EDHEC-Risk Concept Industry Analysis Featured Analysis Latest EDHEC-Risk Surveys Features Interviews Indexes and Benchmarking FTSE EDHEC-Risk Efficient Index Series FTSE EDHEC-Risk ERAFP SRI Index EDHEC-Risk Alternative Indexes EDHEC IEIF Quarterly Commercial Property Index (France) Hedge Fund Index Research Equity Index Research Amundi "ETF, Indexing and Smart Beta Investment Strategies" Research Chair Rothschild & Cie "Active Allocation to Smart Factor Indices" Research Chair Index Regulation and Transparency ERI Scientific Beta Performance and Risk Reporting Hedge Fund Performance Performance Measurement for Traditional Investment CACEIS "New Frontiers in Risk Assessment and Performance Reporting" Research Chair Asset Allocation and Alternative Diversification Real Assets Meridiam Infrastructure/Campbell Lutyens "Infrastructure Equity Investment Management and Benchmarking" Research Chair Natixis "Investment and Governance Characteristics of Infrastructure Debt Instruments" Research Chair Société Générale Prime Services (Newedge) "Advanced Modelling for Alternative Investments" Research Chair CME Group "Exploring the Commodity Futures Risk Premium: Implications for Asset Allocation and Regulation" Strategic Research Project Asset Allocation and Derivative Instruments Volatility Research Eurex "The Benefits of Volatility Derivatives in Equity Portfolio Management" Strategic Research Project SGCIB "Structured Investment Strategies" Research ALM and Asset Allocation Solutions ALM and Private Wealth Management AXA Investment Managers "Regulation and Institutional Investment" Research Chair BNP Paribas Investment Partners "ALM and Institutional Investment Management" Research Chair Deutsche Bank "Asset-Liability Management Techniques for Sovereign Wealth Fund Management" Research Chair Lyxor "Risk Allocation Solutions" Research Chair Merrill Lynch Wealth Management "Risk Allocation Framework for Goal-Driven Investing Strategies" Research Chair Ontario Teachers' Pension Plan "Advanced Investment Solutions for Liability Hedging for Inflation Risk" Research Chair Non-Financial Risks, Regulation and Innovations Risk and Regulation in the European Fund Management Industry Index Regulation and Transparency Best Execution: MiFID and TCA Mitigating Hedge Funds Operational Risks FBF "Innovations and Regulations in Investment Banking" Research Chair EDHEC-Risk Publications All EDHEC-Risk Publications EDHEC-Risk Position Papers IPE EDHEC-Risk Institute Research Insights AsianInvestor EDHEC-Risk Institute Research Insights P&I EDHEC-Risk Institute Research for Institutional Money Management Books EDHEC-Risk Newsletter Events Events organised by EDHEC-Risk Institute EDHEC-Risk Smart Beta Day Amsterdam 2017, Amsterdam, 21 November, 2017 EDHEC-Risk Smart Beta Day North America 2017, New York, 6 December, 2017 Events involving EDHEC-Risk Institute's participation EDHEC-Risk Institute Presentation Research Programmes Research Chairs and Strategic and Private Research Projects Partnership International Advisory Board Team EDHEC-Risk News EDHEC-Risk Newsletter EDHEC-Risk Press Releases EDHEC-Risk in the Press Careers EDHEC Risk Institute-Asia EDHEC Business School EDHEC-Risk Executive Education Yale School of Management - EDHEC-Risk Institute Certificate in Risk and Investment Management Yale SOM-EDHEC-Risk Harvesting Risk Premia in Equity and Bonds Markets Seminar, 11-13 July, 2017, New Haven Investment Management Seminars Contact EDHEC-Risk Executive Education Contact Us ERI Scientific Beta EDHEC PhD in Finance
The Rise of the Robo-Advisors: The Start of a New Industrial Revolution in Wealth Management? - June 23, 2016

The Rise of the Robo-Advisors

Lionel Martellini, Professor of Finance, EDHEC Business School, Director, EDHEC Risk Institute

At the risk of stating the obvious, let us recognise that individual investors, just like institutional investors, are facing complex problems for which they need dedicated investment solutions, as opposed to off-the-shelf investment products. If mass production (as in product) happened a long time ago in investment management, with the introduction of commingled mutual funds, the missing piece of the puzzle is now mass customisation (as in customised solutions).


Lionel Martellini

Mass-customisation and investment solutions: A new industrial revolution in wealth management

At the risk of stating the obvious, let us recognise that individual investors, just like institutional investors, are facing complex problems for which they need dedicated investment solutions, as opposed to off-the-shelf investment products. If mass production (as in product) happened a long time ago in investment management, with the introduction of commingled mutual funds, the missing piece of the puzzle is now mass customisation (as in customised solutions). Unlike mass production, where products are essentially the same for all customers, mass customisation attempts to fill unique customer needs by building customised products off a standardised product platform. Hence, mass customisation is by definition a distribution and manufacturing technique that combines the flexibility and personalisation of “custom-made” products with the low unit costs associated with mass production.

For the wealth management industry, which typically provides individual customers with a basic menu of funds or mandates as a function of a crude risk profiling classification, the challenge is therefore to start offering clients solutions based on their specific needs and goals. Typical examples of relevant goals for most individuals and households are wealth accumulation, financing education for children, financing the acquisition of a home, and/or financing consumption in retirement.

We have reasons to believe that the wealth management industry is about to experience a new industrial revolution. We currently are indeed at the confluence of historically powerful forces. On the one hand, liquid and transparent access to risk premia harvesting 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 expected to go down from their stratospheric levels as the trend towards disintermediation is accelerating through the development of 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. In this context, and since cost-efficient meaningful building blocks have been made available at exactly the same time as digitalisation has started to offer a more relevant goal-based dialogue with investors, one might wonder whether the rise of robo-advisors is precisely the ultimate driving force required to bring about the long-awaited and much-needed access to cost efficient dedicated investment solutions for millions of individuals around the world.

Investment solutions: The implementation of well-understood goal-based investing principles

Individual investors’ investment problems can be broadly summarised as a combination of various wealth and/or consumption goals, subject to a set of dollar budgets, defined in terms of initial wealth and future income, as well as risk budgets such as maximum drawdown limits for example. It is important to note that the success or failure in satisfying these goals subject to dollar and risk budgets does not critically depend upon the stand-alone performance of a particular fund nor that of a given asset class. It depends instead upon how well the performance on the investor's portfolio dynamically interacts with the risk factors impacting the present value of the investor's goals. In this context, it becomes clear that the key challenge for financial advisors is to implement dedicated investment solutions aiming to generate the highest possible probability of achieving investors’ goals, and a reasonably low expected shortfall in case adverse market conditions make the achievement of said goals unfeasible.

In other words, what will prove to be the decisive factor is the ability to design an asset allocation solution that is a function of the kinds of particular risks to which the investor is exposed, or needs to be exposed to fulfil goals, as opposed to purely focusing on the risks impacting the market as a whole. These simple insights have far reaching implications, including on regulatory requirements such as the "prudent man rule", which is the requirement that investment managers or any fiduciary agents must only invest funds entrusted to them with prudence. This prudent approach might actually become counter-productive if it is cast in an isolated context or, in other words, if cast with a sole focus on market risks without a proper integration of the investor's goals. For example, a seemingly safe short-term investment strategy such as the roll-over of money market debt can prove to be very risky from the perspective of meeting long-term consumption needs.

From an academic standpoint, the principles at work behind the construction of customised and optimal investment proposition for each investor based on their individual objectives and constraints are well-understood. Standard portfolio construction methodologies, based on the standard efficient frontier paradigm introduced by Markowitz (1952)1, have a sole focus on market risks and are therefore not suitable for the proper integration of individual objectives. 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 needs to be exposed to fulfil goals, as opposed to purely focusing on the risks impacting the market as a whole, indeed makes the use of modern portfolio theory or standard portfolio optimisation techniques mostly inadequate.

The need for financial advisors to focus on the proper management of personal and aspirational risks in addition to the management of markets risk was perhaps first emphasised in the goal-based wealth allocation framework proposed in Chhabra (2005).1 In a nutshell, goal-based investing (GBI or liability-driven investing) is an investment process which involves the disaggregation of investor preferences into a hierarchical list of goals, with a key distinction between essential goals and aspirational goals (Chhabra, 2005)2, and the mapping of these groups to hedging portfolios possessing corresponding risk characteristics (see Shefrin and Statman (2000)3 and Das et al. (2010)4 for a static analysis of portfolio optimisation with distinct goals, also known as mental accounts, and Deguest et al. (2015)5 for an extension to the dynamic settings).

Investment solutions: Three main sources of added-value

Broadly speaking, the focus of a meaningful GBI solution is to initiate an investor-centric approach to the investment management process which should lead to maximising the probability of investors reaching their aspirational goals, while securing their essential goals. There are three basic requirements for an investment solution to be meaningful for a given investor in the sense of generating a high probability of the investor reaching his/her goals. Meaningful investment solutions should rely on (1) one or several “good” dedicated safe building blocks (meaning truly safe dedicated goal-hedging portfolios), (2) a “good” risky building block (meaning a well-diversified and therefore well-rewarded performance-seeking portfolio), and (3) a “good” allocation to the two building blocks (meaning an allocation that reacts to unexpected changes in objective market conditions as well as subjective dollar and risk budgets).

While each of these sources of value added is already used to some extent in different contexts, 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)6 for a discussion of the three forms of risk management and their relationship to the functions of the financial system).

(1) The first output of the 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 the highest probability and at the lowest cost. Its exact nature depends on the type of goal under consideration. In the example of a retirement goal, the GHP is typically a deferred inflation-linked annuity (or a suitably-defined dynamic replicating portfolio) that will pay the inflation-protected required level of replacement income in retirement.

(2) 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 performance-seeking portfolio in an attempt to harvest risk premia on risky assets across financial markets. A new approach known as factor investing has recently emerged in investment practice, and it recommends that allocation decisions be expressed in terms of risk factors, as opposed to standard asset class decompositions. This evolution has brought 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 amongst sophisticated institutional investors in risk allocation and factor investing (Ang, 20147 and Martellini and Milhau, 20158) 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 Ang, Goetzmann and Schaefer (2009)9 for an analysis of the Norwegian Government Pension Fund Global). In this context, the emergence of smart beta investment solutions is blurring the traditional clear-cut split between active versus passive equity portfolio management (see for example Amenc et al. (2012)10) and smart factor indices, 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 harvest traditional and alternative risk premia (Amenc et al., 2014).11

(3) One natural allocation 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) versus risky 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, whose parameters in general depend on market conditions, should converge to zero when wealth converges to levels required for securing either essential goals or aspirational goals.13

Mass customisation: An outstanding engineering challenge

While goal-based investing principles are well-understood from a theoretical standpoint, the real challenge is to address the scalability constraints required to allow for mass customisation of investment solutions. This poses a tough engineering challenge, since it is hardly feasible to launch a customised dynamic allocation strategy for each individual investor. Investors come in many different shapes and forms, and mass-customisation is needed with respect to many dimensions, including notably:

1. Age and time-horizon;
2. Contribution levels;
3. Date of entry;
4. Essential and aspirational goal levels.

Dimension (1) is not truly problematic, as it can typically be handled through a reasonable number of clusters for individuals. In a retirement context for example, one may thus provide a limited number of strategies/funds for a parsimonious number of different retirement dates (and retirement ages). On the other hand, dimensions (2), (3) and (4) are strong mass customisation constraints.
In a recent paper (Martellini and Milhau, 2016)14 – to which we refer for more details – we analyse this problem and propose a number of practical solutions to the mass-customisation challenge. Regarding dimension (2), we show in particular that the multiplicity of initial (C0) and future (C) contribution levels can be addressed via the introduction of 2 elementary dynamic GBI strategies, corresponding respectively to (C0=$1, C=$0) and (C0=$1, C=$1), which can be mixed so as to achieve any given contribution profile. In the same spirit, dimension (3) can be addressed with the use of floor ratchet and reset mechanisms so as to neutralise the impact of the entry point. Alternatively, one may envision re-launching new strategies at some defined dates (e.g. annually). Finally, dimension (4) can be handled through a focus on dollars invested and not investors. In this context, investors are simply invited to switch to the safe goal-hedging portfolio if and when the target replacement income level is reached, while the underlying strategy maintains a healthy level of risk-taking, thus allowing investors who have not yet reached their aspirational goals to increase the probability of these goals eventually being reached.

Since this latter practice requires a dialogue and reporting focusing on the updated probability of reaching target levels of replacement income, it seems to be ideally adapted to the robo-advisor context.

Mass customisation: Can robo-advisors deliver dedicated investment solutions?

Our ambition is not to provide an exhaustive analysis of the robo-advisor landscape15, but instead to provide a broad overview regarding whether or not existing robo-advisors can be used to meet the challenges of a cost-efficient access to meaningful mass-customised investment solutions for individuals.

Broadly speaking, there are four ingredients needed for the industrial revolution to take place in wealth management. What investors ultimately need is (i) a cost efficient access to (ii) a mass-customised solution based upon (iii) smart factor index building blocks and (iv) a suitable goal-based dialogue.

In a nutshell, it seems that existing robo-advisors do generate interesting benefits regarding dimension (i) because distribution costs are substantially lower due to the digital aspect of the relation. It should however be noted that different levels of service are being provided by different types of robo-advisors. In some cases, the asset allocation recommendation is followed by the creation of the planned portfolio, while in other cases a possible asset allocation is suggested but not executed. In the first case, order executions are typically automatically managed by the platform or a related physical advisor, while in the second the individual is in charge of managing his/her orders either on the platform or externally. This aspect determines the real difference between a mere advisor and a platform which can enable the pursuit of various investment objectives.

On the other hand, it should be recognised that dimension (ii) is mostly missing. Much of current practice in the area of robo-advisors and online investment tools is indeed based on standard portfolio construction techniques and abstracts away from any concept of priority ranking among goals, which is of relevance in most practical applications since investors typically have an explicit, or sometimes implicit, hierarchal ranking of their goals. The presence of priority ranking amongst the goals implies that holding a series of separately optimised portfolios cannot be the correct answer to the challenge raised by the presence of a number of simultaneous goals. Another key limitation of some of the current services is that there is little, if any, connection between the dialogue interface, which provides an estimate of the probability of reaching goals and associated expected shortfall, and the underlying optimisation process, which is somewhat unrelated to the specific nature of the goals at hand. In other words, dimension (iv) is currently well-addressed by some existing robo-advisors, and goal-based reporting is already implemented, but true goal-based investing itself is not yet in place. Finally, it seems that ingredient (iii) is entirely missing, since the building blocks used by existing robo-advisors are typically funds or ETFs based on standard cap-weighted indices, which do not allow for an efficient harvesting of risk premia across and within asset classes.

In conclusion, it seems that we are about halfway towards the emergence of truly meaningful cost-efficient mass-customised forms of investment solutions for individuals. Existing robo-advisors already allow for enhanced cost efficiency and a more meaningful goal-base dialogue, which are massive steps forward. A couple of substantial steps are still needed, however, before we can claim that the industrial revolution in wealth management has finally arrived, since it is still impossible to gain an access to smart goal-based strategies based on smart building blocks. We should perhaps remain confident that this will eventually happen, probably sooner than later, since all of the required ingredients are now readily available. The wait has already been so long!



Footnotes

  1. Markowitz, H. 1952. Portfolio Selection. Journal of Finance 7(1): 77-91.

  2. Chhabra, A. 2005. Beyond Markowitz: A Comprehensive Wealth Allocation Framework for Individual Investors. The Journal of Wealth Management 7(5): 8-34.

  3. Chhabra, A. 2005. Beyond Markowitz: A Comprehensive Wealth Allocation Framework for Individual Investors. The Journal of Wealth Management 7(5): 8-34.

  4. Shefrin, H. and M. Statman. 2000. Behavioral Portfolio Theory. Journal of Financial and Quantitative Analysis 35: 127-151.

  5. 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.

  6. Deguest, R., L. Martellini, A. Suri, V. Milhau and H. Wang. 2015. Introducing a Comprehensive Investment Framework for Goals-Based Wealth Management. EDHEC-Risk Publication (March).

  7. Merton, R. and Z. Bodie. 1995. A Conceptual Framework for Analyzing the Financial Environment, in The Global Financial System - A Functional Perspective. Harvard Business School Press.

  8. Ang, A. 2014. Asset Management: A Systematic Approach to Factor Investing. Oxford University Press.

  9. Martellini, L. and V. Milhau. 2015. Factor Investing: A Welfare Improving New Investment Paradigm or Yet Another Marketing Fad? EDHEC-Risk Publication (July).

  10. 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.

  11. Amenc, A., 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.

  12. Amenc, A., 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 Institute Publication (July).

  13. This can be regarded as a necessary and sufficient condition for ensuring the protection of essential goals with probability 1.

  14. 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).

  15. We refer the reader to the following three sources for useful market analysis:
    - Epperson, T., B. Hedges, U. Singh and M. Gabel. 2015. Hype vs. Reality: The Coming Waves of “Robo” Adoption. Insights from the A.T.Kearney 2015 Robo-Advisory Services Study (June).
    - FINRA. 2016. Report on Digital Investment Advice (March)
    - Accenture. 2015. The Rise of Robo-Advice - Changing the Concept of Wealth Management.