David M. Berns - Modern Asset Allocation for Wealth Management

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

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, thank you for keeping me sane through this endeavor and being so helpful on just about every topic covered. Thank you to my trusted friends in the advisory space—Alex Chown, Jeff Egizi, Zung Nguyen, and Erick Rawlings—for all your thoughtful input. Thank you to Chad Buckendahl, Susan Davis-Becker, John Grable, Michael Guillemette, Michael Kitces, Mark Kritzman, Thierry Roncalli, and Jarrod Wilcox for helpful feedback on special topics. And thank you to Bill Falloon and the rest of the Wiley team for their gracious support and encouragement all along the way. I'd additionally like to thank Mark Kritzman, who through his lifelong commitment to rigorous yet elegant approaches in asset allocation, has inspired me to continue to advance a modern yet practical solution for our wealth management community. And finally, to my science teachers, Peggy Cebe, Leon Gunther, Will Oliver, and Terry Orlando, thank you for the lessons in research that I carry with me every day.

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