Chapter 7The Investment Journey: From Model Asset Allocations to Goal Based Operational Portfolios

“Creating a new theory is not like destroying an old barn and erecting a skyscraper in its place. It is rather like climbing a mountain, gaining new and wider views, discovering unexpected connections between our starting points and its rich environment. But the point from which we started out still exists and can be seen, although it appears smaller and forms a tiny part of our broad view gained by the mastery of the obstacles on our adventurous way up.”

—Albert Einstein (1879–1955)

While FinTechs largely position themselves as revolutionaries in personal finance, they often rely upon simplified portfolio construction methods which seem incomplete with regard to modern risk-management techniques and scenario analysis, and can ultimately lead to inconsistent graphical representation of the potential performance of model portfolios. Therefore, this chapter outlines key aspects of portfolio modelling, which shape the investment propositions of many Robo-Advisors. First, Mean-Variance and Black-Litterman optimizations are drafted, being the most commonly used techniques to construct model portfolios for private wealth. Second, a recent modification to Mean-Variance is introduced, as a relevant development to address client-centric solutions. Thus, Probabilistic Scenario Optimization is discussed, as a risk-based framework whose building blocks and principles can lead Robo-Advisors ...

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