Beyond Diversification: What Every Investor Should Know About Asset Allocation. 2020. Sébastien Page, CFA. McGraw Hill.
Sébastien Page, CFA, explains the pros and cons of different approaches to forecasting returns, risks and correlations between asset classes. Explore methods for building portfolios to meet a range of client requirements.
“If you think you can’t estimate expected returns, you shouldn’t be in the investment business.” — Bernd Scherer, PhD
Within each multi-asset portfolio, whether explicit or implicit, there are forecasts of asset returns, risks and correlations. In this book, Sébastien Page, CFA, lays out the pros and cons of different approaches to forecasting. He offers advice on portfolio construction and offers sample portfolios that put theory into practice. Page has authored scholarly articles on many of these topics. In this book, he skips the math and dives in with practical conclusions.
The capital asset pricing model (CAPM) has flaws, but it provides a useful starting point for forecasting returns. “It ties expected returns to an objective measure of risk and current interest rate levels,” according to Page.
In theory, the market portfolio at the heart of CAPM calculations includes all assets, public and private. In practice, listed stocks and bonds provide a suitable indicator for most investors. The global market was about 60% stocks and 40% bonds in 2000. Today, it’s closer to 40% stocks and 60% bonds, due to buybacks of shares, privatisations, fewer IPOs and large government bond issues. Investors can calculate expected returns for the wide range of assets included in multi-asset portfolios by combining the weighted estimates of stocks and bonds and then multiplying them by the beta of each asset.
A simple inversion of a stock market’s price-to-earnings (P/E) ratio provides a reasonable estimate of equity returns. What P/E? The Shiller CAPE (Cyclically Adjusted P/E) provides a cyclically adjusted measure for the United States. The low return implied by the current high level may be too pessimistic if the increase in return of the last decade is maintained. Higher earnings may be persistent due to the quasi-monopolistic nature of large tech companies. Also, recent earnings may be understated due to accounting issues. In contrast, measures based simply on current earnings may be overly optimistic. The author finds that the combination of historical and current earnings approaches leads to forecasts close to the estimates of a range of industry peers.
Forecasting local currency government bond yields is simple and relatively reliable. Current yield to maturity provides a good estimate of long-term returns. Yield shocks can make bond prices lower (or higher), but they will be offset by higher (or lower) reinvestment rates in the future.
The CAPM is a valuation agnostic model. Equity valuations, however, show a powerful long-term mean reversion effect. Therefore, investors can improve their estimates by incorporating forecasts for valuations. Equity returns can be broken down into three components, with income and growth alongside change in valuation. Dividend payments are persistent, so income forecasts based on current returns are reliable. Profit growth should be anchored to economic growth, since profits as a proportion of economic output must mean a very long-term return.
The page explores a variety of methods for adjusting forecasts, including analyzing institutional investor flows and momentum across asset classes. The large volume of macro data makes it difficult to separate the signal from the noise. Color-coded dashboards are a great way to present data on relationships where macro factors are important to asset prices.
A review of 93 academic studies by Ser-Huang Poon and Clive Granger found that “there is no clear winner in the high-risk forecast horse race.” Investment risks are complex. However, adding complexity to risk models does not necessarily improve predictability. So what should investors do? Page suggests using a number of different models and applying judgment.
The simplest approach is to assume that next month’s volatility for each asset class will be the same as last month’s. This approach is also hard to beat; volatility is persistent from month to month. The opposite, however, is true in the long run. Five years of quiet markets are more likely to be followed by five years of turbulence, and vice versa.
Models based on normal distributions underestimate the probability and magnitude of downside risks. However, Page has not found persistent patterns that help us predict skewness and kurtosis, the statistical measures of these extremes. Instead, it suggests different approaches to modeling tail risks.
Modeling risk-on and risk-off environments separately can provide a more realistic view of potential downside risk by incorporating stressed betas and correlations. Scenario analysis, using both historical events and future scenarios, can add another layer of understanding. Investors must consider, however, how the markets have changed since these historic events. For example, emerging markets are less sensitive to changes in commodity prices today than they were in 2008, while bonds, as measured by the Barclays Aggregate Index, are more sensitive to changes in interest rates because the average duration has increased (from 4.5 years in 2005 to six years). in 2019).
Once investors have forecasts of returns, risks and correlations, they can feed them into an optimizer to calculate the recommended asset mix. Most optimizers suggest concentrated portfolios and are sensitive to small changes in inputs. Investors can use five methods to overcome these limitations:
- Limit weightings to individual asset classes.
- Apply group restrictions, such as exposure to alternative assets. (This is not a random choice. Many forecasts for alternative assets overestimate expected returns and underestimate risk, leading to recommendations for large exposures.)
- Use resampling methods, developed by Richard Michaud, that incorporate forecast uncertainty.
- Take the Black-Litterman approach, which combines forecasts from active investors with forecasts derived from the CAPM, adjusting for confidence in those forecasts.
- Optimize in three dimensions: risk, return, and tracking error in peer group weights.
The mix of stocks and bonds is the most important decision multi-asset investors make, but this mix does not reliably reduce risk. The diversification benefits of government bonds are often seen during the selloff in stocks, but stocks have not protected investors against the bond selloff. Stock-bond correlations were positive in the 1970s and 1980s, when inflation and interest rates drove volatility. This was also true in the “tantrum” of 2013, when the US Federal Reserve signaled that monetary policy would be tightened, and in 2018, when policy rates rose.
Pension investors are more likely to do so match your retirement goals with bonds, especially inflation-linked bonds. Most investors, however, have not saved enough for retirement. They are more likely to to arrive your retirement goals with stocks.
Are carbon-based energy companies a necessary hedge against inflation or stranded assets? How do social and governance issues affect public debt sustainability in emerging markets? Asset allocators have vital decisions to make on these issues, but surprisingly the book does not address environmental, social and governance analysis.
There is no one right approach to asset allocation. Page quotes his father, a now retired finance professor: “We don’t know the results in advance. The information we use is always incomplete and we cannot control the variables. Even so, we have to make decisions because, often, the absence of a decision is worse.” Investors will need to use their judgment to select the right tools for the job. The range of tools that Page lays out in this book can help investors make better decisions.
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All posts are the opinion of the author. Therefore, they should not be construed as investment advice, nor do the views expressed necessarily reflect the views of the CFA Institute or the author’s employer.
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