Adaptive Asset Allocation: Dynamic Global Portfolios to Profit in Good Times—and Bad (Wiley, 2016)
The authors of Adaptive Asset Allocation: Dynamic Global Portfolios to Profit in Good Times (Wiley, 2016)—Adam Butler, Michael Philbrick, and Rodrigo Gordillo— hail from ReSolve, a Toronto-based asset management firm. In 40 brief chapters they take the reader from some basic principles of investing and portfolio design to the research-based meat of the book: how to deal with the reality that “on every meaningful measure of market valuation, stocks are priced to deliver miserable returns over the next 15 to 20 years.” (p. 65)
The four measures, “derived from four distinct facets of financial markets,” are the Shiller PE, which focuses on the earnings statement; the Q ratio, which focuses on assets, or the balance sheet; market cap to GNP, which focuses on corporate value as a proportion of the size of the economy; and deviation from price (aka price residuals), which focuses on a statistical price series. (p. 69) Forecasts based on a model incorporating these measures are much more accurate than those based on the long-term average market return (“1.16 percent annualized return error from [the] model versus 5.55 percent using the long-term average”), “especially over long time horizons and near valuation extremes.” (p. 81)
Given the grim forecast of this model, which as of November 2014 calls for 15-year returns of -1.3%, what is an investor to do? The authors analyze three key portfolio parameters—volatility, correlation, and returns—and merge them with a fundamental understanding of structural diversification “to build optimal portfolios that adapt to ever-changing markets.” (p. 133) For instance, addressing the first parameter, the authors suggest that “when volatility is low, typically in the early and mid-stages of bull markets, [an investor] use leverage to increase volatility up to the target [e.g., 15% annualized]. Conversely, when volatility is high, typically in the early to mid-stages of a bear market, we hold a portion of funds in cash to decrease overall volatility.” (pp. 123-24)
Skipping over several key steps in the authors’ analysis, we reach the gorgeous equity curve of a portfolio that has a Sharpe ratio of 1.60, a maximum drawdown of 8.8%, volatility a stable 9.4%, and compound returns of 15.0%. One dollar invested in 1995 would have grown to $16.31 by the end of 2013. Although they don’t consider the portfolio management approach illustrated in this equity curve optimal (they provide even more impressive equity curves that result from “some minor engineering improvements to parameter estimates and more frequent rebalancing to better control risk”), they suggest that “it provides compelling evidence of the efficacy of an integrated adaptive asset allocation (AAA) framework.” (p. 141)
Any investor or financial advisor who is mathematically literate and has a platform for portfolio backtesting can use this book as a springboard for his own research. Even those with modest quantitative skills can essentially cut and paste some of its findings to improve their portfolio results. The gains could be substantial, many multiples the modest price of this book.
