Single Cycle Degree programme
in xxxxxxxxxx “ordinamento” Final ThesisTitle
Subtitle (optional)
SupervisorCh. Prof. Name Surname Assistant supervisor Ch. Prof. Name Surname Graduand
Name Surname
Matriculation Number xxxxxxxxx Academic Year
200x / 200x
Master’s Degree programme
in Economics and Finance
(D.M. 270/2004)
Final Thesis
The Evolution of Wealth Management:
Transformation and Innovation of Robo Advisory
Supervisor
Ch. Prof. Monica Billio
Assistant supervisor
Ch. Prof. Federico Etro
Graduand
Luca Reddavide
Matriculation Number 860916
Academic Year
“To my family, my bedrock.”
Abstract
The main ambition of this work is to present and outline in the most effective way possible the ongoing transformation of wealth management industry. Primary empha-sis is placed on the digitalization that is hitting the sector. Such evolution is driven mostly by the automatization of investing processes based on algorithmic solutions known as robo advisors. In this dissertation first chapters focus on traditional wealth services whereas the central section reckons with robo advisory systems, inquiring how do they work and why they may be considered a disruptive innovation. A com-parative analysis is left as conclusion; the basic idea is to investigate whether passive strategies, carried out with the assistance of artificial intelligence solutions, are able to create value to common investors, related to the service offered by professional human advisors. Similarly, this thesis strives to investigate the potential effects of massive automatization on the markets and ultimately define, if possible, the optimal structure for a robo advisor.
Contents
Introduction 1
1 Wealth Management Overview and Asset Allocation Foundation 3
1.1 Wealth Management . . . 3
1.2 Asset Allocation . . . 7
2 What is a Robo Advisor? 13 3 Robo Advisory Sector 23 3.1 Main Players . . . 27
4 Advantages and Disadvantages 37 5 Future Developments 45 6 Empirical Study 49 6.1 Mean-Variance Portfolio . . . 50
6.2 Risk Parity Portfolio . . . 62
Conclusions 70 A Appendix 71 A.1 Dataset . . . 71
A.2 Mean-Variance Portfolio . . . 76
A.3 Risk Parity Portfolio . . . 79
A.4 MATLAB Code . . . 82
A.4.2 risk parity.m . . . 86 A.4.3 marginalRiskContributions.m . . . 91 A.4.4 riskCostFunction.m . . . 92
List of Figures
1.1 Example of asset allocation models. . . 9
2.1 Generational divide in world population in 2015 and 2025. . . 18
2.2 Growth of private wealth as global AUM and robo AUM. . . 20
3.1 Asset under management for selected robo advisors in 2017. . . 33
6.1 Efficient frontier, optimal portfolio and individual components. . . 54
6.2 Absolute portfolio performance from 01-04-2010 to 12-29-2017. . . 55
6.3 Comparison of cumulative returns between portfolio, SPY and AGG. 57 6.4 Histogram of portfolio returns with fitted normal distribution. . . 59
6.5 Raw data of algorithmic portfolio returns. . . 60
6.6 Portfolio 30-days moving variance compared to SPY and AGG. . . . 60
6.7 Portfolio 6-months rolling Sharpe ratio compared to SPY and AGG. . 61
6.8 Portfolio weights equalizing risk contribution of components. . . 65
6.9 Risk contribution of each component. . . 65
6.10 Evolutionary trajectories of marginal risk contributions. . . 67
6.11 Evolutionary trajectories of marginal risk contributions after rebalancing. 67 A.1 Optimal weights of ETF portfolio. . . 76
A.2 Histogram of benchmarks returns with fitted normal distribution. . . 76
A.3 30-days moving variance of daily portfolio returns. . . 77
A.4 Box-plots of returns for each selected year. . . 77
A.5 Q-Q plot of returns raw data. . . 78
A.6 Rebalanced portfolio weights equalizing risk contribution of components. 79 A.7 Risk contribution of each component in rebalanced portfolio . . . 79
A.8 Total risk of portfolio over time. . . 80 A.9 Total risk of rebalanced portfolio over time. . . 80 A.10 Additional portfolio analysis provided by Quantopian platform. . . . 81
List of Tables
1.1 Net worth classification of clients. . . 6
1.2 Performance attribution matrix of a managed portfolio. . . 12
3.1 A selection of robo advisors divided by region. . . 30
6.1 Best risk-return ETFs per asset class. . . 51
6.2 Expense ratio for selected ETFs. . . 53
6.3 Optimal portfolio performance statistics. . . 56
6.4 Principal moments of portfolio returns distribution. . . 58
A.1 Covariance matrix of selected ETFs. . . 71
A.2 Correlation matrix of selected ETFs. . . 72
A.3 Investable universe of ETFs − I . . . 73
A.4 Investable universe of ETFs − II . . . 74
Introduction
Technology is a game changer in any sector where exists a chance to employ it, and this means almost everywhere. We may already have an idea of the impacts that tech innovations have on everyday life. Past years provide a clear example: from the inception of computer era to the spreading of internet and advent of smartphones, just to mention some. The wave of technological improvement is far from losing its energy, today more than ever research is heavily focused on robotics, artificial intelligence and machine learning solutions. Doubtless it will massively affect everyone’s life in the next years and probably sooner than many think. Besides others, financial sector as a whole is extremely exposed to this technological urge forcing all parties − say institutions, clients, investors and regulators − to look around and brace themselves in order to face the unknown that is already occurring.
Wealth management field in particular is experiencing an huge transformation of operations and processes, more and more digitilized. Such innovation is forced partly by the changing needs of an increasingly demanding clientele, in part by a necessity of the sector itself to evolve, having been static for too long. Not understanding or, even worse, neglecting the ongoing metamorphosis would likely endanger the same survival of many players.
Chapter 1
Wealth Management Overview
and Asset Allocation Foundation
1.1
Wealth Management
Anytime there is the chance to discuss about wealth management, people asso-ciate this activity to the administration process involving the finances of some Saudi sheikh. Despite being true to some extent, this is just a partial interpretation of the wealth manager job. For years indeed, the only individuals that had access to financial advice products were the components of well-off families or remarkably lucky inheritors. Presumably, the idea that wealth management services are a prerogative of moneyed folks arises here. The billionaire-assistance ground, so beloved to public imagination, is nowadays matter of the private banking sector or ad-hoc independent family office units. In the first case the service-provider is a banking institution focused in furnishing premium-quality products to existing wealthy clients. On the other hand, family offices offer clients the expertize of a range of skilled professionals on a variety of different subjects. As the name suggests, the family is the ultimate recipient of the caring, implying a complex and structure advice. The concept of managing does not include solely the process of growing and defending financial resources, rather comprehends a whole constellation of interests that may also bind
human, social, intellectual, artistic, and philanthropic dimensions, depending first and foremost on client’s goals1. As a matter of fact, when dealing with a group of
people, being clients rarely a single individual, many needs and ambitions coming from several generations have to be minded and blended. All things considered, whenever the delivered service stops at the oversight of investments, from the early advice up to asset class adjustments and taxation planning, it should come in under the wealth management sphere of competence2.
If on one hand an unambiguous formulation of wealth management does not exist, on the other it is possible to highlight the categorization of the activity. In primis it is possible to differentiate managing firms depending on the type of service offered: some, instead of directly sell and administrate the product recommended and thus taking firsthand assets under their own management, may prefer to focus on the mere advisory activity. Similarly, the degree of interaction with clients might differ, whether the advising firm relies on deep and exclusive client’s relationships or instead addresses an extensive set of solutions to a larger consumer base.
Besides the structure chosen, any wealth management company is expected to encompass an array of functions in order to provide a valuable and proficient advice3.
This include, before anything else, a wealth development plan, jointly devised by advisor and client, that considers the objectives sought by the investor and selects the most suitable opportunities to reach the desired target. This step comprises more than an asset combination dilemma; without taking anything away from the election of the optimal asset class partitioning, it should be noticed how crucial the goal visualisation procedure is. In this regard, the advisory entity should act as an educator towards the advice-seeker, assisting in the definition of client’s real needs. In simple words, besides suggesting how the investment should be enforced, a worthwhile advice will also spotlight why the client is actually saving (e.g. for retirement, college tuition, buy a new car, . . . ). These arguments are the cornerstone
1Brunel, 2015. 2Forbes, 2013.
to the goal-based investment theory4.
Providing a wealth protection mechanism corresponds to the second duty accred-ited to the wealth management realm. This means not only applying risk management techniques that of course are required in capital preservation, it entails the shelter from unexpected adverse events too (e.g infirmity). Aside from insurance contracts and portfolio optimization techniques aimed to mitigate idiosyncratic variations, the complexity to adhere to a ever-increasing legislation poses an insidious challenge that must be taken into account. Too often legal issues threat the integrity of patrimonies, the wealth management sector in this sense is asked to provide guidance and safety over their beloved paying clients.
A standard approach further remarks the urgency of periodic portfolio rebalancing as a means of keep client’s finances in track with the goals recognized in the previous phase. Due to market turbulence, the weights of overvalued positions make the port-folio divert from the initial equilibrium, to the detriment of undervalued investments. This would in all likelihood cause a shift in portfolio risk and compromise its own stability. In order to comply with regulators requirements and investor’s preferences, portfolio manager must restore the original balance selling valued stocks and buying unappreciated assets or in technical terms operate a dynamic asset allocation. From a different perspective this behaviour is consistent with the strategy of taking-profit in inflated positions, while increasing the exposure in the underperforming ones. If the lying assumptions are correct, the underestimated stocks will likely grow in value in future times, the portfolio will then benefit from the raised outstanding investment.
Third major commitment for a wealth manager is the administration of tax-related matters. For a considerable time in the past, the taxation has been a minor concern to professionals, until it became clear that a neglectful or inefficient gestio of the issue would widely impact the final result. A practical example is given by the capital
agement in succession or donation circumstances. In the event of asset transmission by reason of inheritance, the tax rate applied is extremely vary: besides a number of states in which such type of taxation does not exist, there are many countries where the levy is significant: starting from a 4% in Italy, there is an average of 40% in US and UK, up to 55% in Japan5. It is evident how ignoring fiscal issues would imply the
erosion of most of the capital. Thankfully, wealth managers are here to minimize such risk and advice clients on the best way to, again, protect their wealth. Speaking of wealth administration, it is imperative to bear in mind how clients are utterly various one from another, pursuing of course different objectives that arise from even more varied needs. On this premise, it appears reasonable to classify the clientele relying, in the first place, on the availability of assets for each individual. The common practice in the wealth management sector requires to differentiate clients on a net worth basis6:
Clientele Investible Assets
Mass Market <$250,000
Mass Affluent $250,000 - $650,000
Emerging Wealthy $650,000 - $1.5 millions
High Net Worth $1.5 millions - $10 millions
Ultra High Net Worth >$10 millions
Table 1.1: Net worth classification of clients.
Historically speaking, the Ultra High Net Worth (UHNW) and High Net Worth (HNW) individuals have been the recipients of conventional advisory; this is because of two main reasons. Firstly, as the wealth increases the complexity of its administration increments as well, making compulsory the support of a professional; secondable, since the cost structure is usually linked with the assets under management, they represent the most profitable segment for advisory firms.
Only in the 70s with the advent of discount brokers and especially with the first 5OECD, 2015
online trading platforms in the 90s, the advice sector opened up to the lower tiers. In recent years, the oncoming wave of digitalization is bringing fresh air to the business, reforming once more the accessibility to financial expertize. Indeed, through robo advisory systems, they expect to shrink service fees and in the meantime enhance the efficiency, leaning on a systematic unbiased advice. Interesting to notice how the business evolution results in a decreasing costs structure proportional to a lower and lower degree of human interaction.
A technical report from JP Morgan7 places emphasis on the service provider role
of advisory firms and on how they must incorporate clients preferences in t heir strategies, as well as defend themselves from newcomer competitors. In particular, the paper addresses special care in process clients behaviour due to trust issues dragged from the last global crisis. The most emblematic case refers to the trend in real estate and private equity investments where is always more common for savers to bypass the wealth management intermediation. In spite of that, JP Morgan provides a positive outlook for the business sector estimating a growth of earnings around 6% - 7% annually.
1.2
Asset Allocation
From a technical point of view, the creation of client’s investment portfolio represents a major concern, as well as the cornerstone of the industry value creation. The traditional approach formalized by Brinson, Hood, and Beebower, 1995 involves an investment allocation policy based on predefined asset classes and associated weights. Any advisor company has relative discretion in selecting the appropriate set of investments so to maximizes customer’s benefit, nonetheless the standard procedure identifies the follow asset classes as starting point:
• Bonds: differentiated in corporate or government, long or short term, along 7J.P. Morgan, 2014.
with several other attributes (e.g. creditworthiness);
• Stocks: choosing among blue chips and penny stocks, value or growth, developed or emerging markets, etc. . . ;
• Investment vehicles: mutual funds, exchange traded funds, etc. . . ; • Alternative investments: real estate, commodities, private equity, etc. . . ; • Foreign currency: both for speculative or hedging strategies;
The real advising ability comes into play in delineating the structure of the port-folio, defined as fraction of wealth assigned to each class reported above. Contingent on weights that have been attributed, each portfolio might appear more aggressive or conservative in accordance to the category of assets preferred by the manager; a predilection for bonds over stocks, for instance, would plainly lead to a cautious investment. Conventionally the literature distinguishes a sequence of allocation models in conformity to a risk/time horizon spectrum8; in particular here we address
to:
• Capital preservation model: perhaps the most conservative strategy, it aims to secure the financial resources to the immediate future; in pursuing such objective, the allocation tends to prefer short term instruments with relative low risk just as treasury notes or commercial papers.
• Income model: provide a steady cash flow over time is the main goal of this allocation; being characterized by a low exposure to volatility, it emerges as a suitable and sound choice for long term savers (indeed appropriate for retirement plans). Its composition tends to reward, over others, value stocks that pay regular dividend, fixed income sources as government bonds and real estate investments.
• Growth model: preferring a greater percentage of stocks and similars, is adequate for clients seeking a bold investment strategy; looking for capital 8Singh and Kaur, 2017.
growth opportunities in the market, it overweights small and medium cap shares for a medium/short term return.
• Balanced model: intuitively, it incorporates elements of both income and growth models, ensuring a flexible approach to the investment process. A shift towards managed instruments is often observable in wealth partitioning.
10%
82% 8%
(a) Capital preservation model
4% 62% 31% 3% (b) Income model 1% 19% 68% 12% (c) Growth model 4% 40% 49% 8%
Cash Bonds Stock Alternatives
(d) Balanced model
4%
40%
49% 8%
Cash Bonds Stocks Alternatives
Figure 1.1: Example of asset allocation models.
The model choice should go hand in hand with the leading ambitions of the wealth owner. Many factors can provide guidance to the most desirable asset allocation; the amount of risk an individual is willing to bear is one of them and depends itself on many other elements. The stage of life, the net worth and the investment objective together contribute to circumscribe the problem. Saver’s age is of particular interest, on average, in fact youngers dispose of a riskier aptitude compared to elders. That is due to the greater amount of time available and absence of financial dependents;
conversely they have access to limited income streams though. On the contrary older people, being closer to the wealth decumulation phase, have to manage more carefully their resources favouring the least risky options (e.g. saving for retirement). Analogous reasoning can be made when considering the disposal of wealth: rich individuals have the capability of incur in more liabilities and bear greater risks than less fortunate ones. In any case, a common denominator refers to the time frame wherein the investment is realized. In this sense a long term horizon allows for a wider range of investing products and ensures meanwhile higher returns coming from risky portfolios which exposure is reduced over time. From a 2015 survey of 300 registered advisors, emerged the average client portfolio is composed by 53% of equities and 25% of bonds, whereas cash and international assets constitute both the 9% of overall portfolio. The remaining 4% of capital is usually allocated to alternative investments9.
The investment process so far introduced, that relies solely on ex-ante determined allocation rules, is to be considered a passive strategy. This view is in conformity with the performance attribution theory developed by Brinson et al., 1995. Under this perspective in fact the managed portfolio should as often as possible match with construction criterions and thus does not allow for deviations, whether they are coming from managers ability or superior information. The concept of exchange-traded funds can be easily adapted to the matter; ETFs are generally built with the aim of replicating a specific index, that is anything other than a portfolio based on an allocation policy in which its weights reflect the theoretical weights of the cloned benchmark. The fairness of the scheme is assured by a periodic rebalancing procedure.
The contribution to the total portfolio return made by any single asset class can be summarized by the relation:
Rp =
X
i
wpi× rpi
Whenever this formula is not respected, the portfolio strategy supposes an active allocation of wealth. The active management can take place operating on two guide-lines. Performing a tactic allocation is the first possibility; this plan of action leaves space to wealth manager in exploiting market inefficiencies through a discretional weighting of asset classes that may differ, temporarily, from the original policy. One tactic, called market timing, permits to anticipate market evolution as an opportunity is foreseen, overweighting (or underweighting) the asset class involved. The second guideline refers to the stock selection approach, commonly known as stock picking, in which singles securities, belonging to the same investment category, are chosen among others. Alleged undervalue (overvalue) is often the ratio behind such actions, whether backed by material information or not. In other words it appraises the information selection capability of market agents.
While market timing strategies involve most of the times the enforcement of concepts acquired from technical analysis, stock selection attributes to asset man-agers the ability of picking best performing stocks out from investable cosmos. Even though there exist countless of methods that presume this capability, two approaches get greater consensus among professionals: top-down investing is based on a com-prehensive macroeconomic analysis that anticipates selection of the best-performing industry and thus the above-the-average equity, vice versa a bottom-up approach focuses on the specific fundamentals of a company, compares its trend among the peers, thence takes advantage of any price distortion.
Security Selection Asset Allo ca tion R =X i wai × rai Rp,a= X i wpi× rai
Actual Portfolio Return Stock Picking
Ra,p= X i wai × rpi Rp = X i wpi× rpi
Market Timing Passive Portfolio
Benchmark
Table 1.2: Performance attribution matrix of a managed portfolio.
The two dynamics above assesses the performance attributable to exclusive active management that, together with the core passive investment, concurs in formalizing the overall portfolio return. The debate on whether active strategies are able to provide superior returns on a consistent basis is widely open and probably would not come to an end very soon; here we limit to present the topic in a general way, in order to ease in next chapters the investigation about where the value of robo advice comes from.
Chapter 2
What is a Robo Advisor?
In previous chapters we have anticipated how the digitalization surge has im-pacted and will continue impacting human activities. Indeed, industries that will not be subjected to such tech transformation are fairly few. Trying to predict the extent to which it will affect businesses is never that simple and it inevitably leaves margins of error. On the other hand, just ignoring the evolution that is under way, would be completely incautious and shortsighted, even fatal for companies exposed to heavily innovative sectors. Perhaps the most threatening effects concern industries that historically have been more conservative and barely inclined to change; for several reasons this can be the case of many financial services. Somebody may find this statement questionable, in some way, nonetheless it is unequivocal that only in the 2000s we started talking about financial technology (widely known as fintech), referring to the application of technological solutions in the financial field. The list of possible examples is huge and it keeps increasing day after day. A special mention has to be addressed to digital payments as in the last years they have constituted a representative topic within the fintech talk, resulting in a disruptive wave for the whole sector that forced many actors to evolve.
Especially in finance, a common evolutionary factor involves the automatization of processes through ad-hoc algorithms or computing solutions. In this sense the high-frequency trading probably enjoys almost all the fame. HFT enters the scene in the
early 2000s when first trading systems were coded and then executed at increasingly speed. The underlining idea was to provide the machine with a set of rules in order to autonomously operate in the market, following predefined instructions for a complete array of scenarios. As time passes and technology advances, the algorithms become more complex and accurate and in the meanwhile the flow of information accelerate at an unheard pace. High-frequency emphasizes the extraordinary speed at which transactions are carried out, most of the times in a fraction of second, even on the or-der of the nanoseconds (10−9 seconds or light-foot for the physicists). The aim of such systems is mainly to exploit market inefficiencies − and of course beat competitors on time − relying more on the volume of trades rather than the nominal amount of single transfers. Official data1 estimates that in 2009 more than 60% of US equity trading
was made by high-frequency traffic, notwithstanding that those numbers reduced in the following years; HFT in fact was accountable for less than 50% of trades in 2015.
Despite the large market’s share it quickly gained and the improvements that many recognized it has introduced − most of all the increase of market liquidity − the high-frequency trading had to deal with several detractors. Indeed numerous operators have blamed the HFT to cause plenty of market failures. Emblematic the accident happened on May 6, 2010 in which major US indexes plumbed almost 9% of their value to just recover nearly the same amount within a couple of minutes. Due to its extremely short time frame, the event was later identified as the Flash Crash2. Albeit not directly involved in the inception of the fact, many traders
incriminated HFT for the amplification of the market drop. Rather than ascertain the responsibilities, we prefer linger on a recurring topos for new technologies: any innovation brings a set of advantages and disadvantages that agents must take into consideration. At this time, following the regulatory scrutiny and a decreasing media coverage (result of a declining excitement), the high frequency trading enjoys somehow a wider acceptance and becomes a regular part of market dynamics: indeed many HFT firms are now acting consistently in the trading process substituting the
1TABB Group, 2017.
traditional figure of market maker. Although the HFT was not the many object of our discussion, we believe its mention was needed since it is strictly correlated with the robo advisory theme and in some way it serves as precursor, the necessary step towards the evolution of the wealth management sector.
The same principle of automatization governing the ultra-fast trading was, a couple of years later, employed to other segments of financial field. A decisive alternative application concerned the creation of algorithms for client guidance and related portfolio administration. We should go back to 2008 in order to track down the first company dealing with such automated investment solutions; Betterment, this is the name of the firm, started offer a constellation of mechanized investment services opening up the digital advice era. Many companies followed the path traced by Betterment, some as a newcomers others as incumbents, with the direct effect of boosting the robo advisory business. Before analyzing how the market evolved and detect the discrepancies among players, we should focus on what actually a robo advisor is and which characteristics it should own to be considered as such. The first thing to bear in mind is that there is no unique definition, rather a set of features that all together allow us to identify an investment platform as a robo advisor. Nevertheless, many consultant firms have tried to denote the phenomena putting the emphasis on the binomial that form the word itself; Deloitte (2016a, p. 2) for instance provides the following definition:
“Robo stands for an automated process without the influence of a human being, utilizing mathematical algorithms to support investment decisions. Advisor stands for wealth management services, in this case in an automated manner, making use of regular online or mobile channels.”
Trying to explain it in other terms, we are facing a change of paradigm in the advi-sory industry, a new way of performing the activity. The software provides automated financial planning services and executes most of the traditional wealth management
tasks (such as goals definition, asset allocation, rebalancing, tax-harvesting, . . . ). The human factor is no more crucial as before, now there is the chance to assist client’s investment needs directly from an online − and mobile − platform that is automated. If the pure version of robotic advice abolish altogether the human presence from the service, in reality also grey models, made up from a robo-human collaboration, might appear efficient and successful in today’s market. In the very same report3, Deloitte consultants identify four different maturity stages for the robo advisory product, with increasing complexity based on the quality of the service offered. Recalling the software developers notation, they described:
• Robo-Advisor 1.0: it embodies the most simplistic and rudimental version of automatic advice in which client receives a single investment proposal based on a default list of possible allocations. Most of the times the choice is subordinate to a questionnaire that is expected to catch the preeminent needs of the client. For instance, a crucial point is to identify the amount of risk a client is willing to bear; in this regard, eventually we will see how this topic is a major concern for all regulators. The whole process of buy, adjust and sell must be performed by the client itself, constituting de facto the rock bottom in the automatization course.
• Robo-Advisor 2.0: rather than focusing on single financial instruments, this version of “robos” operate similarly to a fund of funds, thus reaching a wider spectrum of investment solutions. As above, questionnaire assists in selecting the correct product for the client, with the difference that output is now strictly related to the adequate risk category. The service is also more inclusive, in fact an investment manager takes care of client’s portfolio formalising the set of rules that delineate the managing algorithm. In the end, it represents a semi-automatic system.
• Robo-Advisor 3.0: this generation of advisors is characterized by a greater degree of automatization since portfolio selection and rebalance are delegated 3Deloitte, 2016a.
to autonomous algorithms. Investment managers monitor the final result but their involvement is secondary and for supervision purposes only.
• Robo-Advisor 4.0: at the moment the most advanced robotized advice; it relies on an artificial intelligent network ensuring the platform with self-learning capabilities. In addition to define from the beginning the suitable allocation upon client’s needs and attributes, this kind of technology allows dynamic and autonomous portfolio adjustments depending on market conditions, modified customer’s demands, altered risk preferences.
Interesting to notice how almost the 80% of European robo advisor are recognized as third generation by Deloitte. Even though the report stops at the 4.0 version, this does not prevent further innovations to happen. As a matter of fact, it is extremely likely that as data science techniques advance, the complexity and completion of systems will improve too.
In the attempt to provide a more general and universal definition of the topic, other authors4 tried to detect a bunch of recurring features or common traits able to
unmask robo solutions across the wide advice sea. What follow is a brief description concerning those elements that are believed compelling in the robo investing scope. The first aspect involves the digitalization of the business, as the word robo would suggest, it should incorporate a quite high degree of tech leverage. This technologic requirement is not merely related to the algorithm beneath the investment advice, rather includes the whole platform through which the service is delivered. The massive relentless presence of internet in everyday life led to a proliferation of online and mobile products, transforming the way a multitude of businesses are carried on. As time goes by, more sectors are subjected to this digitalization process, depending on its feasibility and especially on the client base to which they are addressed.
In spite of its disruptive capability, even the robo advisor flame would not have started burning if a particular class of investors was somehow interested in the product offered. The main cause has to deal with the generation gap that nowadays
notably divide the population, mainly under a tech-shrewdness point of view. Indeed the driving force of this type of innovation is represented by the youngest portion of society acknowledged as Millennials. A common categorization used in social sciences involves the separation of population depending on the birth year and thus resulting in a range of different generations: iGeneration (or Generation Z, born 2001 and later), Millennial Generation (from 1981 to 2000), Generation X (1965 to 1980), Baby Boomers (1946 to 1960), Silent Generation (1927 to 1945), Greatest Generation (1901 to 1926).
World Generational Divide
0 500000 1000000 1500000 2000000 2500000 3000000 Gen Z Millennials Gen X Baby Boomers Silent Gen Greatest Gen 2015 2025
Source: UN, 2018 - Data in thousands
Figure 2.1: Generational divide in world population in 2015 and 2025.
Besides any sociological relevance, the generational divide is of major concern for fintech applications. The peculiarities and needs of people coming from different decades are extremely different, so diverse that frequently companies are forced to revolutionize their business in order to fulfil incoming clients’ demands. This is the case of the wealth management sector, for example; as seen in the previous chapter, for years the archetype client in the investment industry has been the wealthy in-dividual, diametrically opposed to the image of young indigent adults. Millennials on their side are largely characterized by low income and wealth, hence they fall
within the segment historically underserved by the financial sector. Root of this market irrelevance is the low margin obtainable by incumbents from managing small individual amounts. The combination of digital advice offered at a relatively low cost ensures robo investing to be a powerful apparatus in handling a broad unserved basin. Younger individuals nonetheless possess a smoother affinity with technology than their parents, as a result they will likely be more comfortable and willing to direct their savings to completely automatized platforms of utterly new entities. For wealth managers this represents a crucial issue; sooner than later Millennials will surpass Baby Boomers as the largest consumer generation in developed countries, benefiting also from the largest ever transfer of wealth: The Wall Street Journal (2011) , citing World Bank data, highlights how the average wealth client in North America is 63 years old and therefore emphasizes how he will bequeath his belongings, before long. As a consequence Millennials will account for almost three-quarters of all income by 2025, estimates say5. In addition, another demographic shift has
been spotted: women in fact will increasingly remain as single, divorced or surviving spouses, thus reflecting a strengthening in their earnings position (do not forget that, at the moment, women hold just 1% of the wealth despite embodying 40% of workforce). All elements combined, financial advisors should expect a totally different approach to wealth management from this forthcoming renewed clientele; only firms able to catch the evolving clients preferences and keep up with the dynamics of technological development will achieve long-term success.
If the imperative for an alternative and digitilized investing guidance was pushed by Millennials, the robo appetite is now increasing even amongst older generations. This trend is mostly baked by a tech hype to which the media coverage of last years has been particularly accountable for. Such shift in client base has several consequences for the business. In the first place the growing disposable wealth constitutes a whole new array of financial resources to harness and thus a remarkable opportunity of expansion in market share for robo advisors. The real question here
is if this transitional appeal for robotic advice will be sufficient to consolidate the position of new-born digital advisors across sector competition. In the second place, the increase of average customer age implies a variation in products demand, for the most towards retirement-related solutions.
Private Wealth Under Management
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 0 10 20 30 40 50 60 70 US$ Trillions Global Robo Source: Statista, 2018
Figure 2.2: Growth of private wealth as global AUM and robo AUM.
A further facet involves the use of ETFs over active investment strategies. Aside the effortless handling of passive solutions, ETFs provide a range of perks barely obtainable via other products (at least at such lower price). The fact that they are highly tradable makes them better off compared to common mutual funds, allows an easier rebalancing process and enables tax efficiency mechanisms, such as the tax-harvesting scheme. Possibly the added value of robotic advice is to be found in this variety of services offered at a fraction of cost in comparison to straight human advisors. Fees charged to operate with Exchange Traded Funds are overall much lower than the ones demanded by an active investment strategy, as consequence operators enjoy a mild cost structure that grants a competitive positioning in the market. Not the least, dealing with already-diversified standardized instruments loads off robo advisors of many compliance and risk management requirements, gaining further efficiency. Nevertheless, relying exclusively on a passive investment
policy could lead to miss investment opportunities that for example a stock-picking strategy would otherwise catch.
Last element, often underrated, copes with the decision-making procedure; in this sense ETFs might appear as more transparent and consistent investment opportuni-ties. While replicating the underlying index, an ETF remains tied to the trends of the market of reference, minimizing the tracking error to an extent hardly achievable by active managers, ergo it emerges as an alternative free of conflict of interests and, most importantly, of emotional factor. Delivering emotionless products through an automated platform, like a robo advisor, does nothing but enhancing the objectivity of the investment, thus boosting customers’ trust. Even if ETFs have proven to be an interesting answer to clients’ demands, more sophisticated systems will likely replace them: automated portfolio indexing for example aims to replicate benchmark implementing algorithm trading strategies on the underlying stocks (direct indexing strategy).
As earlier anticipated, a crucial theme for the sector is to highlight client’s inten-tions in order to implement the most feasible strategy possible. If in words appears as a straightforward task, in reality this process most of the times involves critical issues. Traditional profiling requires filling out paper questionnaire that is believed able to catch largest part of clients’ inclinations, such as appropriate time horizon and individual risk appetite. It is questionable whether those surveys have the ability to collect all meaningful information for investment purposes and particularly if such information is correct and objective. On this subject, robo advisors are expected to leverage the process deploying digital tools capable of enhancing customer expe-rience. The aim is to improve the data gathering procedure while minimizing the information asymmetry in the client-advisor relationship. Being true that is more a formal than material problem, increasing process attractiveness may result, due to a better matching with investor’s needs, in a quality improvement of delivered service (and consequent customer satisfaction). Likewise, the portfolio resulting from an
individual self-assessment is broadly perceived as more coherent option compared to a third party proposal6.
An additional feature imputable to automated solutions, although not yet prop-erly integrated, is the higher engagement level provide by gamification. Gamification is a marketing technique that involves the dichotomy challenge/reward typical of gaming platforms. Together with a pleasing user interface, it aspires to make the investing process more enjoyable without refrain from a complete professional ob-jective advice. It can also offer a new way of gathering data needful to improve wealth management service quality. A simulation of stock market crash, for example, possibly viewed by the investor as an entertaining pastime, would provide exclusive and valuable information about client’s behaviour. Not to be underestimated is the potential educational impact such platforms might have; by means of dedicated informational insights, robotized solution will guide the investor while teaching the predominant investing foundations7. Despite being much more a marketing tool than
a financial instrument, it is expected to refine the overall investment performance, besides attract new resources to the existing clientele basin. In all of this, behavioural finance may represent the intermediary step in optimize robo advisors meeting client needs and thus stand out from competitors.
6Deloitte, 2016a. 7Sironi, 2016.
Chapter 3
Robo Advisory Sector
In order to gain a deeper understanding of robo advisory phenomenon, a closer look to existing marketplace seems reasonable. Statistics show that wealth manage-ment is one of the most vibrant industries within financial services, having remarkable room for growth. In next years indeed, the global volume of net investible assets coming from high and ultra hight net worth individuals is expected to see a notable increase. Estimated figures for private wealth value will stick around 70 trillions of US$ by 2021 in the only North America region1, recording an increase of almost 25%
from actual digits. If Europe in three years will not exceed 50 trillions (a change of 15% approximately), the Asia region scores an impressive increase of 55% while reaching more than 60 trillion US$ of assets value. Even though at a different pace, data acknowledge a reliable global development for wealth management in next future.
JP Morgan, leading institution in private banking services, has recently published a report on the business status2. They have identified how the industry is oddly
scene of a extremely diversified players’ composition, where more than 100 Swiss wealth managers are an emblematic representation. The paperwork exhibits that the 32 biggest companies cumulatively represents only 50% of market share. We only have to think that in other financial divisions, six major institutions control almost 60% of market. Final suggestions address to a general sector consolidation
1Statista, 2018. 2J.P. Morgan, 2014.
through mergers, joint-ventures and acquisitions. At the same time, the worldwide trend set by regulators is aimed towards a stringent legislation, unfolding a pre-vailing aggressive attitude against offshore schemes. Compelling rules are mostly driven by savers demands for transparency and confidence-building frameworks. The bottom line intent is the reassurance of worried investor about fairness of financial markets and objectiveness of market agents. Inclination as for onshore-only structures together with an incredibly fragmented expanding business lends breeding ground to pioneering alternative investment solutions, such as robo advisors.
Taking advantage of the tricky challenges faced by existing players, brand-new participants showed up asserting the need for world-changing digitalized products. Relying on online and mobile platforms, these new competitors set forth a fresh overture to personal finance matters and propose themselves as more efficient inter-mediaries than banks and independent advisors. As seen in previous chapter, the employment of high-tech facilities is the main discriminating factor for automated advice providers. As a matter of fact, it has been years that computing science and engineering solutions were firstly adapted to financial problems.
The revolutionizing contribution brought by robo advisors has to deal with the availability to retail investors of first hand portfolio management tools, of course not unless appropriate filters. Clients are free to autonomously select their optimal portfolio allocation based on their goals and purposes. Given the fact that the assumption of inexperience with financial concepts and portfolio models of final users is taken for granted, a complete guidance framework is indiscriminately provided. Starting from allocation theories that are widely accredited within academia, client is furnished with a whole set of instruments fitted to ease the investment process. As mentioned beforehand, their formative dedication may help to fill the burden dumped by old-fashioned educational systems.
The paramount distinction from traditional advisors resides in the absence of human interaction as its intervention is unnecessary in each step of consulting process:
once platforms are set, robo advisors independently interface and engage users. On top of that and pivotal for value creation, through automated systems they are able to process clients’ inquiries into a proficient investment recommendation, likewise it happens for conventional advice. Proprietary algorithms are able to collect data on customers preferences, such as risk tolerance and preferred time horizon, thus elaborate and deploy the suitable strategy on client behalf. They finally present the outcome comparing achieved versus estimated results; full transparency remains as preeminent ambition. If the collection of documentation is commonly an endless paperwork for regular counselors, levered data analytics allows rapid and unerring aggregation of information, hence resulting in a streamlined service. On the other hand, processing advice in an automated manner ensures its unbiasedness in spite of shady banking relations that often leads to conflicts of interest.
In advisory industry, companies’ margins are usually earned in form of fees charged to clients for delivered service. Such fees should cover firm’s activity and administration costs, from custody to bookkeeping expenses as well as all trading-related and portfolio management charges. It worths noting the purchasing costs − those disbursement demanded to operate with mutual, index or exchange-traded funds − are not comprise3.
Three main structures of compensation are generally contemplated by profession-als in wealth management field. Fee-only arrangements usually reflect a greater extent of impartiality and transparency in the advice, remuneration for consultants is in fact clear from almost all conflicts of interest in this case. This is due to the absence of any incentive in recommending selected products. Such fees may be hourly or commensurate to the volume of assets managed. Different is the case of commission-based advisory in which the profit of wealth manager is subject to an opaque configuration. Client often is unaware, due to a scarce disclosure or a limited understanding, about the presence of monetary incentives behind the proposal. At last, advisory firms may opt for a mixed structure in which some
services are remunerated under a fee-only model, while receiving commissions for others. This structure goes under the name of fee-based compensation.
As investors protection has frequently been a top priority, it is not surprising that national and international regulators are aiming their efforts to encourage and promote spreading of fee-only models. All over the world latest legislations foster its adoption; the Market in Financial Instruments Directive (MiFID II) in Europe as well as the Retail Distribution Review (RDR) in United Kingdom, the Australian FoFa (Future of Financial Advice reform) or FINRA (Financial Industry Regulatory Authority) in U.S. are example of such enactment4.
Clearly, there exists a difference in service charges between passive and active investment, since the latter involves an higher deployment of effort and resources (whether more efficiently allocated, it is no relevant to our study). When a passive strategy is adopted, final investor usually incurs in an annual flat fee calculated on the Assets Under Management (AUM). As detailed in AdvisoryHQ (2017) report, this load is roughly 11−12 basis points for index-tracking funds. Active manage-ment conversely comes up with a variable component often tied to performance incentives. Managers that perform better than the market or specific thresholds are thus rewarded with higher wages. Historically, overall fees claimed by active bond managers range about 0.65 percent of AUM, figures climb to 0.89% considering equity funds. Since ETFs are traded on exchanges in a similar way stocks are, there are no loads connected. Buyers instead face trading costs when exchange-traded funds are purchased or sold; in order to make investors’ lives easier, these expenses are summarized in a so-called expense ratio. On average for U.S. non-leveraged ETFs this ratio equals 0.5% of invested assets. A common construct involves the application of decreasing fees based on invested amount; thereby as the absolute investment in the fund increases, associated percentage expenses will reduce. The market evidence of an inverse correlation between AUM and fees corroborates the thesis.
Going back to robo advisors and focusing on their fee structure, we may draw the attention on charges faced by final investor on their platforms. Vast majority of operators apply fee-only arrangements, particularly to adhere to the prevailing regu-latory direction. Usually users contract a variable annual fee, concentrated around 0.15% to 0.25%, linked to the overall AUM for the period, plus expenses bound to ETFs that, do not forget, compose the investing portfolio. Thus overall expenditures equal on average the 65−75 bps of assets being managed, in line with the expense ratio of 0.73% that according to Fidelity Investments (2011) is loaded when dealing with index mutual funds, and much lower than the percentage of 1.45 demanded by active funds. Higher fees are typically justified by larger human involvement in advisory process; from which arises the price competitiveness of automated products. If private wealth advisory fees are dropping and causing a migration from active to passive solutions, professionals believes it has more to do with the change in advice models rather than in a digitalization surge5.
A further difference from conventional advisory firms concerns minimum invest-ment requireinvest-ments. It is an extremely variable factor across the industry; indeed some investment managers require US$ 150,000, others up to 3 million US$, whereas the average remains at US$ 50,000. Despite this general trend, robo advisors investment prerequisites are much lower, set around a couple of thousand dollars, where actually several players present no investment barriers at all.
3.1
Main Players
The time has come to present the principal robo advising market participants. Having been born in the United States, predominant actors are American-based firms; nonetheless additional virtuous companies are emerging in different regions
worldwide, chiefly in Europe and East Asia. Depending on the location of prove-nance each advisor is characterized by peculiarities reflecting domestic investment culture. In Europe, for example, there exists proof that German advisers allocate on average less resources on equity classes compared to U.S. peers. Instead they have a predilection for fixed income investments6. Further differentiating element concerns
the cost of service slightly lower for U.S. investors, owed to minor ETF-related expenses. Moreover, evidence of home bias occurrence is equally demonstrated; recommendations are distinctly weighted towards familiar markets. American district is definitely the most developed, being far more fragmented and competitive due to a lasting tradition of personal financial advisors. If Asian platforms are still emerging as competitive distribution channels, Old World scene is quickly consolidating. Here, United Kingdom and Germany are two major players: while the former is much closer to overseas cousins, the latter holds the faster growth rate of the region. Apparently, gradual reduction of banking sector preponderance in the distribution market is benefiting the most the growth of personal financial advisors, thus including robo platforms7.
Let the data speak, in 2015, seven years later the establishment of first automated advisor, assets managed by robo platforms were around 100 billions US$ worldwide; such figure doubles in following year and triples in 2017. This trend is expect to persist at least until 2020, where the total AUM will set at 8.1 trillions US$. Albeit these are significant amounts, they pale by comparison with the aggregate of global assets under management. Excluding years of crisis, those amounts have constantly grown over time ending at 69.1 trillions of US dollars in 2016. It is quite clear that just a minimal portion of universal assets is actually managed by robo advisors, a fraction not larger than 0.29%. Notwithstanding, it should be noted that, according to official estimates, managed capital will reach 101.7 trillions by 2020; at that time assets under robotic advice will equal 8% on overall.
6Solactive, 2017.
Furthermore, a report from consultant agency EY (2018) unveils that 55.4 of 69.1 trillions US$ in 2016 correspond to net investible assets coming from High Net Worth and Ultra High Net Worth individuals8. This enormous disparity will likely
reduce in following years, as data suggest: in five years HNW managed wealth will increase to 69.6 trillions. Such raise is in relative terms much lower rate than the increment foreseen in global investments. The difference is likely imputable to less affluent investors tier, that is also the targeted clientele of robo advisory services. On this topic statistics point out how the industry witnesses, alongside a constant growth in customers, a parallel decrease in average managed assets per user. It can be explained as a further indicator of mass market and affluent clients involvement. Basically, there are plenty of elements supporting an affordable market development for automated investment addressed to retail individuals.
Following the financial crisis, new entrants get started proposing a range of client-facing digital financial instruments. New-Yorker Betterment founded in 2008 is universally recognized as the earliest of robo advisors, first offering online rebal-ancing capabilities to long-term passive investors. Few months later from California, Wealthfront began commercialize alike products; from there, many others entered the business. Under a strategic point of view sector evolution is of particular interest. The entrance of new players took place on the general indifference of established competitors, that were busy meeting post-crisis supervisory guidelines. Only when it became clear the disrupting potential of algorithmic advice, incumbents started considering robo advisory as a threat for sector stability. Being unable to contrast the impetus of technical innovation, some well-established wealth managers decided to join the hip; in order to front the robos menace they fell back on joint ventures, acquisitions, outsourcing or internal implementation strategies. Charles Schwab and Fidelity for example launched their own platform in 2015 and 2016, respectively. Financial giant BlackRock announced partnerships with Future Advisor and microin-vesting application Acorns. On its side, Vanguard, strong of a diverse ETFs offering,
introduced an elective investment service for its own clients replicating the business model of automated advisors.
For the sake of completeness, in this context we will mention just a few of actual contestants. Being aware that is far from being an exhaustive list, we have below summarized the principal actors differentiating them by operating area:
North America Europe Asia
Acorns AdviseOnly Ant Fortune
AssetBuilder Easyfolio InvestSMART
Betterment ETFmatic Mizuho Bank Smart Folio
Financial Guard FeelCapital ScripBox
Future Advisor MoneyFarm StockSpot
Personal Capital Nutmeg
Rebalance IRA Scalable Capital
Schwab Intelligent Portfolios Wealthify Vanguard Personal Advisor
Wealthfront Wealthsimple
Table 3.1: A selection of robo advisors divided by region.
Whilst robo advisers share a common algorithmic core, each single firm enjoys small peculiar features that ensure its uniqueness among the others. First of all, any advisor may choose autonomously the adoption of special capital requirements or the amount of fees chargeable to clients, hence creating a broad range of alternatives. Clients are given the possibility to select the most suitable option depending on the spectrum of services offered. Additional services may encompass rebalancing strategies, tax-loss harvesting schemes or different degrees of human interaction.
discriminatory elements too. UK-based Wealthify for instance offers the implementa-tion of limited personal advice, through the guidance of a professional, as well as the opportunity to invest in mutual funds other than exchange-traded funds9. Such kind
of setup is closer to a so-called hybrid model rather than a robo advisor in strict sense.
As a matter of fact, the game is played on the composition and optimization policies of client portfolio. Before all, companies have discretion on asset types to be included; an advisor would prefer to concentrate investments on the domestic market, others will seek exposure to foreign resources for diversification sake. Park, Ryu, and Shin (2016) have identified the most evident distinctions among major robo advisors. At the outset, they emphasize how in terms of asset allocation the Black-Litterman model is generally preferred to the conventional Markowitzian approach. Market leaders such as Betterment and Wealthfront reported to have implemented Black-Litterman in order to estimate rates of returns, even though its final employment is subordinated to the significance levels pertaining the market equilibrium rate of return.
B-L model is an asset allocation model that combines two main ideologies of modern portfolio theory, the Capital Asset Pricing Model (CAPM) and Markowitz’s mean-variance optimization. The model’s main benefit is that it provides investors with a systematic approach for combining their personal views about returns of individual asset classes with the market equilibrium implied returns, under a the mean-variance perspective. Given historical asset covariances, the Black-Litterman model calculates through a reverse optimization what each asset’s expected return should be in order to generate a portfolio having the same weights as the market portfolio. Such expected returns are then the CAPM equilibrium returns for each asset class. Once investor has expressed its own views on expected returns, B-L fuses market equilibrium implied returns with investor’s expectations, producing a new vector of expected returns. Note that in the absence of investor views,
sulting returns are those implied by the market equilibrium, thus investor should hold the entire market. Even though blended returns leads to reasonable portfolio weights without additional constraints on the portfolio optimization process − unlike Markowitz approach − it also true estimating market portfolio may be not that straightforward as it seems: public markets for example might not fully represent the universe of risky assets.
According to Park et al. (2016) Schwab does not consider B-L model at all. Instead, Schwab Intelligent Portfolio employs a version of the Gordon Growth model to predict expected rates of return, incorporating portfolio past returns, market interest rates, credit risk spreads as well as GDP growth rates. A remarkable at-titude from Wealthfront involves the enforcement of basic client needs analysis in the optimization process. For what concerns client profiling, Betterment adopts a plain asset allocation method, differently from Wealthfront and Schwab that exploit behavioural approaches and questionnaires to assess clients appetite.
A report on fintech by Statista10 has investigated the amounts administrated by a selected range of robos in 2017. Looking at its findings, it is readily observable how incumbents platforms have a pronounced vantage over newborn advisors. Even though entered the market years later, they experience harvesting of AUM at a faster pace. The answer likely lies on the existing broad base that characterizes major companies such as Vanguard and Schwab. Indeed they have the chance to address algo-solutions to actual partners in form of supplemental appealing benefit, on top of conventional advice service. Moreover, leaning on a strong reputation, they are able to extend their offering to a wider audience targeting a far less affluent segment than their traditional clients. Attraction of established firms towards automated advice is among the major concerns and greater threats questioning the survival of modern advisers.
AUM of Selected Robo Advisors - 2017 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 US$ Millions Vanguard Intelligent Portfolios Betterment Wealthfront Personal Capital Future Advisor Nutmeg AssetBuilder Wealthsimple Financial Guard Rebalance IRA Scalable Capital Source: Statista, 2017b
Figure 3.1: Asset under management for selected robo advisors in 2017.
A current trend for robotic advice is the inclusion of smart-beta and risk parity strategies in portfolio definition. On this regard, factor-based asset allocations are gaining more and more attention from academia and professionals. Many claim this approach as superior than traditional asset-class allocation, even though no theoretical evidence suggests a persistent dominance exists11. Risk-factor models
attempt to identify a number of common factors able to explain the returns of individual portfolios. Every single portfolio is arranged in a way that offers different degree of sensitivity to individual risky elements; usually it is the result of multiple linear regression between asset returns and peculiar risk factors, of the form:
ri = αi+ βi1Fi1+ βi2Fi2+ · · · + βikFik+ εi
In a multifactor model as the one shown above, the betas are said to be smart because, opposed to CAPM beta that tracks the overall systematic exposure of a stock, account for the specific risk with respect to the associated factor.
Common smart-beta strategies involve value, quality, momentum, or size criteria; 11Idzorek and Kowara, 2013.
value approach trails presumed excess returns from low prices stocks compared to their fundamentals. Quality is a generalization for stable earnings and consistent asset growth equities. While the tendency for stocks that have outperformed in the past to replicate in the future, is the ground idea behind momentum. The size criterion instead relies on the the historical evidence that small cap stocks provide consistently abnormal returns than blue chips. On this topic, a good synthesis is offered by the Carhart four factor model12, an extension of well-known French − Fama three factor model, in which asset returns are explained as a linear relationship between size, value and momentum, respectively small-minus-big (SMB), high-minus-low (HML) and up-minus-down (UMD) factors. Of course, including market risk premium too. Tests have shown that a similar model accounts for almost all of the cross-sectional variation in expected returns on portfolios of mutual funds.
r = αt+ βm(Rm− Rf) + βSM BSM Bt+ βHM LHM Lt+ βU M DU M Dt+ εi
Lately, smart-beta ETFs have become popular among robo advisors since ever more players started to include them in their offering. Such instruments enjoy all the peculiarities of vanilla ETFs, while major distinctions reside in the possibility of choosing correct stocks to fit in the index, in conformance with agreed criterion. That is weighting assets as opposed to weighting by market capitalization. Strategic-beta ETFs, alternative name for smart-beta, seem to be the link between passive and actively managed ETFs. While actively managed ones assume the activity of a human team in picking out over-performing equities, smart ETFs employ computer models to overweight index stocks that are better positioned according to statis-tics and historical data. As the complexity of the product increases, so does the price.
On the other hand, risk parity is a portfolio management technique that, rather focusing on capital allocation, aims its attention to the allocation of risk, commonly defined as standard deviation. This approach heavily relies on the assumption that,
when allocations are adapted to the same risk level, the designed portfolio can achieve superior risk-adjusted returns and ensures a better handling of market downturns than regular portfolios. In other terms, risk parity presumes the maximization of risk diversification rather than optimize return per unit of risk.
As the portfolio portioning decisions are based on single stocks variances rather than covariances, the final outcome may result in a more robust and thus more efficient solution than conventional mean-variance optimization. We will deeper investigate the matter in final chapters while an empirical application of this ap-proach will be presented. It worths noting that in many cases, risk parity is seen as a particular case of smart-beta strategy; while both perspectives pursue a risk-driven plan, in reality the former does not involve any factor modelling underneath.
Chapter 4
Advantages and Disadvantages
In this chapter we evaluate the benefits that robo advisors are expected to bring into the industry. The analysis will focus on those features that provide robotics with an edge over human competitors and therefore are cause of turmoil for the sector. The rising popularity for such alternative mediators is mostly imputable to a set of advantages that are assumed capable of increase the efficiency of investment recommendations. It is equally true that flawless solutions are unlikely to exist. As a matter of fact, robo advisors are not exempt from limitations, hence in the same way we will try to produce a comprehensive overview of the topic as we deepen the inquiry.
As previously stated, the vast majority of existing automated advisors proposes ETF-based investments. The underlying reasons for this strategic decision are easy to say, and cost-effective benefit is just one of them. Since established, exchange-traded funds were intended to pursue a certain investment policy in an automatic manner, that is replicate a specific index aiming at the lowest tracking error possible. If at first only major market indexes were mirrored, as time passed the investable universe expanded at exponential rate, now includes a countless variety of benchmarks. Less liquid investments, real estate in primis, and asset classes historically distant from retail investors purview − such as commodities − also fell in ETFs orbit constituting a brand-new way of investing. There are few doubts regarding the contribution of asset diversification offered by exchange-traded funds, for sure they played a
significant role in the robo advising surge.
An Exchange-Traded Fund, abbreviated ETF, is in all respects an investment vehicle that combines characteristics of both funds and stocksU.S. Securities and Exchange Commission, 2018.Although it does pooling financial resources in the same way as a mutual fund would do, ETF holders enjoy several virtues proper of financial stocks or bonds. Above all, this category of funds is traded on the stock exchanges similarly to company shares. The price of an ETF quote in fact reflects at anytime fund Net Asset Value, opposed to what happens for a straight mutual fund in which investors must wait funds NAV disclosure in order to acquainted with position’s value. While in the first case real-time investment evaluation is possible looking at trading price, NAV disclosures may take place on a daily, weekly or monthly (even annual) basis depending on fund’s policy reported on prospectus.
The high marketability is just one edge; since the primary target is the replication of an index and considering that indexes are baskets of selected stocks, ETFs seek exposure on a range of different assets. Hence investing in ETFs means select an already-diversified financial solution and thus reach a considerable reduction of risk by automatic means. Being offered on all the main exchanges, all these products are easily accessible by retail investors, on top of that the charges required are a fraction of those demanded by mutual funds for the very same set of stocks but actively managed. A further facet that should not go unnoticed concerns the perks obtainable by wealth managers when recommend them to clients; indeed all the administrative and operating costs are largely narrowed. This is because investors are dealing with standardized bundles of stocks already subjected to strict regulatory scrutiny. Implications are that many funds’s procedures, such as risk analysis of single stocks or compliance-bearing schemes, are brought to a lesser extent by advisors, resulting in a reduced cost structure. Not the least, by using exchange-traded funds investors are always certain the employed strategy will be always consistent with the approach defined by the KIID. ETFs daily disclose underlying trades, differently from mutual
