Global Case For Strategic Asset Allocation And An Examination Of Home BiasVW Staff
Global Case For Strategic Asset Allocation And An Examination Of Home Bias by Brian J. Scott, CFA; James Balsamo; Kelly N. McShane; Christos Tasopoulos – Vanguard
- Broadly diversified balanced funds with limited market timing tend, over time, to move in tandem with overall financial markets. Our empirical analysis, as well as that originally performed by Brinson, Hood, and Beebower (1986), illustrates the significance of a broadly diversified asset allocation.
- Active management has produced significant performance dispersion across portfolios. Our analysis, based on work first published by Jahnke (1997), also supports the possibility of outperformance based on an investor’s selecting a “winning” actively managed fund. We found, on average, that active management reduces a portfolio’s returns and increases its volatility compared with a passive index-based implementation of the portfolio’s asset allocation policy. At the same time, our findings support the view that active management can create an opportunity for a portfolio to outperform.
- As a result, when building portfolios, market-capitalization-weighted global indexes are a valuable starting point for all investors. Yet we find that many investors tilt their portfolios away from market cap, either consciously or unconsciously. Perhaps the most prominent tilt investors make is toward a home bias. To the extent this is an unconscious choice, we provide a framework for considering the benefits of global diversification.
The seminal 1986 paper by Brinson, Hood, and Beebower (henceforth BHB), “Determinants of Portfolio Performance,” concluded that asset allocation is the primary driver of a portfolio’s return variability for broadly diversified portfolios. Yet disagreements or misunderstandings about the findings’ relevance to investors still make the topic valuable to clarify for investors.
We examine two key questions: How does asset allocation affect your risk/return expectation? And how much home bias is reasonable? We analyze these questions in five major markets: the United States, Canada, the United Kingdom, Australia, and Japan. We briefly review two studies at the core of this debate: BHB’s paper and Jahnke’s “The Asset Allocation Hoax” (1997). We then expand upon Vanguard’s past research, most notably The Global Case for Strategic Asset Allocation by Wallick et al. (2012). Finally, we discuss the role of home bias tilts in relation to asset allocation.
The ongoing asset allocation debate
In their landmark paper, BHB concluded that a portfolio’s static target asset allocation explained the majority of a broadly diversified portfolio’s return variability over time. These findings were confirmed by Vanguard and other research, including Ibbotson and Kaplan (2000), suggesting that a portfolio’s investment policy is an important contributor to return variability (Hood, 2005). Investment advisors have generally interpreted this research to mean that selecting an appropriate asset allocation is more important than selecting the funds used to implement it. Vanguard’s findings indicate that both are important, yet we suggest the following sequence for portfolio construction: Start with an asset allocation policy decision, then consider the implementation strategy.
In 1997, Jahnke argued that BHB’s focus on explaining return variability over time ignored the wide dispersion of returns among broadly diversified active balanced funds over a specific time horizon. In other words, he maintained that a portfolio could achieve very different terminal wealth levels, depending on which (active) funds were selected. Jahnke’s analysis emphasized that, as a result of active management strategies, actual returns earned should be examined across different active balanced funds within a set holding period. It is correct that the BHB study did not show that two funds with the same asset allocation can have very different holding-period returns. The research we report here confirms the findings of both studies and views them as separate analyses that ultimately helped us address this question: Can active management increase a portfolio’s returns without increasing the volatility experienced?
Our analytical framework
Vanguard’s latest research updates analysis from 2012. It covers the United States, Canada, the United Kingdom, Australia, and Japan from January 1990 to September 2015. Previous versions of this research analyzed a longer data history, but the current analysis was shortened to cover a common time period and include additional markets. This research confirms our earlier conclusions that, over time and on average, most of the return variability of a broadly diversified portfolio that engages in limited market timing is due to its underlying static asset allocation. Active investment decisions such as market timing and security selection had relatively little impact on return variability over time.
To determine the relative performance of asset allocation and active management, we needed to distinguish between a portfolio’s policy return (or asset allocation return)—that is, what a portfolio could have earned if it recreated its policy allocation with passively managed index funds—and the actual return earned by the active balanced fund over the period. Our empirical case tested BHB’s and Jahnke’s studies on a global scale, using a greater number of balanced mutual funds.1
Time-series regression (per BHB, 1986)
Return variability measures the extent to which actual returns diverge from the policy returns. Therefore, greater variability in returns would suggest a wider possibility of returns and a lessened ability to predict results, inherently indicating increased portfolio volatility. The variation in the policy return that explains the percentage of variations in the actual return is measured by the adjusted R-squared (R2) derived from a time-series regression analysis of the fund’s actual return versus its policy return. A high adjusted R2 would mean that variations in the policy return explained a high percentage of the variation in fund returns.
BHB’s 1986 conclusions were derived from the results of a time-series analysis measuring the effect of asset allocation on return variability. Such an analysis compares the performance of a policy (long-term) asset allocation represented by market indexes with the actual performance of a portfolio over time. Our results confirmed BHB’s findings that, on average and over time, most of the return variability of a broadly diversified portfolio that engages in limited market timing was attributable to the ups and downs of its policy asset allocation. Active investment decisions—such as market timing and security selection—had relatively little impact on return variability over time.
It is important to acknowledge that BHB’s data set was pension funds, which were typically exposed to a high level of systematic market risk, resulting in high R2 numbers versus the returns of their policy portfolios over time. BHB’s analysis concluded that more than 90% of return variability over time could be explained by the asset allocation policy. Ibbotson and Kaplan (2000) and Vanguard research found similar results for the balanced mutual fund universes in the United States, Canada, the United Kingdom, Australia, and Japan, with percentages slightly lower than BHB’s findings (see Figure 1).
As the figure shows, asset allocation largely contributed to return variability over time. As a result, asset allocation is key in managing the range, or variability (experienced volatility), of a portfolio’s returns over time.
Cross-sectional regression (per Jahnke, 1997)
The adjusted R2 derived from a cross-sectional regression analysis of the fund’s actual return versus its policy return is used to measure the degree to which an asset allocation (passive) policy compared with an active management strategy and explains the dispersion of returns across funds over a set time horizon.
In considering Jahnke’s emphasis on determining how much asset allocation affects actual portfolio return dispersion across funds, we ran a cross-sectional analysis to compare actual returns with policy returns. Both our and Jahnke’s analyses resulted in lower R2 numbers (see Figure 2). In other words, active management implemented by taking idiosyncratic risks and differential exposure to systematic risk factors (such as factor or tactical overweights) can create significant return dispersion across active balanced funds, resulting in a lower R2. Jahnke believed that investors cared about actual returns and the range of possible investment outcomes at the end of their time horizons, rather than about return variability experienced over time. Jahnke’s analysis confirmed that some individual actively managed funds can outperform their policy portfolios.
What matters most to investors: Return and risk
Vanguard’s research supports both BHB’s and Jahnke’s findings. In fact, there is not really a debate between the two positions; rather, they refer to two different aspects of portfolio construction: Jahnke refers to holding-period return or terminal wealth, and BHB to day-to-day portfolio volatility, which can be defined as portfolio risk.
The risk interpretation of BHB’s finding is that about 90% of the volatility of a broadly diversified balanced portfolio comes from its policy asset allocation decision and broad market movements. Jahnke’s finding that actual portfolio returns can vary significantly over a specific investment horizon means that the selection of active managers and strategies can lead to outcomes very different from the policy asset allocation benchmark.
Thus, once the policy allocation has been determined, the portfolio’s expected risk will not depend much on how it is implemented (passive index or active); however, the ultimate performance of the portfolio relative to the policy benchmark will depend critically on the selection of a particular active manager or strategy.
The question then comes down to the challenges in selecting managers or implementing active portfolio strategies that will prove to outperform the policy benchmark (See Wallick, Wimmer, and Balsamo, 2015). Manager selection is challenging—so much so that a reasonable starting point is to presume that an investor has average skill in selection and that a passive marketcap-weighted implementation is a valuable starting point for portfolio construction.
We examined actual return performance by comparing actual versus policy returns. We calculated the average return of a fund’s asset allocation policy as a percentage of the fund’s long-term average return and computed the ratio of a fund’s policy volatility over its actual volatility. These two calculations helped us determine how both an investor’s policy and active management strategies have performed in the past. We found that, on average, active funds added to volatility levels and underperformed the benchmark (as reflected in Figures 3 and 4). From January 1990 through September 2015, on an equal-weighted basis, only 4% of U.S. actively managed balanced funds produced statistically significant alpha. At the same time, the outperforming assets made up 17% of the assets under management.
We found that, on average, using an equal-weighted count methodology, a greater degree of active management reduced both time-series and cross-sectional R2 but did not necessarily increase performance. On average, active management risk is not compensated (Sharpe, 1991), yet it is compensated if skill overcomes hurdles such as tendencies toward higher costs and turnover of active management. Indeed, Vanguard’s research on active management (Wallick, Wimmer, and Balsamo, 2015) identifies three key components that improve the odds of success: identifying top talent, obtaining access to that talent at a reasonable cost, and being patient enough to hold the funds over time.
The Sharpe ratio helps us measure the risk/return tradeoff. The ratio is the equity-risk premium divided by the standard deviation, which provides a better measure of how much return we derive from every unit of risk taken. The higher the ratio, the better the risk-adjusted return you will have on the chosen investment. Figure 3 shows a clear spike in returns per unit of risk taken in the policy over the fund’s actual returns. The higher risk taken in the fund relative to the policy comes from active management strategies such as market timing and security selection.
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