Balancing the Balanced Fund (Canadian Edition) with Dynamic Volatility Weighting

Balancing the Balanced Fund (Canadian Edition) with Dynamic Volatility Weighting
December 24, 2011


This is a ‘Canadian-ized’ version of an article we published on Monday, December 19, 2011, which featured a study of US equity and fixed-income markets. As we are located in Canada, we were motivated to see how well the same techniques work in our home market using the S&P/TSX Composite.

As expected, it turns out that they work quite well.

The investment community is in the midst of an identity crisis, though admittedly many in the industry don’t know it yet. At the heart of the matter is the following misconception:

Investors perceive that investment professionals add value via security selection and market timing. What’s worse, most investment professionals believe that they add value via security selection and market timing. This perception is dangerously misguided.

Repeat after me: Investment professionals add value via asset allocation, not security selection. Again: Investment professionals add value via asset allocation, not security selection.

The following chart is from Pawley (2004) who sourced Brinson, Hood and Beebower (1986) and Simon (1998). The chart contrasts perceived sources of investment value from a large survey of investors with the empirical sources of investment value from the Brinson study. The average investor thinks that their Advisor adds value by picking stocks and bonds; my sense is that the average Advisor thinks that too. The reality however is that a good Advisor adds value by having a system to emphasize stocks versus bonds or cash, and vice versa. That is, a good Advisor adds value through intelligent asset allocation.

Click for a larger image

The Brinson study is controversial, mostly because it is improperly cited as validation for pseudo (read false) asset class diversification, such as small-cap versus large-cap, or value versus growth. It is also used to justify Strategic Asset Allocation (SAA) whereby very long-term averages (returns, volatility and correlation) are used to model an ‘optimal’ allocation to stocks, bonds and cash for each individual based on their risk tolerance. While this justification for SAA makes intuitive sense, we will demonstrate how traditional SAA is a suboptimal diversification approach by every metric except perhaps ‘simplicity’. But then, why do you pay your Advisor those big fees?

The Magic of Simple Rebalancing

Strategic Asset Allocation requires one further step beyond the initial asset allocation decision: periodic rebalancing. This is the process whereby each asset is bought or sold on a fixed schedule to bring the stock/bond allocation ratio back into alignment. The assets frequently move out of alignment when one asset class outperforms the other in any period.

While adherents to a Strategic Asset Allocation approach are explicitly expected to perform rebalancing on a pre-established schedule, for example annually or bi-annually (defined in your Investment Policy Statement), in my experience many Advisors do not revisit the rebalancing decision on a regular basis, and so many clients miss out on the value of this simple exercise over time.

Let’s conceive of a real life example, say a retired couple with just enough money to sustain a reasonable lifestyle assuming that they are able to receive average returns in retirement. These Canadian domestic investors may have been advised to adopt a 50/50 stock/bond Strategic Asset Allocation with quarterly rebalancing. If they had started with this approach in Canada in 1993 (our earliest data), and stuck with the strategy through to the present, their returns would look something like this:

Case 1: 50/50 stock/bond portfolio with quarterly rebalancing
Source: Butler|Philbrick & Associates, Click for a larger image

The table at the bottom may require some explanation. For our purposes, you want to focus on the following data:

  • CAGR (second from the top on the left): This is the annualized return to the portfolio over the entire duration of the test. This strategy delivered a CAGR of 9.89% per annum.
  • Sharpe (third from the top on the left): This is perhaps the most common measure of the ‘efficiency’ of a portfolio, and in this case it measures the annualized return to the strategy divided by the standard deviation, which is the most common measure of portfolio risk. The higher this ratio the better. This strategy had a Sharpe ratio of 1.11.
  • Max Daily Drawdown (six from the top on the left): This is the worst drop in the portfolio from peak-to-trough measured from the highest closing high to the highest closing low. It is a measure of how much loss an investor had to bear when investing in this strategy. This strategy had a Max Daily Drawdown of -25.05%.
  • % Winning Months (top right): This is the percentage of months in which the strategy delivered positive absolute returns. This strategy delivered positive returns in 69% of months.

Let’s contrast the performance of this 50/50 SAA portfolio with the return to a 100% stock portfolio over the same time frame:

Case 2. S&P/TSX Composite ‘Buy and Hold’
Source: Butler|Philbrick & Associates, Click for a larger image

Canadian investors have enjoyed two decades of very strong returns, benefitting from the strong U.S. economic expansion of the 1990s and then again from China’s decade- long infrastructure boom during the ‘aughts’ which drove prices for Canada’s commodities to record levels.

Over the past 18 years Canadian stocks delivered a remarkable 9.41% per year including reinvested dividends. To compare, Canadian stocks delivered 1.83 percentage points per year more than US stocks, and 12% per year more than Japanese stocks. Of course, investors still had to endure two near 50% drops, and a 6-year period of zero returns (from 1998 through 2003), which would have wreaked havoc on retirement plans. Further, despite the strong overall performance, stocks only delivered positive returns in 74% of 12-month periods — not a very consistent experience.

While a traditional SAA approach definitely improved results over a pure Canadian equity portfolio, we can improve the performance even more by reconsidering how we think about risk.

True Risk Optimization

While a simple, traditional SAA portfolio with periodic rebalancing delivered much stronger, and more efficient returns over the period tested than did stocks on their own, the simple SAA framework as described still has some very serious drawbacks.

Let’s revisit the true objective of the SAA process: to ensure that an investor achieves the maximum return available at a specified level of risk that is a function of the investor’s risk tolerance. Unfortunately, we know from experience, and a mountain of research, that in real life market risk is constantly changing. When markets are rising in a nice orderly uptrend, market risk (volatility) is generally very low. When markets are falling, or even going sideways, uncertainty and risk (volatility) are generally elevated. (See our article Jekyll or Hyde Markets for more on the market’s multiple personalities.)

If the objective of SAA is to maintain a fixed level of portfolio risk that is commensurate with each investor’s risk tolerance, then shouldn’t we reduce our allocation to each asset class dynamically when we start to experience amplified levels of risk (volatility), and increase our allocation when volatility declines? In this way we can preserve a much more consistent level of risk within the portfolio. Such expansion and contraction in portfolio allocations might be considered at each rebalance period.

If we simply alter the traditional SAA strategy so that at each rebalance date we reduce relative allocations to stocks or bonds when they exhibit relatively risky behaviour (geek note: based on 60 day trailing volatility), and increase allocations when they exhibit low relative risk, we can achieve a much more efficient portfolio, again just with stocks and bonds:

Case 3: SAA with Dynamic Volatility Weighted Rebalancing, 50/50 stocks/bonds
Source: Butler|Philbrick & Associates, Click for a larger image

Note that the objective of this portfolio is to keep the risk stable by reducing allocations to assets when they are exhibiting risky behaviour (high trailing volatility), and increasing allocations to assets when they are exhibiting low risk behaviour (low trailing volatility). In traditional SAA, the focus is on maintaining a fixed allocation. In contrast, and in keeping with the broader objective of SAA, this risk-weighted approach is focused on maintaining a fixed risk allocation.

It will come as no surprise by now that the volatility weighted rebalancing framework performs much better than the traditional 50/50 approach. Indeed, the relative volatility approach delivered 10.28% annualized returns, maximum drawdown of just 15.3%, and 90% positive rolling 12-month periods. In fact, this simple approach produced a Sharpe ratio over 1.5!

Not bad for a simple and intuitive twist on an old idea. The following chart draws on US data to illustrate how this approach also exposes an investor to a much more consistent portfolio experience as the grey line in the chart below (relative volatility weighted portfolio) tracks well below the black line (SAA 50/50) for most of the past 18 years, indicating much lower and more consistent volatility for the investor. The blue line is beyond the scope of this article, but suffice to say that by explicitly holding risk constant by systematically adding cash, portfolio risk and return characteristics can be improved even more dramatically.

Source: Butler|Philbrick & Associates, Click for a larger image

Opportunities for Action

We have demonstrated that over several market cycles a diversified portfolio substantially outperforms an all-equity portfolio, both in absolute terms and on a risk-adjusted basis. The period studied, from 1993 through 2011 is especially interesting because it includes two record-setting equity bull markets during the 1990s and 2000s, interspersed with two intense bear markets in 2001-2003, and 2008.

While the success of the diversified and rebalanced stock and bond portfolio relative to stocks on their own is not a revelation, many investors might be surprised at just how well this portfolio has done over the past 18 years on both an absolute and risk adjusted basis. Further, while we would in no way espouse this model as an optimal framework, not least of which because the stock / bond diversification framework ignores the myriad opportunities available from other markets and asset classes, this simple portfolio outperformed the average retail investor by 8% per year over the same period (See Dalbar, 2011).

We also demonstrated the conceptual and empirical validity of implementing portfolio allocations based on a true risk target that is commensurate with each individual’s risk tolerance, rather than on static Strategic Asset Allocation percentages. In a traditional SAA approach, a stock/bond allocation is chosen at the inception of the investment process, and the portfolio is altered at each rebalance date to move it back toward its long-term target allocation. In a risk-optimized framework however, the allocation to both equities and bonds depends on the relative risk associated with each asset class based on their relative volatilities at each rebalance date. In this way, portfolio allocations to stocks and bonds will ebb and flow according to their respective risk, holding aggregate portfolio risk near the initial target over time.

Empirically, this simple technique measurably improved absolute returns, but dramatically improved portfolio efficiency: Sharpe ratio improved by 36% and Maximum Daily Drawdown was reduced by 65%.

In closing, we would assert that Advisors and investors should consider an approach to Strategic Asset Allocation that incorporates explicit ‘buffers’ which expand and contract allocations to assets when they are volatile so as to keep aggregate portfolio volatility constant. This approach has merit conceptually, mathematically, and empirically as seen in the associated tests. This type of framework should be robust to asset classes, market regimes, and exogenous shocks, and provide a much more stable return experience for investors.