6.1 Introduction

The spirit of the wealth management industry is to provide families and individuals with consistent and up-to-date insights, reasoning, and advice to make wiser and more informed financial decisions about investments, liabilities, and possibly the management of real assets. Any financial investment entails a level of risk, whether a full capital loss or a lower than expected return, which advisors are required to identify and possibly measure. Compared to the boom years of the post-war economy in the US, when Modern Portfolio Theory was initially formulated, investment products have become more complex and leveraged, while markets have exhibited unprecedented levels of volatility and risk contagion among regions and asset classes. This has posed a significant challenge to both advisors and portfolio managers to discuss investment opportunities with taxable investors, not just for the short but also for the long term. Financial advisors cannot be expected to be mathematicians, thus demanding robust yet intuitive digital tools to support their risk management queries (e.g., Robo-4-Advisors). Experts in quantitative finance, instead, cannot solve mathematically all unknowns related to the dynamics of investment returns. No investment algorithm can be programmed to describe with certainty the potential dynamics of financial markets, and measure and master investment uncertainty in a timely fashion. However, investment managers have historically tended to rely too optimistically on the robustness of pricing and trading models, as was made clear by the default of Lehman Brothers or Long Term Capital Management. A fundamental misconception seems quite often to affect the investment behaviour of many professionals: that risk and uncertainty are interchangeable terms, although they are in fact not the same thing. Risk refers to the lack of knowledge about what is going to happen next, but we know what the distribution of such a potential event looks like. When tossing a coin, we are aware that such a “heads or tails” game is governed by well-known probabilities. Uncertainty instead refers to the lack of knowledge about what is going to happen next, but where we do not know what the possible distribution of such an outcome looks like. When forecasting asset prices we “estimate” the shape of probability distributions but we do not know them ex-ante with certainty. Therefore, algorithms are always refined approximations and cannot model the dynamics of asset prices with probabilistic precision, even though some rules or laws can be found to govern well-known problems in physics. A gravitational force can be measured and replicated under varying assumptions, so that an unmanned spaceship can be propelled to reach Mars with great exactitude. Instead, the dynamics of financial markets cannot be framed mathematically once and for all: there seems to be far more uncertainty than risk walking down Wall Street. This explains why portfolio modelling is only a starting point in investment decision-making and needs to be complemented by other pieces of information, to facilitate a well balanced and informative investment journey.

Why bother? Because portfolio modelling is at the core of Robo-Advisors and client-centric financial advice, and although essential to the risk management of personal investments, it does not solve all the challenges related to the management of financial uncertainty merely by the automation of portfolio rebalancing. Portfolio modelling is essential to clear the table of the wealth management discussion from the appraisal of potential risks and returns that can be reasonably measured. Thus, it will give way to further conversations about what is uncertain (e.g., stress tests or market views) and feature meaningful what-if analysis about how financial decisions (e.g., sell in a downturn), personal or market events (e.g., unexpected need of cash) can hinder the achievement of financial goals. Modern technology allows us to gamify conversations about quantitative trade-off on digital tools, to become the heart and soul of competitive and added-value investment relationships. Since this book is about FinTech innovation in wealth management, we explain the reasons why a technological revolution would be incomplete without concomitant innovation in the methods of finance.

“How should wealth managers change their approach to finance, so that technology fosters greater fairness, transparency and personalization of investment decision-making?”

Modern risk/return measurement owes its methods to physics, although physics and economics are very different disciplines. To quote Nobel prize winner Richard Feynman in his speech at the Caltech graduation ceremony, right after the October 1987 market crash, imagine how much harder physics would be if electrons had feelings! The mismanagement of the emotional relevance in economics is one of the main causes of individuals' tendency to buy high and sell low, hindering trading strategies that seemed to be robust ex-ante. Financial markets and private investors are not governed by rules or law, but are dominated by self-interest, greed, and fear. Therefore, respond to the emotions of the emotionally exuberant or over-conservative.

John Coates (2013) has provided an insightful point of view about the biological sources of emotional trading, leading to market bubbles or crashes, and has discussed what happens to the levels of hormones in people's bodies when they are engaged in risk-taking activities. Coates has recognized that human behaviour follows a biological pattern and the subsequent interaction among people is one of the strongest forces to affect the fate of financial markets. This clearly happens at the macro level, dominated by the play of corporations, political agendas, tax legislation, market regulation, and international flows of liquidity. But it also happens at the micro level, made up of periodic conversations between advisors and their clients, or being part of a do-it-yourself interaction with a Robo-Advisor. The greatest innovation in finance and technology would be to embed elements of investment psychology and cognition within advisory workflows, and recognize the implications of emotions, ambitions, and fears as part of the investment decision-making process. In essence, the investors' goals and their personality should take centre stage, as opposed to traditional approaches which focus primarily on the dynamics of markets and benchmarks.

As simple as it sounds, this change in perspective is not proof against relevant hurdles, because “it does change everything” in most wealth management practices, as described in Brunel (2015). The wealth management industry is traditionally shaped around a mechanism of “product-driven” distribution, while such a change would require the adoption of a “portfolio-driven and client-centric” advisory model. However, widespread revision of market regulation, born out of the ashes of the Global Financial Crisis, demands higher levels of transparency on costs, risks, and incentives. The tightening of the compliance framework significantly increases the costs of red tape for wealth management firms, as well as smaller financial advisors, favouring de facto scalable fee-only businesses versus traditional distribution models. The resulting need to reward the advisory relationship with more added value gives lifeblood to the projects of digitalization, and highlights the urgency of creating more emotional and engaging investment experiences. There is nothing more engaging than discussing portfolio construction and investment performance in the light of personal goals, as opposed to market dynamics, and depicting graphically the chances of achieving a desired financial ambition, which in turns translates into a higher probability of attaining personal goals. Individual ambitions and fears, and the “estimated” probabilities attached to their goals, are the main focus in a Goal Based Investing process and replace the traditional conversation about tracking the risks and expected returns of benchmarks, to remind us that financial markets are dominated by risks and uncertainty which need to be reviewed against personal targets. As Chhabra (2015) put it: “If the markets don't really care about you, as surely they do not, then why should you spend all your time and effort trying to beat them?”

Rudimentary attempts to embrace GBI principles lie behind the curtains of the digital interfaces of many Robo-Advisors. They were created as effective on-boarding mechanisms capable of attracting customers on the basis of their simplicity, coolness, and convenience. They are attempting to keep investors explicitly engaged through the cycle, by focusing on long-term performance toward final goals as opposed to idiosyncratic discussions. They are offering their services at discount prices, fostering a realignment of the asymmetry of information. But in essence, they have showcased the feasibility of institutionalizing the private banking relationship, by inviting clients to invest in portfolios which are more clearly labelled around thematics (e.g., retirement, education, housing) displaying different purposes and investment horizons. This is a relevant discontinuity from the DotCom propositions of the 1990s, since the business focus is shifting from idiosyncratic investing (e.g., stock picking) towards passively managed portfolios for long-term targets. However, current Robo-Advisors cannot yet compare to best GBI practices, as articulated by the work of Brunel (2015). Notwithstanding, the need to differentiate further within the wealth management and FinTech ecosystems, and move beyond initially price-driven and single minded business models, will push innovators to innovate further. This time around innovation will not be “disruptive” but “sustaining”, since the final battle will be fought by means of competitive and added-value GBI Gamification.

“Today's investment is tomorrow's competitive growth.”

Investors have a difficult time ahead: the student loans crisis is impending in the US, as is the retirement crisis globally. Therefore, understanding the interaction between these risks and uncertainty is of the utmost strategic relevance for financial institutions and FinTech entrepreneurs, because their capability to provide adequate answers and solutions could ultimately decide the fate of their businesses, as opposed to the search for digital excellence alone which can be easily commoditized. While already transforming, wealth managers need to understand the competitive landscape in 2020 to succeed as winners instead of becoming laggards.

This chapter provides an understanding of what Goal Based Investing means to help build long-term strategies for the development of digital offers. It features some highlights of academic theory, presents the building blocks of the approach, discusses the aspects of a consistent elicitation of goals and risk profiles, and showcases the advantages of GBI graphical reporting. Discerning the essence of Goal Based Investing supports our advocacy for a renewed interpretation of portfolio theory, based on probabilistic scenario simulations, and prepares the terrain to discuss the insights of investment Gamification.

6.2 Foundations of Goal Based Investing

Goal Based Investing is about informing individuals on how to invest to achieve personal goals and invite them to dedicate time to a balanced elicitation of personal ambitions and fears, as opposed to an attempt to tame the markets and formulate investment policies based solely on tracking of benchmarks. Traditional wealth management practices have been primarily driven by an asset management perspective. This perspective focuses on reporting ex-post performance and expected returns of preferred indices or benchmarks to influence asset allocations, without embedding consistently the elicitation of the many ambitions individuals formulate when investing their money, their varying level of risk acceptance, the existence of different investment horizons, and liquidity constraints.

So far, three elements have prevented GBI principles from becoming mainstream, despite being fair and valuable. First, technology was not accessible to many financial advisors and family officers to help them institutionalize a GBI-driven investment relationship with intuitive and economically convenient processes. Robo-technology, digital experiences, and Gamification principles are available nowadays to close this gap, as Robo-Advisors have started to demonstrate. Second, individuals are not rational investors and are truly dominated by traits such as greed and fear. They tend to compare themselves quite often to what their peers or other professional investors might have gained in financial markets, instead of pondering the risks involved and the impact on personal goals. The Global Financial Crisis has reduced people's comfort and confidence in tracking the performance of financial markets, and has affected the reputation of traditional firms as sources of investment advice. This has ignited widespread discussions about the costs and values of active management and idiosyncratic investing, compared to long-term and more passive investment management in the light of personal goals and thematics, as Robo-Advisors have begun to show. Third, Modern Portfolio Theory has dominated portfolio management ever since its first Mean-Variance formulation in the early 1950s. MPT is a model of portfolio diversification, which assumes the existence of a unique efficient frontier which identifies optimal portfolio allocations for given levels of return or risk targets over a single investment horizon. However, individuals exhibit multiple goals, multiple risk attitudes, and multiple investment horizons. Human beings exhibit biases and references, which make the understanding of how they truly decide more relevant than the modelling of how they should react rationally in principle. Most investors would feel more pain when they lose money than the pleasure they would get when they earn the same amounts. Investors are not consistently risk averse and do not have a global view of their investments, but hold separate mental accounts and are willing to gamble more from some of the accounts than from others. Brunel (2002, 2003, 2015) and Chhabra (2005, 2015) have pioneered the use of mental accounts to shape investment advice around well-defined Goal Based Investing principles.

Yet, most wealth management practices remain confined to MPT-related approaches, notwithstanding the known pitfalls, due to an apparent lack of valuable alternatives. Most likely, a professional tendency to simplify investment decisions to the dynamics of benchmarks, and rely on mainstream theories for compliance purposes, has also played a relevant role. The turning point was the publication of a seminal paper by Das, Markowitz, Scheid and Statman (2010), in which the authors concluded that mental accounts and Mean-Variance optimization are mathematically equivalent, hence resolving an initial criticism that GBI approaches might lead to sub-optimal investing due to the allocation of wealth into separate optimal buckets, instead of single optimal portfolios on a unique efficient frontier. However, a discussion about mathematical optimality or sub-optimality of mental accounts versus a single minded optimization might miss the main added-value and business point of GBI. Traditional portfolio optimization is typically about measurement of the measurable unknowns, not about understanding the impact of uncertainty on portfolio returns and the affordability of personal goals. The true added value of GBI resides in moving the investment discussion from expected returns and volatility towards the probability of achieving or missing an investment goal. The game changer resides in the chance to support a more consistent, intuitive, balanced, and informative wealth management experience with modern and fit-for-purpose quantitative methods, and help to resolve investment biases within a robust risk based framework, more than finalizing a debate on mathematical preferences.

This is why the remainder of this book discusses the opportunity to update portfolio choice beyond classical MPT configurations, and presents the principles of Probabilistic Scenario Optimization (PSO), as in Sironi (2015). GBI frameworks can be strengthened by means of scenario analysis, joint simulation of multiple investment horizons, stress tests, market views over time, and risk management of real products (especially fixed income and derivatives). Scenario modelling opens the way for educational Gamification, as a means of helping investors to gauge risk and uncertainty by reconciling ex-ante the potential impact of their decision-making across the short, medium, and long term. If not academia, then digital practice is dictating the relevance of Goal Based Investing at a time when portfolio management is becoming commoditized, within an industry that shifts from product-driven toward portfolio-driven and client-centric models. Wealth managers are asked to showcase added value to their techno-literate clients who are becoming more demanding in terms of personalization, thematics, and transparency.

6.3 About Personal Needs, Goals, and Risks

Abraham H. Maslow (1943) formulated an insightful theory of human motivation which sheds light on the relevance of personal unconscious motivations as opposed to conscious statements, centring upon ultimate goals instead of partial ones, recognizing that humans arrange their preferences in hierarchies of relative predominance so that one need usually rests on the prior satisfaction of another, which is influenced by the field in which an individual reacts, whether in an integrated fashion or as a set of isolated decisions. Although Maslow's theory is about motivation and not behaviour, it is a key starting point for subsequent advances in behavioural finance and Goal Based Investing. Human needs are organized as shown in Figure 6.1:

1. Physiological needs: which correspond to the physical drives such as food, water, and shelter.
2. Safety needs: which refer to human preference for safe, orderly, predictable, organized environments in which unexpected things (danger) cannot occur.
3. Love needs: which indicate the relevance of affectionate relations within a community or in the intimacy of a family.
4. Self-esteem needs: which emphasize the search for recognition, reputation, or prestige.
5. Self-actualization needs: which are the desires for self-fulfilment, such as becoming everything that one is capable of becoming.

Kahneman and Tversky (1979) laid the foundations of prospect theory. They articulated that individuals tend to fear losses more than appreciate gains of the same monetary magnitude (as in Figure 6.2), and therefore make inconsistent decisions with regard to the level of risk aversion. The contradiction in people simultaneously owning an insurance policy (e.g., safety need, low risk) and playing the lottery (self-actualization need, high risk) is well known.

Further advancing from the theory of motivation and prospect theory, Shefrin and Statman (2000) centred their research on the idea that individuals have multiple goals, similar to Maslow's idea that human beings have multiple needs, and that individuals have different risk profiles for each goal, which is reflected in a hierarchy of prepotency. Instead of possessing a global view of their investments, they tend to reason according to separate mental accounts which leads them to accept at once very different gambles.

Brunel's Behavioural Portfolio (2002, 2003, 2015) and Chhabra's Wealth Allocation framework (2005, 2015) have refined the original discussion, by redrawing Maslow's hierarchy as a hierarchy of goals and associated risk profiles, as shown in Figures 6.3 and 6.4.

Taking from Chhabra (2005, 2015), investors have multiple goals which can be organized in three main buckets:

1. Essential goals: which would ideally correspond to physiological and safety needs, and refer to building a safety net that protects from a variety of risks such as mitigating the loss of employment, severe health issues, lack of retirement income, children's and spouses' well-being in case of investor's death.
2. Important goals: which refer to the achievement of personal and family stability such as a constant or growing standard of living within a community, nation, or group of peers.
3. Aspirational goals: which entail pursuing personal dreams and aspirations, such as philanthropic giving, significantly expanding a business, or achieving a unicorn type of investment return. By pursuing such goals, individuals might be prepared to face significant investment losses as a price for the highly aspirational potential.

Within a Goal Based Investing framework, investors are required to take their time, isolate their personal goals, prioritize them, project potential cashflows over time to identify most appropriate investment horizons, aggregate them into fewer buckets and figure out how much money should be invested today or periodically contributed into an adequate set of suitable investment policies, which would be designed to enhance the probability of fulfilling all goals. Thus, people need to make sure that aspirational investment returns do not jeopardise the fulfilment of more essential ambitions. The key rationale would be to insulate investors' essential goals from the dynamics of the financial markets, while granting them enough probability to achieve important targets and allowing for the opportunity to achieve aspirations.

Since each goal represents a different return ambition, they can be remapped into different types of risks:

1. Personal risks: which require protection against falling short of fundamental needs, such as granting access to essential cashflows and avoiding a dramatic decrease in the standard of living.
2. Market risks: which arise from investments required to improve personal finances in order to keep up with increases in the cost of living as well as comparative increases in average wealth due to financial market trends. Market risks are therefore linked to the dynamics of financial markets and no cost-effective portfolio can fully diversify them away.
3. Aspirational risks: which stem from idiosyncratic risks aimed at fostering wealth mobility, hence entailing the potential to generate substantial capital gains or losses.

To go back to investment practice, the elicitation of goal buckets and the definition of the corresponding risk profiles constitute the building blocks of an informative and transparent set of investment policies which can be implemented separately, reviewed individually, optimized and stress tested holistically, should theory and financial engines permit:

1. Safety portfolios: consisting of protective assets (e.g., liquidity, primary residence, retirement savings, short-term and highly rated Fixed Income or Inflation Linked).
2. Market portfolios: with the objective of stability in the long term (e.g., bonds, stocks, mutual funds, alternative assets... all with the most balanced and adequate mix).
3. Aspirational portfolios: to target aspirational goals (e.g., family-owned businesses, private equity ownership).

Table 6.1 summarizes the mapping between buckets, risk typologies, and model asset allocations.

The fact that personal goals can be organized into hierarchical categories is common knowledge nowadays, and this approach is useful for the majority of investors, irrespective of their worth. Clearly, affluent and UHNW would place different personal emphasis on goals like “saving for retirement” and “philanthropy”. A consistent periodic assessment of personal preferences, ambitions, fears, and current investments (e.g., share of wallet) would be essential for any financial advisor or Robo-Advisor to provide holistic Goal Based Investing advice. However, financial advisors might not be able to collect all relevant information from their clients and gain a full picture of their assets and liabilities. Investors might not be used to dedicating enough time and discussing goals, time horizons, and risk appetite due to insufficient financial literacy as well as entrenched investment habits. Furthermore, Robo-Advisors are currently focused on very simplistic engagement mechanisms based on easy-to-complete self-assessment questionnaires and limited investment proposals (e.g., thematic portfolios), although a great effort in the area of design has been made to avoid potential clients' perception of being pigeon-holed. Yet, most of them already organize their engagement model around thematics, hence potential goals. Although this seems to be the right first step, going forward it might not be enough to fulfil the growing demand for personalization stemming from a very competitive marketplace which serves Generation X and especially Millennials. The fight for personalization is not just a problem of technology, such as Big Data analytics to personalize news and insights on a digital tablet, nor can it be confined to the creation of a smarter and cognitive dialogue before investments are made. The fight for personalization will be fought by providing seemingly unique financial advice and related investment policies, which correspond to actual goals and comply with individual core values with regard to personal, social, or environmental issues.

The innovative search for successful and competitive investment experiences needs to be affordable as well, and allow streamlining of all aspects of pre-investment compliance without missing out on the chance to garner cognitive insights on clients and generate asset allocations which correspond to the right thematics, align with desired impact investing, and preserve essential goals from the fate of aspirational bets. Leading FinTechs, platforms, or financial institutions will be those capable of using technology to support the procedural hurdles of Goal Based Investing, and making efficient use of robo-technology, Big Data analytics, cognitive computing, innovative quantitative finance, and digital Gamification to create a compliant and relevant engagement. All these aspects will be discussed in the remainder of this book.

6.4 Goal Based Investing Process

GBI principles can become the competitive skeleton of a digital transformation based on affordable workflows, the backbones of which are cognitive computing, deep learning, Big Data analytics, social media insights, and scenario analysis. The generation of an added-value investment experience requires the building of an informative dialogue whose outcome is a personalized and compliant investment policy that matches individuals' values and goals. GBI workflows designed to engage individuals and let them invest consciously into relevant thematics (i.e., sub-investment policies or model portfolios) do not need to be very different between family offices, Digital-Advisors (i.e., Robo-4-Advisors) and Robo-Advisors. The workflow can be institutionalized and unbundled into five assessment steps, as shown in Figure 6.5, which enhance the compulsory Know Your Customer (KYC) processes and contribute to the final investment allocation.

1. Personal values: which identify personal beliefs and sensitivities, to facilitate the personalization around specific thematics or impact investment opportunities.
2. Goals: which are the needs behind any investment portfolio, and are ultimately formulated as a percentage total return, a target portfolio value, or a required income stream (e.g., post-retirement income or de-cumulation).
3. Time horizons: which qualify the minimum/maximum holding periods for the sub-investment policies (e.g., short, medium, long, or generational transfer).
4. Risk tolerances: which assign to each goal the most coherent risk limit (e.g., maximum shortfall probability).
5. Goal priority: to organize each goal within the most coherent risk bucket (e.g., essential, important, aspirational).

The GBI engagement model is fairly streamlined but must allow for a recursive revision of every decision-making step. It is therefore essential that the financial engine dedicated to supporting portfolio construction allows for interactive what-if analysis, in order to qualify and quantify the impact of any preference and investment decision on potential future outcomes.

6.5 What Changes in Portfolio Modelling

Setting aside for a moment the relevance of personal values, Goal Based Investing approaches attempt to personalize the investment experience by identifying optimal portfolios which comply with four postulates, that is:

1. taxable investors have multiple goals,
2. exhibiting different priorities,
3. which target multiple time horizons,
4. all potentially characterized by different risk tolerances.

Thematic labels have been showcased by many Robo-Advisors and are useful to organize investment goals and prioritize them within risk buckets. Goals can be assigned a quantitative target in terms of desired asset value (or total return percentage) within a time frame, which is also conditional on the initial invested amount and any periodical contribution. Defining optimal portfolios for each goal, in the presence of money in-flows and out-flows, might not be trivial for most financial engines which rely upon classical MPT assumptions. Clearly, given a model portfolio the more an investor contributes, the higher the chances of achieving higher portfolio values over time. But how much is potentially due to the evolution of financial markets and how much would be a function of in-flows? Given that disposable wealth is constrained, is there a way to understand ex-ante what would be the best balance? Moreover, long-term investing might require an even more complex design than myopic bets. First of all, although target date is distant, risk constraints need to be verified periodically: we have learned in Chhabra (2005) that the journey matters as much as the destination. Secondly, more complex goals aimed at supporting post-retirement needs (e.g., income targets or de-cumulation out-flows) require quantification of the investment objective in terms of affordability to buy an annuity or conform to a certain pattern of wealth de-cumulation, which is not always a trivial quantitative task.

Traditional portfolio construction techniques do not aways seem to be fit for purpose. Therefore, further financial innovation is required to exploit all the added-value advances that GBI approaches seek to deliver. In particular, compared to traditional MPT, it seems relevant to:

1. change the risk measure and introduce the probability of achieving or missing a goal as key criteria;
2. embed a multi period verification mechanism within portfolio modelling, to account for the relevance of the journey as well as the destination;
3. facilitate the simulation of real products within portfolio modelling, to enhance risk management, add valuable insights, and improve compliance;
4. simulate and optimize portfolios by accounting for in-flows and out-flows;
5. allow the expression of investment goals as the affordability of future investment decisions (e.g., buying an annuity at retirement).

Das, Markowitz, Scheid and Statman (2010), Chhabra (2005, 2015), and Brunel (2002, 2003, 2015) have provided the academic imprimatur for the advocacy of the change of risk measure, indicating the probability of achieving a goal (hence its complement, the probability of missing a goal) as the key objective of GBI portfolio construction. Sironi (2015) has attempted to address the remaining issues with Probabilistic Scenario Optimization (PSO), and opened the GBI framework to the use of risk factor simulation and scenario analysis, which are building blocks of an investment Gamification based on sound quantitative methods. While traditional approaches typically define portfolio risk as the volatility of potential returns, or a quantile of their distribution (e.g., Value at Risk), what seems relevant for individuals is not the minimization of an arbitrary level of loss but the minimization of the probability of missing their financial goals. Since most solutions assume normality of portfolio returns, in a Mean-Variance framework we can look at the simplified normal distribution of potential portfolio returns and identify the probability associated with any level of return ambition, as shown in Figure 6.6. The optimal portfolio would correspond to the asset allocation that would produce at a given time horizon the highest target return, with a minimum required probability of success.

Further distancing from the Mean-Variance limitations, Sironi (2015) has allowed definition of the optimal portfolio module as the asset allocation that exhibits the highest probability of success (hence minimum probability of failure) along the time horizon, constrained by a multi-period risk limit that can be expressed with quantile measurement (e.g., VaR profile over time), as shown in Figure 6.7.

The quantitative aspects of these optimization approaches will be drafted in the next chapter. What follows is a revision of the five assessment steps of the GBI workflow, as shown in Figure 6.5: personal values, goals, priorities, time horizons, and risk tolerances.

6.6 Personal Values

Understanding ethical and behavioural thought processes of clients provides valuable insights about their emotional preferences to drive the whole GBI workflow efficiently. Yet, advisors are not psychologists, time is money, and individuals can be reluctant to engage in an investment conversation centred on personal ethics. What clients care about, what they believe is worth doing, and what makes them passionate are all answers to probing questions which modern analytics can help to collect and transform into an engaging interaction. This is not just relevant as a first step with a prospect, but provides emotional suggestions to keep clients engaged over time, allowing for less formal conversations about reassessment of goals and leading to risk-effective portfolio rebalancing. How can technology enhance the understanding of personal values? With particular regard to Millennials, their use of social media can provide Robo-Advisors and Robo-4-Advisors with a relevant source of information about personal interests and values which can be scanned by analytics and deep learning engines to provide insights about the personal relevance of environmental issues, sensitivity to tobacco usage, sports preferences, interests about social issues in certain regions more than others, etc. This would allow the positioning of a filtered set of thematics (e.g., travel, children's education, tobacco-free investments), which can be referred to when making investment proposals and increase the level of customization within an institutionalized framework. Moreover, smart digital tools could allow association of the right emotional images to thematic portfolios, which would be perceived as more aligned with personal preferences and values. For example, a Digital-Advisor could start to ascertain an aspirational bucket by showing on a digital tool the image of a Ferrari instead of a vineyard in Tuscany, should social media analytics report that the client has been frequently tweeting about Formula One races as opposed to the latest wine rankings by Robert Parker. Moreover, cognitive computing can be used by Robo-Advisors to create insightful yet automated conversations based on personality insights, whenever human interaction is not part of the assessment process.

6.7 Goal Elicitation

Shefrin and Statman (2000) stated that individuals have multiple goals and different risk profiles for each goal (e.g. lottery versus insurance, pension versus IPO). Goal elicitation, prioritization, and mapping to buckets are the essence of Goal Based Investing. Paradoxically, Brunel (2015) reminded us that the major challenge with a client-centric process is that clients are required to remain engaged through the whole workflow, while traditional approaches obviate this need since clients are more simply asked to pick a portfolio out of a risk-tolerant selection and be satisfied with the outcome. Therefore, creating the right engagement and experience seems to be a winning factor to help financial advisors in guiding their clients through the steps of the process, in particular in visualizing goals which are far into the future. That most of us will depend on retirement money is obvious, although many individuals fail to understand how relevant it is to start saving and investing for retirement while still being young professionals. Moreover, it might not be easy to discern how much retirement income will be deemed enough by looking far into the future, and hence set a meaningful quantitative target.

Digital tools can help to create life planning experiences with graphical representations of personal needs and most likely timelines (Figure 6.8), so that the process of goal elicitation can become more interactive and possibly gamified. Every goal can be conceived as a personal scenario into the future: having US$ 20,000 more in 5 years, or buying an annuity to yield US$ 1,000 every month after retirement. Since financial goals can be represented as thresholds which can be achieved conditional to specific scenarios, financial engines operating on scenario analysis seem to provide greater added value because they allow simulation of future statements of wealth performance over time and visualization of their effects on potential goals. This helps to understand if portfolios are exposed to excessive risk, but also if goals are too ambitious or insufficient given the implications of the passage of time on compounded returns and the likelihood of economic realization. The winning factor lies in the availability of a financial engine capable of marking to future investment products and strategies, which decouples product performance and the evolution of the underlying variables (i.e., risk factors). Brunel (2015) also advocates that asset allocations based on risk factors rather than asset classes become the norm, as it allows for a more differentiated and finer analysis and management of the risks involved in a portfolio and, we would add, the affordability of personal ambitions.

6.8 Goal Priority

Shefrin and Statman (2000) state that individuals have multiple goals and have different risk profiles for each goal. The scope of Goal Based Investing resides in allowing individuals to invest with purpose, and hence associate more clearly financial needs and the investments that best fit. At the same time, Goal Based Investing seeks to ensure that invested amounts and investment compositions are thoroughly crafted so that lower priority goals can never jeopardize essential ones. Building a hierarchy of prepotency is truly valuable as it allows account to be taken of emotions during a market downturn, because it can provide a clear picture of the risks undertaken and the potential still open to reach personal financial ambitions.

6.9 Time Horizons

The essence of any financial decision-making process is always about the consistent appraisal of three elements: portfolio risk, personal ambition, and investment horizon. Yet, although much time is devoted to the understanding of risk and return, not enough is usually dedicated to making sure that the passage of time, which elapses between the present and the realization of desired goals, is properly modelled and represented. Brunel (2015) reminded us that Goal Based Investing enables the establishment of the link between “My Wealth” and “My Life”, which means understanding how needs can change over time, how relevant it becomes to anticipate strategies aimed at resolving future funding needs, and that future life is not deterministic but that personal events, decisions, or external factors can influence our minimum requirements, sometimes suddenly.

Investing is therefore a journey, in which private investors should be allowed to travel with the tools and equipment necessary to enjoy the trip, as well as cope with the abrupt changes in the terrain. Goals are meaningful only if minimum and maximum investment horizons are also set to judge their effective realization. As individuals have multiple goals, they clearly have multiple investment horizons, and hence need to discuss investment opportunities across the timeline and through economic cycles. The capacity to absorb losses in the short term for goals whose horizon is set far into the future can be fundamental to avoid the tendency to buy high and sell low, and thus enhance final investment returns. It is therefore paramount to be capable of designing potential scenarios to assess the interrelation between essential, important, and aspirational goals over different time steps. Chhabra (2011) reminded us that the journey matters, as indicated in Figure 6.9. Hence, understanding how different sub-investment policies interact over time is essential to build a holistic view of personal wealth changes.

Yet, classical advice relies upon expected return and variance, which are hardly meaningful indicators of investment risk/return potential for the short term, and clearly not suitable indicators for the medium to long term. This is why Probabilistic Scenario Optimization (PSO) is drafted in the remainder of this book, because only a financial engine built on scenario analysis over time can facilitate ex-ante understanding of the risks that lie ahead and accommodate for stress tests to check the effective robustness of the hierarchy of prepotency to face uncertain events.

6.10 Risk Tolerance

The risk profile is the result of a process of investigation aimed at identifying which investments are suitable and adequate for an individual, that is how much risk (e.g., potential losses) a client can tolerate. Post-GFC market regulation has strengthened the relevance of achieving a consistent elicitation of investors' risk profiles, although principles and recommendations are not always aligned internationally. Most strikingly, although risk tolerance is a key hub of the compliance process, regulators have been setting principles more than prescriptive mechanisms, which has favoured the appearance of differing practices among industry participants which have been largely relying upon static paper questionnaires. The effectiveness of the risk assessment, which is part of the on-boarding mechanism of Robo-Advisors, can be significantly improved by using smart technology and become an educational opportunity for investors. Enhancing the compliance framework allows for more suitable and risk adequate investment propositions, hence lowering potential attrition and regulatory costs.

An individual's risk tolerance is a combination of subjective and objective elements, namely risk aversion and risk capacity, both combining to shape an individual's perception of financial risks, hence relevant to a consistent calibration of the GBI workflow. Risk aversion (e.g., pain from losses) is the subjective factor which determines the willingness to take on risks as a result of psychological traits and emotional responses. Risk capacity (e.g., personal wealth) is the objective factor which determines the capability to sustain financial losses of a certain magnitude without jeopardizing essential goals. Clearly, the fact that clients are wealthy yet conservative does not imply that they should be allowed to invest part of their wealth in very risky bets, just because potential losses would not affect their financial well-being. More importantly, knowing individual risk capacities is a relevant factor to mitigate framing biases during the elicitation of risk aversion. According to Klement (2015), risk capacity and risk aversion are closely linked and common practices for risk questionnaires seem to be too weak, as limited or excessively standardized, leading to the underestimation of investors' risk tolerance. Individuals dealing with paper questionnaires react with more vivid emotions when presented with larger loss scenarios than smaller amounts, or abstract figures such as percentage losses. That is, confronting the emotions for a potential loss of US$ 20 for US$ 100 invested would be different than presenting a loss of US$ 2,000 for a US$ 10,000 investment. Therefore, it seems fundamental to shape investigations about risk aversion by framing the dialogue with due knowledge of individuals' risk capacity and actual investment amounts.

What drives and influences the forging of personal levels of risk tolerance? Klement and Miranda (2012) seem to indicate that genetic imprints, past experiences, and the environment we interact with are key drivers. We have already mentioned the work of Coates (2013) on the biology of risk, linking traders' exuberance and over-conservativeness to the level of hormones, hence genetic predisposition. Yet, asking for a DNA test does not seem to be a viable step of a risk assessment phase. Instead, looking at life experiences and the interaction with the community in which a client lives and works seems to be more easily accessible and convenient. It is well known that after a market downturn investors are less willing to invest in stocks than after the recovery of a prolonged bull market, because the memory of a recent loss is still vivid and influences emotional reactions to investing. However, past experiences are also very relevant, such as the social conditions and market environments during formative years, which can affect individuals as well as whole generations. Brown, Ivković, Smith and Weisbenner (2008) have also discussed the relevance of peers and communities, presenting insightful evidence about their potential influence on the amount of risky asset ownership: it seems that moving an individual to a community characterized by higher stock participation would consequently increase the acceptance of stock investing.

Financial advisors and regulators might still fall short in recognizing that investors tend to exhibit multiple risk tolerances, and failing to account for this might lead to very inefficient dialogues and asset allocations. GBI workflows allow us to calibrate risk tolerance to individual goals. Yet, all goals and underlying risks can be aggregated in a hierarchy of prepotency to fit single minded compliance. Smart financial engines would allow the embedding of a hierarchy of risk tolerances in the construction of portfolios, similarly to what financial institutions would do when allocating risk capital to trading desks. No sub-investment policy of higher risk and ambition should hinder the probability of reaching essential goals when investments are jointly simulated over time, acting as a global limit to the holistic asset allocation. However, the risk definition needs to be enhanced. Classical approaches define the risk profile starting from the traditional MPT assumption that investors are willing to take on extra risk only if they can garner higher anticipated returns to compensate them for higher risk, thus identifying which investments are suitable and adequate. Nothing is necessarily said about the consistency between the levels of risk and declared ambition. Goal Based Investing innovates on the definition of risk, and introduces the probability of missing a target as a key driver of the asset allocation process. This allows definition of an asset allocation as adequate, not solely against a personal risk tolerance, but also against a specific goal and helps gauge how reasonable investors' expectations are compared to the risks they are willing to on-board.

How can technology enhance the elicitation of the risk profile? First, digital tools allow us to move out of tick-box questionnaires and create smart dialogues between personal financial advisors or Robo-Advisors and respective customers. Questions and answers could be generated in the most inbiased fashion to avoid framing, placed in the context of an individual's life cycle (e.g., Generation X), experiences, and communities. Second, social media analytics could provide valuable insights into cognitive dialogues by contextualizing questions and answers to the profession, location, religion, and ethical considerations of customers. Third, quantitative finance enhances risk measurement and assessing the probability of achieving or missing goals across different buckets and time horizons without losing consistency.

6.11 Reporting Goal-Centric Performance

So far, we have discussed the advantages of Goal Based Investing to enhance a transparent intuitiveness in financial decision-making, by mapping thematic portfolios to investors' emotional needs. Since this client-centric approach is far more demanding than a traditional brokerage or advisory model, it is relevant to make clients feel comfortable during the whole process, which does not seem to be affordable without institutionalizing the workflow. GBI innovates the way individuals make investment decisions, by themselves or by consulting with personal financial advisors or Robo-Advisors, because portfolio propositions become aligned to the way people think about their money, more than the way institutions think about creating MPT portfolios. Clearly, GBI also innovates the way investment performance is reported, because the focus moves away from quarterly analysis of benchmarks and asset returns, toward the establishment of a progress-to-goal dialogue. Implementing client-centric policies and reporting goal-centric performance can be time consuming and too expensive without appropriate technology, which involves a revision of back-ends and front-ends of established firms. Robo-Advisors have a competitive advantage, as they can construct their system architectures without much reliance on the past. We have learned that clients seem to care about mental accounts, but custodians have no knowledge of this and tax authorities care about the asset's ownership irrespective of the investing purpose (setting aside tax advantaged retirement savings). Therefore, the following complexities need to be addressed:

1. Money is not always deposited with a single entity and financial advisors require tools of account aggregation and disaggregation to map holdings or part thereof to individual goals and buckets.
2. Discussing the aggregated investment policy is still relevant, due to compliance and tax reasons related to regulatory risk tolerance and investment rebalancing. Regulators and tax authorities do not have mental accounts.
3. Clients are used to traditional reporting, which makes it advantageous to provide a reconciliation of both views.

Therefore, robo-solutions need to solve the tasks of aggregation and disaggregation across buckets and goals, as depicted in Figure 6.10.

We can assume a simplified example in which US$ 100 are invested in six products at the same start date. Table 6.3 exemplifies a traditional report (numbers and quantities are only indicative).

Table 6.3 exemplifies instead a GBI report for an investor having two goals with different investment horizons.

The probability of reaching a goal is a function of the performance so far, as well as the remaining potential evolution of the financial market variables affecting the price of any security over time. It is also a powerful way to isolate those portfolios, hence clients, among the thousands, hundreds of thousands, or millions whose asset allocation does not seem sufficiently robust within a financial planning context. Therefore, alerts can be generated to inform financial advisors about those clients needing more care, while providing an intuitive though quantitative rationale for the rebalancing discussion. Figure 6.11 features a simplified graphical representation of portfolio performance, which depicts intuitively ex-post performance (which is known after disaggregation of asset ownership from the individual goals) and ex-ante performance (which is estimated with a Monte Carlo process).

The remainder of this book describes how to enhance portfolio modelling and make insightful graphical representations of investment performance.

6.12 Conclusions

Goal Based Investing is a game changer in wealth management as it moves the advisory dialogue from the advisor-centric approaches of MPT based portfolio construction to the hierarchy of client-centric goals. Financial innovation can be supported by technological innovation to institutionalize the GBI approach and make it affordable to financial advisors, as well as entertaining and engaging for final investors. The rest of this book will discuss portfolio construction, without delving too much into its mathematics, simply to highlight the main assumptions underling Modern Portfolio Theory and its current implementations by many Robo-Advisors and financial institutions (e.g., Mean-Variance, Black-Litterman), the modifications already mentioned to comply with GBI principles, and further advances (e.g., PSO) to build a more robust risk based simulation framework, which strengthens GBI added value and allows for scenario analysis and Gamification.