Modern Asset Allocation for Wealth Management. David M. Berns

to deploy the primary framework of the book, the hope is that the materials presented here can minimally help advisors navigate the wide world of asset allocation solutions out there in a more informed and fiduciary manner.

      While a primary goal of this writing is to provide a practical solution to asset allocation, I must warn you that the final framework falls short of being a simple solution. Any asset allocation solution that truly respects client preferences and the foundations of modern financial economics will require a certain foundation of knowledge and measured care for proper implementation. My hope is that, with education and practice, the refined perspective presented here will quickly become second nature to wealth management practitioners and ultimately lead to a scalable process that financial advisors can truly stand behind. To help streamline this education, I have decided not to present an encyclopedic review of asset allocation tools, but instead to focus on a limited number of tools for each step of the asset allocation process. To this end, I have consciously focused on the most accurate methods that were simultaneously practical, which includes elements that are undeniably optimal (and need never be replaced) and others that are clearly not optimal (and may warrant replacement). While the ultimately singular framework presented here indeed has its limitations, my truest intention was to create a modern yet practical process that the wealth management community could readily and confidently deploy today.

For the purposes of this book, asset allocation is defined as anything related to creating an investment portfolio from scratch. This includes setting client risk preferences, deciding which assets to include in client portfolios, forecasting future asset performance, and blending assets together to form optimal client portfolios. Following the first chapter, which reviews some key preliminary concepts and presents the general framework pursued here, this book is organized in the order in which each asset allocation task is carried out when creating a client's portfolio in practice. Hence, Chapters 2–5 are meant to serve as a step-by-step guidebook to asset allocation, where the aforementioned software follows the exact same workflow. Below is a brief overview of what will be covered in each chapter.

      Chapter 1. Preliminaries. Utility theory and estimation error, two key concepts that underlie much of the book's discussions, are introduced. Asset allocation is then defined as the maximization of expected utility while minimizing the effects of estimation error, which will ultimately lead to the book's modern yet practical process for building portfolios. MPT and other popular models are shown to be approximations to the full problem we would like to solve. Key concepts from behavioral economics are also introduced, including a modern utility function with three (not one) risk parameters, that can capture real-world client preferences. We then review how to minimize estimation error and its consequences to create a practical framework that advisors can actually implement. The chapter ends with a formal definition of the overall framework that is pursued in the remainder of the book.

      Chapter 2. The Client Risk Profile. The chapter begins with a review of how to measure the three dimensions of client risk preferences (risk aversion, loss aversion, and reflection) via three lottery-based questionnaires. The concept of standard of living risk (SLR) is introduced to help determine whether these preferences should be moderated to achieve the long-term cash flow goals of the portfolio. SLR is then formally assessed with a comprehensive yet simple balance sheet model, which goes far beyond the generic lifecycle investing input of time to retirement, and leads to a personalized glidepath with a strong focus on risk management. The final output of the chapter is a systematic and precise definition of a client's utility function that simultaneously accounts for all three dimensions of risk preferences and all financial goals.

      Chapter 3. Asset Selection. The third chapter presents a systematic approach to selecting assets for the portfolio that are simultaneously accretive to a client's utility and minimally sensitive to estimation error. By combining this asset selection process with the concept of risk premia, the chapter also introduces a new paradigm for an asset class taxonomy, allowing advisors to deploy a new minimal set of well-motivated asset classes that is both complete and robust to estimation error sensitivity.

Chapter 4. Capital Market Assumptions. This chapter justifies the use of historical return distributions as the starting point for asset class forecasts. We review techniques that help diagnose whether history indeed repeats itself and whether our historical data is sufficient to estimate accurately the properties of the markets we want to invest in. A system is then introduced for modifying history-based forecasts by shifting and scaling the distributions, allowing advisors to account for custom forecasts, manager alpha, manager fees, and the effects of taxes in their capital market assumptions.

      Chapter 5. Portfolio Optimization. In the fifth and final chapter, we finally maximize our new three-dimensional utility function over the assets selected and capital market assumptions created in the previous chapters. Optimizer results are presented as a function of our three utility function parameters, showcasing an intuitive evolution of portfolios as we navigate through the three-dimensional risk preference space. By comparing these results to other popular optimization frameworks, we will showcase a much more nuanced mapping of client preferences to portfolios. The chapter ends with a review of the sensitivity of our optimal portfolios to estimation error, highlighting generally robust asset allocation results.

      There are three key assumptions made throughout this book to simplify the problem at hand dramatically without compromising the use case of the solution too severely: (1) we are only interested in managing portfolios over long-term horizons (10+ years); (2) consumption (i.e. withdrawals) out of investment portfolios only occurs after retirement; and (3) all assets deployed are extremely liquid. Let's quickly review the ramifications of these assumptions so the reader has a very clear perspective on the solution being built here.

      Assumption 1 implies that we will not be focused on exploiting short-term (6–12 month) return predictability (AKA tactical asset allocation) or medium-term (3–5 year) return predictability (AKA opportunistic trading). Given the lack of tactical portfolio shifts, it is expected that advisors will typically hold positions beyond the short-term capital gains cutoff, and it can be assumed that taxes are not dependent on holding period, allowing us to account completely for taxes within our capital market forecasts. One can then assume there is little friction (tax or cost) to rebalancing at will, which leads to the following critical corollary: the long-term, multi-period portfolio decision can be reduced to the much simpler single period problem. Finally, the long horizon focus will help justify the deployment of historical distribution estimates as forecast starting points.

      The first key ramification of assumption 2 is that we only need to consider “asset only” portfolio construction methods, i.e. asset-liability optimization methods with regular consumption within horizon (common for pension plans and insurance companies) are not considered. Additionally, it allows us to focus on the simpler problem of maximizing utility of wealth, rather than the more complex problem of maximizing utility of consumption.

Assumption 3 has two main consequences: (1) liquidity preferences can be ignored while setting the client risk profile; and (2) the liquidity risk premium need not be considered as a source of return. This assumption also keeps us squarely focused on the average retail client, since they don't have access to less-liquid alternative assets (like hedge funds and private equity/real estate) that are commonly held by ultra-high-net-worth individuals.

      I hope this book and the accompanying software empowers advisors to tackle real-world asset allocation confidently on their own, with a powerful yet intuitive workflow.

       David Berns

      New York

      January 10, 2020

Acknowledgments

      First and foremost, thank you to my amazing family and friends for all their love and support throughout the writing of this book. Carolee, thank you for selflessly taking care of me and our family through all of the anxiety-laden early mornings, late nights, and weekend sessions; I couldn't have done this without you. Craig Enders,