Outside of diversity: What every investor needs to know about asset distribution. 2020. Sebastian Page, CFA. McGraw Hill.
Sebastian Page, CFA, explains the pros and cons of different approaches to predicting returns, risks, and interrelationships across asset classes. He explores portfolio building methods to meet a range of client requirements.
“If you don’t think you can predict the expected return, you shouldn’t be in the investment business.” – Bird Scarer, Ph.D.
Buried প্রতিটি explicit or implicit মধ্যে asset return, risk, and interrelationship forecast within each multi-asset portfolio. In this book, Sebastian Page, CFA, outlines the advantages and disadvantages of different methods of forecasting. He provides advice on portfolio construction and offers sample portfolios that put the theory into practice. Page has written academic papers on many of these topics. In this book, he avoids mathematics and dives into practical decisions.
The Capital Asset Pricing Model (CAPM) has errors but provides a useful starting point for predicting returns. “It combines the expected return with an objective measure of risk and current interest rate levels,” according to Page.
Theoretically, the market portfolio at the center of the CAPM calculation includes all assets, public and private. In practice, listed equities and bonds provide a suitable proxy for most investors. The global market in 2000 consisted of about 60% equity and 40% bonds. Today, it is close to 40% equity and 60% bonds, due to share buybacks, privatization, low IPOs and government bond issues. Investors can calculate the expected return for a wide range of assets included in a multi-asset portfolio by adjusting weighted estimates for equities and bonds and then multiplying each asset by beta.
A simple breakdown of the price-to-earnings ratio (P / E) of an equity market provides a reasonable back-of-the-envelope estimate for equity returns. Which P / E? Schiller Cape (Cyclically Adjusted P / E) provides cyclically adjusted measurements for the United States. The low returns inherent in today’s highs could be very pessimistic if profitability growth over the past decade could be sustained. High earnings can be sustained due to the semi-exclusive nature of large tech companies. In addition, recent earnings may be lower due to accounting problems. Conversely, measures based on today’s earnings can be very optimistic. The authors find that the combination of the historical and current earning methods predicts industry peers closer to estimates.
The forecast return for local currency government bonds is straightforward and relatively reliable. The current yield of maturity provides a good estimate of the long-term return. The yield push may push bond prices lower (or higher) but will be offset by higher (or lower) reinvestment rates.
CAPM is an evaluation-agnostic model. Equity valuations, however, show a strong average opposite effect in the long run. Therefore, investors can improve their estimates by including valuation forecasts. Equity returns can be divided into three components with value change as well as income and growth. Dividend payments are fixed, so revenue forecasts based on current production are reliable. Earnings growth should be linked to economic growth, considering that profits as part of economic production must be returned in the long run.
Page explores various methods for fine-tuning forecasting, including flow analysis of institutional investors and asset class dynamics. The perfect volume of macro data makes it difficult to distinguish signals from sound. Color-coded dashboards are a great way to present relationship information where macro elements are important for asset value.
A review of 93 academic studies by Ser-Huang Pun and Clive Granger found that “there is no clear winner for predicting horse racing.” Investment risk is complex. Adding complexity to risk models, however, does not improve prediction. So, what should investors do? The page suggests using different models – and enforcing judgment.
The easiest way is to assume that next month’s volatility for each asset class will be the same as last month’s. This method is also difficult to lose; The instability continues from month to month. But the opposite is true in the long run. Five years of turmoil after five years of calm market and vice versa are more likely.
Models based on general distribution reduce the likelihood and level of negative risk. Page did not find any permanent patterns that would help us to predict oblique and curtosis, a statistical measure of this extreme. Instead, he offers different approaches to tail risk modeling.
Modeling risk-on and risk-off environments can provide a more realistic view of potential negative risks by incorporating stressed beta and interactions separately. Scene analysis ব্যবহার using both historical events and foresight-can add another level of understanding. Investors need to consider, however, how markets have changed since that historic event. For example, emerging markets are less sensitive to commodity price changes than in 2008, while bonds measured by the Barclays Aggregate Index are more sensitive to interest rate changes because the average period increased (from 4.5 years in 2005 to six years in 2019).
Once investors have forecasts of returns, risks and correlations, they can input them into an optimizer to calculate the proposed asset mix. Most optimizers recommend a centralized portfolio and are sensitive to small changes in inputs. Investors can use five methods to overcome this limitation:
- Limit weights to individual asset classes.
- Apply group constraints, such as exposure to alternative resources. (This is not a random choice. Many forecasts for alternative assets reduce expected earnings and devalue risk, leading to higher exposure recommendations.)
- Use the Richarding method, developed by Richard Mitchaud, which incorporates the uncertainty of the forecast.
- Adopt the Black-Litterman method, which combines active investor forecasts with forecasts from CAPM, adjusting for confidence in those forecasts.
- Optimize in three dimensions: risk, return and tracking error peer group weight.
The stock-bond mix is the biggest decision of multi-asset investors, but this blend does not reliably reduce risk. The diversification advantage of government bonds is often seen during stock selloffs, but stocks do not protect investors against selling bonds. The stock-bond correlation was positive in the 1970s and 1980s, when inflation and interest rates led to instability. This was also true of the “Temper Tantrum” in 2013, when the US Federal Reserve signaled that monetary policy would be tightened, and in 2018-2018, when policy rates would rise.
Pension investors are more likely Match Their retirement goals with bonds, especially inflation-related bonds. Most investors have not saved enough to retire. They are more likely Reach Their retirement goals, including equity.
Are carbon-based energy companies a necessary hedge against inflation or future stuck assets? How do social and governance issues affect the sustainability of government debt in emerging markets? Resource allocators have to make important decisions in this regard, yet surprisingly, the book does not address environmental, social and governance analysis.
There is no proper method of allocating resources. Page quotes his father, now a retired finance professor: “We don’t know the results in advance. The information we use is always incomplete and we cannot control variables. Still, we must make decisions because often the absence of decisions is worse. Investors need to use their judgment to select the right tools for the job. The tools that Page has set out in this book can help investors make better decisions.
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All posts are the author’s opinion. As such, they should not be construed as investment advice, or the opinions expressed must not reflect the views of the CFA Institute or the author’s employer.
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