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Preface

      Modern portfolio theory (MPT) is one of the most insightful tools of modern finance. It introduced the world to the first intuitive framework for portfolio risk and how one can optimally combine investable assets to form portfolios with high return and low volatility. The elegance and power of mean-variance (M-V) optimization garnered its inventor Harry Markowitz a well-deserved Nobel Memorial Prize in Economics and has always been a great personal inspiration for how quantitative insights can assist us all in our daily life.

      The actual process of implementing MPT for clients can be challenging in practice, though. Setting client risk preferences, accounting for client financial goals, deciding which assets to include in the portfolio mix, forecasting future asset performance, and running an optimization routine all sound simple enough conceptually, but when you actually sit down to implement these tasks, the true complexity of the problem becomes apparent. This implementation hurdle forces many advisors either to outsource the asset allocation component of their process or deploy simple portfolio construction heuristics (rules of thumb) that are sub-optimal and lack connection to client preferences.

      The financial ecosystem has also seen tremendous evolution since MPT was first introduced in 1952. The world has been introduced to non-linear investable assets such as options and certain alternative risk premia (AKA style premia, factor investing, or smart beta), which have rapidly become more available to retail investors over the past two decades. Additionally, our understanding of human behavior when it comes to decision-making under uncertainly has markedly shifted with the discovery of prospect theory (PT) in the 1970s. Homo economicus, the perfectly rational investor, is no longer the client we are building portfolios for. These evolutions cannot be handled in the MPT framework: non-linear assets cannot be represented by mean and variance and the M-V approach cannot capture the nuances of behavioral risk preferences.

While MPT is both practically challenging and theoretically antiquated, there are wonderful new methods available for both simplifying the challenging tasks in the asset allocation process and addressing the realities of human decision-making in today's markets. Unfortunately, this progress has not been widely assimilated by the wealth management community, which includes both traditional advisors and robo-advisors. Most advisors still utilize the original formulation of MPT or deploy heuristic models that help avoid the challenges of implementation altogether. This book is a first step to bridging the gap between the original formulation of MPT and a more modern and practical asset allocation framework.

      This book was written to enable advisors to more accurately design portfolios for real-world client preferences while conquering the complexities of the asset allocation process that often push advisors into sub-optimal heuristics or outsourcing. To empower advisors fully in being able to implement the framework catalogued in this book, the complete machinery is available as a cloud-based SaaS: www.portfoliodesigner.com. Just as the book is meant to provide a modern and intuitive system for creating portfolios, the software is also intended to provide an accurate and scalable solution for real-world asset allocation based on the methods presented here. And for those who

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