2.1 Introduction

Many of us enjoy our morning rites, reading a newspaper while toasting bread, drinking a cup of coffee before going to work. The youngest and most tech-savvy might scroll the latest tweets on global finance. Recently, there has not been a day without a new blog post about Robo-Advisors and their disruptive potential. This topic has clearly made an impact on professional media, although there seems to be a lot of journalistic biz-buzz around it, which might not facilitate a rational debate about the characteristics of these FinTechs.

We can just ponder for a moment the term “Robo-Advisor”. This bloggers' term conveys a biased perception about what these companies really are and do. For the many tech enthusiasts, Robo-Advisors are fully automated machines which make investment decisions without any human interaction and can fully replace professionals to provide advice that eliminates any conflict of interests to benefit final investors. Many others instead focus on the perils of excess automation, and promote a sort of luddite juxtaposition between customer-committed human advisors and unmanned automated services. This book takes a more balanced stance and shows that techno-literate advisors have the most to gain from robo-technology. Digital-Advisors can use digital solutions to become more efficient in assessing, investing and reporting on clients' goals, thus saving time to focus on prospecting and added-value conversations. A way of disentangling from this conflict of minds would be to use a more appropriate appellation, such as “Automated Investment Solutions” (AIS). But we are aware that AIS would not be a headline stealer, because it does not convey the same emotional emphasis as “robotics”. So Robo-Advisors it is!

This chapter provides a review of what they are and what they are not: neither fully “Robots”, nor truly “Advisors”.

2.2 What is a Robo-Advisor?

Robo-Advisors were born recently within the FinTech ecosystem to advise or manage private wealth and disintermediate traditional wealth managers. Although a truly global phenomenon, with established players in Europe and Asia-Pacific, the US is their biggest market in terms of number of players and assets under management (AUM). This is due to structural differences in the marketplaces, the US landscape being far more fragmented and competitive with a longer tradition of personal financial advisors and firms. From a timeline perspective, they first appeared between 2008 and 2010, and then made a leap onto the top chart of disruptive innovations towards 2013 due to a set of concomitant factors, among which:

• a widespread tightening of international regulations to foster investors' protection and favour de facto fee-only advice: for example, the European Market in Financial Instruments Directive (MiFID II), the UK Retail Distribution Review (RDR), the Australian Future of Financial Advice reform (FoFA), the FINRA and DOL rules in the US;
• a significant growth of their assets under management or under advice, favoured by a period of relative strength of US stocks;
• the impressive market penetration of smartphones, which allows a larger population of consumers to benefit from the internet any time, anywhere, than ever before;
• the recognition that Robo-Advisors do not appeal only to low margin customers of retail banking or HENRYs (High Earners, Not Rich Yet), but also to affluent and wealthier patrimonies, which were previously thought to be the apanage of traditional advisory firms.

Don Abbondio, one of the main characters of Alessandro Manzoni's masterpiece The Betrothed (1840), was a quiet clergyman. Pondering in his armchair while reading a small book, yet unaware of the disruptive events which were about to shake up his life, he found himself thinking: “Carneades, who was he?”. Similarly, we might have been asking ourselves:

“Robo-Advisors, who are they?”

A short definition cannot easily be found, because there seems to be more than one business model in the ecosystem. They differentiate by the degree of passive management, depth of investment automation, self-assessment mechanisms, and target clientele. Not all FinTechs addressing personal finance can be classified as Robo-Advisors, and not all Robo-Advisors are pure FinTechs. As a matter of fact, recognized established institutions have also launched robo-services, as add-on offers to their traditional operations. They are growing at an even faster pace than FinTech innovators. Other retail and private banks, platforms, and asset managers are following suit. Therefore, we need to articulate a rich enough definition that embraces a few of the main features of these tech based, finance-orientated firms. Although they appear to be simple solutions, there is much more complexity behind the scenes, as with anything at the crossroads between finance and technology.

“Robo-Advisors (1.0) are automated investment solutions which engage individuals with digital tools featuring advanced customer experience, to guide them through a self-assessment process and shape their investment behaviour towards rudimentary goal-based decision-making, conveniently supported by portfolio rebalancing techniques using trading algorithms based on passive investments and diversification strategies.”

By extrapolating from this definition, Robo-Advisors 1.0 show at least some of these five facets, if not all:

• to be digital businesses, with automation and technology at their core;
• to conform by and large to passive investment and diversification principles;
• to institutionalize automated portfolio rebalancing and tax optimization (often);
• to use attractive self-assessment approaches which attempt to personalize investment decisions to individual goals and behaviour;
• to display a high degree of business focus.

Existing practices are just a first step in the journey of industry transformation, as further change is underway. So far, Robo-Advisors have been characterized by a strong focus on simplicity and cost efficiency compared to more advanced alternatives. Although a long-term limitation, this is also a key feature of successful innovations: simplicity usually pays off at the start of a new disruptive journey. Figure 2.1 provides a high-level example of the most common workflow of a Robo-Advisor 1.0.

On-boarding new customers seems to be one of their strengths, since they use digital experiences to reach out and facilitate intuitive self-profiling compared to paper questionnaires. Yet, we will discuss the limitations of current experiences and the need to adopt a stronger profiling mechanism to achieve a more insightful elicitation of individuals' risk tolerances and ambitions, which cognitive computing seem to facilitate. Investment advice is moving from products to model portfolios based on simple ETF strategies, attempting to lock clients into longer-term investing instead of myopic trading. This seems to be key to providing simpler investment opportunities which are linked to broader market movements instead of idiosyncratic names, reducing efforts in investment design and performance reporting. Account aggregation capabilities are also extremely valuable, if not one of the most relevant features. Individuals might not want their advisors to know about all their invested assets but they might enjoy receiving self-directed robo-advice on their full wealth allocation. The advisory workflow is improved by automating the rebalancing activities, which also partially mitigates clients' anxiety during a downturn as decision-making is delegated. Reporting becomes more interactive and visual compared to traditional reports.

The remainder of this chapter reviews the five characteristics which make up the definition of Robo-Advisors, and highlights strengths and weaknesses: digital tools, passive investments, automated rebalancing, efficient on-boarding, single minded business.

2.3 Automated Digital Businesses for Underserved Markets

Robo-Advisors offer financial services by leveraging on advanced digital technology, which grants scalability and enhanced customer experience to optimize clients' on-boarding and investment management. The digital transformation of everyday life is a global trend which creates the fundamental fertilizer for the success of automated investment services. This process of virtualization of the consuming experience is driven by two relevant factors: ubiquitous connectivity and generational shifts. First of all, the web has become omnipresent. The entrepreneurial exuberance of the late 1990s, which ended with the DotCom bubble, was based on too optimistic assumptions about the use and penetration of the internet. Those assumptions are realistic today: smartphones, tablets, and wearables have made digital much easier and more affordable. Second, a new generation of consumers has been growing up with total familiarity in the use of social media and digital tools. Millennials display a level of digital instincts which Baby Boomers do not possess and which makes them very receptive to FinTechs' propositions. This prompts a radical shift in behaviours. However, we can observe that digital technology is changing the consuming behaviour of all generations, not just that of young tribes.

Robo-Advisors are taking advantage of the digitalization of everyday life. They lower the barrier to entry by automating investment processes with seemingly unmanned interaction, hence minimum operating costs. Initially, they were targeting low income Millennials, thus a population with limited access to human financial advice. Given their perceived market irrelevance, this family of investors had been rather neglected by banks. Prima facie Robo-Advisors were meant to be a breakthrough in such an underserved (i.e., low competition from banks) but highly digitalized (i.e., sensitive to innovation) market segment. Yet, Robo-Advisors started to appeal very fast to a broader public of investors, such as affluent and high net worth individuals, who are instead at the core of incumbents' strategies. The average age, wealth, and disposable income of today's robo-clientele are all growing, highlighting that clients are segmenting themselves according to their tech-savviness instead of traditional criteria. Figure 2.2 reports on total and average AUM per client of a set of “independent” Robo-Advisors, as reported by MyPrivateBanking (2015).

As unexpected competition gathered in front of banking gates, incumbents' digital strategies were shaken by an earthquake, making Robo-Advisors one of the most debated topics at conferences and in the financial press.

2.4 Passive Investment Management with ETFs

The second facet of Robo-Advisors is passive investment management, which is a form of trading seeking to gain or shed exposure to broader market indices, sectors, or geographies. While passive investing tracks a benchmark or a well defined subset of its components, active management attempts to achieve above market returns by trading or shorting the constituents of an index based on rules, sentiment, or portfolio managers' views. Financial literature and academic research have openly criticized the performance of active management funds compared to passive investments. Arnott, Berkin and Ye (2000) have shown that active mutual funds have underperformed the Vanguard S&P 500 index fund by an average of 2.1% per year pre-tax over a 20-year period. Their poor historical performance can be explained by a mix of factors. First, active funds ask taxable investors for higher fees, which eat up on their net returns over time. Second, they might have suffered from poor securities selection, due to a forced overweight bias towards small cap stocks compared to large ones. The investigation period (1978–1998) was dominated by large cap performances and a significantly skewed distribution of market capitalization towards large issuers: only a very tiny fraction of stocks enjoyed market capitalization above index average. Last, active trading might have triggered capital gains more often than economically advisable, leading to tax inefficiency and affecting post-tax returns of taxable investors.

The wider public of private investors might not have easy access to academic evidence, but Robo-Advisors picked up the reasoning on their behalf and heavily promoted indexing and tax optimization as key features of their offering. In the aftermath of the GFC, private investors became effectively more dubious about direct investing on Wall Street, opening up their appetite for different investment services. At the same time, market regulation started to foster higher transparency when reporting investment costs to individuals. Social media blogs caught the momentum to provide intuitive comparison of prices and historical returns across financial products, granting better education to a broader public and advocating for a change of investment behaviour. The existence of the asymmetry of information might have shielded retail banks, private banks, and asset managers from the duty to transform and do more.

Adding to their competitive stance, Robo-Advisors typically invest in ETFs instead of passive mutual funds, following tighter US regulation enforcing the ban of inducements, because they generate lower investment costs on average. Moreover, they can be traded throughout the day on open markets, which facilitates the processes of automated portfolio rebalancing and tax-loss harvesting. According to Morningstar, the average expense ration of ETFs in 2010 was 6bps, which compares with 73bps for index mutual funds, whose average tax cost has been estimated at 130bps in the period 2004–2014. While the first US mutual funds to track a market index were launched back in the 1970s, the first ETF tracking the S&P500 began trading only in 1993. Initially, SEC exemptive relief was granted only to passive ETFs providing direct or inverse exposure to specific indices, while from 2008 onwards a new family of rule based ETFs was also approved, providing a higher degree of active management to meet particular investment policies. Notwithstanding lower costs and appealing trading features, ETF shares still account for only around 12% of total net assets managed by US investment companies 20 years after they first appeared. According to the Fact Book 2015 published by Investment Company Institute (ICI), which is the national association of US investment companies (comprising mutual funds, exchange-traded funds, closed-end funds, and unit investment trusts), the total net assets managed by US investment companies in 2014 amounted to US$ 18.2 trillion, of which US$ 15.9 trillion in mutual funds and US$ 2.0 trillion in ETFs (as can be seen in Table 2.1). Yet, in the last 10 years the net assets of ETF shares more than quintupled. This growth follows a shift in the investment practice of institutional investors, but also an increased awareness of retail investors, fee-only financial advisors, and last but not least Robo-Advisors.

Robo-Advisors have used ETFs to construct long-term taxable portfolios, and to achieve the following:

• compress the price tag to the minimum, dumping the market of traditional financial advice;
• commoditize automated portfolio management and rebalancing;
• make performance reporting an easier task;
• reduce compliance costs, risk management efforts, and market data expenses by working with a more efficient catalogue of investment products;
• link investors to market trends instead of individual stories to make the narrative of investment decision-making more affordable, transparent, and less emotional through the cycle.

US trading costs have also been lowering steadily, as reported by Jones (2002) in Figure 2.3. This has allowed some Robo-Advisors to offer automated portfolio indexing to their wealthier clients. Automated portfolio indexing uses algorithm trading mechanisms to replicate indices by trading on the underlying stocks directly, hence replacing mutual funds and ETFs altogether. The cost/benefit advantage seems to benefit from an improved performance of tax-loss harvesting, which is optimized by trading on individual stocks. ETF providers might also become commoditized in the not too distant future.

The main differences between ETFs and mutual funds are summarized in Table 2.2.

2.5 Algorithms of Automated Portfolio Rebalancing

Algorithms of portfolio rebalancing are the third identified facet of Robo-Advisors, and take care of the periodical revision of the asset allocation through the investment cycle. Investors are typically presented with an allocation which is chosen out of a set of pre-defined model portfolios according to a self-assessment procedure that judges on their age, risk tolerance, return appetite, financial knowledge, initial or periodic invested amount, and time horizons. Therefore, effective personalization is fairly limited even though some Robo-Advisors feature more refined thematics or allow the inclusion of personal market opinions within optimization routines. Portfolio modelling often refers to Mean-Variance or tilted optimizations (Black-Litterman), which allow the embedding of subjective views of expected returns or their relative difference across investments. The most common asset classes are stocks, bonds, currencies, commodities, and protection against inflation. In essence, rebalancing is a risk management technique which enforces the asset allocation to revert back towards its desired long-term equilibrium, because market dynamics might lead invested portfolios to deviate. Running a new mathematical optimization at every rebalancing time is not strictly required, but is recommended if markets have drifted significantly from their initial state. Existing Robo-Advisors exhibit different rebalancing rules, which are not always part of a fully automated process:

• discrete schedules (e.g., once a month);
• discretional decisions of fund managers (e.g., personal views on single asset classes);
• statistical triggers to avoid unnecessary trading and minimize costs (e.g., widening of tracking error volatility against a benchmark);
• reoptimization as new asset classes are made available or the economic environment changes abruptly (e.g., a market crash or a fundamental shift in monetary policy).

Their typical long-term and automatically rebalanced model portfolios are an attempt to keep clients invested through the market cycle, in the belief that the choices of asset allocation dominate portfolio returns in the long run. Clearly, this assumption is aligned with their revenue model, often based on fee-only agreements as a percentage of AUM. Although rebalancing is meant to facilitate the risk management of model portfolios, we must acknowledge that these are constructed with fairly simplistic or straightforward optimization routines, whose limitations are discussed in the second part of this book. Yet, are incumbents doing any better?

2.6 Personalized Decision-Making, Individual Goals, and Behaviour

The fourth facet is the personalization of the investment experience across individual goals and personality. This is possibly the most compelling but challenging feature, which has been attracting a substantial amount of investment in research and development, not just from rampant technology providers but also from incumbents. Creating a truly disruptive and emotional dialogue between investors and digital firms would be the tipping point of industry robotization. As already depicted in Figure 2.1, the first and most recognized element of interaction between prospects and Robo-Advisors resides in their on-boarding mechanisms. While conventional wealth managers largely rely upon paper questionnaires to document individual investors' traits, Robo-Advisors take advantage of digital technology to shape the process of enrolment with enhanced customer experience. Today's Robo-Advisors certainly emerge as lighter engagement devices, but they are clearly not free from red tape either. Market regulation imposes that investors' risk profiles are to be properly elicited and kept up to date, although no specific approach nor criterion is defined to validate their robustness. Most configurations start from the assumption that investors are rational, inherently risk averse, and accept more risk only if they can garner higher expected returns to compensate for it. Therefore, the key difference compared to conventional wealth managers does not seem to reside so much in the underlying assumptions but rather in a more attractive process, which makes questionnaires look less so and enhances the perception of investors' participation in the decision-making process. The model portfolio identified at the end of the self-assessment should be perceived by investors as a more logical choice of their own opinions, instead of a third party's recommendation: given their age, their capability to absorb losses and their declared return ambitions. The delicacies of the processes dedicated to risk profiling are an unresolved problem both in practice as well as in academia, irrespective of the level of automation. Hence, this should be a top priority for wealth management firms, Robo-Advisors included: only a consistent and transparent elicitation of individual goals and fears is a guarantee that the subsequent steps of automated investing are truly robust and suitable. Moreover, only a thorough and informative process can open up to further advances in personal finance, like consistent Goal Based Investing. Should Robo-Advisors provide only a better experience, but then rely on the same principles of conventional questionnaires, they might improve the framework but not solve the problem. Recent seminal papers have discussed the inability of conventional questionnaires to elicit investors' attitudes and behaviour with regard to risk-taking. Kahneman and Tversky (1979), Foerster, Linnainmaa, Melzer and Prebevitero (2014), Burns and Slovic (2012), Weber, Weber and Nosic (2012), and Klement (2015) are all valid references and will be discussed more thoroughly in the second part of this book, which is dedicated to investors, risk profiling and advanced technology to better account for behavioural finance, the framing bias, the evidence stemming from the biology of risk, and the variability of risk feelings. Table 2.3 provides a summary of the strengths and weaknesses of Robo-Advisors, compared to conventional wealth managers, with respect to self-assessment and enrolling tasks.

2.7 Single Minded Businesses

The last common feature of Robo-Advisors resides in their highly focused propositions, which attempt to unbundle one aspect at the time of the banking experience. Such single mindedness is clearly a strength in the short term, as disruptive innovation has a chance to succeed only if consumers can understand the new offer without ambiguity, are granted easy access to the new product, recognize the differences compared to conventional providers, and feel they can afford it. Moreover, simple front ends allow Robo-Advisors to be primarily and de facto very efficient enrolling mechanisms, to reduce the attrition rate during the steps of self-assessment and minimize the percentage of customers who drop out before signing up their commitment to invest. These aspects might not be the only ones to play in favour of their single mindedness. Robo-Advisors are still fairly indebted companies, they need to grow fast and reward venture capital investments. This might further reinforce their need to push aggressively on their original message and exploit the current momentum of favourable coverage by the press and social media. Notwithstanding, the landscape is changing fast and will change even faster in the next years. On-boarding new customers is not cost free and requires significant marketing expenditures to augment AUM from the first billion to the first trillion, and fully exploit digital economies of scale. As the cost to acquire a new client is fairly insensitive to a client's disposable wealth (at least when looking at customers of retail banking and affluent investors), Robo-Advisors might feel the pressure to reach out to individuals outside their original retail focus. Yet, the greater the amount of wealth invested by an individual, the more likely the request for extra added value compared to current configurations, inducing them to articulate a broader offer, such as opening to other investment options or a more refined identification of personal goals. The following set of elements concur to transform existing AIS into Robo-Advisors 2.0:

• competition from established institutions, which are starting to adopt robo-technology as standalone products or to support the work of human financial advisors, potentially reducing the clients' perception of the gap between fully automated solutions and hybrid business models;
• pressure from institutional investors to improve their return on investment, by increasing the profitability per customer;
• opportunity to optimize marketing costs, and appeal to wealthier individuals.

Robo-Advisors 2.0 might therefore expand their initial propositions and feature some of the following characteristics:

• transform from Business to Consumer models (B2C) into Business to Business to Consumer services (B2B2C) or Robo-4-Advisors, and provide personal financial advisors with the opportunity to use robo-technology for those parts of their workflow related to account aggregation, model portfolio selection, rebalancing, and reporting;
• in some cases, transform to Business to Business models (B2B) or Robo-as-a-Service, and provide On Cloud services to Tier 2 financial institutions looking for automation but lacking the expertise to develop proprietary solutions;
• extend the services offered towards a better definition of personal goals along the time axis, hence fostering convergence between financial advice and planning;
• expand into saving and payment platforms, not just investment solutions;
• augment the effective personalization of model portfolios, and account more explicitly for the opinions of more sophisticated investors or demanding financial advisors;
• transform into fully fledged digital family offices, adding specialized services of wealth optimization beyond financial investments;
• engage clients with Gamification to further align their investment behaviour to long-term money management, solve the educational burden, and sell more complex and higher margin services which would otherwise require human interaction.

Clearly, unbundling financial services and focusing on one aspect of the banking relationship in isolation might be a good starting point for a FinTech, but not necessarily a proper long-term strategy for an institution. Banks are well aware of the relevance of creating a marketplace, where wealth management is an essential entry point to create a banking relationship that facilitates cross-selling: loans, mortgages, and insurance products.

2.8 Principles of Tax-Loss Harvesting

Robo-Advisors have been positioning aggressively to provide above average returns compared to passive investments managed by conventional financial advisors. Above average returns are meant to come from a potentially superior performance of long-term asset allocations which do not attempt to tame the markets, as well as methodical attention to expense ratios by minimizing management fees, trading costs, and tax implications. They attempt to generate slightly better returns by enriching rebalancing algorithms with tax-saving mechanisms that optimize tax liabilities (after-tax benefits) and reinvest tax savings for longer periods (before-tax advantages). This seems to be particularly relevant in those constituencies like the US in which the tax code allows taxable investors to take advantage of losses generated by declined investments, which are disposed of to harness tax reductions and lower personal taxes.

Tax-loss harvesting does not provide tax avoidance, but is a tax-deferral mechanism which exploits the different tax treatment between short and long term. Most of all, it combines the need to respect the asset allocation constraints at any point in time with the restrictions of the tax code (e.g., wash sale rules). These algorithms search for declined investments to generate losses, provide tax reductions, lower an investor's taxes, and minimize the negative impact of wash sales, which disallows a loss if taxable investors do not truly dispose of the investment across all their accounts (e.g., accounts held by their spouses). With regard to the US tax code, the rule would be triggered by selling a security and purchasing a “substantially identical” security 30 days before or after the sale. Since Robo-Advisors cannot freely dispose of declined losses, they typically replace declined assets with correlated ones, that is assets which are not “substantially identical” for the tax code but whose returns are highly correlated to the original ones from a portfolio management perspective. That could be the case for ETFs which provide the same market exposure but formally track different market indices (e.g., MSCI Emerging Markets versus FTSE Emerging Markets). Investment catalogues are therefore made up of primary and secondary lists, which the algorithms can choose from. The use of correlated securities allows us to maintain the target asset allocation and optimize the cash drag that would be generated by the application of wash sale rules. When harvesting losses without replacement, wealth managers are exposed to the risk that within the 30-day period the potential tax losses stemming from a decline in the security are more than offset by a reversal of the security's price in the open market, which will end up generating a capital gain and leave the investor worse off if the resulting gains exceed the harvested losses. The use of the correlated asset instead, allows harvesting of further losses if after 30 days the security's price has further declined, or generated portfolio performance without triggering any capital gain if the security price has gone up since no further buy/sell is required.

Hence, tax-loss harvesting attempts to generate so-called TaxAlpha advantages, which can be attributed to the reinvestment of tax savings and the difference in the tax rate between short and long term. The benefits of tax-loss harvesting clearly disappear when a portfolio is liquidated and taxes are finally due, as long as the portfolio is not passed on to the investor's heirs or a charity fund. Since TaxAlpha measurement is exposed to the uncertainty of liquidation, it is typically computed yearly, the year being the time frame under which taxes are fully due and investment losses can offset other capital gains or income taxes.

2.1

where CL_ST is the short-term capital loss, CL_LT is the long-term capital loss, and X_ST and X_LT are the corresponding short-term and long-term federal and state capital gains tax rates.

Portfolio rebalancing is performed at least once a month and taxable gains and liabilities can be automatically assessed with regard to the characteristics of individual investors. Therefore, automated robo-technology allows FinTechs to save time and achieve economies of scale beyond the capabilities of most conventional financial advisors. In general, not all investors can benefit. First of all, only long-term investors can be advantaged since tax codes usually impose higher taxes to short-term compared to longer-term capital gains. Second, wealthier investors bearing higher tax rate obligations or living in higher tax rate constituencies have more to gain than those falling into lower tax brackets. The differences in tax code among countries are not discussed in this book and the presentation of tax optimization techniques is kept to the level of principles. The following example is only indicative and does not necessarily correspond to any practice.

Example

Frank is our investor. He lives in a zero cost trading environment and falls into a tax bracket which imposes 25% on short-term capital gains and 15% on long-term capital gains. The financial market is made up of three different opportunities:

• ETF_A tracking the FTSE US Index;
• ETF_B1 tracking the MSCI Emerging Markets Index;
• ETF_B2 tracking the FTSE Emerging Markets Index.

ETF_B1 and ETF_B2 are known to be perfectly correlated (to simplify our example), but are not “substantially identical” according to the tax code. Frank has US$ 200,000 sitting in his account and wants to invest half of his money with a short investment horizon (i.e., 1 year) and the remaining half with a longer horizon (i.e., 5 years). Therefore, he makes the following investment decision at the beginning of the first year:

• invest US$ 100,000 into ETF_A for 1 year;
• invest US$ 100,000 into ETF_B1 for 5 years.

Frank is committed to his investment style. Thus, he plans to disinvest from ETF_A at the end of the first year and disinvest from ETF_B1 at the end of the fifth year.

Table 2.4 shows the evolution of the market value of the three investments over time.

As planned, Frank disposes of ETF_A at T₁ and incurs a short-term capital gain equal to US$ 7,000, which will be taxed at 25%. Frank now has two options:

1. pay taxes on his short-term capital at T₁ and carry on with his investment in ETF_B1 until T₅, leading up to a long-term capital gain of US$ 30,000 taxable at 15%;
2. optimize his taxes by disposing of ETF_B1 at T₁, harvesting a tax-loss of US$ 7,000 to offset the capital gains generated by ETF_A and immediately reinvest the proceeds of ETF_B1 into ETF_B2, which is then kept in the portfolio until T₅ to yield a long-term capital gain equal to US$ 37,000 taxable at 15%.

We can easily see that:

1. in the first case, Frank pays taxes of US$ 1,750 at T₁ and taxes of US$ 4,500 at T₅;
2. in the second case, Frank compensates short-term capital gains and losses at T₁ and defers taxation until T₅ of an amount equal to US$ 5,550.

2.9 Conclusions

Robo-Advisors are automated investment solutions which have been showcasing how digital technology, automated investment algorithms, and passive investment management can be bundled together to transform the functioning of the wealth management industry. Born to serve the needs of taxable investors directly, they are already transforming into Business to Business models to support the work of personal financial advisors and planners. The personalization of the investment decision-making experience around personal goals and fears is part of their success story, and configures as a very rudimentary implementation of Goal Based Investing principles. Finally, the strengths and weaknesses of Robo-Advisors 1.0 can be summarized as in Table 2.5.

The next chapter discusses the functioning of the investment management industry and how robo-technology is poised to change the competitive landscape of the supply-demand chain: asset managers, ETF providers, platforms, personal financial advisors, retail and private banks.