Van Lanschot today presents a new-style wealth planning advisory service: an innovative concept that meets the increasing need of private banking clients for creation and preservation of their wealth.
Richard Bruens, Member of the Board of Managing Directors, observes: At Van Lanschot, we aim to provide excellent service to our clients at every stage of their lives, to be a partner in life. Planning wealth isnt just about financial objectives, it also encompasses other personal and social goals - and its a logical extension of Van Lanschots wealth management strategy.
More and more down to the individual
People often dont have the time to take a step back and look at their long-term goals. With all the changes taking place in pensions, healthcare costs, mortgage tax relief and numerous other areas, individuals are increasingly in charge of their own wealth creation. Van Lanschot has designed its new wealth advisory service precisely to meet the needs of clients looking to create and preserve wealth while keeping a clear view of the viability of their goals and the risks they need to factor in.
Scanning for the day after tomorrow
Van Lanschot has developed a new approach supported by specially designed tools which helps private bankers to identify client needs and requirements even better. They sit clients down and take them through a special app on a large tablet computer, identifying client priorities and resulting in a detailed wealth planning report taking a long-term perspective. This overview of goals, solutions and risks is tracked actively and regularly updated.
Enough for what?
The new service is being launched in a radio, online and billboard campaign with the teaser question: Enough for what? Its new tagline Geef uw vermogen een horizon (Set the horizon for your wealth) underlines Van Lanschots promise to use its innovative service to give people insight into their wishes and goals, and how to achieve them.
This is the fourth article in an introductory series that takes a look at a basic factor return model that governs the bulk of equity portfolio returns. So far we have seen how the risk-free rate is important for returns, and in the last article, we looked at the equity premium. In this article, we shall be looking at the risk scale factor, beta, for the equity premium from our basic equation, to wit:
return = riskFreeRate + beta*equityPremium + value + momentum + small + alpha + error
As a reader of this, I assume that most you have been at least notionally exposed to beta as part of the capital asset pricing model (CAPM). (Investopedia has a 98 second video if you have not.). As a quick reminder, it is defined as:
beta = covariance[stock, marketPortfolio]/variance[marketPortfolio]
The concept of beta
The basic premise behind beta is that the market portfolio is the most risk-efficient portfolio available to investors. That is to say investors cannot do better in terms of risk-adjusted returns than the market portfolio, and that all assets get priced for their risk relative to this market portfolio. Given that (giant) assumption, beta is essentially a measure of market risk (as opposed to business risk); naturally, the two are linked however. Figure 1 diagrams this relation.
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Figure 1: The returns of ten portfolios formed on beta 1928-2003 (Fama amp; French 2004: 33)
As you can see there is a trade-off between risk and returns as one would expect, but the empirical relation between beta and returns is less than one would expect. Based on these measurements, quadrupling risk, eg going from beta 0.5 to a beta of 2 would probably net the investor only about a 4% gain in portfolio returns instead double-digit returns predicted by the model. In other words, the risk-efficiency is declining in beta; per unit of relative risk investors are getting less returns that they should expect.
So while the basic story of more return for more risk holds for a portfolio (or ETF) of such stocks, the use of the model is of very little use for forecasting actual equity returns. To illustrate the point, I calculated the betas for about 67 stocks against the ETF equivalent of the world portfolio, VT, and then used these to predict the returns in the subsequent 12-month period; Figure 2 reports the results.
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Figure 2: October 2013-2014 returns against October 2012-2013 betas. Authors calculations based on random securities data from finance.yahoo.com.
As you can see the model does not perform particularly well at predicting an individual stocks return. Naturally, we could get fancy by searching for the right beta calculation period and best subsequent return period. Some authors have had luck calculating beta against an index of the real economy, but even the better estimates of beta can hope to explain only about 3-7% of the subsequent return. That type of small edge might be good enough for a quant fund to make use of in some sort of strategy given its access to expensive specialized data, a good IT/trading set-up, lower trading costs, and cheaper margin. Nevertheless, I have my doubts as to whether its big enough for a retail investor to capitalize on, especially when idiosyncratic equity returns are likely to dominate systematic risk-factor effects in the highly concentrated types of portfolios retail investors tend to hold. There are some risk-efficient beta-based strategies that might be feasible for retail investors, but discussing them in detail is beyond the scope of this article; I hope to return to them later.
So is beta broken?
Not necessary. It could be simple measurement error. There are all sorts of dirty and arbitrary choices that have to be made in order for an econometrician to test such an elegant theory. For example, how long do you have to observe a stock to know its beta and return? Beta in theory ought be measured against the entire universe of assets; I have never met such an index, but let me know when you do so I can test the theory more accurately. Furthermore, as we have seen earlier in this series, even measuring the risk-free rate can be a challenge that can give even a tenacious empirical economist fits. In addition, there are also major statistical issues of how to account for discontinuities in the variance, skewed returns, etc.
Apart from issues of empirical identification, a few hypotheses hold that theory is essentially correct, but real-world investment restrictions hide beta. For example, many institutional investors face margin restrictions by charter, so rather than borrowing and leveraging the risk-efficient portfolio as would be optimal, they opt to load up their portfolio with beta stocks as an inefficient but feasible substitute. While individual investors do not necessarily face margin restrictions, interest rates can be well in excess of the risk-free rate. As an example, the current risk-free rate for USD as measured by the one-year LIBOR is ca. 0.55%. The margin rates at TD-Ameritrade and ScottTrade, retail brokerages, are an eye-watering 7.75-9% for the lowest tier accounts; Swissquote, their Helvetic analog, charges 3.15%. Even at this lower rate, retail investors are essentially entirely cut-off from constructing a risk-efficient leveraged portfolio-margin interest will likely eat their returns alive before they get ahead. The proliferation of leveraged ETPs further bespeaks to this phenomenon. Studies of individual brokerage accounts reveal that retail investors do typically choose both higher beta stocks and higher beta portfolios, lending additional evidence to this theory of margin constraints. Both these retail and institutional investors are essentially bidding up beta in their search for absolute returns, and are willing to bear inefficient returns given no or inferior alternatives.
Alternatively, the theory could be simply wrong, and there have been numerous attempts to fix the beta over the years. Many of which forego the elegant simplicity of the original model for less convincing and sometimes convoluted mathisms such as by incorporating specific properties of the irregular stock market returns, or individuals risk and wealth planning.
Wherefore ought we care about beta?
The reality is some investors just do not care. To paraphrase one of my readers, I dont need empirical analysis; my scorecard is my account. So aside from rationally over-weighting high-beta stocks due to margin constraints, individual investors could simply be beta-indifferent (or ignorant) in their portfolio selections. Furthermore, there is possibly quite a large pool of such beta-neutral investors, be it because of their total wealth, high risk-propensity, or infinite investment horizon, or combination thereof, which would make beta less important for them. For me personally, profit and financial risk worry me much more, and there is a reason why I use credit scoring for my stock picks, but have yet to compute their betas.
Similarly, stock gurus and individuals do seem to concentrate more on total returns rather than their beta. In fact, some of the superior returns shown in the popular press implicitly rely on beta returns. Peter Lynch and his devotÃ, Jim Cramer, two business-case investors, are essentially beta-indifferent in their advice; they just want a good thesis and let the market take care of the rest. Both prefer growth stocks at a decent price (ie PEG lt; 1), which likely implies a beta tilt.
At the other end of the spectrum, James OShaughnessy, more of a quantitative/value investor, advocates the use of various value metrics, one of which is the cash-flow yield; he reports that these cash-cows yield a higher returns, but also exhibit higher betas (OShaughnessy (2005): 110).
But before you glibly dismiss this 50-year old theory, which still survives despite weak evidence for its central empirical proposition, its worth considering more deeply why. The main reason for this in my opinion is that beta can be understood: first, a specific mathematical relation between returns and the equity premium; and second, but perhaps most importantly, as an investors parable about how we should regard market risk. Because the model captures a kernel of truth about something deeply rooted in the human psyche about risk-and-return, it lives on.
What does this mean for the retail investor?
1. Be risk efficient.
I have harked on this point before. We have just seen how higher beta stocks do imply higher returns, but be aware this relation is not one-for-one, and that risk efficiency is declining in beta.
As a corollary, it is worth considering the risk-efficiency of your entire portfolio. If your brokerage or accountant does not calculate a Sharpe ratio (ie equityPremium/standardDeviation) for you, you can easily do this in Excel by keeping track of your portfolios monthly gains and losses against the VT ETF, which is akin to the world portfolio, and QLTA ETF as a proxy for the risk-free rate. In order to beat the market, your Sharpe ratio should be higher. Ill leave this to you as a homework exercise.
2. Remember that beta scales the equity premium.
Beyond the risk parable of beta, the mathematical relation is also worth heeding. Since beta only scales one component of the return equation, high beta stocks have the greatest effect where and when equity premia are high. Hence, it is important to know whether the bulk your returns are coming from the risk-free rate or the equity premium. The next chart illustrates my point.
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Figure 3: Expected equity premia and risk-free rates based on authors calculations using Reuters Fundamentals and tradingeconomics.com
Given the dominance of the risk-free rate in Brazil and Mexico a low volatility strategy would be more efficient, whereas higher beta names in Greece and Portugal would be a more prudent use of your beta budgetâ¦provided you have one of course. The essential logic of deploying beta when there are high relative risk premia also applies to sectors and periods as well. In doing so, you may not have any measurable alpha, but your returns should be higher.
In our next article, we will be looking at value as a source of intuitive but potentially risky equity returns.
With many people planning on supplementing their retirement savings by working longer or obtaining part-time jobs, financial advisors may need to take on the role of health and fitness coach to help achieve that outcome. A new study by Merrill Lynch and Age Wave shows that over half of retirees had to retire earlier than planned because of health problemsone in three because of health problems.
Poor healthcare knocks people out of the workforce earlier than they expected or worse, says Andy Sieg, head of global wealth amp; retirement solutions for Bank of America Merrill Lynch.
The study, based on responses from over 3,000 respondents, found that 45 percent of retirees actually retired on time or later than expected, while 55 percent had to retire early.
Thinking about health in the planning process is prime time, says David Tyrie is head of retirement amp; personal wealth solutions for Bank of America Merrill Lynch. Investing in your wealth is the best way to improve your wealth.
The study found that while 29 percent of baby boomers are proactively taking charge of their health and healthcare finances, 32 percent fall into the challenged and confused category, struggling with heath issues and worrying about the impact of their illnesses on their financial future.
Were in an environment now where, in many cases, its overwhelming and confusing, Tyrie says. Even among people over 50 years of age who have a financial advisor (22 percent of the population), 59 percent have never discussed healthcare costs with their advisor, says Ken Dychtwald, President and CEO of Age Wave.
According to the study, healthcare costs are the greatest retirement financial concern (41 percent.) And thats true even among wealthy Boomers. About 60 percent of investors over 50 with more than $5 million in investable assets cited not being able to afford healthcare costs as their biggest fear.