Editor’s Letter

Before going any further, let me offer heartfelt congratulations on behalf of all at The Journal of Wealth Management to James Chong and Mike Phillips whose article, “Sector Rotation with Macroeconomic Factors,” published in the Summer 2015 issue of The Journal of Wealth Management, won the William F. Sharpe Award for 2015

A recent client experience led me to dig deeper into the issue of how to construct and present portfolios to wealthy individuals, and in particular, to the extent to which their goals can or cannot be reasonably achieved. Our main question relates to the definition and appropriate use of asset classes, strategies, factors and sub-portfolios. It would be wonderful if one or several authors took the lead and put more meat on this skeleton.

Years ago, the classic approach to analyzing portfolio composition was to focus on three, and eventually four, asset classes: cash, bonds, and equities—and eventually real assets. Each asset was assigned a primary role, such as liquidity and safety for cash, income for bonds, and growth for equities—with equities having a further role together with real assets in protecting the portfolio from inflation. Although this was an attractively simple framework, it gradually became evident that a number of corners had been cut to make it as simple as possible. Indeed, the fundamental differentiation across the small number of asset classes was driven by three main elements: the relative importance of income in total returns, the relative predictability of income, and the term of the instrument. More recently, I suggested that a fourth differentiator might involve the size and volatility of the premium that must be earned over and above inflation. This four-dimensional framework did not naturally or directly fit with the role definition that has been adopted, and these roles did not really consider hybrid asset classes. Convertible bonds anyone?

Conceptually, each of these asset classes could be represented as some combination of four building blocks to which certain “return” or “risk” premiums could be added. For instance, our own model considers four distinct building blocks: inflation, real short-term rate, maturity premium, and equity premium, but certainly several equally valid ways exist to skin that cat. In our model, which considers all forecasts in an equilibrium state, cash is equal to the sum of inflation and real short-term rates, bonds are equal to cash plus the maturity premium, equities equal to bonds plus the equity premium, and real assets the sum of inflation plus a “real asset premium.” The latter is defined as the return required by investors to move away from any of the other asset classes to “buy” inflation protection, with an equivalent tracking error for that premiums with respect to inflation and the correlation between the variations in that real asset premium and those of inflation.

The need to focus on tax-efficiency as a fundamental requirement for investors concerned with after-tax results has led to the introduction of “strategies” as a substitute for asset classes. The difference between a strategy and an asset class involved two different steps. The first required becoming a bit more detailed in the analysis of the main components of each asset class to account for the fact that the overall market portfolio risk spectrum is a continuum rather than a neat set of four different spots. For instance, meaningful differences exist in the risk profile of top credit and lower credit bonds as exist between the equities of more established and less mature companies. The second difference relates to the way a manager actually makes investment decisions—primarily security selection decisions. A simple way to think of this relates to the move imposed by the focus on tax efficiency from a two- to a three-dimensional space. Tax-oblivious investors live in a two-dimensional space, introduced by modern portfolio theory, in which the only way to achieve extra return (i.e., alpha) is to incur some incremental risk, which is often measured in terms of the portfolio’s tracking error relative to its benchmark index. Tax awareness requires the addition of a third dimension, tax-efficiency, as it gives rise to another potential trade-off: the exchange of some tracking error for higher tax efficiency. In short, different strategies applied to the same asset class, or sub-asset class, can produce materially different expected after-tax returns; for instance, compare low tracking error equity index portfolios to somewhat concentrated and actively managed equity portfolios.

At this point, the “fundamental roles” assigned to each of these asset classes or strategies had not changed, although the foundation was a bit shaken. Indeed, the margin of difference between the roles played by a high yield bond index and those played by a shorter-duration bond index is material, although both of them would be viewed as sub-asset classes or strategies within the broader bond asset class.

More recently, the industry has also been shifting away from the concept of asset class, but rather than focusing solely on sub-asset classes and management processes, it started examining risk factors. Risk factors represent the various drivers of systematic risk embedded in a strategy or an asset class. Clearly, using bonds as an example, one can argue that they are exposed to interest rate risk (which parallels the real short-term interest rate component of our model), to inflation risk (which parallels the inflation component of our model), to yield curve shape risk (which deals with the variability of the maturity premium), and to credit risk, at a minimum. Although one can begin to refine the analysis and consider the difference between parallel shifts in the yield curve (which could be interpreted as an increase in either inflation or real short rates), several questions arise: What about yield curve twists or change in concavity? What about the influence of economic or corporate profit growth on credit risk? What about industry sector risk? One could easily add to the list. Further similar analyses can be carried out with other asset classes, thus leading to a potential portfolio allocation to factor risks rather than asset classes.

Looking at factor risks changes the neat thought framework that enabled the assigning of different roles to different asset classes. Thus, although one or another of these roles may persist—for instance, one might have a liquidity factor that will clearly tell us whether that portfolio element is more or less liquid—a definite classification becomes orders of magnitude more challenging.

The most recent development, chronologically, came with the seminal articles by Das et al. [2010, 2011],¹ which demonstrated the value of using “mental accounts” as a means of helping individuals meet their goals and supported the work of a few pioneers who had written about goals-based wealth management for nearly a decade. Behavioral finance indeed suggested that these mental accounts were the real distinction from one investor to the next, as individuals had multiple goals, multiple time horizons, and multiple levels of risk tolerance for each of them. More importantly, it taught us that the real measure of “risk” was not so much the volatility of returns, but the chance of missing out on achieving a goal—note that there is a clear relationship between these two definitions, but the latter changes the focus. To the extent that this shortfall risk is a function of both expected return and expected return volatility, together with the time frame over which the goal has to be met, the focus on individual asset classes, strategies, or risk factor was no longer appropriate. It had to shift to the sole real determinant that matters: the sub-portfolio that would have the highest probability-adjusted return over the relevant time horizon. It could, in effect, be viewed as offering the lowest funding cost for that goal. Individual asset classes, strategies, or risk factors were no longer sufficient because the return volatility of the sub-portfolio depended both upon the individual return volatilities of their components and on their correlation. Thus, two higher risk assets that had a low and stable correlation could well create a better combination to achieve income—within reasonable liquidity parameters—than a single lower-risk asset.

Although it may well be true that individuals and families often desire the simplest possible answers, it is important for all advisors to refrain from falling into the obvious and dangerous trap: distorting reality to favor sound bites over substance. We would encourage all advisors to take the time to educate families on the importance of sub-portfolios, which we have called “goals-based modules,” and to shy away from trying to break the overall portfolio into anything other than these modules. This, to this humble observer, is the key to structuring and maintaining the necessary feedback loop that “weds” families to their wealth by showing them which part of their portfolio and how much of their wealth is needed to meet each goal over the desired time horizon and with the required probability of success, or, in plainer English: a sense of urgency.

The Spring 2016 issue of The Journal of Wealth Management may well be setting another record: this time, it is not due to a larger than usual number of articles, but to the length of three of the pieces we publish. We encourage readers to review these articles in depth as they offer unusual insights. Again, this issue dispenses with our usual book reviews, because we simply ran out of space.

The first article, by Lisette Cooper, Jeremy Evnine, Jeff Finkelman, Kate Huntington, and David Lynch, stands on its own and discusses a possible “post-modern” portfolio theory that incorporates metrics of social return into the portfolio construction process, offering wealth advisors a simple approach to implementation that is amenable to clients with varying degrees of interest in social return.

Our next two articles focus on the financial planning industry. The first, by Inga Chira, explores survey respondents’ attitudes towards the financial planning profession and the reasons that may preclude individuals from using financial planning services. She finds that there is significant confusion about how financial planners differ from other financial professionals and that the reasons for bypassing financial planning services are both tangible and intangible. The second, by Meysam Safari, Shaheen Mansori, and Stephen Sesaiah, covers a somewhat similar topic but focuses on Malaysia. The authors present results of a survey on the general public’s understanding about the profession by assessing the impact of five major influential factors on individuals’ intention to hire a financial planner’s services—awareness, acceptability, affordability, accessibility, and assurance—and conclude with policy recommendations that may lead to higher penetration of financial planning services in Malaysia.

The next article again stands on its own and is one of the longest we have ever published. It is by frequent contributor John Haslem and provides an in-depth discussion of important issues related to mutual fund distribution, particularly fund distribution channel characteristics, Rule 12b-1 fees and distribution, direct-sold and broker-sold services, Rule 12b-1 fees and revenue sharing, revenue sharing issues, “soft-dollar” trading, distribution and flows, opacity and agency conflicts, expense-shifting agency conflicts, and intermediated distribution and portfolio managers.

Our next group comprises three articles that are more specifically focused on broad asset allocation issues. The first, by Javier Estrada, notes Warren Buffett’s recent recommendation that a pure static 90% in stocks and 10% in short-term bonds allocation makes a great deal of sense, and presents a couple of simple dynamic strategies adjusting this recommendation, which provide better upside potential and downside protection. The second, by Chi Keong Lee, describes a different approach to constructing portfolios, based on the investor’s forward-looking, long-term expectation of peak-to-trough loss from investments (expected drawdown). Last but not least is an article by Kamphol Panyagometh and Kevin Zhu, who wade into the classic debate on the relative efficiency of dollar-cost averaging and lump sum investing, offering an admittedly somewhat theoretical analysis and conclusion: that dollar-cost averaging should be recommended only to moderately risk-averse investors and should be compared with a 50% to 65% risky asset allocation instead of executed through a lump sum approach.

Our next two articles are timely, as they relate to the commodity price universe. The first, by Srinidhi Kanuri, Robert McLeod, and D.K. Malhotra, looks into the performance of commodity-based mutual funds, concluding that these funds have not been able to create positive alphas for their investors, have negative or insignificant performance persistence, and have no market timing ability. Looking at specific time periods of market downturns, however, commodity-based mutual funds’ performance was significantly positive, indicating that they provide a good hedge during bear markets/financial crises. The second, by Hilary Till, discusses the (potential) structural sources of return for both commodity trading advisors and commodity indices based on a review of empirical research from both academics and practitioners.

The last two articles do not form a group, as they are as different from one another as can be. The first, by Wai Mun Fong and Zhehan Ong, focuses on high dividend yield stocks and suggests that a combination of high gross profitability and high dividend yield stocks inherit the defensive qualities of high yield stocks in bad times while underperforming the market portfolio only modestly in good times. Our last piece is by Li Xian Liu and Milind Sathye, who look at the Chinese mutual fund industry, as China recently permitted foreign joint ventures as well as off-shore investing, and find that there is considerable room for funds management companies to improve technical efficiency to ultimately bring increased benefits to investors, although neither foreign ownership nor off-shore investing help enhance efficiency.

Jean L.P. Brunel

Editor

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