.
Diversifying the Diversifiers
and Tracking the Tracking
Errors: Outperforming
Cap-Weighted Indices
with Limited Risk of
Underperformance. Journal of
Portfolio Management 38(3):
72-88.
The death of the policy portfolio, the first
pillar of the old investment paradigm,
had actually been announced, or rather
predicted, by Peter Bernstein as early
as 2003 in his “Economics and Portfolio
Strategy” newsletter, independently of
the emergence of an increased focus
on liability risk management. This
early announcement resulted from the
recognition that there is no such thing as
a meaningful policy portfolio; one should
instead think in terms of a meaningful
dynamic policy portfolio strategy. The
claim here is that the need to react to
changes in market conditions as well as
changes in margin for error with respect
to investors' most important goals
invalidates the relevance of any optimal
portfolio that would be held constant
for a sustained period of time (Merton,
1971).7 When transported to an assetliability context, this recognition leads to
the emergence of dynamic, as opposed
to static, liability-driven investing (see
Badaoui et al., 2014, for more details on
the benefits of such strategies and its
adoption by sophisticated institutional
investors).8
In parallel to the emergence of dynamic
liability-driven investing, the second
driving force behind the paradigm
change is the progressive adoption of a
new approach known as factor investing,
which has recently emerged in investment
practice and which recommends that
allocation decisions be expressed in terms
of risk factors, as opposed to standard
asset class decompositions. Once again,
the focus is to move away from a marketcentric perspective towards an investorcentric perspective, which should start
with a thorough analysis and proper
understanding of the risk factors that have
a meaningful influence on the probability
of asset owners achieving their goals.
This evolution has delivered a fatal blow
to the second pillar of the old investment
paradigm, namely the focus on manager
selection. Indeed, while risk factors have
long been used for risk and performance
evaluation of actively managed portfolios,
the growing interest among sophisticated
institutional investors in risk allocation
and factor investing (Ang, 2014; Martellini
and Milhau, 2015)9,10 leads to a disciplined
approach to portfolio management that
is meant to allow investors to harvest
risk premia across and within asset
classes through liquid and cost-efficient
systematic strategies, without having
to invest with active managers (see in
particular the analysis of the Norwegian
Government Pension Fund Global by Ang,
Goetzmann and Schaefer, 2009).11
In this context, the emergence of smart
beta investment solutions is blurring the
traditional clear-cut split between active
and passive equity portfolio management
(Amenc et al., 2012),12 while smart factor
indices, formally defined as efficient and
well-diversified replicating portfolios for
rewarded risk factors, now form a basis
of cost-efficient investment vehicles that
can be used by institutional investors to
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The Death of the Policy Portfolio and the Emergence
of Liability-Driven Investing and Factor Investing in
Institutional Money Management
harvest traditional and alternative risk
premia (Amenc et al., 2014).13
13 - Amenc, N., R. Deguest,
F. Goltz, A. Lodh and L.
Martellini. 2014. Risk
Allocation, Factor Investing
and Smart Beta: Reconciling
Innovations in Equity
Portfolio Construction.
EDHEC-Risk Publication (July).
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The Evolution from Mass
Customisation to Mass Production
and the Emergence of Goal-Based
Investing in Individual Money
Management
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The Evolution from Mass Customisation to Mass
Production and the Emergence of Goal-Based
Investing in Individual Money Management
While developments have started in
institutional money management, I view
as a critically important challenge the need
to transport them to individual money
management, where the massive shift of
retirement risks onto individuals is laying
a great responsibility at the feet of the
investment management industry in terms
of how to provide households with suitable
retirement solutions.
Investment management is actually
justified as an industry only to the extent
that it can demonstrate a capacity of adding
value through the design of meaningful
investment solutions that allow investors
to meet their meaningful goals.
14 - Bodie, Z., R. Fullmer and
J. Treussard. 2010. Unsafe at
Any Speed? The Designed-In
Risks of Target-Date Glide
Paths. Journal of Financial
Planning (March).
15 - Chhabra, A. 2005.
Beyond Markowitz: A
Comprehensive Wealth
Allocation Framework
for Individual Investors.
The Journal of Wealth
Management 7(5): 8-34.
16 - Lopes, L. 1987.
Between Hope and Fear: The
Psychology of Risk. Advances
in Experimental Social
Psychology 20: 255-295.
17 - Deguest, R., L. Martellini,
A. Suri, V. Milhau and H.
Wang. 2015. Introducing a
Comprehensive Investment
Framework for Goals-Based
Wealth Management. EDHECRisk Publication (March).
Unfortunately,
currently
available
investment options hardly provide a
satisfying answer to the retirement
investment challenge, and most individuals
are left with an unsatisfying choice
between, on the one hand, safe strategies
with very limited upside potential, which
will not allow them to generate the kind
of target replacement income they need
in retirement and, on the other hand, risky
strategies offering no security with respect
to minimum levels of replacement income
– see Bodie et al. (2010) for an analysis
of the risks involved in the use of target
date fund investments in a retirement
context.14
This stands in contrast with a well-designed
retirement solution that would allow
individual investors to secure the kind of
replacement income in retirement needed
to meet their essential consumption goals,
while generating a relatively high probability
of them achieving their aspirational
consumption goals, with possible additional
12
goals including healthcare, old age care
and/or bequest goals.
I argue that some dramatic changes with
respect to existing investment practices are
needed to facilitate the development of
such meaningful retirement solutions. Just
as in institutional money management, the
need to design an asset allocation solution
that is a function of the kinds of particular
risks to which the investor is exposed, or to
which the investor needs to be exposed, to
meet liabilities or to fulfil goals, as opposed
to purely focusing on the risks impacting
the market as a whole, makes standard
approaches (which are based on balanced
portfolios invested in a mixture of asset
class portfolios actively and passively
managed against market benchmarks)
mostly inadequate.
This recognition is leading to a new
investment paradigm, which has been
labelled goal-based investing (GBI) in
individual money management (Chhabra,
2005),15 where investors' problems can
be fully characterised in terms of their
lifetime meaningful goals (see Lopes, 1987,
for an analysis of investors' aspirational
goals throughout their life cycle),16 just as
LDI has become the relevant paradigm in
institutional money management, where
investors' problems are broadly summarised
in terms of their liabilities.
In a nutshell, GBI includes two distinct
elements (see Deguest et al., 2015, for
a detailed analysis).17 On the one hand,
it involves disaggregating investor
preferences into a hierarchical list of goals,
with a key distinction between essential
and aspirational goals, and the mapping
of these groups according to hedging
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portfolios that possess corresponding
risk characteristics. On the other hand, it
involves an efficient dynamic allocation
to these dedicated hedging portfolios
and a common PSP. In this sense, the GBI
approach is formally consistent with the
fund separation theorems that serve as
founding pillars for dynamic asset pricing
theory, as was the case for the LDI approach
(see Shefrin and Statman, 2000, and Das et
al., 2010, for an analysis of the relationship
between Modern Portfolio Theory portfolio
optimisation with mental accounts in a
static setting).18,19
18 - Shefrin, H. and M.
Statman. 2000. Behavioral
Portfolio Theory. Journal of
Financial and Quantitative
Analysis 35: 127-151.
19 - Das, S., H. Markowitz, J.
Scheid and M. Statman. 2010.
Portfolio Optimization with
Mental Accounts. Journal of
Financial and Quantitative
Analysis 45(2): 311-334.
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Investing in Individual Money Management
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The Relationship between Dynamic
Asset Pricing Theory and
Goal-Based Investing
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When observed more precisely, the first
output of the new investment framework
consists in designing a goal-hedging
portfolio (GHP) for each essential goal.
The general objective assigned to this
portfolio is to secure the goal with
certainty and to do so at the cheapest
cost. Its exact nature depends on the type
of goal under consideration.
20 - This condition can be
regarded as a necessary
and sufficient condition
for ensuring the protection
of essential goals with
probability 1.
21 - Karatzas, I., J.P.
Lehoczky and S.E. Shreve.
1987. Optimal Portfolio and
Consumption Decisions for
a Small Investor on a Finite
Horizon. SIAM Journal on
Control Optimization 25:
1557-1586.
22 - Cox, J. and C.F. Huang.
1989. Optimal Consumption
and Portfolio Policies
when Asset Prices Follow a
Diffusion Process. Journal of
Economic Theory 49: 33-83.
For a retirement goal, the GHP is typically
a deferred inflation-linked annuity (or
a suitably-defined dynamic replicating
portfolio for a deferred inflation-linked
annuity) that will pay the inflationprotected required level of replacement
income in retirement. In addition to
financing hedging portfolios associated
with all essential goals, the investor also
needs to generate performance so as to
reach aspirational goals with a non-zero
probability. In this context, investors
should allocate some fraction of their
assets to a well-diversified PSP in an
attempt to harvest risk premia on risky
assets across financial markets, as was
also advocated within institutional money
management under the LDI paradigm.
One natural benchmark strategy consists
in securing all essential goals, and
investing the available liquid wealth in
a performance portfolio allowing for the
most efficient harvesting of market risk
premia. This strategy, which is appealing
since it secures essential goals with
probability 1 and generates some upside
potential required for the achievement
of important and aspirational goals, is in
fact a specific case of a wider class of (in
general) dynamic goals-based investing
strategies.
These strategies advocate that the
allocation to the safe (with respect to
investors' goals) rather than the risky
16
portfolio should be taken as some function
of the current wealth level and the
present value of the fraction of essential
goals that is not financed by future
cash inflows, with the key property that
this function (the parameters of which
generally depend on market conditions)
should converge to zero when wealth
converges to levels required for securing
essential goals.20
From a formal standpoint, the problem
can be handled via the so-called convex
duality or martingale approach to dynamic
asset allocation problems (Karatzas,
Lehoczky and Shreve, 1987; Cox and
Huang, 1989),21,22 where one first defines
an optimal state-contingent wealth for
investors given their long-term goals and
constraints, and then obtains the optimal
dynamic asset allocation strategy as the
dynamic replicating portfolio strategy for
the non-linear contingent pay-off.
I emphasise that the framework should
not only be thought of as a financial
engineering device for generating
meaningful investment solutions with
respect to investors' needs. It should
also, and perhaps even more importantly,
encompass a process dedicated to
facilitating a meaningful dialogue with
the investor. In this context, the reporting
dimension of the framework should focus
on updated probabilities of achieving
investors' meaningful goals and associated
expected shortfalls, as opposed to solely
focusing on standard risk and return
indicators, which are mostly irrelevant in
this context.
At the risk of stating the obvious, let me
again state the fact that institutional
and individual investors alike are facing
complex problems, emphasised by the
aforementioned recent changes in the
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Theory and Goal-Based Investing
retirement landscape, for which they
need dedicated investment solutions,
as opposed to off-the-shelf investment
products.
23 - Bodie, Z. and R.
Merton. 1995. A Conceptual
Framework for Analyzing the
Financial Environment (in
The Global Financial System
- A Functional Perspective).
Harvard Business School
Press.
These problems can be broadly summarised
by the need to finance substantial levels
of consumption with relatively limited
dollar budgets (limited contributions from
the beneficiaries and/or their sponsors) as
well as relatively limited (regulatory- or
self-imposed) risk budgets. Against this
backdrop, I would propose the following
definition of investment management as
the art and science of efficiently spending
investors' dollar and risk budgets through
a disciplined use of the three forms of
risk management, namely risk hedging
(for efficient control of the risk factors in
investors' liabilities/goals), diversification
(for efficient harvesting of risk premia)
and insurance (for securing investors’
essential goals while generating attractive
probabilities of attaining their aspirational
goals).
While each of these sources of value added
is already used to some extent in different
contexts, I argue that a comprehensive
integration of all these elements within
a comprehensive disciplined investment
management framework is required for
the design of meaningful investment
solutions (see Bodie and Merton, 1995,
for a discussion of the three forms of risk
management and their relationship to the
functions of the financial system).23
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The True Start of the Industrial
Revolution in Investment
Management
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Mass Customisation versus Mass Production in Investment Management — January 2016
The True Start of the Industrial Revolution
in Investment Management
Mass production (in terms of products)
happened a long time ago within
investment management, through the
introduction of mutual funds and, more
recently, exchange-traded funds. I would
argue that what will trigger the true start
of the industrial revolution is instead mass
customisation (as in customised solutions),
which by definition is a manufacturing and
distribution technique that combines the
flexibility and personalisation of “custommade” solutions with the low unit costs
associated with mass production. The true
challenge is indeed to find a way to provide
a large number of individual investors
with meaningful dedicated investment
solutions.
24 - Merton, R. 2003.
Thoughts on the Future:
Theory and Practice in
Investment Management.
Financial Analysts' Journal
(Jan./Feb.): 17-23.
25 - Duffie, D. 2001. Dynamic
Asset Pricing Theory (Third
Edition). Princeton University
Press, Princeton.
Within Modern Portfolio Theory, mass
customisation is trivialised: if investors’
problems can be fully characterised by a
simple risk-aversion parameter, then the
aforementioned fund separation theorems
state that all investors need to hold a
specific combination of two common
funds – one risky fund used for risk premia
harvesting, and one safe (money market)
fund.
In reality, different investors have different
goals, as discussed above, and the suitable
extension of the fund separation theorems
implies that if the performance-seeking
building block can be the same for all
investors, the safe building block(s), which
are known as goal-hedging portfolio(s)
and are the exact counterparts in
individual money management of LHPs
in institutional money management,
should be (mass) customised. Besides, the
allocation to the safe rather than risky
building blocks should also be engineered
so as to secure investors’ essential goals
(e.g. minimum levels of replacement
income) while generating a relatively high
probability of achieving their aspirational
20
goals (e.g. target levels of replacement
income).
That mass customisation is the key
challenge that our industry is facing
has long been recognised, but it is only
recently that we have developed the
actual capacity to provide such dedicated
investment solutions to individuals. This
point was very explicitly made by Merton
(2003)24: “It is, of course, not new to say
that optimal investment policy should not
be “one size fits all”. In current practice,
however, there is much more uniformity
in advice than is necessary with existing
financial thinking and technology. That is,
investment managers and advisors have a
much richer set of tools available to them
than they traditionally use for clients. (…)
I see this issue as a tough engineering
problem, not one of new science. We know
how to approach it in principle (…) but
actually doing it is the challenge.”
Paraphrasing Robert Merton, I would like
to emphasise that designing meaningful
retirement solutions does not indeed
require a new science.
I have actually argued in this paper that all
the required ingredients are perfectly wellunderstood in the context of dynamic asset
pricing theory (see for example Duffie,
2001),25 namely (1) a safe (goal-hedging)
portfolio that should be truly safe; (2) a
risky (performance-seeking) portfolio
that should be well rewarded; and (3) an
allocation to the risky portfolio that (3.i)
reacts to changes in market conditions and
(3.ii) secures investors’ essential goals while
generating a high probability of reaching
aspirational goals. On the other hand,
scalability constraints required to address
mass customisation do pose a tough
engineering challenge, since it is hardly
feasible to launch a customised dynamic
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Mass Customisation versus Mass Production in Investment Management — January 2016
The True Start of the Industrial Revolution
in Investment Management
allocation strategy for each individual
investor. There are in fact two distinct
dimensions of scalability – scalability with
respect to the cross-sectional dimension
(designing a dynamic strategy that can
approximately accommodate the needs of
different investors entering at the same
point in time) and scalability with respect
to the time-series dimension (designing a
dynamic strategy that can approximately
accommodate the needs of different
investors entering at different points in
time). The good news is that financial
engineering can be used to meet these
challenges – see Martellini and Milhau
(2016) for a detailed analysis.26
26 - Martellini, L., and
V. Milhau. 2016. Mass
Customisation versus Mass
Production in Retirement
Investment Management:
Addressing a “Tough
Engineering Problem”.
EDHEC-Risk Working Paper
(forthcoming).
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The True Start of the Industrial Revolution
in Investment Management
22
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Conclusion
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Conclusion
27 - Technically, this requires
the use of Monte-Carlo
simulations under the
risk-neutral probability
measure.
28 - This rather requires
Monte-Carlo simulations
under the historical
probability measure, with
a change in measure from
historical to risk-neutral
that involves risk premium
estimates.
24
In closing, let me state that the magnitude
of what is happening should not be underestimated. I do believe that our industry is
truly about to experience somewhat of an
industrial revolution within the next 5 to 10
years. We currently are at the confluence of
historically powerful forces. On the one hand,
liquid and transparent access to risk premiaharvesting portfolios is now feasible with
smart factor indices, which are cost-efficient
and scalable alternatives to active managers.
On the other hand, distribution costs are
bound to go down from their stratospheric
levels as the trend towards disintermediation
continues to accelerate, particularly
through the development of FinTech and
robo-advisor initiatives, which are putting the
old business model under strong pressure, and
forcing wealth management firms to entirely
rethink the value that they are bringing to
their clients.
Risk management, defined as the ability
for investors, or asset and wealth managers
acting on their behalf, to efficiently
spend their dollar and risk budgets so as
to enhance the probability of reaching
their meaningful goals, will play a central
role in this industrial revolution that will
eventually lead to scalable, cost-efficient,
investor-centric and welfare-improving
investment solutions. Sophisticated financial
engineering techniques have too often been
used in the past to hide fees and risks within
complex products that were sold to investors
as safe and inexpensive products, and
which were anyway entirely irrelevant with
respect to investors' meaningful goals. It is
about time that we use the same financial
engineering techniques to help investors
address the most important challenges they
face, including the retirement financing
challenge.
The combination of two relatively new
ingredients is required for wealth and
asset management firms to take an active
role in this new investment paradigm.
First, they should internalise the financial
engineering expertise that is most
commonly found in investment banking,
namely the expertise needed to design statecontingent pay-offs and efficient dynamic
replicating portfolios for these pay-offs.27
The convergence between investment
management expertise, where a substantial
amount of accumulated knowledge can
be found about how to efficiently harvest
risk premia, and investment banking
expertise, where a substantial amount of
accumulated knowledge can be found about
how to efficiently structure underlying risk
exposures, is a key central requirement for
this industrial revolution to take place.
Secondly, they should equip themselves with
suitably designed distribution and reporting
platforms and tools that will allow them to
engage ex-ante and ex-post in a meaningful
dialogue with asset owners – a dialogue
centred on the time-varying probabilities
of achieving investors' goals. 28
In the profound soul-searching process
that is currently under way in investment
management, I believe it is important for
all parties involved to maintain a proper
perspective and see what is happening
as what it actually is, namely a unique
opportunity for our industry as a whole
to add value to society. Incidentally, asset
and wealth managers willing and able to
embrace this challenge will be able to grow
a profitable business as they will start to
address the needs of their clients more
suitably.
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References
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References
• Amenc, N., R. Deguest, F. Goltz, A. Lodh and L. Martellini. 2014. Risk Allocation, Factor
Investing and Smart Beta: Reconciling Innovations in Equity Portfolio Construction.
EDHEC-Risk Publication (July).
• Amenc, N., F. Goltz, A. Lodh and L. Martellini. 2012. Diversifying the Diversifiers and
Tracking the Tracking Errors: Outperforming Cap-Weighted Indices with Limited Risk of
Underperformance. Journal of Portfolio Management 38(3): 72-88.
• Ang, A. 2014. Asset Management: A Systematic Approach to Factor Investing. Oxford
University Press.
• Ang, A., W. Goetzmann and S. Schaefer. 2009. Evaluation of Active Management of the
Norwegian Government Pension Fund Global. Available at: http://www.regjeringen.no.
• Badaoui, S., R. Deguest, L. Martellini and V. Milhau. 2014. Dynamic Liability-Driven
Investing Strategies: The Emergence of a New Investment Paradigm for Pension Funds?
EDHEC-Risk Publication (February).
• Bernstein, P. 2003. Are Policy Portfolios Obsolete? Economics and Portfolio Strategy
(March).
• Bodie, Z., R. Fullmer and J. Treussard. 2010. Unsafe at Any Speed? The Designed-In Risks
of Target-Date Glide Paths. Journal of Financial Planning (March).
• Bodie, Z. and R. Merton. 1995. A Conceptual Framework for Analyzing the Financial
Environment (in The Global Financial System - A Functional Perspective). Harvard Business
School Press.
• Chhabra, A. 2005. Beyond Markowitz: A Comprehensive Wealth Allocation Framework
for Individual Investors. The Journal of Wealth Management 7(5): 8-34.
• Cox, J. and C.F. Huang. 1989. Optimal Consumption and Portfolio Policies when Asset
Prices Follow a Diffusion Process. Journal of Economic Theory 49: 33-83.
• Das, S., H. Markowitz, J. Scheid and M. Statman. 2010. Portfolio Optimization with
Mental Accounts. Journal of Financial and Quantitative Analysis 45(2): 311-334.
• Deguest, R., L. Martellini, A. Suri, V. Milhau, and H. Wang. 2015. Introducing a
Comprehensive Investment Framework for Goals-Based Wealth Management. EDHECRisk Publication (March).
• Duffie, D. 2001. Dynamic Asset Pricing Theory (Third Edition). Princeton University
Press, Princeton.
• Fabozzi, F., L. Martellini and J. Mulvey. 2014. Proper Valuation Rules for Pension Liabilities.
Investment and Pension Europe, EDHEC-Risk Institute Research Insights Winter (4-5).
• Fernandes, D., J. G. Lynch Jr and R. G. Netemeyer. 2014. Financial Literacy, Financial
Education and Downstream Financial Behaviors. Management Science 60(8): 1861-1883.
• Karatzas, I., J.P. Lehoczky and S.E. Shreve. 1987. Optimal Portfolio and Consumption
Decisions for a Small Investor on a Finite Horizon. SIAM Journal on Control Optimization
25: 1557-1586.
26
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Mass Customisation versus Mass Production in Investment Management — January 2016
References
• Lopes, L. 1987. Between Hope and Fear: The Psychology of Risk. Advances in Experimental
Social Psychology 20: 255-295.
• Martellini, L., and V. Milhau. 2016. Mass Customisation versus Mass Production in
Retirement Investment Management: Addressing a “Tough Engineering Problem”. EDHECRisk Working Paper (forthcoming).
• Martellini, L. and V. Milhau. 2015. Factor Investing: A Welfare Improving New Investment
Paradigm or Yet Another Marketing Fad? EDHEC-Risk Publication (July).
• Martellini, L. 2006. The Theory of Liability Driven Investments. Life & Pensions Magazine
2(5): 39-44.
• Merton, R. 2003. Thoughts on the Future: Theory and Practice in Investment Management.
Financial Analysts' Journal (Jan/Feb): 17-23.
• Merton, R. C. 1971. Optimum Consumption and Portfolio Rules in a Continuous-Time
Model. Journal of Economic Theory 3(4): 373-413.
• Shefrin, H. and M. Statman. 2000. Behavioral Portfolio Theory. Journal of Financial and
Quantitative Analysis 35: 127-151.
• Tobin, J. 1958. Liquidity Preferences as Behavior Towards Risk. Review of Economic
Studies 25: 65-86.
• Towers Watson. 2014. Retirement in Transition for the Fortune 500: 1998 to 2013. Insider
Report (September). Available at: https://www.towerswatson.com/en/Insights/Newsletters/
Americas/Insider/2014/retirement-in-transition-for-the-fortune-500-1998-to-2013.
• van Binsbergen, J., M. Brandt and R. Koijen. 2008. Optimal Decentralized Portfolio
Management. Journal of Finance 63(4): 1849-1895.
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References
28
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About EDHEC-Risk Institute
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Mass Customisation versus Mass Production in Investment Management — January 2016
About EDHEC-Risk Institute
Founded in 1906, EDHEC is one
of the foremost international
business schools. Accredited by
the three main international
academic organisations,
EQUIS, AACSB, and Association
of MBAs, EDHEC has for a
number of years been pursuing
a strategy of international
excellence that led it to set up
EDHEC-Risk Institute in 2001.
This institute now boasts a
team of close to 50 permanent
professors, engineers and
support staff, as well as 36
research associates from the
financial industry and affiliate
professors.
The Choice of Asset Allocation and
Risk Management and the Need for
Investment Solutions
EDHEC-Risk has structured all of its
research work around asset allocation
and risk management. This strategic
choice is applied to all of the Institute's
research programmes, whether they involve
proposing new methods of strategic
allocation, which integrate the alternative
class; taking extreme risks into account
in portfolio construction; studying the
usefulness of derivatives in implementing
asset-liability management approaches;
or orienting the concept of dynamic
“core-satellite” investment management
in the framework of absolute return or
target-date funds. EDHEC-Risk Institute
has also developed an ambitious portfolio
of research and educational initiatives in
the domain of investment solutions for
institutional and individual investors.
Academic Excellence
and Industry Relevance
In an attempt to ensure that the research
it carries out is truly applicable, EDHEC has
implemented a dual validation system for
the work of EDHEC-Risk. All research work
must be part of a research programme,
the relevance and goals of which have
been validated from both an academic
and a business viewpoint by the Institute's
advisory board. This board is made up of
internationally recognised researchers,
the Institute's business partners, and
representatives of major international
institutional investors. Management of the
research programmes respects a rigorous
validation process, which guarantees the
scientific quality and the operational
usefulness of the programmes.
30
Six research programmes have been
conducted by the centre to date:
• Asset allocation and alternative
diversification
• Performance and risk reporting
• Indices and benchmarking
• Non-financial risks, regulation and
innovations
• Asset allocation and derivative
instruments
• ALM and asset allocation solutions
These programmes receive the support of
a large number of financial companies.
The results of the research programmes
are disseminated through the EDHECRisk locations in Singapore, which was
established at the invitation of the
Monetary Authority of Singapore (MAS);
the City of London in the United Kingdom;
Nice and Paris in France.
EDHEC-Risk has developed a close
partnership with a small number of
sponsors within the framework of
research chairs or major research projects:
• ETF and Passive Investment Strategies,
in partnership with Amundi ETF
• Regulation and Institutional
Investment,
in partnership with AXA Investment
Managers
• Asset-Liability Management and
Institutional Investment Management,
in partnership with BNP Paribas
Investment Partners
• New Frontiers in Risk Assessment and
Performance Reporting,
in partnership with CACEIS
• Exploring the Commodity Futures
Risk Premium: Implications for Asset
Allocation and Regulation,
in partnership with CME Group
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About EDHEC-Risk Institute
• Asset-Liability Management in Private
Wealth Management,
in partnership with Coutts & Co.
• Asset-Liability Management
Techniques for Sovereign Wealth Fund
Management,
in partnership with Deutsche Bank
• The Benefits of Volatility Derivatives
in Equity Portfolio Management,
in partnership with Eurex
• Structured Products and Derivative
Instruments,
sponsored by the French Banking
Federation (FBF)
• Optimising Bond Portfolios,
in partnership with the French Central
Bank (BDF Gestion)
• Risk Allocation Solutions,
in partnership with Lyxor Asset
Management
• Infrastructure Equity Investment
Management and Benchmarking,
in partnership with Meridiam and
Campbell Lutyens
• Risk Allocation Framework for
Goal-Driven Investing Strategies,
in partnership with Merrill Lynch
Wealth Management
• Investment and Governance
Characteristics of Infrastructure Debt
Investments,
in partnership with Natixis
• Advanced Modelling for Alternative
Investments,
in partnership with Société Générale
Prime Services (Newedge)
• Advanced Investment Solutions for
Liability Hedging for Inflation Risk,
in partnership with Ontario Teachers’
Pension Plan
• Active Allocation to Smart Factor
Indices,
in partnership with Rothschild & Cie
• Solvency II,
in partnership with Russell Investments
• Structured Equity Investment
Strategies for Long-Term Asian
Investors,
in partnership with Société Générale
Corporate & Investment Banking
The philosophy of the Institute is to
validate its work by publication in
international academic journals, as well
as to make it available to the sector
through its position papers, published
studies, and global conferences.
To ensure the distribution of its research
to the industry, EDHEC-Risk also provides
professionals with access to its website,
www.edhec-risk.com, which is entirely
devoted to international risk and asset
management research. The website, which
has more than 70,000 regular visitors,
is aimed at professionals who wish to
benefit from EDHEC-Risk’s analysis and
expertise in the area of applied portfolio
management research. Its monthly
newsletter is distributed to more than 1.5
million readers.
EDHEC-Risk Institute:
Key Figures, 2014-2015
Number of permanent staff
48
Number of research associates &
affiliate professors
36
Overall budget
€6,500,000
External financing
€7,025,695
Nbr of conference delegates
1,087
Nbr of participants at research
seminars and executive education
seminars
1,465
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About EDHEC-Risk Institute
Research for Business
The Institute’s activities have also given
rise to executive education and research
service offshoots. EDHEC-Risk's executive
education programmes help investment
professionals to upgrade their skills with
advanced risk and asset management
training across traditional and alternative
classes. In partnership with CFA Institute,
it has developed advanced seminars based
on its research which are available to CFA
charterholders and have been taking
place since 2008 in New York, Singapore
and London.
In 2012, EDHEC-Risk Institute signed
two strategic partnership agreements
with the Operations Research and
Financial Engineering department of
Princeton University to set up a joint
research programme in the area of assetliability management for institutions
and individuals, and with Yale School
of Management to set up joint certified
executive training courses in North
America and Europe in the area of risk
and investment management.
As part of its policy of transferring knowhow to the industry, in 2013 EDHEC-Risk
Institute also set up ERI Scientific Beta.
ERI Scientific Beta is an original initiative
which aims to favour the adoption of the
latest advances in smart beta design and
implementation by the whole investment
industry. Its academic origin provides the
foundation for its strategy: offer, in the
best economic conditions possible, the
smart beta solutions that are most proven
scientifically with full transparency in
both the methods and the associated
risks.
32
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EDHEC-Risk Institute
Publications and Position Papers
(2013-2016)
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EDHEC-Risk Institute Publications
(2013-2016)
2015
• Blanc-Brude, F., M. Hasan and T. Whittaker. Cash Flow Dynamics of Private Infrastructure
Project Debt (November).
• Amenc, N., G. Coqueret, and L. Martellini. Active Allocation to Smart Factor Indices (July).
• Martellini, L., and V. Milhau. Factor Investing: A Welfare Improving New Investment
Paradigm or Yet Another Marketing Fad? (July).
• Goltz, F., and V. Le Sourd. Investor Interest in and Requirements for Smart Beta ETFs
(April).
• Amenc, N., F. Goltz, V. Le Sourd and A. Lodh. Alternative Equity Beta Investing: A
Survey (March).
• Amenc, N., K. Gautam, F. Goltz, N. Gonzalez, and J.P Schade. Accounting for Geographic
Exposure in Performance and Risk Reporting for Equity Portfolios (March).
• Amenc, N., F. Ducoulombier, F. Goltz, V. Le Sourd, A. Lodh and E. Shirbini. The EDHEC
European Survey 2014 (March).
• Deguest, R., L. Martellini, V. Milhau, A. Suri and H. Wang. Introducing a Comprehensive
Risk Allocation Framework for Goals-Based Wealth Management (March).
• Blanc-Brude, F., and M. Hasan. The Valuation of Privately-Held Infrastructure Equity
Investments (January).
2014
• Coqueret, G., R. Deguest, L. Martellini, and V. Milhau. Equity Portfolios with Improved
Liability-Hedging Benefits (December).
• Blanc-Brude, F., and D. Makovsek. How Much Construction Risk do Sponsors take in
Project Finance. (August).
• Loh, L., and S. Stoyanov. The Impact of Risk Controls and Strategy-Specific Risk
Diversification on Extreme Risk (August).
• Blanc-Brude, F., and F. Ducoulombier. Superannuation v2.0 (July).
• Loh, L., and S. Stoyanov. Tail Risk of Smart Beta Portfolios: An Extreme Value Theory
Approach (July).
• Foulquier, P. M. Arouri and A. Le Maistre. P. A Proposal for an Interest Rate Dampener
for Solvency II to Manage Pro-Cyclical Effects and Improve Asset-Liability Management
(June).
• Amenc, N., R. Deguest, F. Goltz, A. Lodh, L. Martellini and E.Schirbini. Risk Allocation,
Factor Investing and Smart Beta: Reconciling Innovations in Equity Portfolio Construction
(June).
• Martellini, L., V. Milhau and A. Tarelli. Towards Conditional Risk Parity — Improving Risk
Budgeting Techniques in Changing Economic Environments (April).
34
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EDHEC-Risk Institute Publications
(2013-2016)
• Amenc, N., and F. Ducoulombier. Index Transparency – A Survey of European Investors
Perceptions, Needs and Expectations (March).
• Ducoulombier, F., F. Goltz, V. Le Sourd, and A. Lodh. The EDHEC European ETF Survey
2013 (March).
• Badaoui, S., Deguest, R., L. Martellini and V. Milhau. Dynamic Liability-Driven Investing
Strategies: The Emergence of a New Investment Paradigm for Pension Funds? (February).
• Deguest, R., and L. Martellini. Improved Risk Reporting with Factor-Based Diversification
Measures (February).
• Loh, L., and S. Stoyanov. Tail Risk of Equity Market Indices: An Extreme Value Theory
Approach (February).
2013
• Lixia, L., and S. Stoyanov. Tail Risk of Asian Markets: An Extreme Value Theory Approach
(August).
• Goltz, F., L. Martellini, and S. Stoyanov. Analysing statistical robustness of crosssectional volatility. (August).
• Lixia, L., L. Martellini, and S. Stoyanov. The local volatility factor for asian stock markets.
(August).
• Martellini, L., and V. Milhau. Analysing and decomposing the sources of added-value
of corporate bonds within institutional investors’ portfolios (August).
• Deguest, R., L. Martellini, and A. Meucci. Risk parity and beyond - From asset allocation
to risk allocation decisions (June).
• Blanc-Brude, F., Cocquemas, F., Georgieva, A. Investment Solutions for East Asia's
Pension Savings - Financing lifecycle deficits today and tomorrow (May)
• Blanc-Brude, F. and O.R.H. Ismail. Who is afraid of construction risk? (March)
• Lixia, L., L. Martellini, and S. Stoyanov. The relevance of country- and sector-specific
model-free volatility indicators (March).
• Calamia, A., L. Deville, and F. Riva. Liquidity in European equity ETFs: What really
matters? (March).
• Deguest, R., L. Martellini, and V. Milhau. The benefits of sovereign, municipal and
corporate inflation-linked bonds in long-term investment decisions (February).
• Deguest, R., L. Martellini, and V. Milhau. Hedging versus insurance: Long-horizon
investing with short-term constraints (February).
• Amenc, N., F. Goltz, N. Gonzalez, N. Shah, E. Shirbini and N. Tessaromatis. The EDHEC
european ETF survey 2012 (February).
• Padmanaban, N., M. Mukai, L . Tang, and V. Le Sourd. Assessing the quality of asian
stock market indices (February).
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EDHEC-Risk Institute Publications
(2013-2016)
• Goltz, F., V. Le Sourd, M. Mukai, and F. Rachidy. Reactions to “A review of corporate
bond indices: Construction principles, return heterogeneity, and fluctuations in risk
exposures” (January).
• Joenväärä, J., and R. Kosowski. An analysis of the convergence between mainstream
and alternative asset management (January).
• Cocquemas, F. Towards better consideration of pension liabilities in European Union
countries (January).
• Blanc-Brude, F. Towards efficient benchmarks for infrastructure equity investments
(January).
36
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EDHEC-Risk Institute Position Papers
(2013-2016)
2014
• Blanc-Brude, F. Benchmarking Long-Term Investment in Infrastructure: Objectives,
Roadmap and Recent Progress (June).
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For more information, please contact:
Carolyn Essid on +33 493 187 824
or by e-mail to: [email protected]
EDHEC-Risk Institute
393 promenade des Anglais
BP 3116 - 06202 Nice Cedex 3
France
Tel: +33 (0)4 93 18 78 24
EDHEC Risk Institute—Europe
10 Fleet Place, Ludgate
London EC4M 7RB
United Kingdom
Tel: +44 207 871 6740
EDHEC Risk Institute—Asia
1 George Street
#07-02
Singapore 049145
Tel: +65 6438 0030
EDHEC Risk Institute—France
16-18 rue du 4 septembre
75002 Paris
France
Tel: +33 (0)1 53 32 76 30
www.edhec-risk.com
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