GestaltU - Butler|Philbrick|Gordillo and Associates

Sky-High Bullish Sentiment Suggests Caution is Warranted

2012-02-10T05:11:00.000-08:00

We have not yet tested these sentiment indicators for significance, but with stock market sentiment reflecting such extreme bullish confidence, one has to wonder who is left on the sidelines with cash ready to push this market higher.

S&P500 Price (top) and Mutual Fund Cash as a Percent of Total Assets (bottom)

Source: SentimentTrader

Source: SentimentTrader via Evilspeculator.com

Low Volatility as a Fiduciary Duty

2012-02-03T13:43:00.000-08:00

Mike Moody, John Lewis and the Dorsey Wright team run a superb shop, and our views overlap about 80% of the time (systematic vs. clinical, anti-forecast, RS, etc.), but I feel compelled to address a recent post on their SystematicRelativeStrength blog (SRS).

The SRS blog took issue with a quote by investment legend and individual investor advocate David Swenson, who has led Yale's endowment fund successfully since 1985. We endorsed his statement yesterday in a post, but here it is again:

“A fiduciary would offer low-volatility funds and encourage investors to stay the course,” he said. “But the for-profit mutual fund industry benefits by offering high-volatility funds.”

SRS was troubled by the assertion that investors are more likely to stick with a low volatility strategy, and used Dalbar's study of investment behaviour to prove their point. Specifically, they noted that historically investors have been no more inclined to hold onto lower volatility bond funds than higher volatility stock funds. From SRS:

"I have a few issues with this. First, the data argues that low-volatility funds are not the answer. If low volatility were the answer, customers would hold their low-volatility bond funds longer than they hold their high-volatility stock funds—but they don’t."

I find this argument disingenuous and logically flawed. For evidence, I have drawn a chart using data from the 2011 Dalbar study (see full study here):

Source: Dalbar (2011)

Note that investors are indeed no more likely to hold onto bond funds than equity funds, with the average holding period for both around 3.2 years or so. However, what the SRS article did not mention (strangely, since they have several asset allocation funds), was that the investors tended to hold asset allocation funds for a full 33% more time than either stock or bond funds on their own. Huh.

The category of Asset Allocation funds is overwhelmingly dominated by balanced funds of stocks and bonds. Investors who allocated capital to asset allocation funds explicitly outsource the asset allocation decision to the fund manager, who they expect will bias the fund toward or away from stocks or bonds as opportunity knocks.

Dorsey Wright manages a Balanced Fund (Arrow DWA Balanced Fund) with annual turnover of 59%, which implies an average holding period of 1.69 years (1/0.59). The highest ranked fund in the U.S. Balanced Fund category, the Intrepid Capital Fund, had an annual turnover of 88%, implying that the fund turns over almost its entire portfolio about once per year (every 1.14 years to be exact).

Does this high turnover mean that these fund managers are trading their portfolio on intuition or emotion? Does it demonstrate a lack of expertise or conviction in their approach? I don't think so. Rather, the managers of these successful funds are moving capital around to take advantage of opportunities they identify, in DWA's case through their proven relative strength system.

Turnover is not a measure of investors' preferences; rather investors prefer the best returns for the lowest risk. Neither stocks nor bonds on their own can deliver this objective because they are much better together than either is on its own. Investors know intuitively that there are times to emphasize bonds and times to emphasize stocks. The less successful ones try to time these changes on their own, and make binary decisions to switch between them, while the successful ones leave the decision to experts who adapt portfolios incrementally over time.

Literature from the institutional space is beginning to identify the power of dynamic asset allocation (DAA). This approach advocates more frequent rebalancing based on changing expected return, volatility and correlation dynamics. This type of strategy often has higher turnover, but can be engineered for quite low volatility. Their objective is to capture a large proportion of upside returns from a diverse set of asset classes, but continuously optimized diversification protects portfolios from major losses. As a result, investors are more likely to stick with these strategies because they experience a steady return trajectory like bonds, but capture substantial equity returns as well.

Low volatility DAA strategies are also highly optimal for retirement outcomes because of the sensitivity of retirement plans to large sustained losses, especially in early years.

In summary, Swenson asserts that investment advisors with their clients' best interests at heart would guide clients toward low volatility strategies, mostly because these strategies are much easier to stick with through thick and thin. We have also shown, counterintuitively, that low volatility strategies may provide stronger absolute returns as a result of their smoother ride. This makes them especially compelling for investors nearing or in retirement, and who wish to maximize retirement spending while sleeping well at night.

We practice what we preach. Find our whitepaper here.

2012-02-02T11:18:00.001-08:00

David Swensen, the legendary manager of Yale University's endowment, arguing that acting as a fiduciary for other people's money and maximizing profits are incompatible activities.

"A fiduciary would offer low-volatility funds and encourage investors to stay the course," he said. "But the for-profit mutual fund industry benefits by offering high-volatility funds."

Consulting Behemoth Russell Investments Advocates Volatility Sizing

2012-02-02T08:46:00.000-08:00

We published a whitepaper in November that demonstrated the importance of sizing asset class exposures based on volatility that received a lot of attention. It turns out that global investment consultant Russell Investments also advocated for this approach in a recent study targeted at institutional asset allocators.

Their study includes some neat charts that further validate the mechanics of this approach. For example, the success of this approach is based on gambling theory which describes optimal bet sizing. If we assume expected return is either constant or unknowable, but positive, then it is optimal to always bet the same amount. However, markets force most investors to increase and decrease their bet sizes, sometimes dramatically, based on changing volatility. An investor's exposure to risk is much larger when the market's volatility is 30% than when it is 15%, but most investors fail to adjust portfolios for this higher risk.

The charts below illustrate how this plays out in markets. The charts at the top prove that returns in one month have little relationship to returns in the next month (left), but the volatility in the current month does a good job of forecasting the volatility in the subsequent month (right). So adjusting one's exposure based on the most recent month's volatility makes sense.

Source: Russell Investments

Some might argue that higher volatility should equate to higher expected returns, but the chart below invalidates that assumption. There is no relationship between volatility and expected return.

Source: Russell Investments

If higher volatility does not improve expected returns, then we should concern ourselves with optimal bet sizing. By doing so, we substantially improve investment results, especially after accounting for risk.

Source: Butler|Philbrick|Gordillo & Associates, 2012. Results are pro-forma and for illustrative purposes only.

Rebalancing Canada

2011-12-24T14:15:00.000-08:00

Prologue:

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.

Rebalancing Japan

2011-12-24T13:37:00.000-08:00

Prologue:
This is a 'Japan-amized' version of an article we published on Monday, December 19, 2011, which featured a study of US equity and fixed-income markets. The Japanese experience since 1993 was dramatically different than the U.S. experience. While U.S. stocks climbed 267% over the past 18 years, Japanese stocks dropped 48% over the same period, which annualizes to losses of 3.43% per year.

Of course, Japanese investors endured a seemingly endless series of intermediate term extremes of hope and despair as markets oscillated wildly above and below their long-term negative trend. Japan's multi-decade crash and stagnation is unique among modern market economies (so far), so we wanted to see how well our volatility adjusted rebalancing framework worked in this difficult environment.

The results are even better than we had any right to expect, which gives us some hope for investors over what we forecast to be a very difficult decade for equities going forward.

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 Japanese 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 Japan 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 Japanese stock/bond portfolio with quarterly rebalancing

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

With such a long-term downtrend, even traditional SAA with quarterly rebalancing couldn't salvage a Japanese investor's portfolio from near-zero returns, as illustrated in the following equity-only example.

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 1.84% 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 0.16.
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 -33.6%.
% Winning Months (top right): This is the percentage of months in which the strategy delivered positive absolute returns. This strategy delivered positive returns in 55% of months.

Of course, the 50/50 portfolio did much better than stocks on their own. Let's contrast the performance of the traditional 50/50 SAA portfolio with the return to a 100% stock portfolio over the same time frame:

Case 2. Nikkei 'Buy and Hold'

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

Over the past 18 years Japanese stocks delivered a truly dismal -3.43% per year including reinvested dividends for a total aggregate loss to investors of 48% top date. To compare, US stocks delivered 11 percentage points per year more than Japanese stocks.

While a traditional SAA approach definitely improved results over a pure Japanese equity portfolio, it probably didn't serve as much comfort to Japanese investors.

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.

While a traditional 50/50 allocation with rebalancing struggled to deliver returns (but delivered an abundance of hope and despair), relative volatility weighting between stocks and bonds provided investors with tolerable, if not robust, results of 4.71% annualized over the period, with a very respectable Sharpe ratio of 0.78. Further, the portfolio never experienced a loss greater than 14% from peak to trough, less than half the drawdown experienced by a traditional balanced portfolio.

Not bad for a simple and intuitive twist on an old idea. The following chart uses US data to illustrate how a volatility based approach also exposes investors 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, even in Japan!

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 a long-term secular bear market with several bull-market episodes.

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, it is much better than typical 'Aggressive' all-equity allocations.

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 substantially improved absolute returns, but also dramatically improved portfolio efficiency: in the Japanese study above, the Sharpe ratio improved by 700% and Maximum Daily Drawdown was reduced by 240% over traditional SAA.

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.

Rebalancing Resurrected

2011-11-10T12:09:00.001-08:00

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.

Click chart for a bigger version.
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 reasonably lifestyle assuming that they are able to receive average returns in retirement. This investor may be advised to adopt a 50/50 stock/bond Strategic Asset Allocation with quarterly rebalancing. If they had started with this approach in mid-1994 (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

Click chart for a bigger version. Source: Butler|Philbrick & Associates.

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 8.54% 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 0.84.
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 -24.33%.
% Winning Months (top right): This is the percentage of months in which the strategy delivered positive absolute returns. This strategy delivered positive returns in 66% 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 500 ‘Buy and Hold’

Click chart for a bigger version. Source: Butler|Philbrick & Associates.

Note that stocks alone over this period delivered 7.58% annualized returns, with a Sharpe ratio of 0.37, a Max Daily Drawdown of 55% (!!), and delivered positive returns in 61% of months.
This means a simple SAA portfolio with 50/50 stocks/bonds delivered 24% more total growth (330% vs. 267%), with twice the efficiency (Sharpe ratio of 0.73 vs. 0.37), half the investor anxiety (Max Daily Drawdown -24% vs. -55%), and more winning months (66% vs. 61%).
These are actually pretty good stats, but SAA only scratches the surface of what is possible with more thoughtful asset allocation techniques.
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

Click chart for a bigger version. Source: Butler|Philbrick & Associates.

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.
This approach delivers much more efficient performance than the traditional SAA approach. While the annualized returns to this strategy improve by just 0.15% per year, the real benefit is clear from the risk metrics.
The Sharpe ratio for this approach is 0.99, which represents 18% greater efficiency than traditional SAA, and 300% more efficiency than a pure stock portfolio. Of even greater interest for most investors, the Maximum Daily Drawdown drops to 17% from 24% for traditional SAA and 55% for stocks, an improvement of 40% and 300% respectively.
Not bad for a simple and intuitive twist on an old idea. The following chart shows 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.

Click chart for a bigger version. Source: Butler|Philbrick & Associates.

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 1994 through 2011 is especially interesting because it includes a record setting equity bull market during the 1990s and a volatile sideways market through the 2000s.
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 18% and Maximum Daily Drawdown was reduced by 40%.

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.
Adam Butler and Mike Philbrick are Portfolio Managers with Butler|Philbrick & Associates at Macquarie Private Wealth in Toronto, Canada.

Demographic Blues

2011-08-23T12:05:00.000-07:00

The Federal Reserve Bank of San Francisco published a fascinating piece of research on Monday relating U.S. stock market performance to demographic trends. The results are not encouraging for long-term 'Buy and Hold' type investors.

Boomer Retirement: Headwinds for U.S. Equity Markets?

By Zheng Liu and Mark M. Spiegel

Historical data indicate a strong relationship between the age distribution of the U.S. population and stock market performance. A key demographic trend is the aging of the baby boom generation. As they reach retirement age, they are likely to shift from buying stocks to selling their equity holdings to finance retirement. Statistical models suggest that this shift could be a factor holding down equity valuations over the next two decades.

Historical data suggests a strong relationship exists between the age distribution of the U.S. population and stock market performance. A key demographic trend is the aging of the baby boom generation. As they reach retirement age, Boomers are likely to shift from a bias toward saving and buying stocks, to selling their equity holdings to finance retirement. Statistical models suggest that this shift could substantially depress equity valuations over the next 15 years or more.

Without belaboring the mechanics of the study, the researchers analyzed trends in the proportion of middle-aged workers in the U.S. economy relative to the proportion of retired workers to forecast future stock market valuations. This research follows other studies which found that the booming markets of the 1980s and 1990s were largely attributable to the bulge bracket of baby boomers who were entering their prime saving and investing years during those decades.

In contrast, the current Federal Reserve study finds that, as the ratio of middle aged workers to retired persons is forecast to fall persistently through 2025 as the bulk of baby boomers retire, these same boomers will be withdrawing savings from stock and bond markets, thereby exerting slow but steady downward pressure on prices of financial assets, including stocks, for the foreseeable future.

The specific demographic ratio analyzed in the study is the M/O ratio, which is the population ratio of those aged 40 - 49 to those aged 60 - 69. This ratio broadly captures the number of people in prime saving and investing years relative to the number of people who are beginning to withdraw from savings to fund retirement. From 1981 to 2000 this ratio increased from 0.18 to 0.74 at the same time stock valuations rose from roughly 8 times earnings to almost 30, and the main U.S. stock market index exploded from 150 to almost 1500.

Source: Stockcharts.com

Sadly, the U.S. Census Bureau is forecasting exactly the opposite dynamic to play out over the next 15 years as boomers retire. As this demographic scenario unfolds, the Federal Reserve Bank's model suggests that stock prices will enter a persistent decline until 2021. Further, stocks are not expected to exceed their 2010 levels until 2027, after adjusting for inflation.

Key findings:

The M/O ratio explains about 61% of the movements in the P/E ratio during the sample period. In other words, the M/O ratio predicts long-run trends in the P/E ratio well.
Given the projected path for P/E* and the estimated convergence process, we find that the actual P/E ratio should decline from about 15 in 2010 to about 8.3 in 2025 before recovering to about 9 in 2030.
The model-generated path for real stock prices implied by demographic trends is quite bearish. Real stock prices follow a downward trend until 2021, cumulatively declining about 13% relative to 2010.
Inflation adjusted stock prices are not expected to return to their 2010 level until 2027.
On the brighter side, as the M/O ratio rebounds in 2025, we should expect a strong stock price recovery. By 2030, our calculations suggest that the real value of equities will be about 20% higher than in 2010.

Source: Federal Reserve Bank of San Francisco

Interestingly, the conclusions from the Federal Reserve paper mirror the conclusions from our own proprietary 'Estimating Future Returns' models, which suggest investors should expect near zero returns after inflation for at least the next 15 years.

Source: Shiller (2011), DShort.com (2011), Chris Turner (2011), World Exchange Forum (2011), Federal Reserve (2011), Butler|Philbrick & Associates (2011)

While this outcome may appear inconceivable to many, I would urge you to examine the path of Japanese stocks from their peak in 1989 at almost 40,000 to their current price under 10,000. Japan suffered from too much debt and an unhealthy property sector, but these headwinds were amplified by another challenge which we in the West share (though not quite as badly): a declining share of working persons relative to retired persons.

Source: Stockcharts.com

Perhaps not surprisingly, U.S. stock markets have been tracking the performance of Japanese stocks since U.S. stocks peaked in 2000. When we overlay the two stock market indices and align their respective peaks, the resemblance is uncanny (and not a little bit shocking for 'Buy and Hold' investors). If we continue to track the Japanese experience, we may be setting up for another major drop, perhaps to new lows.
Source: Bloomberg, Ritholtz.com

You probably aren't hearing this message from pundits on TV or in the papers, or economists at the major banks or investment firms. However, I urge you to keep three thoughts in mind when you hear these experts speak:

1. If a person's job depends on them not knowing something, then they won't know it.

2. Investment firms make much higher margins from clients who hold or trade stocks or stock mutual funds than from clients who hold bonds or cash instruments like GICs or money market funds. As such, they have a strong incentive to keep clients invested in stocks and stock mutual funds at all times, per the 'Buy and Hold' approach.

So what is a person to do when long-term Canadian bonds are yielding 0.64% after inflation, and developed market stocks are likely to yield no returns (but probably typically high volatility) for the next 15 years or more?

At Butler|Philbrick & Associates, we have a plan. At its core, our approach embraces two major areas of differentiation:

Broaden the investment opportunity set for portfolios to include international and emerging market stocks, real estate, commodities,etc. Cash is an asset class!
Apply a proven process to decide which asset classes to own (including cash when markets are risky), and when to own them.

As a proof of the effectiveness of our approach in difficult markets, we published a study on how to profitably trade the Japanese bear market back in February (see here for full study). We have suspected for some time that the Japanese template was the most likely trajectory for developed stock markets over the next several years.

In the study, we applied our trend following approach to the Japanese stock market to see how it would have performed over its 21 year downhill roller coaster ride.

Source: Butler|Philbrick & Associates

You can see in the chart that by taking advantage of both positive and negative trends using a simple timing system over this period, our technique delivered over 16% annualized investment performance, while never dropping more than 22.9% from any peak to trough (see "CAGR%" and "Max Total Equity DD" respectively in the table above the chart).

Pretty good results generally, but especially when they're compared with the 75% cumulative loss that most Japanese investors experienced by holding stocks over the past 20 years.

We are following a proven trend following model like the one above to deliver prospective returns for clients no matter what happens in markets. What are you (or your Advisor) doing to prepare for this potential investment outcome?

Adapt or Fail

2011-07-29T06:12:00.000-07:00

The previous article in this series discussed the ebb and flow of stock markets as they move through long periods of strong and weak returns, often lasting 15 years or more at a time. As such, there are periods when investors can set their sails and ride the prevailing winds to a sunny investment horizon. Alternatively, there are long periods where markets go sideways or down, and where any growth in investment portfolios must necessarily come at the expense of someone else.

Unfortunately, our analysis suggests that we are in the early, or perhaps middle stages of a multi-year period of low market returns, where investors will need to ‘row’ their way to positive investment performance. During such ‘rowing’ periods it is important to consider active strategies that have the potential to provide substantial risk-adjusted returns in any type of market.

Heave Ho

One such rowing strategy was recently outlined in a paper entitled Optimal Momentum by Gary Antonacci. This paper has practical relevance because it closely mirrors the general principles we apply in our proprietary investment models for clients. In the study, Antonacci constructs an investment universe that broadly captures the set of global opportunities available to investors. We simplified the strategy presented in the paper slightly, and altered the investable universe in our study to accommodate the options available to Canadian investors by investing in Exchange Traded Funds (ETFs).

At any given time, our model was able to invest in any two of the asset classes below (in bold) via a corresponding ETF (in italics).

· US Real Estate - iShares Cohen & Steer Realty REIT

· Gold Bullion - SPDR Gold Shares

· Japanese Stocks - iShares MSCI Japan Index Fund

· European Stocks - iShares S&P Europe 350 Index Fund

· Cash - Barclays Low Duration Treasury

· Asian Stocks (ex-Japan) - iShares Pacific ex-Japan

· US Treasury Bonds - iShares Barclay 7-10 Yr. Treasury - 7-8 Yr.

· US Stocks - Vanguard MSCI Total U.S. Stock Market

ETFs trade on the major stock exchanges just like regular stocks, but they behave largely like very low-cost index mutual funds. That’s because each Exchange Traded Fund, or ETF, is constructed to deliver the same performance as a very broad index of underlying securities. For example, investors can purchase a single ETF to experience the exact returns of the entire S&P/TSX Index, the S&P 500, theNasdaq, gold, European stocks, or a wide variety of other asset classes or indices.

In his paper, Antonacci presents a strategy that ranks the investment opportunities above based on how each investment has performed over the prior six months. The concept is based on the well-documented phenomenon in markets called ‘Momentum’, whereby securities that have done well over the recent past have a high probability of continuing their positive performance over the subsequent 1 to 3 month period.

Accordingly, in our study we ranked the above basket of investments at the end of each month according to their relative price performance over the previous six months. At the end of each month, we altered the holdings of the portfolio so that the portfolio always held the two most highly ranked investments. In other words, the portfolio adapted at the end of each month to hold the most prospective investments over the following month based on our ranking criteria.

Table 1.
SYMBOL	EXCHANGE TRADED FUND (ETF)/ INVESTMENT MODEL
ICF	US Real Estate - iShares Cohen & Steer Realty REIT
GLD	Gold Bullion - SPDR Gold Shares
EWJ	Japanese Stocks - iShares MSCI Japan Index Fund
IEV	European Stocks - iShares S&P Europe 350 Index Fund
SHY	Cash - Barclays Low Duration Treasury
EPP	Asian Stocks (ex-Japan) - iShares Pacific ex-Japan
IEF	US Treasury Bonds - iShares Barclay 7-10 Yr. Treasury - 7-8 Yr.
VTI	US Stocks - Vanguard MSCI Total U.S. Stock Market

To illustrate the effect of this ranking and rotation into the most prospective markets each month, in the diagram below, we tracked our model’s holdings for each month of the year 2008 in Figure 1. to demonstrate how the ETF holdings of our model change over time. The numbers represent the monthly total returns for each investment, and coloured squares identify the months where our model actually held the investment.

You can see that our model rotated into gold (GLD) and U.S. bonds (IEF) for the first 7 months of 2008, and then moved out of gold and into cash (SHY) for the remainder of the year.

Figure 1: Highlighted Monthly Model Holdings in 2008

	January	February	March	April	May	June	July	August	September	October	November	December
VTI	-6.17%	-2.50%	-0.90%	4.89%	2.02%	-8.12%	-0.62%	1.46%	-9.24%	-17.48%	-8.01%	1.78%
IEV	-8.80%	-0.47%	1.18%	4.43%	0.41%	-9.34%	-2.57%	-3.97%	-12.36%	-21.25%	-6.72%	7.70%
EWJ	-4.29%	-1.49%	-1.28%	7.36%	1.96%	-7.47%	-3.77%	-4.92%	-6.57%	-15.57%	-3.78%	11.56%
EPP	-7.88%	-2.57%	-2.09%	7.76%	2.86%	-9.16%	-4.66%	-4.95%	-12.67%	-26.52%	-6.90%	9.45%
SHY	1.65%	1.03%	0.25%	-0.84%	-0.35%	0.24%	0.43%	0.47%	0.78%	1.10%	1.10%	0.56%
IEF	3.36%	1.24%	1.34%	-2.41%	-1.78%	1.14%	0.72%	1.53%	-0.14%	-0.87%	7.75%	5.15%
GLD	10.84%	5.23%	-6.00%	-4.16%	0.92%	4.52%	-1.44%	-9.29%	4.11%	-16.14%	12.57%	7.73%

Source: Antonacci (2011), Butler|Philbrick & Associates (2011)

It Pays to Row

In terms of performance, our modified Antonacci Model achieved a 407% total return between 2003 and July 2011. In the same period, the S&P 500 index of the largest U.S. stocks returned 68% including dividends. This works out to an average annual return of 21.8% for our simple strategy versus 6.6% annualized for U.S. stocks. Further, while U.S. stocks were dropping over 50% in 2008 our Antonacci study portfolio delivered a positive return of 16.1% (see Figure 2 below). Figure 2. demonstrates the relative performance of our model in each calendar year relative to all of the available investments. Notice that from 2003 to 2011 our modified Antonacci Model never dropped out of the top half in terms of annual calendar-year performance, and never experienced a losing year.

Figure 2:

Source: Antonacci (2011), Butler|Philbrick & Associates (2011)

SYMBOL	EXCHANGE TRADED FUND (ETF)/ INVESTMENT MODEL
ICF	US Real Estate - iShares Cohen & Steer Realty REIT
EWC	Canadian Stocks - iShares MSCI Canada Index Fund
GLD	Gold Bullion - SPDR Gold Shares
EWJ	Japanese Stocks - iShares MSCI Japan Index Fund
IEV	European Stocks - iShares S&P Europe 350 Index Fund
SHY	Cash - Barclays Low Duration Treasury
SPY	US Large-cap Stocks - SPDR S&P 500 Index
ANT	Modified Antonnacci Model
EPP	Asian Stocks (ex-Japan) - iShares Pacific ex-Japan
IEF	US Treasury Bonds - iShares Barclay 7-10 Yr. Treasury - 7-8 Yr.
VTI	US Stocks - Vanguard MSCI Total U.S. Stock Market

Even more impressive, the modified Antonacci model’s worst full calendar year return was positive 10%, versus minus 35% or more for the major stock markets. In fact, the Antonacci model never dropped more than 10% from any peak to trough on its ride to the top, while markets dropped 50% or more!

The Long and Short

The take away from this study is that, if left alone, and especially during low-return periods, financial markets do not deliver returns in a consistent fashion. As such, it is of paramount importance that any investment strategy be able to adapt over time as markets change - moving to bonds, cash or perhaps gold when markets are falling, or into the most prospective markets when markets are rising – rather than sticking with a constant allocation to one market or another over time, per the traditional approach.

After studying the performance of Antonacci’s momentum based strategy over the past decade and considering the returns that buy and hold investing delivered during the same period, it should be clear that a systematic adaptive investment strategy has the potential to add tremendous value in all types of market environments, and especially during highly uncertain and volatile times like today.

For more information about how adaptive, systematic strategies may work well for you, or to read our Estimating Future Returns piece, please visit us on the Web at www.ButlerPhlibrick.com/CaseStudies.html.

Time to Row

2011-07-29T06:11:00.000-07:00

We have discussed at length the merits of our “Estimating Future Returns” model and its history of providing more accurate forecasts of future returns to stocks than traditional methods (for more on our real returns model click here).

It is essential to consider the myriad potential outcomes and the likelihood of each outcome occurring when determining whether your current investment strategy is appropriate, or whether an alternative strategy is warranted.

Considering the expected returns for the foreseeable future allows the wealth management process to be more adaptive to market conditions, a characteristic that can add tremendous value to a portfolio and a wealth plan over time. One of the single greatest failings of the traditional wealth management approach is its inability to integrate these ever changing projected future returns into the portfolio management process.

In fact investors almost never receive the very long-term rate of return that the dominant investment theory would predict (Dalbar, 2011). Instead, investors have historically received a random mix of very low and very high returns year in and year out. Worse, these high return and low return periods often cluster together over many years at a time. This is problematic if you happen to retire during a period of sustained below average returns.

These types of multi-decade bull and bear markets exist and are driven by market regimes where economic and psychological forces compel prices to move broadly higher or lower over many years at a time. If you refer to Figure 1 below you will see that retiring in a regime of persistent low returns is just as likely as retiring in a period where markets experience sustained above average returns.

The question becomes: What type of market are you retiring in? Further, what can you do to maximize your chance of a successful retirement?

Our “Estimating Future Returns” model allows us to better inform investors about where they sit today and what their likely prospects are for the medium term. As Ed Easterling of Crestmont Research puts it, valuation models allow us to more accurately gauge whether investors will need to ‘row’ for the foreseeable future or whether they can ‘sail.’

Over the last 100 years there have been four extended periods during which investors were forced to ‘row’ and three periods where investors could ‘sail.’ There was the raging bull market of the roaring twenties, which seemingly defied Washington’s policy makers. The brutal bear market of the Great Depression followed the ‘Roaring Twenties’, and lasted until the end of World War II.

The end of the Second World War preempted the post-war economic boom and concurrent long-term bull market that raged from the late 1940s to the late 1960s. The almost decade and a half long stagnation that followed was sparked off in earnest by the 1973 OPEC oil crisis and the subsequent dramatic rise in inflation expectations which pushed interest rates to their century highs by 1981.

This period was followed by the longest sustained bull market in the history of the United States, from the early 1980s to the ‘Tech Bubble’ in 2000. We have subsequently experienced the current decade’s financial turmoil which has seen financial markets recover from the depths of the Tech Bubble only to be met with substantial difficulties stemming from the collapse of the U.S. housing market in 2008; a state from which the market still has yet to fully recover.

Figure 1

Source: Butler|Philbrick & Associates (2011)

What this should illustrate is that markets come in all shapes and sizes, and that the traditional ‘Buy & Hold’ approach to investing may not be the optimal strategy in all market environments. Additionally, sustained bear markets inevitably follow sustained bull markets. Unfortunately, no one knows precisely when the switch will flip from one market regime to the other.

This creates the need for a strategy that is not only effective during both rowing and sailing environments, but which can also be effective during the transition between these two market regimes.

Rowing requires a dynamic and adaptive approach with periodic readjustments, while ‘sailing’ only necessitates letting the prevailing wind propel you toward your financial goals, while making minor adjustments once in a while to stay on course. Passive strategies like ‘Buy & Hold’ work adequately, but only in ‘sailing’ markets. In ‘rowing’ markets the only positive returns that investors receive are necessarily taken from someone else, as it is a zero sum game.

If you look at Table 1 below, you will notice that our “Estimating Future Returns” model very accurately estimated the forward fifteen-year market environment for both bullish and bearish market regimes of the past century. While traditional Advisors would have you believe that the best estimate of portfolio returns is always the very long-term average of 6.5% after inflation, our model has historically delivered much higher levels of accuracy. You can see in Table 1. that in sailing environments this 6.5% estimate is usually too low, while in rowing environments it is much too high.

Table 1

Source: Shiller (2011), Doug Short (2011), Butler|Philbrick & Associates (2011)

If the model’s past success is any indication, then we observe a high likelihood that investors will face a rowing environment over the next fifteen years. During these types of market regimes, it is essential to have a strategy that is adaptable to many different market environments, and can scour the globe for the best opportunities. In our next installment, we discuss a strategy that has the potential to deliver substantial risk-adjusted returns in all market environments, and demonstrate how it managed to deliver positive returns, even during one of this decade’s most challenging years.

Buy and Hold? Dead Money.

2011-04-10T17:28:00.000-07:00

At Butler|Philbrick & Associates, we don’t take anything on faith. Nor do we take expert opinions to heart, as we have shown time and again that experts make poor oracles. Instead, we believe in crunching the numbers ourselves to discover meaningful relationships in data. Where meaningful relationships exist, we apply statistical models to improve our chances of success.

Now that we have developed and deployed Version 2 of our systematic investment model, we decided to shift our attention to the development of a more robust model for forecasting long-term stock market returns. Traditional Advisors assume that the best estimate of future market returns in all market environments is the simple long-term average return on stocks: about 6.5% per year after inflation.

We hypothesized that it is possible to construct a statistical model using long-term market data which will allow us to make much more accurate predictions about long-term returns. It turns out that we were right. Those who are interested in the process we used, and the specifications of our model, are encouraged to read our full report.

There are several reasons why it may be useful to have a more robust estimate of future expected returns on stocks:

People who are approaching retirement need to estimate probable returns in order to budget how much they need to save.
A retiree’s level of sustainable income is largely dictated by expected returns over the early years of retirement.
Investors of all types must make an informed decision about how best to allocate their capital among various investment opportunities.

Many investors do not know that traditional wealth advice is rooted in the assumption that the best estimate of future returns is always the average long-term return to stocks. No matter where markets are on the continuum from very cheap to very expensive, traditional Advisors will make recommendations on the assumption that investors should expect 6.5% inflation adjusted returns on stocks over all investment horizons.

To illustrate, imagine a retiree who visited a traditional Investment Advisor at the peak of the technology bubble in early 2000, when markets were more expensive than at any other time in the prior 130 years. This investor would have been advised to expect returns on his stock portfolio of 6.5% per year over his or her investment horizon, based on very long-term averages.

Our models suggest that this retiree should have expected inflation-adjusted returns to his portfolio of negative 2% per year over the subsequent 15 years, a difference in returns of 8.5% per year versus the long-term average. In fact, this investor would have experienced returns of negative 1.55% per year through December 31, 2010, and would need a return of almost 22% per year through 2015 to realize the traditional advisor’s year 2000 projections.

Table 1. applies this same analysis to other important periods over the past 100 years. We contrasted the return forecasts from a traditional long-term average approach with the forecasts from our valuation-based model, at a variety of transitional dates in stock markets, to demonstrate the improved accuracy of our valuation-based approach.

Source: Shiller (2011), DShort.com (2011), Butler|Philbrick & Associates

You can see that forecasts derived from long-term average returns yield over 400% more error than estimations from our valuation-based model over these 15-year forecast horizons (1.24% annualized return error from our model versus 5.24% using the long-term average). Clearly our model offers substantially more insight into future return expectations than simple long-term averages, especially near valuation extremes.

Chart 1. shows how closely our valuation-based model forecasted actual market returns over subsequent 15-year periods. The blue series is our model forecast, and the red series tracks actual market returns. The red line near the middle of the chart reflects a 6.5% annualized return.

Source: Shiller (2011), DShort.com (2011), Butler|Philbrick & Associates

So what does our model suggest about current market valuations and future expected returns? You can see from the chart’s blue line that expected returns from current levels are well below average. Even at the market’s lows in March of 2009, expected returns to stocks over the subsequent 15 years was just average, suggesting that markets simply achieved long-term average valuations at the market’s low. We were, and are, a far cry from the generational low valuations achieved around 1920, 1950, and 1980. If history is any guide, we may achieve those generational-low valuations – which represent once-in-a-lifetime opportunities to buy stocks – at some point in the next 5 to 10 years. Of course, we must endure another period of very low returns to achieve such low valuations.

So what level of annualized returns should we expect from stocks, after adjusting for inflation, over the next 5, 10, 15 and 20 years based on current valuations? Table 2. summarizes our model’s forecasts over these horizons. Only time will tell how accurate they might be, but history clearly proves that future returns will be much closer to these values than the long-term average of 6.5%.

Source: Shiller (2011), DShort.com (2011), Butler|Philbrick & Associates

The investment industry has a large vested interest in convincing you that the same approach that delivered poor returns over the prior decade will deliver much more robust results over the next few years. That way, you will be convinced to hold your money in the same traditional, high margin products that made banks so much money, and lost investors so much money, over the last 10 years.

In periods of low returns, investors must have the courage to adopt a different approach if they hope to achieve better-than-average results. Our Gestalt Architecture was engineered to deliver strong returns in all markets, including markets which drop in value over several years or months. How does your Advisor plan to deliver robust results in the likely event that future returns are well below average?

For a wealth of evidence about momentum investing, market timing, asset-class rotation and ETFs, please visit our Case Studies web page at www.ButlerPhilbrick.com/CaseStudies.html.

Estimating Future Returns

2011-03-17T13:06:00.000-07:00

"Mankind are so much the same, in all times and places, that history informs us of nothing new or strange in this particular. Its chief use is only to discover the constant and universal principles of human nature." - David Hume

Long-time readers will know that we do not make predictions in the normal sense. That is, we endorse the decisive evidence that markets and economies are complex, dynamic systems which are not reducible to normal cause-effect analysis. However, we are willing to acknowledge the likelihood that the future is likely to rhyme with the past. Thus, we apply simple statistical models to discover mean estimates of what the future may hold over meaningful investment horizons (10+ years), while acknowledging the wide range of possibilities that exist around these averages.

There are several reasons why it may be useful to have a more robust estimate of future expected returns on stocks:

People who are approaching retirement need to estimate probable returns in order to budget how much they need to save.
A retirees level of sustainable income is largely dictated by expected returns over the early years of retirement.
Investors of all types must make an informed decision about how best to allocate their capital among various investment opportunities

Many studies have attempted to quantify the relationship between Shiller PE and future stock returns. Shiller PE smoothes away the spikes and troughs in corporate earnings which occur as a result of the business cycle by averaging inflation-adjusted earnings over rolling historical 10-year windows.

This study contributes substantially to research on smoothed earnings and Shiller PE by adding three new valuation indicators: the Q-Ratio, and deviations from the long-term price, and total-return, trends. The Q-Ratio measures how expensive stocks are relative to the replacement value of corporate assets. Deviations from the long-term trends of the S&P price and total-return series indicate how 'stretched' values are above or below their long-term averages.

These three measures take on further gravity when we consider that they are derived from three distinct facets of financial markets: Shiller PE focuses on the earnings statement; Q-ratio focuses on the balance sheet; and deviation from trend focuses on a technical price series. Taken together, they have capture a wide breadth of information about markets.

We analyzed the power of each of these 'valuation' measures to explain inflation-adjusted stock returns over subsequent multi-year periods. Our analysis provides compelling evidence that future returns will be lower when starting valuations are high, and that returns will be higher in periods where starting valuations are low.

This last point may seem obvious, but I want to emphasize a critical point about traditional wealth management of which most investors are not aware:

Traditional investment planning does not account for whether markets are cheap or expensive. An investor who visited a traditional Investment Advisor at the peak of the technology bubble in early 2000 would, in practice, be advised to allocate the same proportion of his wealth to stocks as an investor who visited an Advisor near the bottom of the markets in early 2009. This despite the fact that the first investor would have had a valuation-based expected return on his stock portfolio from January 2000 of negative 2% per year, while the second investor would expect inflation-adjusted compound annual returns of 6.5%. For an investor with $1,000,000 to invest, this would represent a difference of more than $1.26 million in cumulative wealth over a decade.

Said differently, traditional wealth advice is rooted in the assumption that the best estimate of future returns is the average long-term return to stocks. No matter where markets are on the continuum from very cheap to very expensive, traditional Advisors will make recommendations on the assumption that investors should expect 6.5% inflation adjusted returns on stocks over all investment horizons.

John Hussman at Hussman funds is careful to qualify the value of this anaylsis: "Rich valuation is strongly associated with weak subsequent returns, but only reliably so over periods of 7-10 years. In contrast, the present syndrome of overvalued, overbought, overbullish, rising-yield conditions is typically associated with abrupt and often steep losses, but is more commonly resolved over a period of months rather than years." (Hussman, Feb 14, 2011). Thus, we are not making a forecast of market returns over the next several months; in fact, markets could go substantially higher from here. However, over the next 10 to 15 years markets are very likely to revert to average valuations, which are much lower than current levels.

This study will demonstrate that investors should expect 6.5% returns to stocks only during those very rare occasions when the stock market passes through 'fair value' on its way to becoming very cheap, or very expensive. At all other periods, there is a better estimate of future returns than the long-term average, and this study will quantify that estimate.

Investors should be aware that, relative to meaningful historical precedents, markets are currently expensive and overbought by all three measures, indicating a strong likelihood of low inflation-adjusted returns going forward over periods as long as 20 years.

This prediction is also supported by evidence from an analysis of corporate profit margins. In his recent book, Vitaly Katsenelson provided the following chart (Chart 1.) of long-term profit margins to U.S. companies. Companies have clearly been benefitting from a period of extraordinary profitability.

Source: Vitaly Katsenelson (2011)

The profit margin picture is critically important. Jeremy Grantham recently stated, "Profit margins are probably the most mean-reverting series in finance, and if profit margins do not mean-revert, then something has gone badly wrong with capitalism. If high profits do not attract competition, there is something wrong with the system and it is not functioning properly." On this basis, we can expect profit margins to begin to revert to more normalized ratios over coming months. If so, stocks may face a future where multiples to corporate earnings are contracting at the same time that the growth in earnings is also contracting. This double feedback mechanism may partially explain why our statistical model predicts such low real returns in coming years.

Caveat Emptor.

Modeling Across Many Horizons

Many studies have been published on the Shiller PE, and how well (or not) it estimates future returns. Almost all of these studies apply a rolling 10-year window to earnings as advocated by Dr. Shiller. But is there something magical about a 10-year earnings smoothing factor? Further, is there anything magical about a 10-year forecast horizon?

Kitces (2008) demonstrated that "the safe withdrawal rate for a 30-year retirement period has shown a 0.91 correlation to the annualized real return of the portfolio over the first 15 years of the time period". So there is clearly merit in studying a 15-year forecast horizon as well. Further, the tables below will demonstrate that statistical models have the greatest explanatory power at the 15 year horizon.

This study will attempt to address the question of 'perfect forecast horizon', perfect valuation factor, and 'perfect earnings smoothing factor', by analyzing the explanatory power of earnings, the Q-Ratio, and regressed historical stock returns, over return horizons from 1 to 30 years. We will also put all of the factors together to construct an optimized model.

Table 1. below provides a snapshot of some of the results from our analysis. The table shows estimated future returns based on several factor models over some important investment horizons. The "Best Fit Multiple Regression" is by far the most accurate model, but other results are provided for context.

Table 1. Factor Based Return Forecasts Over Important Investment Horizons

Source: Shiller (2011), DShort.com (2011), Butler|Philbrick & Associates (2011)

You can see from the table that every single valuation factor model generates results which suggest a very low future return environment for stocks. Further, the 'Best Fit Multiple Regression', which has historically provided a surprising degree of forecast accuracy, confirms this outlook with a high degree of confidence (see explanation below). Those who are not interested in our process can skip to the bottom sections, 'Putting the Predictions to the Test', and 'Conclusion'.

Process

The following matrices show the R-Squared ratio, regression slope, regression intercept, and current predicted forecast returns for each valuation factor. The matrices are heat-mapped so that larger values are reddish, and small or negative values are blue-ish. Click on each image for a large version.

Matrix 1. Explanatory power of valuation/future returns relationships

Source: Shiller (2011), Doug Short (2011), Butler|Philbrick & Associates (2011)

You will note that the R-Squared (top chart), which is a measure of the explanatory power of the relationship, is highest for the Q-ratio over all forecast horizons up to 9 years, but that deviations from the real price regression are more predictive over horizons of 10 years or more. The explanatory power of smoothed earnings ratios gets better consistently as we extend the forecast horizon, with peak ratios at the 20-year range. No factors posses any material explanatory ability at forecast horizons less than 5 years, so we have omitted results for these horizons.

Many analysts quote 'Trailing 12-Months' or TTM PE ratios for the market as a tool to asses whether markets are cheap or expensive. If you hear an analyst quoting the market's PE ratio, odds are they are referring to this TTM number. Our analysis slightly modifies this measure by averaging the PE over the prior 12 months rather than using trailing cumulative earnings through the current month, but this change does not substantially alter the results. As it turns out, TTM average earnings have very mild explanatory value over periods greater than 8 years. However, the explanatory power of TTM earnings is substantially less reliable than all other factors studied in this analysis, so investors may wish to pay little heed to this indicator of whether stocks are cheap or expensive.

One interesting take-away from the analysis is that the simple real price series carries much better explanatory power than the real total-return price series, which includes reinvested dividends. The total return series reflects the actual returns to investors over any given period, while the price series ignores dividends altogether. Strangely however, the total return series does a relatively poor job of forecasting future total returns to stocks, and is, statistically speaking, a much less powerful explanatory factor than either the Q-ratio or the simple real price regression. As so often happens in complex fields, the evidence makes a mockery of the theory.

Forecasting Expected Returns

The next matrices provide the slope and intercept coefficients for each regression. We have provided these in order to illustrate how we calculated the values for the final matrix below of predicted future returns to stocks.

Matrix 2. Slope of regression line for each valuation factor/time horizon pair.

Source: Shiller (2011), Doug Short (2011), Butler|Philbrick & Associates (2011)

Matrix 2. Intercept of regression line for each valuation factor/time horizon pair.

Source: Shiller (2011), Doug Short (2011), Butler|Philbrick & Associates (2011)

Our final matrix below shows predicted future real returns over each time horizon, as calculated from the slopes and intercepts above, by using the most recent values for each of the 13 earnings series, the Q-Ratio, and the return series as inputs. For statistical reasons which are beyond the scope of this study, we have substituted the ordinal rank for the nominal value for each factor in running our analysis. Therefore, when we solve for future returns based on current monthly data, we apply the monthly rank in the equations.

For example, the 9-year return prediction based on the current Q-Ratio can be calculated by multiplying the ordinal rank of the current Q-Ratio (1266) by the slope from the matrix at the intersection of 'Q-Ratio' and '9-Year Rtns' (-0.0001152), and then adding the intercept at the same intersection (0.1378). The result is -0.0080, or -0.80%, as you can see in the matrix below at the same intersection (Q-Ratio : 9-Year Rtns). Please click the matrix for a larger version.

Matrix 4. Modeled forecast future returns using current valuations.

Source: Shiller (2011), Doug Short (2011), Butler|Philbrick & Associates (2011)

Finally, at the bottom of the above matrix we show the forecast returns over each future horizon based on our best-fit multiple regression from the factors above. We began testing the multiple regression against the Q-ratio, the 15-year Shiller PE, the price regression, and the total return regression as a 4 factor model. However, we discovered that the 15-year PE and total return regression series provided more noise than signal to the regression (that is, these factors were not statistically significant and reduced the F-score), so we narrowed the regression to include just the Q ratio and the real price series over each forecast horizon. We provided the R-squared for each of these multiple regressions below the forecasts; you can see that at the 15 year forecast horizon, our regression explains 76% of total returns to stocks. Further, the regression is very highly statistically significant, with a p value of effectively zero. Chart 2. below demonstrates how closely the model tracks actual future 15-year returns. The red line tracks the model's forecast annualized real total returns over subsequent 15 year periods using the Q ratio and deviation from price regression as inputs at each period. The blue line shows the actual annualized real total returns over the same 15-year horizon.

Chart 2. 15-Year Forecast Returns vs. 15-Year Actual Future Returns

Source: Shiller (2011), Doug Short (2011), Butler|Philbrick & Associates (2011)

You can see that 15-year 'Regression Forecast' returns are 0.51% per year, and 10-year returns are forecast to be just -0.48% per year. To be clear, this means our model forecasts negative real total returns to U.S. stocks over the next decade given current levels of valuations.

Putting the Predictions to the Test

A model is not very interesting or useful unless it actually does a good job of predicting the future. To that end, we tested the model's predictive capacity at some key turning points in markets over the past century or more to see how well it predicted future inflation-adjusted returns.

Table 2. Comparing Long-term average forecasts with model forecasts

Source: Shiller (2011), Doug Short (2011), Butler|Philbrick & Associates (2011)

You can see we tested against periods during the Great Depression, the 1970s inflationary bear market, the 1982 bottom, and the 2000 technology bubble top. The table also shows expected 15-year returns given market valuations at the 2009 bottom, and current levels. These are shaded green because we do not have 15-year future returns from these periods yet. Note real total return forecasts of 6.2% annualized from the bottom of the market in February 2009. This suggests that prices just approached fair value at the market's bottom, but they were nowhere near the level of cheapness that markets achieved at bottoms in 1932 or 1982. As of the end of February 2011, expected future returns over the next 15 years are under 1% annualized.

We compared the forecasts from our model with what would be expected from using just the long-term average real returns of 6.5% as a constant forecast, and demonstrated that estimates form long-term average returns yield over 400% more error than estimations from our model over these 15-year forecast horizons (1.24% annualized return error from our model vs 5.24% using the long-term average). Clearly the model offers substantially more insight into future return expectations than simple long-term averages, especially near valuation extremes.

Conclusions

The 'Regression Forecast' return predictions along the bottom of Matrix 4. are robust predictions for future stock returns, as they account for over 100 different cuts of the data, using 3 distinct valuation techniques, and utilize the most explanatory statistical relationships. The models explain up to 76% of future returns based on R-Squared, and are statistically significant at p=0. It is worth noting, however, that even this model has very little explanatory power over horizons less than 6 or 7 years, so almost anything is possible in the short-term.

Returns in the reddish row labeled "PE1" in Matrix 4 were forecast using just the most recent 12 months of earnings data, and correlate strongly with common TTM PE ratios cited in the media. These expected return numbers are substantially higher than any other numbers in the matrix save the forecasts from the total return series. This anomaly probably helps to explain the general consensus among sell-side market strategists that markets will do just fine over coming years. Just remember that these analysts have no proven ability whatsoever in predicting market returns (see here, here, and here). Further, it can be argued that their firms have a substantial incentive to keep their clients invested in stocks.

Investors would do much better to heed the results of robust statistical analyses of actual market history, and play to the relative odds. This analysis suggests that markets are currently expensive, and asserts a very high probability of low returns to stocks (and possibly other asset classes) in the future. Remember, any returns earned above the average are necessarily earned at someone else's expense, so it will likely be necessary to do something radically different than everyone else to capture excess returns going forward.

Note: I would like to thank Doug Short of the always illuminating dshort.com for sharing his data on the Q-ratio which he painstakingly compiled from several sources. The most recent data is derived from Federal Reserve data; more information on this, and much more, at dshort.com

The Cult of Social Conformity

2011-03-11T11:31:00.000-08:00

As a psychology graduate and a student of behavioural economics, I know from the literature that fear is a more powerful force than greed. In fact, fear of loss is about 2.5 times more powerful than lust for gains across a broad spectrum of studies. I take a fair amount of time with clients to try to get a good sense of a person's true tolerance for loss. This leads to discussions about what people are most afraid of; what keeps them awake at night. Very often, people tell me that they are most afraid of not having 'enough'. The definition of 'enough' consumes most of the rest of our conversations.

How Much is Enough?

Clients often define 'enough' in terms of how often they can travel, how much they can give to grandchildren or children, whether they can keep their golf club membership, or whether they can buy a vacation home or boat. People generally want to know that their lifestyle won't change that much when they retire, and that they will have enough money to fill their free time with 'adventure' of whatever sort. Sometimes that means trekking through the Andes or African safaris, and other times people find adventure in their gardens, or in pursuing cerebral passions.

While everyone's definition of 'enough' is different, one criteria is common to hundreds of conversations with clients from many walks of life, though clients very rarely express it directly. People are terribly afraid of failing to keep up with their friends and peers. Further, clients are quite aware of, and sensitive to, their relative standing.

Absolute versus Relative Success and Failure

For example, I recently spoke with a client who had been investing for many years with an investment company that is majority owned by his professional association. Each year during his annual portfolio review, the salaried Advisor from this company would nod reassuringly and tell him that he was 'on track'. For evidence, the Advisor would state that the client's financial condition was on par with the financial condition of higher paid specialists in his field of a similar age.

This is interesting from two perspectives. First, it may be that the average specialist in this professional field is in poor financial condition. Second, why should a comparison with the average financial position of his peers be of some comfort?

It turns out that people generally are sensitive to two dimensions of risk. First, there is the risk of absolute failure. In the financial domain generally, this is the risk of running out of money before you die. But equally important, there is the risk of relative failure, and this risk causes far greater anxiety. We can summarize the differences between the two types of risk from a financial perspective with the following matrix.

While clients are anxious about not achieving absolute financial success, they are far more comfortable with the notion so long as all of their friends follow the same process, and end up in the same boat. If all of their peers invest the same way and experience a poor result, they can all commiserate together, and this becomes tolerable.

However, people are very wary of adopting an approach that is different from their peers, because they run a far more terrifying risk: that they will fail while their friends are successful. This outcome is truly intolerable.

Informational Social Influence

For this reason, most investors are heavily influenced by a psychological dynamic called 'informational social influence'. Wikipedia describes this phenomenon as:

"a type of conformity. When a person is in a situation where s/he is unsure of the correct way to behave, s/he will often look to others for cues concerning the correct behavior. When "we conform because we believe that other's interpretation of an ambiguous situation is more accurate than ours and will help us choose an appropriate course of action," it is informational social influence."

This effect is especially strong where educated people have lost faith in the traditional 'experts' within a specialized domain. Many professional organizations, for example, have created wealth management arms to allow their members to act upon the informational social influence present within their association. These wealth management arms are often positioned as independent from the influence of untrustworthy outside 'expert' sources, so that the best interests of their members are uncorrupted. Of course, this provides fertile soil for the cultivation of wrongheaded dogma and even stronger forms of informational social influence.

A critical outcome from informational social influence is that it "often leads not just to public compliance (conforming to the behavior of others publicly without necessarily believing it is correct) but private acceptance (conforming out of a genuine belief that others are correct)" (Wikipedia) That is, people who are under a strong form of informational social influence are likely to truly believe in the consensus of their peer group, and are strongly resistant to alternative views. Interestingly, these are the exact qualities used to describe a cult.

Source: Indexed

It is my humble opinion that this phenomenon drives the majority of decisions in markets. It drives investors slowly into new trends and market fads, which provides the steady flow of capital into these trends which causes them to move well beyond what might be justified by fundamentals. This results in the age-old patterns of bubbles and busts. It also drives the majority of investors, large and small, into the hands of traditional managers and consultants who apply the same techniques and generally deliver the same mediocre results. In this way, investors can not be held accountable for poor results, as they are just conforming to the established norm. Further, individuals can take comfort that, if they experience poor results, they won't be suffering alone.

Definition of Insanity

It is a common refrain that the definition of insanity is repeating the same actions over and over while expecting a different result. Given what we now know about the cultish tendencies of groups whose members are guided in their actions by the actions of others in their peer group, I would like to offer some contrarian advice:

Do not rely on the opinions or actions of your peers, as they are probably being informed and influenced by their peers, of which you are one. Further, do not confidently rely on opinions expressed by the emissaries of any organization that relies on the confidence of your peer group to justify its existence. Further, it may prove dangerous to benchmark your financial health against the financial state of your peers.

Instead, and if something is important to your future, do your own research and come to your own conclusions. You may discover that trying something new and different from your peers could substantially improve your lot.

Open your mind; there is a better way.

Timing the Bull

2011-03-09T15:15:00.000-08:00

In prior posts (here and here) we examined the value of simple market timing systems in generating strong risk-adjusted returns during low or negative return periods in markets. This research is useful for two reasons:

While there is strong evidence to suggest that markets move through long bullish periods followed by long bearish periods, and that these cycles tend to last about 17 years on average, it is difficult to identify with precision the beginning and end of any long cycle. For this reason, investors should adopt a strategy that has the potential to perform equally well when markets go sideways or down, as this condition is not just likely, but virtually inevitable over most investors' investment horizons.

Timing systems should be stress-tested in all types of market environments before we can conclude that they are broadly effective. Many systems are uniquely effective in certain markets and under certain conditions, but break down in other market situations. As it is impossible to predict what kind of markets investors will face in the future, we don't want to apply a system that is only effective in certain types of markets.

Timing Strategies Work in Bear Market Periods

CXO Advisory Group recently published a study of timing systems and their effectiveness over the 1999 - 2009 period. This period was chosen deliberately, as during this period a buy and hold strategy of U.S. stocks actually lost 0.7% annually (see Chart 1.). During this period, the authors show that applying a simple coin-flip strategy, where at the end of each month a coin is flipped to decide whether to own stocks for the next month, or sell stocks and be in cash, would have beat the market 85% of the time. While no volatility statistic is cited in the study, the authors do mention that volatility experienced by coin flippers is lower than the volatility of buy and hold.

Chart 1. S&P 500 Total Returns Dec. 31, 1999 through Dec 31, 2009

Source: TradingBlox, Shiller (2011)

The CXO study tested 5 simple systems (plus Buy and Hold), per their report:

Buy and Hold: Buy and hold S&P 500 stocks (as a benchmark).
EW SPY-Cash: Hold equal amounts of SPY and cash, rebalancing monthly.
10-Month SMA: Hold SPY (cash) when the monthly close of the S&P 500 Index is above (below) its 10-month simple moving average (SMA).
6-1 Momentum: Hold SPY (cash) when the past 6-month return for the S&P 500 Index is positive (negative).
6-1-1 Momentum: Hold SPY (cash) when the past 6-month return, with a skip-month, for the S&P 500 Index is positive (negative).
100 Monthly Coin Flippers: Hold SPY (cash) when the coin comes up heads (tails).

The chart below from the report (Chart 2.) plots the relative returns to each strategy over the period studied. Gratifyingly (but not unexpectedly given our prior tests), the simple 6-month price momentum timing system (green line) is most effective, delivering 138% of cumulative outperformance over the period (Source: CXO paper). The simple 10-month moving average system (orange line) is almost as effective. The many blue lines represent 100 trials of monthly coin flipping; it is easy to see that most of the blue lines lie above the black line near the bottom, which is the return to Buy and Hold.

Chart 2. Simple Strategy Cumulative Returns Comparison

Source: CXO Advisory Group

Do Timing Strategies Work in Bull Market Periods Too?

While we would intuitively expect timing systems to add value during bear market periods, and in fact we can see that a dart-throwing monkey would probably outperform in bear markets with a coin-flip strategy, investors need a system that is reliable during bull markets too.

We set out to test 4 of the systems described in the CXO study on U.S. stocks during the period 1982 through 1999. This period represents the most consistent and powerful bull market in U.S. stocks of the past 140 years in terms of both duration and aggregate returns. If a timing system can add value during this type of bull market, in addition to adding value during difficult bear market periods, we can be much more confident of its efficacy in the future.

Chart 3. shows the total returns to a buy and hold strategy over the test period. You can see that stocks delivered a total return of 17.7%, and with a maximum drop in portfolio value from any peak to trough of 26% (all results are as of the end of the month). A buy and hold investor was never underwater for more than 20 months during this period. This compares very favorably with the period 1999 – 2009 where a buy and hold investor was underwater for 73 months during the “Tech Wreck”, and remains underwater still from the 2008 crash (Chart 1.). The annualized monthly volatility of returns was 10.7% during the 1982 - 1999 bull market period (note that daily volatility was closer to 14% over this period, but the monthly data is sufficient for comparison).

Chart 3. S&P500 Total Return Dec. 1981 through Dec. 1999

Source: Shiller (2011)

The Bull Market Test

With these types of returns, a timing system has its work cut out for it. Let's see how 3 of the timing systems above stack up when markets are rising, rather than falling or moving sideways. Note that all of the tests below account for trading costs of 0.25% per trade to make the comparison more realistic.

Table 1. Timing versus Buy and Hold in a Bull Market

Source: Shiller (2011), Butler|Philbrick & Associates

The table requires some explanation. First, the columns:

Annualized return - simply the annualized compound return to each strategy over the time period
Volatility - annualized monthly standard deviation, a traditional measure of risk
Maximum Drawdown - another measure of risk that is simply the largest percentage drop from any peak in portfolio value along the way
Randomized Annual Return - our testing software randomizes the order of monthly returns, and the signals generated by each system, to provide a more robust estimate of the worst likely average returns over the period
Randomized Max Drawdown - similar to randomized returns, this is the worst probable drawdown that resulted from many randomly ordered monthly series, and provides a more robust estimate of true risk
Randomized Return / Randomized Drawdown - a more robust measure of risk-adjusted returns; higher values are better

As our test used a very specific start and end date, and necessarily represents just one of an almost infinite number of possible monthly return histories, we prefer to analyze the value of any strategy based on a randomized sample of many possible return paths (in this case 2000), rather than just the one that we lived through. The "Randomized Return / Randomized Drawdown" measure provides a much more robust way for us to evaluate the effectiveness of each strategy.

On this measure, we can see that the strategy of holding an equal amount of stocks and cash, and rebalancing back to even on a monthly basis, provided the best risk-adjusted return over the duration of this bull market. It is worth noting however that the annualized return to cash from 1982 - 1999 was over 7% per year, while cash currently yields less than 1%, so it is difficult to draw any conclusions about future returns from this analysis. Further, this balanced strategy delivered absolute returns of less than 13% per year versus 17.7% for buy and hold.

Of greater consequence, both the 10-month moving average and 6-Month Momentum strategies substantially outperformed buy and hold on a risk adjusted basis, according to the more robust randomized measure (far right column in Table 1.). While buy and hold delivered slightly higher returns after trading costs during the period (17.7% versus 16.1% for 10-Month MA and 17% for 6-Month Momentum), the timing strategies realized almost a 50% reduction in maximum drawdown. Further, the randomized annual return was higher for the 6-Month Momentum strategy, indicating that from a probability weighted perspective, 6-Month Momentum timing is more likely to deliver higher absolute returns than buy and hold.

Conclusion

We saw in prior posts (here and here), and from the CXO study above that a timing system can deliver much better returns to investors when markets move sideways or down over many years. This study aimed to test these systems against a period of prolonged market strength. The value of the timing systems would be called into question if they performed much worse during long-term bull markets, as an investor would not know in advance if future returns are going to be sideways, up or down for many years.

Our study demonstrates that simple timing systems can deliver substantial risk-adjusted value to investors, even during periods of very strong bull markets. The timing systems reduced portfolio volatility by approximately 20%, and reduced month-end portfolio drawdowns by almost 50%, which makes for a much smoother experience for investors, and many less sleepless nights. Further, absolute returns were comparable even after accounting for trading costs.

Given that simple systematic investment strategies can deliver much better results during bear markets, and better risk-adjusted results during bull markets, it is difficult to imagine why any small investor would not apply a timing system to improve their chances for long-term investing success.

Chart 4. Equal Weight S&P500 and cash, with monthly rebalancing.

Source: Shiller (2011), Butler|Philbrick & Associates

Chart 5. 10-Month SMA: Own the S&P500 when the index closes any month above a simple 10-month moving average; hold cash otherwise.

Source: Shiller (2011), Butler|Philbrick & Associates

Chart 6. 6-1 Momentum: Own the S&P500 when the index delivers 6 month price performance above the 6 month return to cash; hold cash otherwise

Source: Shiller (2011), Butler|Philbrick & Associates

Goldilocks and the Three Grizzlies

2011-02-27T10:52:00.000-08:00

We have seen that a simple timing system can add substantial value to U.S. stocks over the long term, and to a long-term market bubble and collapse, using the Nikkei as our proxy.

Both of these studies quite successfully applied timing systems to a single index only. In practice however, the real power of trend following systems is their ability to identify and ride many trends simultaneously. If U.S. stock markets are choppy, perhaps there is a nice trend underway in oil, Treasuries or international small-cap stocks. If so, the trend following system will find them and ride them to generate returns.

The arch enemies of trend following systems are flat sideways markets, as well as extended markets with big, fast reversals in direction. Sailors will understand this implicitly, as a sailor's greatest enemy is low winds, followed closely by blustery winds which constantly change direction. So long as there is a fairly constant breeze blowing in the same general direction, a sailor can set his sails and ride, with only minor regular adjustments. When the winds change direction, the sailor adjusts. Similarly, so long as markets spend a few months going in the same general direction, trend-following systems will ride along and capture profits. When they spend a few months moving in the opposite direction, or no general direction, trend followers will "tack" or "lower their sails" respectively as the market requires.

With this study we have set out to confound our trend following system by testing it against 3 of the most difficult market periods of the past 140 years:

The Nasdaq tech bubble and subsequent crash
The Nikkei property bubble and subsequent crash
The 'Roaring 20s' stock market bubble and subsequent Great Depression

This is an especially interesting study because it provides for no 'escape valve' to earn returns while these markets are thrashing their way through their respective "Grizzly Bear" markets. The best a system can do is rest in cash while the markets churn. For this reason, this study is also an excellent 'stress-test' of timing and rotational systems, as it is difficult to conceive of any period where markets have provided a more intense challenge for investors.

It is important to note that, even during these periods, it was possible to find assets somewhere which were trending. For example, during the Great Depression, gold prices moved substantially higher over many years. During the Nikkei's long plunge from 1990 through 2000, U.S. stocks experienced one of their most intense bull markets. For this reason, a thoughtfully conceived asset class rotational system rarely encounters periods where no trends are to be found anywhere. Further, where such a period has occurred, such as during the years 2000 - 2002, it didn't last long, and losses were very well contained.

The 3 Grizzlies

NASDAQ
The period from early 2000 through late 2002 saw U.S. markets peak following one of the richest parabolic runs in market history: the tech bubble. The collapse that followed in the Nasdaq stock market lasted almost 2 years, and took the index down 75% from its highest monthly close in April 2000. After bottoming in 2002, the market gained over 100% through late 2007 before experiencing a second collapse in 2008 which cut the value of the index in half. The market then experienced a very steep rally, which we are still enjoying today.

Chart 1. Nasdaq Composite 1998 - 2010

Source: Wikiposit.com, Butler|Philbrick & Associates

Japanese Stocks
The Nasdaq peak and collapse looks similar to the parabolic peak and collapse in the Japanese stock market which occurred in the late 1980s and throughout the 1990s. Chart 2. below closely aligns the Nikkei's final surge from 1988 through 1990, and its subsequent collapse through the year 2000, with the Nasdaq's peak and collapse. Note how closely the trajectory of the Nasdaq tracked the Nikkei's waterfall - the price series are not exactly the same, but they certainly 'rhyme'.

Note that the Nikkei return series was transformed to match dates with the Nasdaq series above, so the dates on Charts 2. and 3. are not accurate for these markets.

Chart 2. Nikkei Japanese stock market composite 1988 - 2000

Source: Reuters, Butler|Philbrick & Associates

The U.S. Great Depression

Last, but certainly not least, we tested against the granddaddy of all challenging markets - the 'Roaring Twenties' bull market and the ensuing Great Depression. In order to overlay this longer bear market with the Nasdaq and the Nikkei, we shortened the testing period to include just the very tail end of the market rally in 1929, and all of the ensuing Depression through 1942. You will note that this period resembles the other two 'Grizzly' bear markets, but only modestly.

During the Depression, stocks dropped almost 90% from peak to trough, though the maximum loss from the highest month-end close to the lowest month-end close was about 82%. The return series charted below captures total returns including dividends over this period (again, using the dates of the Nasdaq collapse for testing purposes). Interestingly, the Nikkei and U.S. stocks over the Depression years delivered the same annualized return: ~ negative 3.3% per year for 12 years (see CAGR% in charts 2. and 3.)

Chart 3. U.S. stock market during the Great Depression 1929 - 1942

Source: Shiller (2011), Butler|Philbrick & Associates

Buy and Hold with Rebalancing

These were clearly very challenging periods for investors, and God forbid that we ever experience a period where all markets behave this way at the same time. However, given the strange powers at work in markets today, and the amount of aggregate leverage (debt) in the system, we can't entirely rule out this possibility during our investment horizon. So how would an investor fare in an environment where all investable markets were to collapse at once? This study provides a proxy for just this situation.

A typical investor who believed in traditional risk management via diversification might have an equal amount invested in all three markets, and might rebalance between the markets every year to maintain his 'Strategic Asset Allocation'. While most investors might have some bonds as well, we could envision an aggressive investor with equal exposures to, for example, North American, European and Emerging Market stocks, where all markets collapsed en masses as in 2008.

Chart 4. assumes an investor chooses to allocate equally to all 3 of the markets above for the entire period shown, and that he rebalances his portfolio at the end of every year.

Chart 4. Buy and Hold with Rebalancing

Source: Shiller, Reuters, WikiPosit.com, Butler|Philbrick & Associates

You can see that diversification works - sort of. While the combined portfolio didn't gain in value over the period, neither did it lose any, with annualized returns of 0.06% per year. The portfolio did gain almost 60% in the first couple of years, and then lost over 50% from its peak value (see Max Total Equity DD in Chart 4.), but it ended up right back where it started by the end. This is much better than it would have done by holding the Depression market or the Nikkei alone.

Market Timing and Rotation

As with our other studies, we will apply three simple strategies to this basket of 3 investments which have the potential to substantially improve returns, and with much less risk of loss.

Moving Average Timing System

The first approach involves a simple moving average timing system. In this case, we own an equal amount of each index, rebalanced annually. However, if any of the 3 indices closes any month below its 10-month moving average, the system will sell that index and hold cash instead (at the U.S. dollar rates prevailing during the Nasdaq's run and collapse around the turn of the century). When an index closes back above its 10-month moving average, the system will re-purchase the index at the then prevailing price.

A simple moving average system improves risk-adjusted performance dramatically. With this system, we are able to achieve substantial positive returns over the period of 5.4% per year, which compares with returns of ~0% per year for the 'Buy and Hold' example. At the same time, risk is reduced by 50%, with the worst total peak-to-trough loss over the period of just 24%, versus 56% for the 'Buy and Hold' approach.

Chart 5. 3 Grizzlies with 10-Month Moving Average Timing System

Source: Source: Shiller (2011), Reuters, WikiPosit.com, Butler|Philbrick & Associates

Rotational Timing System with Cash Alternative

For a second cut, we applied our relative strength rotational strategy to signal when to hold cash, and when it makes sense to hold each stock index. Conceptually, this approach makes use of a simple momentum system where we own a stock index when it has outperformed cash over the prior year or so, and we hold cash otherwise. This decision is applied for each index independently, and capital is rebalanced at the end of every year.

In this way, stocks earn their way into the portfolio by demonstrating positive returns over any prior 6- to 12-month period. If they haven't earned their place in the portfolio by exhibiting strong prior returns, then statistically it makes better sense to hold cash.

We can see that by applying this technique against the three indices we were able to extract returns of over 8% per year, even while each of these markets independently peaked and crashed over a period of many years. Risk is further reduced as well: investors using this system only had to endure a 2 year period of modest losses, with a maximum peak-to-trough drop of less than 20%.

Chart 6. Grizzlies with Relative Strength Rotational System with Cash Alternative

Source: Source: Shiller (2011), Reuters, WikiPosit.com, Butler|Philbrick & Associates

Rotational Long/Short - The Goldilocks System

The 3 Grizzly Bears were periods where market prices dropped substantially over many years. Yet the above systems were only able to take advantage of periods where these markets went up; when markets were dropping, they sat in cash. It follows logically that if we were to allow a system to also take advantage of periods where markets were dropping, by selling short, that we might be able to extract even higher returns, especially against markets that are experiencing long declines.

Our rotational long/short system applies the same momentum ranking system as the rotational timing system described directly above. However, instead of moving to cash when index returns have been weak, our long/short system sells the index short in order to capture gains while the market is going down.

Amazingly, while the 3 Grizzlies are raging and tearing a big hole in most investors' portfolios, Goldilocks is able to coax the 3 bears into delivering returns of almost 13% per year! The risk in the system is only slightly higher than the risk to the rotational system with cash, with a maximum peak-to-trough loss of 24% versus 20%, and an investor is never below water on his investments for much longer than 2 years.

Chart 7. Goldilocks and the 3 Grizzlies

Source: Shiller (2011), Reuters, Butler|Philbrick & Associates

Conclusion

We endeavored to test some simple timing systems under very extreme conditions to see how they held up. It is important to test how systems behave under a variety of market conditions so that we can be as certain as possible that our systems will work no matter what the market holds in the future.

We chose the three most volatile, deepest bear markets of the past 140 years for our test case. While we can't be certain that these markets represent the most challenging conditions possible under which our systems might be forced to operate, we can say that these are some of the most challenging conditions investors have faced in history. While future markets will throw new and unexpected challenges at us, studies like this help to demonstrate how simple systems can adapt to almost any market environment to deliver more consistent and robust returns than 'Buy and Hold', and with substantially less risk.

Taming the Japanese Bear: Nikkei Case Study

2011-02-19T17:39:00.000-08:00

The last post explored the efficacy of two simple strategies -- a moving average approach, and a momentum strategy -- in timing the S&P500, and compared these systems to a buy and hold strategy over the period 1971 - 2010. This period included an 11 year secular sideways market from 1971 until the Volker bottom in 1982, followed by one of the biggest and longest bull markets in history, from 1982 through the peak of the technology bubble in 2000. Post 2000, US stocks languished for three years before embarking on another 4-year rally to new all-time highs, and then they crashed again, by more that 50% in 2008-9. Of course, since then we have rebounded 100% from the lows in the sharpest rally nice 1937. What a wild ride!

We saw that two simple timing strategies would have beat the markets in terms of absolute returns, but more importantly, the strategies would have achieved these returns with a fraction of the risk experienced from buy and hold.

This post will examine how the same timing systems would have performed if applied against Japanese stocks since 1984. What makes this study especially interesting is that the price of the Nikkei has lost 22% of it's value in absolute terms, which works out to annualized returns of -1% per year, over that 26 year period. At the same time US stocks are up 555%, or 7.75% per year.

The question is, can a timing system provide substantial positive returns even during a period where markets are substantially negative?

Chart 1. shows the price performance of the Nikkei from 1984 through the end of 2010. We can see that the market experienced a parabolic run to it's peak in 1989 before beginning a 20 year bear market which took prices down 80% through the end of 2008. Note the annualized returns of -0.94%, and maximum total loss from peak value of 80% over this period (see "CAGR%" and "Max Total Equity DD" respectively in the table above Chart 1.)

Chart 1.

Source: Butler|Philbrick & Associates

Chart 2. shows the results of a test which applies a simple moving average to signal when to move to cash. As in the prior post, the system owns Japanese stocks when the index closes any month above the moving average, and holds cash otherwise. The results are substantially better than buy and hold, with positive returns of 4.7% per year, and a maximum loss of 43% from any peak, though the return series is a little choppy.

Chart 2.

Source: Butler|Philbrick & Associates

For Chart 3. we applied a relative strength rotation strategy to signal when to hold cash versus the index. Conceptually, this system makes use of a simple momentum system where we own the index when it has outperformed cash over the prior year or so, and we hold cash other wise. In this way, stocks earn their way into the portfolio by demonstrating positive returns over any prior 6 to 12 month period. If they haven't earned their place in the portfolio by exhibiting strong prior returns, then it makes better sense to hold cash.

We can see that this technique delivered returns on the Nikkei of over 10% annually even while the index itself dropped 22% in value. Even better, this portfolio never dropped more than 19% from any peak value.

Chart 3.

Source: Butler|Philbrick & Associates

Lastly, and for giggles, we ran the same system as in Chart 3 above, but we also allowed the system to short the index in periods where the market exhibited negative returns over the prior 6 to 12 months. In this way we are able to earn returns while the market trends higher, and to also earn returns when the market trends lower.

Chart 4.

Source: Butler|Philbrick & Associates

By allowing the system to move into a short posture when markets are exhibiting negative price momentum, rather than just holding cash during these periods, we are able to extract over 16% annualized investment performance, while never dropping more than 23% from any peak portfolio value. And this during a period where the index dropped in value by 22%, from start to finish, and by 80% from peak to trough!

You may believe that markets only ever go up, despite having experienced no less than 2 major bear markets in the last 10 years, each of which erased half of total stock market value, and ignoring the evidence from Japan, which has experienced 20 years of declining prices. It is folly, however, to not at least acknowledge the possibility that market may not go up all the time. Accepting this possibility, doesn't it make sense to explore other approaches to investing which may better position your portfolio for whatever the future might hold?

Make Stocks Earn Their Way Into Your Portfolio

2011-01-31T09:04:00.000-08:00

The investment industry has us pre-programmed to be fully invested all the time, for reasons we have discussed ad nauseum in many prior articles. Let's try, just for a moment, to reverse that thinking. Let's assume instead that we want to hold cold, hard cash (well, in the bank), until stocks prove their merit. Only once they have demonstrated their potential to provide better risk adjusted returns than cash over the intermediate term do we part with our cash in order to buy stocks. The following simple case study illustrates 2 simple ways for stocks to earn their way into your portfolio. If they don't, then we will hold cash.

The first method applies a momentum filter, such that stocks must have provided a higher total return than cash over a prior multi-month period in order to qualify for portfolio inclusion. If the the returns to stocks over the past year or so have been less than the returns to cash, we will stick with cash. Otherwise, if stocks have delivered a better return than cash over the same period, we will purchase the S&P 500 stock index for our portfolio.

(Any investor can gain exposure to the S&P500 stock index by purchasing any number of exchange traded funds, the most popular of which is SPY, traded on the New York Stock Exchange.)

The second method applies a simple moving average filter, such that stocks can earn their way into the portfolio only if they close out any month above their 9 month moving average. If they close out any month below this moving average, they are sold and we hold cash instead.

Note that we use the prevailing cash interest rate available in each historical period when calculating the results below, which are for the period from December 1971 to the present:

Observations:

Total returns improve for both strategies over Buy and Hold
Volatility, the traditional measure of portfolio 'risk' is lower in both strategies, and by almost 50% with the Momentum approach
Max Drawdown, or the worst drop in value from peak-to-trough, is over 100% worse for Buy and Hold than for either of the strategies
Longest Drawdown, or the longest period of being 'underwater' in your investments, is between 80% and 120% Longer for Buy and Hold

Conclusion:

There is absolutely no justification for a Buy and Hold approach. Even simple systematic approaches deliver at least the same returns, and with much less risk.

Appendix: Test results

Test 1. S&P 500 Buy and Hold Total Return, 1971 - 2010

Test 2. S&P 500 vs. Cash Ranked By Multi-Month Relative Strength

Test 3. Hold S&P500 above Moving Average, hold cash otherwise

The Fee Delusion

2011-01-29T15:11:00.000-08:00

Fees are perhaps the most frequently discussed, and least well understood, factor in financial product marketing. Financial organizations that serve professional groups often focus on low fees to obscure the fact that their investment arm is understaffed, under qualified, and generally delivers poor service and performance. The simple reality is that in the field of investments, as in many complex fields, differentiated value isn't free, but mediocrity can come very cheap.

To illustrate, consider the following 3 proposed investments:

Advisor A proposes an investment in a fund with a history of performing in-line with stock markets. The manager performs slightly better during really poor periods in the market, slightly worse in really good markets, and averages returns of 1% better than stocks over the last 10 years, after accounting for fees and costs. Note that this performance would place the fund in the top 95% of all funds, as most funds don't come close to this track record. This fund has dropped by more than 35% from its peak twice in the past ten years. This fund charges a fee of 1%.

Advisor B proposes an investment in a systematic strategy, with a history of performing very differently than stock markets over time. The performance of this fund is largely independent of the direction of stock markets, and the fund has never been down more than 25% from its peak. It has performed about 10% better than stocks over the last 3 years, after fees and expenses. This manager charges a fee of 2%.

Advisor C proposes an investment in a fund with a history of exactly tracking the performance of stocks over the very long-term. This fund does well when markets to well, and poorly when markets do poorly. This fund has also been down more than 35% from its peak twice in the past 10 years. This fund charges 0.4% in fees.

All other things equal, which Advisor's proposal would you go with?

Mutual Funds: A Low Probability Bet

Before answering, I urge you to consider the following chart, which illustrates the probability that a fund which ranks in the top 25% of its category will even be ranked in the top half of its category four years later. Remember that funds selected at random would, on average, be in the top half of their category about 50% of the time. However, we see that top funds in one year have, on average, only about a 40% chance of being in the top half 4 years later. That is worse than random chance.

Source: Gene Hochachka, February 2008

http://ssrn.com/abstract=1094246

The Passive Option

Given the chart above, one could be forgiven for believing that it is not possible to deliver differentiated performance, and in fact there is a very large and credible contingent of the investment community that operates on that basis. This way of thinking is called 'passive investing', and I am willing to concede that for the majority of investors, this is probably the way to go. I concede this for most investors because it is actually very hard to find an Advisor or a fund manager who actually does add value over time, especially after fees and commission. Hard, but certainly not impossible, and potentially highly rewarding for those who do.

If you embrace a passive approach, the only logical strategy is indexing. This is a low-cost strategy which involves purchasing a diversified basket of broad-market Exchange-Traded Funds and holding them forever, with periodic rebalancing. A buy and hold strategy that emphasizes a diversified basket of global asset classes is almost certain to outperform cash in the bank over periods longer than 10 years. However, investors are vulnerable to behavioral biases that will make it hard for them to stick with this strategy when things inevitably turn ugly. Further, the unpredictable path of returns may have a substantial negative impact on your ability to achieve, or maintain, financial independence.

For investors that choose this route, we would be delighted to provide guidance on how to construct a well diversified portfolio of ETFs that will benefit from the ebb and flow of capital and opportunity in global stocks, commodities, real estate and bonds. Under IIROC guidelines, I am not allowed to provide specific investment advice here without first knowing your long-term objectives and risk tolerance. A fairly quick phone call can establish these basic parameters (believe it or not), and we will provide this quick advice at no cost, and with no further obligation.

One final thing on passive investing: there is no conceivable reason to use mutual funds for a passive approach. If you embrace this philosophy, you acknowledge, quite legitimately, the low probability that a manager will add value over time. So why are you investing in mutual funds which charge you 1% or more for the slim chance that the manager will do just that, when you can open an account at a discount broker and purchase Exchange Traded Funds or index mutual funds for as low as 0.25%? It's madness!

Where an Active Approach Can Add Value

I made the case in the previous section for most investors to apply a passive approach to their investments. This means taking your money out of mutual funds and putting them into a basket of very low cost Exchange Traded Funds. However, while a passive approach is likely the best option for most investors, who lack the time, motivation, or acumen to find quality active managers, smart, motivated and open-minded investors who take the time to learn the nuances of different active styles can realize tremendous value. This task is difficult, but certainly not impossible.

Managers that deliver consistent returns over time generally possess some combination of the following 4 qualities:

Insider information - yes, this happens all the time, and is quite illegal. No, your mutual fund manager doesn't have any. If he did, he would be making 2% plus 20% of the fund's profit at a hedge fund.

A systematic approach with very little qualitative interference based on manager ‘intuition’
A focus on asset allocation, not stock-picking
A rigorous strategy for risk management

There are some superb talents in the investment industry who have demonstrated incredible performance persistence through time: George Soros, Steve Cohen, John Paulson, and a few others. So far as I know, these managers do not use any specific system to manage money, but instead are able to see into the future via a black box in their heads. Notably, none of these managers is available for average investors, though they charge extremely high fees (5% plus a percent of total portfolio growth). Further, I could count the number of consistently successful investors of this type on two hands. I don't include Warren Buffet because he is not an investor in the contemporary sense. Rather, he is a business man who purchases companies and then adds value through extraordinary management. This is an important distinction!

Thankfully, there are far more examples of true systematic approaches with very long track records of adding extraordinary value to investors. Here is a list of some of these funds, along with their December 2010, and calendar year 2010 returns, and total assets under management, courtesy of Jez Liberty of Au.Tra.Sy blog:

Organisation / Fund	Return	YTD ^*	AUM ^**
Abraham Trading¹	8.28%	8.30%	$456M
Altis Partners²	5.66%	11.52%	$1,609M
Aspect Capital³	6.16%	15.35%	N/A
BlueTrend⁴	6.71%	15.98%	$8,000M
Campbell & Company⁵	N/A	N/A	N/A
Chesapeake Capital⁶	13.90%	10.86%	$835M
Clarke Capital⁷	9.08%	42.62%	$38M
Drury Capital⁸	9.05%	0.63%	$305M
Dunn Capital⁹	10.95%	30.75%	$275M
Eckhardt Trading¹⁰	8.87%	21.08%	$418M
EMC Capital¹¹	5.90%	6.72%	$180M
Hawksbill Capital¹²	11.79%	57.58%	$83M
Hyman Beck & Co.¹³	12.82%	8.49%	$512M
JWH & Co.¹⁴	-5.18%	5.20%	$25M
Man AHL Diversified¹⁵	4.80%	11.60%	$1,223M
Millburn Ridgefield¹⁶	5.58%	12.65%	$1,090M
Rabar Market Research¹⁷	10.51%	24.59%	$179M
Saxon Investment¹⁸	1.18%	6.10%	$89M
Superfund¹⁹	10.93%	12.93%	N/A
Tactical Investment Mgt²⁰	10.60%	68.99%	$66M
Transtrend²¹	3.60%	14.88%	$5,826M
Winton Capital²²	3.73%	14.43%	$17,010M

Many of these systematic managers charge hefty fees, but have delivered remarkable long-term performance. For example, Jim Simons’ Renaissance Medallion Fund (not shown) charges 5% management fees and a 44% (!!) performance fee. Despite these burdensome costs, Medallion has consistently returned 35% per year in performance after deducting all fees. Unfortunately, Medallion fund has been closed to new investors since 1993. MAN Group’s AHL Diversified Fund has been open to new investors since its inception in 1990. This fund has returned 15.4% per year for investors after charging a 3% management fee and a 20% performance fee. It has reported fund losses over a calendar year only once: in 2009.

There are several Canadian systematic funds, most notably Acorn Diversified in Oakville, and Auspice Capital out of Calgary. Of course, Butler|Philbrick & Associates applies a systematic approach for its clients as well.

Conclusions

Unfortunately, high fees do not necessarily guarantee superior service or investment performance. You are, however, unlikely to receive superior service or performance for low fees. Investment advisors and funds who rely on low fees to capture and keep clients generally fall into 3 categories:

They are in the early stages of building their businesses, and with few qualifications
They have a focus on client volume instead of performance and/or superb service
They do not offer differentiated value

Highly qualified professionals that deliver differentiated value to their clients are confident charging a reasonable fee for their services. For other professionals, this should not come as a surprise.

Perhaps the main point of this essay is to demonstrate that there is no logical basis for investing in traditional actively managed mutual funds, no matter how low their fees are. Given that your probability of choosing a strong traditional mutual fund which will remain in the top half of its category just 4 years from now is worse than random, an average investor should ALWAYS prefer a diversified basket of passive Exchange Traded Funds.

Investors with the acumen, motivation and perseverance to investigate active managers will discover that there are phenomenal managers and strategies out there that have the potential to fundamentally alter the trajectory of their investments, and the landscape of their retirements. These managers do not compete on price - but smart investors won't care.

Notes

^{* YTD: Year-To-Date performance.
** AUM: Assets Under Management.
1. Abraham Trading was founded by Salem Abraham, after he was introduced to Managed Futures and Trend Following by Jerry Parker. He is considered as a “second-generation” Turtle.
2. Altis Partners started trading in 2001 and now manage over a $1B with their Altis Global Futures Portfolio. The figures referenced in the performance table are not provided by Altis Partners and no reliance should be taken as to their accuracy, and as a consequence the figures may not be in accordance with any CFTC / NFA performance reporting requirements.
3. The four founders of Aspect (Eugene Lambert, Anthony Todd, Michael Adam and Martin Lueck) were significant members of one of the most succesful funds in managed futures – AHL (Adam, Harding and Lueck).
4. BlueTrend, from BlueCrest Capital, is one of the largest Trend Following funds – headed by Ms. Leda Braga
5. Campbell & Company is one of the oldest Trend Following firms, operating for around 4 decades.
6. Chesapeake Capital was founded by Jerry Parker, a former Turtle.
7. Clarke Capital was founded by Michael Clarke in 1993. The programme tracked here is Millenium.
8. Drury Capital, Inc., was founded in Illinois in 1992 by Mr. Bernard Drury.
9. Dunn Capital was founded by Bill Dunn.
10. Eckhardt Trading is the firm managed by William Eckhardt, who co-led the Turtle experiment with Richard Dennis
11. EMC Capital was founded by Liz Cheval, a former Turtle.
12. Hawksbill Capital was founded by Tom Shanks, a former Turtle.
13. Hyman Beck & Co. main principals are Alexander Hyman and Carl Beck.
14. JWH & Co. was founded by John W. Henry, Owner of the Boston Red Sox.
15. Originally ED & F Man. Became a succesful CTA under Larry Hite and went on to form part of The Man Group plc, which subsequently bought AHL to form the Man AHL: the systematic trading division of the Man group.
16. Millburn Ridgefield have been trading Trend Following models since the early 1970’s.
17. Rabar Market Research is the company of Paul Rabar, a former Turtle.
18. Saxon Investment was founded by Howard Seidler, a former Turtle.
19. Superfund founder and CEO: Christian Baha.
20. Tactical Investment Management was founded by David Druz, a student of Ed Seykota.
21. Transtrend is a Trend follower CTA based in Netherlands
22. Winton Capital is a London-based CTA founded by Dave Harding (also co-founder of AHL).}

Newton's First Law of Market Mechanics

2011-01-27T12:41:00.000-08:00

"A sound banker, alas, is not one who foresees danger and avoids it, but one who, when he is ruined, is ruined in a conventional and orthodox way along with his fellows, so that no one can really blame him.” - Sir Maynard Keynes

Magical Momentum

The venerable Jeremy Grantham, one of the world's greatest and most experienced value investors, devoted considerable space in his most recent quarterly missive to the perennial thorn in the side of traditional value investors: price momentum.

A market or stock exhibits price momentum if, over a period of 6 to 12 months, it demonstrates strong price performance relative to its peers. More importantly, it has been clearly proven across all time periods, markets, and geographies, that strong historical price momentum is a superb predictor of future price momentum, at least over a 3 to 6 month horizon. That is, markets and stocks that have performed most strongly over the prior 6 to 12 month period are more likely, on average, to also perform most strongly over the next 3 to six months.

The chart below, from Charles Kirkpatrick's stock market bible, Beat the Market demonstrates the degree to which the prior 12 month returns to stocks are able to explain the following 3 month performance. In fact, the relationship is nearly perfect, with the prior 12 month returns explaining almost 90% of the 3 month future returns (note R2 of 0.893 in the chart below). Further, the prior 12 months performance explains almost 73% of future performance over the successive 6 month period as well, though the relationship breaks down by 12 months out.

Source: Charles Kirkpatrick, "Beat the Market" (2008)

This flies in the face of both intuition and the dominant theory about how markets work. Intuition and common sense support the ubiquitous mantra of, 'Buy low, sell high'. Modern Portfolio Theory asserts that markets are efficient, and trends can not persist. Why would a rational investor chase an expensive market?

It turns out that, as happens so often in investing, the common sense approach is dead wrong. In fact, stocks, sectors and markets that have dropped the most over the prior 6 to 12 months will perform poorly on average, despite potentially being 'cheap' by traditional measures. Cheap stocks tend to get cheaper. On the other hand, buying stocks near current highs, and which have gone up the most over the prior 6 to 12 months, has been an extraordinarily successful strategy over time.

As an illustration of this effect, please examine Chart 2, which shows the relative performance of U.S. stocks from 1927 through 2008 ranked according to their prior 12-month price momentum. For example, the red line shows the performance of the top 10% of stocks, as ranked by their 12-month prior price performance, and re-selected quarterly, while the dark blue line at the bottom shows the performance of the bottom 10% of stocks. The red line shows how you would have done if you had stuffed your portfolio at the end of every quarter with the stocks that were up the most over the prior 12 months. Vice versa with the blue line. You can see that strong stocks outperformed weak stocks by 10,000 times over this time period.

Source: Ken French Data Library

Chart 3 below demonstrates how constructing a portfolio by holding the top performing stock market sector (i.e. energy, technology, financial services, etc.) over the prior 12 months would have been a very effective strategy over the long term. In this case the green line tracks the performance of a portfolio which buys the top performing stock market sector over the prior year. The blue line holds the top two best performing sectors, while the red line reflects a basket of the top 3. The blue line at the bottom shows the performance of a simple "Buy and Hold" strategy. Note that holding the top single sector provided performance 6 percentage points per year higher than "Buy and Hold" over this very long period.

Source: Mebane Faber (2010)

Professional Lemmings

Jeremy Grantham offers us a typically shrewd theory for why momentum effects are so prevalent in markets. His theory rests on the sound premise that career risk drives the vast majority of investment decisions. In his words, everyone who works in the markets, "behaves as if his job description is 'keep it'." Referring to Sir Maynard Keynes' economic texts, Grantham adds, "Keynes explains perfectly how to keep your job: never, ever be wrong on your own." [emphasis mine]

In other words, Advisors and managers know that, even if they lose substantial capital for clients, they are unlikely to lose clients, or be fired from their jobs, so long as everyone else is losing big money too. The rational approach for Advisors and managers from this perspective is to look around and see what the other guy is doing, and beat him to the draw.

Grantham again: "And if everyone is looking at everybody else to see what’s going on to minimize their career risk, then we are going to have herding. We are all going to surge in one direction, and then we are all going to surge in the other direction. We are going to generate substantial momentum, which is measurable in every financial asset class, and has been so forever. It’s the single largest inefficiency in the market. There are plenty of inefficiencies, probably hundreds. But the overwhelmingly biggest one is momentum". [emphasis mine]

Mutual fund managers are a great case in point. Would you be surprised to learn that, if a mutual fund manager is charged with a Canadian equity mandate, and Canadian stocks drop by 50% in a year while the manager's fund drops by 45%, the manager receives her full bonus? She earns her full compensation despite the fact that she lost 45% of her clients' capital in that year. This is because she has very narrowly and explicitly been charged with keeping up with the performance of the Canadian stock market. The manager's greatest risk is in deviating from the performance of Canadian stocks; she is much less concerned with losing money.

Given the risks she faces, would you expect this manager to be more concerned with maximizing her funds' risk-adjusted performance, or will she be more concerned with what her peers are doing so she isn't left behind?

Keynes also discussed in great detail the convention of 'extrapolation', which is the tendency for people to base predictions on what has happened in the past. All of us embrace extrapolation in order to make sense of a very uncertain world, even though we know from experience that predictions of any kind are unstable, especially out further than a few months. Analysts are famous for extrapolating because, by definition, if you make a prediction of any kind, you are susceptible to career risk. This can be observed in how economists and analysts provide estimates that cluster together, like a school of fish trying to confuse a shark. Even when very substantial fundamental changes are afoot, analysts will only change their estimates of the future in small increments over time, as they wait for confirmation from their peers. For this reason, the investment community is very slow to adapt to change, which allows for trends to form and amplify over time.

Let He Who Is Without Sin Cast the First Stone

As you can imagine, all investors, not just professionals, help to create momentum. Individual investors are also extremely sensitive to another important emotional factor: envy thy neighbor. Don't feel bad, though; even some of the greatest minds of the last millennium were equally susceptible. Take Sir Isaac Newton, for example, who invested quite early in the 'South Sea Bubble' in Britain in the early 1700s. The following chart shows the trajectory of the bubble, and Newton's decisions along the way.

Source: Marc Faber

Note that Newton did quite well in recognizing the opportunity early on, and exited quite happily less than 6 months later with a tidy profit. One might surmise that he felt prices of ships were fairly valued at that point, and having substantially improved his economic lot, felt satisfied - at least for a while. For as prices continued to rise, past $300 where Newton sold, past $500 where all of his friends were becoming rich beyond their wildest dreams, he eventually couldn't take the social pressure of watching his friends get rich while he stood on the sidelines. So he did what many investors do: he entered the market with his total personal fortune (how else to catch up to his friends?), and was eventually wiped out as the bubble collapsed.

Nortel anyone?

Conclusion

I couldn't possibly wrap up this essay more eloquently, or with more credibility, than did Mr. Grantham himself.

"Let me end by emphasizing that responding to the ebbs and flows of major cycles and saving your big bets for the outlying extremes is, in my opinion, easily the best way for a large pool of money to add value and reduce risk. In comparison, waiting on the railroad tracks as the “Bubble Express” comes barreling toward you is a very painful way to show your disdain for macro concepts and a blind devotion to your central skill of stock picking. The really major bubbles will wash away big slices of even the best [value] portfolios. Ignoring them is not a good idea."

A strategy of following the market's strongest trends - that is acknowledging the tendency for investors and managers to herd, extrapolate and focus on career risks - has demonstrated consistently strong risk-adjusted performance over time. Mr Grantham's explanation of this phenomenon helps to cement our long-term focus on these factors to deliver superior risk-adjusted investment performance.

Retirement at the Casino

2011-01-21T13:30:00.000-08:00

Clients and others who have seen us speak, read our material, or know us at all, understand that we at Butler|Philbrick & Associates don't make any investment decisions without a proper understanding of the odds, and the range of possible outcomes. We are optimists, but err on the side of caution.

The Quantitative Wealth Management Analytics Group, or QWeMA Group, based out of the Fields Institute near the University of Toronto, also embraces this focus on probability, and has applied it to the field of retirement planning in a novel way.

Traditional retirement plans have no way of accounting for the range of possible future outcomes, in the markets, with inflation, or in your own lifespan. The QWeMA Group's solution, on the other hand, accounts mathematically for the full range of possible outcomes, and assigns a probability to retirement success. As you will see in some of the videos produced by QWeMA's founder, Dr. Moshe Milevsky, a closer inspection of traditional plans shows that they will fail up to 50% of the time. For an in-depth case study about the vulnerabilities in traditional retirement plans, please see our article in Dental Practice Management's December issue, starting on page 36.

The following video by Dr. Milevsky explains the tradeoffs facing retirees, and how they can effectively plan for their future by finding an optimal mix of retirement income, product mix, and investment strategy. The video is over 17 minutes long, but grab your spouse, a glass of wine, and invest in your future. Be sure to return to this article after watching by hitting your browser's 'Back' button.

Welcome back!

Dr. Milevsky demonstrated how you can alter the sustainability of your retirement by reducing your income and altering your product mix. He also illustrated how different income levels and product mixes affect retirement sustainability and the legacy you leave behind.

There are other ways to improve your retirement options that Dr. Milevsky did not discuss in the video. One of the ways is to broaden your investment opportunity set to include international stocks, commodities, REITs and alternative strategies, in addition to domestic stocks and bonds. If you are like most Canadian investors, your portfolio is focused almost exclusively on Canadian stocks. While this has worked out well over the past ten years, investors may be missing important future opportunities by sticking so close to home. Further, adding even a very simple risk management system, such as the timing system we described in our Ontario Dentist article, can improve your sustainable retirement income by over 50%.

As an example, for a couple with a $1,000,000 retirement portfolio, following this simple system would have increased sustainable income from less than $50,000 per year for a traditional balanced portfolio, to over $90,000 per year. The same applies to expected legacy, where this simple investment approach would have more than doubled the amount left behind at the end.

Chart source: Ontario Dentist (2010)