BOE Paper Signals Worrisome Outlook for Equities Post QE

We don’t write very often on macroeconomic policy anymore, mostly because we are acutely aware of the complexity of economic systems. But it’s also because we are deeply upset at how we’ve permitted our policymakers to act over the past few years. While the Krugmans of the world loudly advocate mortgaging our childrens’ futures to preserve a bloated, over-indebted, short-termist, consumption focused status quo that channels virtually all of society’s wealth creation into the hands of the privileged few, we can’t help but think this will all end very badly. Worse, those who stand to lose the most – our children – are the least to blame for this predicament.

But I digress. This article is actually about how central banks have orchestrated a large overshoot in asset prices, and what this means for likely future returns to stocks and bonds in particular. We’ve written on this topic before, but this article approaches the problem from the point of view of central bank policy rather than analyzing valuation metrics.

The motivation for this article comes from a Bank of England “Quarterly Bulletin” entitled, “The United Kingdom’s quantitative easing policy: design, operation and impact“. We feel this bulletin has profound implications for investors, as it speaks to central banks’ goal of stimulating demand and inflation expectations primarily through the channel of ‘portfolio balance effects’: that is, inflating prices of stocks and bonds.

Specifically, the BOE believes that the primary impact of QE in the short term is to cause holders of QE eligible securities – primarily non-bank financial institutions such as insurance companies and pensions – to surrender these securities to central banks in exchange for newly printed money. The BOE then expects this cash to be redeployed toward higher yielding securities, such as credit and equities. This dynamic, compounded by effects from policy signalling (central bank jaw-boning) and liquidity enhancements, would  serve to drive the real prices of these assets materially higher in the short term. The ultimate goal is to engender a ‘wealth effect’ whereby asset holders feel more confident and ratchet up spending commensurately, increasing end demand and inflation.

Figure 1. QE Transition Channels

Source: Bank of England

From the paper:

`Portfolio balance effects: Central bank asset purchases, through this channel, push up the prices of the assets bought and also the prices of other assets. When the central bank purchases assets, the money holdings of the sellers are increased. Unless money is a perfect substitute for the assets sold, the sellers may attempt to rebalance their portfolios by buying other assets that are better substitutes.(3) This shifts the excess money balances to the sellers of those assets who may, in turn, attempt to rebalance their portfolios by buying further assets — and so on. This process will raise the prices of assets until the point where investors, in aggregate, are willing to hold the overall supplies of assets and money. Higher asset prices mean lower yields, and lower borrowing costs for firms and households, which acts to stimulate spending. In addition, higher asset prices stimulate spending by increasing the net wealth of asset holders.`
`While policy signalling effects affect expected policy rates, portfolio balance effects work by reducing the spreads of longer-term interest rates over expected policy rates (term premia) and the required return on risky assets relative to risk-free assets (risk premia) more generally.`
```... in the impact phase, asset purchases change the composition of the portfolios held by the private sector, increasing holdings of broad money and decreasing those of medium and long-term gilts. But because gilts and money are imperfect substitutes, this creates an initial imbalance. As asset portfolios are rebalanced, asset prices are bid up until equilibrium in money and asset markets is restored. This is reinforced by the signalling channel and the other effects of asset purchases already discussed, which may also act to raise asset prices. Through lower borrowing costs and higher wealth, asset prices then raise demand, which acts to push up the consumer price level.

...```
`The Bank of England’s asset purchase programme has attached particular importance to the portfolio balance channel. That is why purchases have been targeted towards long-term assets held by non-bank financial institutions, like insurers and pension funds, who may be encouraged to use the funds to invest in other, riskier assets like corporate bonds and equities.`
`Before asset purchases began, the main holders of gilts were UK non-bank financial institutions and overseas investors. Gilts only represented a modest part of UK non-bank financial institutions’ overall portfolios, suggesting they might be prepared to reinvest some of the money from gilt sales in other assets. Overseas investors might be more inclined to choose to invest in foreign assets. However, to do so they would need to change their sterling for foreign currency, putting downward pressure on the exchange rate. And, since all central bank money has to be held by someone, those who received the sterling might then choose to invest in other sterling assets.`

The “Impact Phase” of quantitative easing,  as described in the above paragraphs, is illustrated in Figure 2 below. The red line shows the effect on real asset prices like stocks and credit markets, while the blue line shows the increase in the broad money supply as a function of QE. Note how asset prices track excess broad money creation, but with a meaningful amount of excess torque. Clearly we have seen this asset pricing dynamic play out around the world wherever central banks are aggressively pursuing quantitative easing, and the effect is most profound in regions with the most aggressive monetary policies, such as the U.S. and Japan.

Figure 2. The ‘Impact Phase’ of Quantitative Easing (red line: real asset prices | blue line: broad money)

Source: Bank of England

It’s clear from Figure 3. that the Bank of England believes the mechanism of action for QE to result in massive short-term asset price inflation during the ‘Impact Phase’ of Quantitative Easing. However, when asset purchases slow or stop during the ‘Adjustment Phase’, they fully anticipate real asset prices to revert to prior equilibrium valuations. If we apply this concept to equity markets, we would expect markets to revert to long-term average valuations which, depending on the valuation method implies a real price drop of between 50% and 80%, per Figure 4. below.

Further, markets rarely just revert to ‘equilibrium’; just as markets clearly overshoot to the upside from leverage effects, herding and other feedback mechanisms, they are equally likely to overshoot to the downside. Indeed, in order to preserve a stable equilibrium, market must definitionally trace out the same magnitude of time and value below equilibrium as they do above it.

Figure 4. Current valuation versus long-term equilibrium

Source: DShort.com

It’s also worth noting that central banks have a very poor track record of managing this ‘Adjustment Phase’ effectively, as we have seen repeatedly in Japan over the past two decades or more, and as we saw in the U.S. and Europe in 1937 when policymakers unwound stimulative policies after the Great Depression. Figure 5., which we captured from a recent Bridgewater presentation illustrates how monetary tightening periods have negatively impacted asset prices at various periods in the past.

Figure 5. Drawdowns for traditional 60/40 endowment model portfolio and Bridgewater’s ‘All Weather’ risk parity based strategy during monetary tightening periods

Source: Bridgewater

Speaking of Bridgewater, Ray Dalio was on TV yesterday claiming that investors should expect a nominal 4% in total returns from equity markets over the next 10 years. That’s a little bit more ‘balanced’ than our own statistical forecast of about 0% real, or 2.5-3% nominal returns over the same period, but definitely in the same ballpark.  Figure 6. from the same presentation as Figure 5. shows how Bridgewater may have constructed its forecast. Returns since 1970 on a traditional 60/40 endowment style portfolio have averaged about 9.9%. This return can be decomposed into the return on cash plus the excess return on risky assets in the asset allocation. Note that over the full period cash returned 5.6% nominal while the risk premium on the asset allocation amounted to just 4.3%.

If we fast-forward to today, we note that current cash yields (for 5 year money) are 1.4%, about 4% lower than the average yield over the past 43 years. The question is, what should we assume in terms of excess returns on a traditional asset allocation in order to arrive at our total return estimate? I guess Bridgewater expects about 3%…

To reiterate what we’ve said many times in the past, while long term performance – 7 to 20 years – may look pretty bleak, anything could happen in the short term. All our research suggests markets are nearly impossible to forecast in the intermediate term (several months to 5 years or so), and markets have a history of moving well beyond equilibrium once momentum takes hold, but over the long-term we expect gravity to exert its inexorable pull. Looks like the BOE agrees.

Path Dependency in Financial Planning: Retirement Edition

`"And now the sequence of events in no particular order" - Dan Rather`

Imagine for a moment sitting at the kitchen table, steaming coffee in hand. The sun is streaming in the windows, bacon is popping in the pan, and Rover drops the paper at your feet. A brilliant Saturday morning by any measure, but today is extra special. Now you’re retired!

When you open the paper to the front page you notice that something is strange. First, the pictures are moving as though there is a movie embedded in the page instead of a picture. The text is in a strange font, and is so clear that it almost jumps from page to eye. When you start to scan the first article, you are startled to discover that a woman’s voice seems to be reading the words directly into your mind.

Your eyes dart to the top of the page, searching for the publishing date of the paper: August 1, 2045. Your gut falls, pulse is pounding; deep breath, calm yourself. A few minutes later, coffee and bacon forgotten, you are flipping urgently through the paper – maybe it will disappear just as quickly as it appeared!

Strange names, strange places, strange devices; what wonders!

Then you are in the business section. Numbers scroll across the top of the paper: every major market is listed and some new ones, along with their current prices. A bell goes off in your head, and a smile paints its way across your face.

You work the math. You’re 63 years old today. Your retirement plan was engineered to last you and your spouse 32 years until you turn 95 in the year 2045. How fortuitous!

You scan the business pages, but can’t find any individual stock listings, just closing values for major markets and asset classes. Quickly, you compare the prices listed for indexes in the paper to the most recent closing prices until you find the market with the best returns over the next 32 years.

You discover that the best market will deliver returns of 8% per year. Retirement Nirvana is just a few clicks away with your trusty Excel spreadsheet. No more anxiety, no more sleepless nights. You’re set.

Or are you?

Bookends

While you now have information about average returns for the best market over the next 32 years, you have no idea what path that market will take to get there. In your minds’ eye, it looks like the trajectory in Chart 1; a beautiful arcing growth curve from point A today to point B 32 years from now.

Chart 1. The retirement curve in your minds’ eye

But in reality, the paths that the index might take to achieve that level are limitless. For example, the index might surge in the first few years and then move sideways for the last couple of decades, like the red line in Chart 2. Or it might move sideways for the first three decades, and deliver all the returns in the final two years, like the green line.

Chart 2. Two alternate futures

I know what you’re thinking: Why does it matter in what sequence the returns are generated if we know exactly where the index will end up? Why do I care if returns come early or late, or are spread evenly through time?

These are great questions. Indeed, if we were to invest a lump sum in the index today and leave it there for 32 years it would not matter – at least mathematically – which path the index traveled to get from A to B.

Unfortunately, most retirees aren’t in that situation. Rather, most people expect to draw a steady income from their portfolio, withdrawing monthly, quarterly or annually an amount that keeps them in a certain lifestyle, adjusted each year for inflation. As we will see, this subtle change in objective can have a profound impact on financial outcomes.

Nick and Nancy: A Case Study

Let’s zoom in for a moment on Nick and Nancy, both 63. They are about to embark on the great retirement adventure. Through sacrifice, wisdom, perseverance – and some luck - the couple has  accumulated \$3,000,000 in savings over Nick’s almost 4 decades as a corporate lawyer. Nancy is pretty good with a spreadsheet so, with the help of their accountant, she created one to determine how much pre-tax income they can draw each year from their combined nest egg. Their analysis yielded the results depicted in Chart 3.

The red bars in Chart 3 represent the income that Nick and Nancy expect to draw each year from their portfolio, adjusted for inflation. You can see that in the year after retirement they expect to draw about \$180,000, and this amount scales by 3% per year, to account for expected inflation. The blue line describes the evolution of Nick and Nancy’s wealth after accounting for investment growth at 8%, and their annual withdrawals. Note that their total wealth seems to peak at around age 75 near \$3.5 million before tapering off aggressively toward their estimated age of mortality: 95.

Chart 3. Example retirement schedule from age 63 through 95

To summarize:

• `Nick’s current age: 63`
• `Retirement savings: \$3 million`
• Modeled sustainable income: \$180,000

So, from our future newspaper Nick and Nancy know that they are going to invest in a portfolio that delivers 8% per year, on average, over their 32 year retirement horizon. What they don’t know is the sequence in which those returns will materialize. In the next section we will demonstrate how this seemingly benign missing piece to the puzzle will decide Nick and Nancy’s retirement fate.

Lucky Now, or Lucky Later

For the sake of illustration, let’s assume that the best index over Nick’s and Nancy’s investment horizon is the Dow Jones Industrial Average. Nick and Nancy confidently withdraw an income each month to fulfill their lifestyle expectations, and all of their money is invested in this index.

Let’s also assume that the Dow delivers exactly the same returns over the next 32 years that it delivered over the years 1966 through 1997. This period was chosen because the returns to the Dow over the period were exactly 8% (or near enough for government work), and because it captured a long sideways market in the 1970s as well as a long powerful bull market from 1981 through the late 1990s. Chart 4. plots the price performance of the Dow over this period.

Chart 4. Dow Jones Industrial Average Index (1966 – 1997)

Data source: Bloomberg

Again, note that the average return to the Dow over this period is 8% per year. However, if you look closely you will see that the returns are not evenly spaced over time. Rather, from 1966 through 1982 there are essentially no returns, as the index began the period at 1000 and ended the period at the same level. Then, from 1982 through 1997 the Dow grew at over 15% per year taking the index from 1000 to about 8000. Charts 5 and 6 zoom in to provide a better perspective on these two very different market regimes.

Chart 5. Dow Jones Industrial Average Index (1966 – 1981)

Data source: Bloomberg

Chart 6. Dow Jones Industrial Average Index (1982 – 1997)

Data source: Bloomberg

Path Dependency

In our first instance, let’s take the straightforward example where Nick and Nancy experience exactly the same returns, in exactly the same order, as the Dow index delivered over the 32 years from 1966 to 1997. Remember, in the early years Nancy and Nick’s portfolio is large relative to the amount of their annual withdrawals, while in the back half of their retirement horizon, after they’ve spent many years drawing down income, the portfolio is relative small. Obviously the portfolio is more vulnerable to poor returns when the portfolio is large than it is when the portfolio is small.

In this example where the markets play out as they actually did historically, market growth would materialize in a manner that somewhat resembles the green line in Chart 2 above: sideways early on with a late surge. Chart 7 shows the trajectory of their retirement wealth given this market return trajectory, including withdrawals and growth.

Chart 7. Retirement wealth trajectory: weak early returns and strong late returns

Data source: Bloomberg

Remember, Nick and Nancy knew for certain that they would achieve 8% average compounded returns over the full duration of their retirement horizon. However, in this first example, where the poor returns come early in the retirement horizon, the couple is completely broke over 15 years before their expected age of mortality. That is, Nick and Nancy must endure their remaining 5, 10 or 15 years in retirement on CPP (social security) and old age social subsidies alone. A pretty dire outcome.

For our second example, we will mix things up. Rather than experiencing the long, volatile sideways market from 1966 to 1981 first, followed by the strongly surging market from 1982 to 1997 later, we will reverse the order. In this case, imagine a situation where Nick and Nancy experience the long bull market from 1982 to 1997 in the early part of their retirement period when their nest egg is very large, and then experience the 16 years of poor sideways markets in the latter half. Chart 8 shows Nick’s and Nancy’s retirement trajectory under these new assumptions.

Chart 8. Retirement wealth trajectory: weak early returns and strong late returns

Data source: Bloomberg

Note how this this chart looks much more like Chart 3., which was the retirement model that Nancy created when she analyzed their prospective retirement trajectory. In this example, Nick and Nancy are able to withdraw their desired income each year, adjusted for inflation, and still end up with over \$4 million in terminal wealth. Now we see why Albert Einstein allegedly claimed that compound growth is the most powerful force in the universe.

Individual Rate of Return

It is critical to remember that the Dow Jones Industrial Average delivered average returns of 8% per year in both examples above. With no cash flows going in or out of the portfolio, the order of returns is completely inconsequential. It doesn’t matter whether returns come early or late; the Dow still ends in exactly the same spot.

However, when we introduce cash flows into the equation, things change dramatically. We saw that, with exactly the same withdrawals, and exactly the same long-term average market returns, Nick and Nancy ended up in two dramatically different situations. On one hand they died with over \$4 million in wealth for heirs, charity or other valued causes. On the other hand they were flat broke at age 79, fully 16 years earlier than expected. So what matters most to Nick and Nancy, the average return from the market, or the average growth of their portfolio?

The average rate of return on a series of cash flows is referred to as the Internal Rate of Return. It is also referred to as the dollar weighted rate of return. We refer to the average dollar weighted rate of return that a person receives on his personal portfolio as his Individual Rate of Return, and it is the only rate of return that should matter to investors who are either saving money, or withdrawing money from their portfolio over time.

Table 1. compares the average rate of return on the Dow Jones to the Individual Rates of Return that Nick and Nancy experienced in their portfolios in the two examples above. Note that the IRR from the model is less than 8% due to the way dollar weighted returns are calculated over time contrast to the 8% arithmetic return of the index.

Table 1. Comparing Individual Rates of Return (IRR%)

 `IRR %` `Model` `7.37%` `Good returns come early` `8.36%` `Good returns come late` `-2.41%`

Chart 9. Early returns versus late returns

Table 1 clearly shows that an investor with the exact same savings and the exact same average market return can experience a +10% difference to actual realized portfolio growth, or Individual Rate of Return, simply due to luck. An investor who is lucky enough to experience strong market growth in the front half of their retirement horizon may experience double, triple, or better growth than an investor who is unlucky enough to have most of his strong returns come in his later retirement years. In the case of Nick and Nancy, a weak sequence of returns resulted in total depletion of their wealth after 16 years of retirement, while a strong sequence resulted in a \$4 million legacy.

Do you feel lucky, sir?

Nick and Nancy discovered that it is not enough to know the long-term average returns that they can expect from their investment portfolio. It is also important to account for potentially adverse sequences of returns. For savers, it is problematic to invest in a portfolio with the potential to deliver high returns in early periods followed by low returns in later periods. For retirees (and endowments and pensions) in a net withdrawal situation, sequence of returns is equally important. However, the situation is reversed, such that investors in withdrawal will be adversely affected if returns cluster later in the investment period, with lower near-term returns.

For investors in traditional portfolios this sensitivity to the sequence of returns can be seriously problematic. That’s because both stocks and bonds are prone to periods of low, volatile returns that last many years or even decades.

The following chart shows the long-term returns to the Dow Jones Industrial Average back to 1998. Note that periods of strong returns – coloured green in the chart – have lasted between 5 and 17 years, and delivered cumulative returns ranging from 150% to 1000%. These green periods of long-term growth are invariably followed by red periods of low or negative returns that have lasted from 17 to 25 years historically, and delivered cumulative returns between -4% and 12% over their entire duration.

Chart 10. Long term Dow with secular bull and bear markets

Source: Guggenheim Investments

Note that we are currently about 13 years into the most recent red sideways period. The question is, are we near the middle of the sideways period, or did we turn the corner in 2008, setting up for a new long-term growth cycle?

The answer has profound implications for investors. Are we setting up for a decade of strong returns right now, which would prove to be a boon for recent retirees but unfortunate for young savers? Or will we experience another 5 or 10 years of sideways to set up for a long growth market a decade in our future?

At Butler|Philbrick|Gordillo & Associates, we don’t believe that a person’s chances for financial happiness should be held hostage to whether they happen to start saving, or enter retirement, at a lucky point in history. That’s why we spent 5 years developing our Adaptive Asset Allocation  (AAA) investment framework with the goal of specifically optimizing the saving and retirement objectives of real investors. It’s an investment approach designed to get as close as possible to the wealth trajectory in your mind’s eye (see Chart 1.)

By actively managing portfolio risk and dynamically assembling diversified portfolios of global assets twice per month, our AAA process is engineered to minimize risks associated with sequence of returns. Instead, in simulations back to 1995 the AAA approach implies the potential for extremely stable returns regardless of market conditions. As a result, even under onerous assumptions, we believe a portfolio invested in AAA is much more likely to hit your financial targets than a traditional portfolio approach, especially given the poor prospects for stocks at this point in the market cycle.

BOE Paper Signals Worrisome Outlook for Investors Post QE

We don’t write very often on macroeconomic policy anymore, mostly because we are acutely aware of the complexity of economic systems. But it’s also because we are deeply upset at how we’ve permitted our policymakers to act over the past few years. While the Krugmans of the world loudly advocate mortgaging our childrens’ futures to preserve a bloated, over-indebted, short-termist, consumption focused status quo that channels virtually all of society’s wealth creation into the hands of the privileged few, we can’t help but think this will all end very badly. Worse, those who stand to lose the most – our children – are the least to blame for this predicament.

But I digress. This article is actually about how central banks have orchestrated a large overshoot in asset prices, and what this means for likely future returns to stocks and bonds in particular. We’ve written on this topic before, but this article approaches the problem from the point of view of central bank policy rather than analyzing valuation metrics.

The motivation for this article comes from a Bank of England “Quarterly Bulletin” entitled, “The United Kingdom’s quantitative easing policy: design, operation and impact“. We feel this bulletin has profound implications for investors, as it speaks to central banks’ goal of stimulating demand and inflation expectations primarily through the channel of ‘portfolio balance effects’: that is, inflating prices of stocks and bonds.

Specifically, the BOE believes that the primary impact of QE in the short term is to cause holders of QE eligible securities – primarily non-bank financial institutions such as insurance companies and pensions – to surrender these securities to central banks in exchange for newly printed money. The BOE then expects this cash to be redeployed toward higher yielding securities, such as credit and equities. This dynamic, compounded by effects from policy signalling (central bank jaw-boning) and liquidity enhancements, would  serve to drive the real prices of these assets materially higher in the short term. The ultimate goal is to engender a ‘wealth effect’ whereby asset holders feel more confident and ratchet up spending commensurately, increasing end demand and inflation.

Figure 1. QE Transition Channels

Source: Bank of England

From the paper:

`Portfolio balance effects: Central bank asset purchases, through this channel, push up the prices of the assets bought and also the prices of other assets. When the central bank purchases assets, the money holdings of the sellers are increased. Unless money is a perfect substitute for the assets sold, the sellers may attempt to rebalance their portfolios by buying other assets that are better substitutes.(3) This shifts the excess money balances to the sellers of those assets who may, in turn, attempt to rebalance their portfolios by buying further assets — and so on. This process will raise the prices of assets until the point where investors, in aggregate, are willing to hold the overall supplies of assets and money. Higher asset prices mean lower yields, and lower borrowing costs for firms and households, which acts to stimulate spending. In addition, higher asset prices stimulate spending by increasing the net wealth of asset holders.`
`While policy signalling effects affect expected policy rates, portfolio balance effects work by reducing the spreads of longer-term interest rates over expected policy rates (term premia) and the required return on risky assets relative to risk-free assets (risk premia) more generally.`
```... in the impact phase, asset purchases change the composition of the portfolios held by the private sector, increasing holdings of broad money and decreasing those of medium and long-term gilts. But because gilts and money are imperfect substitutes, this creates an initial imbalance. As asset portfolios are rebalanced, asset prices are bid up until equilibrium in money and asset markets is restored. This is reinforced by the signalling channel and the other effects of asset purchases already discussed, which may also act to raise asset prices. Through lower borrowing costs and higher wealth, asset prices then raise demand, which acts to push up the consumer price level.

...```
`The Bank of England’s asset purchase programme has attached particular importance to the portfolio balance channel. That is why purchases have been targeted towards long-term assets held by non-bank financial institutions, like insurers and pension funds, who may be encouraged to use the funds to invest in other, riskier assets like corporate bonds and equities.`
`Before asset purchases began, the main holders of gilts were UK non-bank financial institutions and overseas investors. Gilts only represented a modest part of UK non-bank financial institutions’ overall portfolios, suggesting they might be prepared to reinvest some of the money from gilt sales in other assets. Overseas investors might be more inclined to choose to invest in foreign assets. However, to do so they would need to change their sterling for foreign currency, putting downward pressure on the exchange rate. And, since all central bank money has to be held by someone, those who received the sterling might then choose to invest in other sterling assets.`

The “Impact Phase” of quantitative easing,  as described in the above paragraphs, is illustrated in Figure 2 below. The red line shows the effect on real asset prices like stocks and credit markets, while the blue line shows the increase in the broad money supply as a function of QE. Note how asset prices track excess broad money creation, but with a meaningful amount of excess torque. Clearly we have seen this asset pricing dynamic play out around the world wherever central banks are aggressively pursuing quantitative easing, and the effect is most profound in regions with the most aggressive monetary policies, such as the U.S. and Japan.

Figure 2. The ‘Impact Phase’ of Quantitative Easing (red line: real asset prices | blue line: broad money)

Source: Bank of England

You may be thinking, ‘So far, so good. What’s the problem?” Here is where things get interesting. Again directly from the paper:

`In the adjustment phase, rising consumer and asset prices raise the demand for money balances and the supply of long-term assets. So the prior imbalance in money and asset markets shrinks, and real asset prices begin to fall back. The boost to demand therefore diminishes and the price level [inflation] continues to increase but by smaller amounts. The whole process continues until the price level has risen sufficiently to restore real money balances, real asset prices and real output to their equilibrium levels.`

The language in the paper is predictably benign, so as not to alarm readers about the implications. But the benign language is misleading, especially with respect to the impact of attenuating QE on real asset prices like stocks, bonds and real estate. Essentially, portfolio balance effects are driven by the flow of newly printed money which is used to purchase government bonds primarily from insurance companies, wealth funds and pensions. While bond purchases – QE – continues to take place,  these institutions are continually faced with holding newly minted low-yielding cash assets, which they then roll out the risk curve in search for higher yields. As assets are priced ‘at the margin’, these marginal dollars serve to continually apply upward pressure to prices. No doubt this engenders other feedback mechanisms related to (over)confidence, leverage effects, and momentum which carries markets far beyond equilibrium. However, when this positive feedback mechanism ends, “real asset prices begin to fall back“. Let’s see what that looks like according to the Bank of England in Figure 3.

Figure 3. The ‘Adjustment Phase’ after the end of Quantitative Easing

Source: Bank of England

It’s clear from Figure 3. that the Bank of England believes the mechanism of action for QE to result in massive short-term asset price inflation during the ‘Impact Phase’ of Quantitative Easing. However, when asset purchases slow or stop during the ‘Adjustment Phase’, they fully anticipate real asset prices to revert to prior equilibrium valuations. If we apply this concept to equity markets, we would expect markets to revert to long-term average valuations which, depending on the valuation method implies a real price drop of between 50% and 80%, per Figure 4. below.

Further, markets rarely just revert to ‘equilibrium’; just as markets clearly overshoot to the upside from leverage effects, herding and other feedback mechanisms, they are equally likely to overshoot to the downside. Indeed, in order to preserve a stable equilibrium, market must definitionally trace out the same magnitude of time and value below equilibrium as they do above it.

Figure 4. Current valuation versus long-term equilibrium

Source: DShort.com

It’s also worth noting that central banks have a very poor track record of managing this ‘Adjustment Phase’ effectively, as we have seen repeatedly in Japan over the past two decades or more, and as we saw in the U.S. and Europe in 1937 when policymakers unwound stimulative policies after the Great Depression. Figure 5., which we captured from a recent Bridgewater presentation illustrates how monetary tightening periods have negatively impacted asset prices at various periods in the past.

Figure 5. Drawdowns for traditional 60/40 endowment model portfolio and Bridgewater’s ‘All Weather’ risk parity based strategy during monetary tightening periods

Source: Bridgewater

Speaking of Bridgewater, Ray Dalio was on TV yesterday claiming that investors should expect a nominal 4% in total returns from equity markets over the next 10 years. That’s a little bit more ‘balanced’ than our own statistical forecast of about 0% real, or 2.5-3% nominal returns over the same period, but definitely in the same ballpark.  Figure 6. from the same presentation as Figure 5. shows how Bridgewater may have constructed its forecast. Returns since 1970 on a traditional 60/40 endowment style portfolio have averaged about 9.9%. This return can be decomposed into the return on cash plus the excess return on risky assets in the asset allocation. Note that over the full period cash returned 5.6% nominal while the risk premium on the asset allocation amounted to just 4.3%.

If we fast-forward to today, we note that current cash yields (for 5 year money) are 1.4%, about 4% lower than the average yield over the past 43 years. The question is, what should we assume in terms of excess returns on a traditional asset allocation in order to arrive at our total return estimate? I guess Bridgewater expects about 3%…

To reiterate what we’ve said many times in the past, while long term performance – 7 to 20 years – may look pretty bleak, anything could happen in the short term. All our research suggests markets are nearly impossible to forecast in the intermediate term (several months to 5 years or so), and markets have a history of moving well beyond equilibrium once momentum takes hold, but over the long-term we expect gravity to exert its inexorable pull. Looks like the BOE agrees.

Robust Risk Parity [ Dynamic Asset Allocation for Practitioners Part 5 ]

In our article on Structural Diversification we explored the idea of holding a universe of assets which, when assembled in thoughtful proportion, might be expected to protect investors against the four major market regimes that they might encounter over the long term. In that article we applied an understanding of the theoretical financial pricing drivers of each asset to engineer a theoretically diversified portfolio. For example, we theorized how bonds should respond to changes in growth and inflation expectations versus how gold or developed market stocks might respond.

If we make the strong assumptions that:

• our understanding of the financial theory about each asset is thorough and consistent through time;
• our assumptions about the relative risk contribution by each asset are stationary through time, and;
• the growth and inflation dynamics we applied to create our four economic regimes represent the only drivers of asset prices

…then we can feel relatively confident that the portfolio will achieve our stated goal of providing consistent positive real returns in any environment.

Unfortunately, there are many reasons to doubt these three strong assumptions. Much ambiguity still exists about the relative sensitivity of assets to changes in growth expectations versus changes in interest rates. We also know from our naive risk parity article that asset risk changes – sometimes dramatically – through time. Later in this article we will demonstrate how correlations change too, and how that impacts assets’ marginal risk. Lastly, it is naive in the extreme to believe that asset pricing can be decomposed into just two factors.  For example, fixed income assets, even Treasuries, may be sensitive to other sources of risk, such as credit risk as we are beginning to witness with recent downgrades of U.S. debt markets. Gold and commodities also react differently to different types of inflation and growth. And these are just a few examples of how our assumptions may not hold through time.

When we have cause to doubt our foundational assumptions, we must begin to contemplate whether we can engineer diversification in other ways. This prompts the question, “What can we assume about the long-term character of asset returns, and what can we estimate dynamically in order to reduce our reliance on these assumptions?” Figure 1 proposes a decision waterfall whereby investors can choose an appropriate portfolio construction methodology on the basis of what they assume about assets versus what asset parameters they feel confident they can estimate over time.

Figure 1. Portfolio Optimization Decision Waterfall

In this article we will introduce methods to quantitatively account for the diversification properties of assets in the portfolio in order to equally distribute the total risk in portfolios rather than equally distributing nominal risk. In keeping with the fairly loose nomenclature around these concepts in the literature, we will brand frameworks that help to balance the total risk in portfolios ’robust’ risk parity, in contrast to the ‘naive’ risk parity explored in the last article. As such, we have revealed a new section of our Portfolio Optimization Machine corresponding to what we will discuss today.

Figure 1. Portfolio Optimization Machine

Robust Risk Parity

Remember that naive risk parity weights are proportional to the inverse of asset volatilities, so that higher volatility assets will have a smaller weight in the portfolio, and vice versa. In effect, naive risk parity implicitly assumes that all asset pairs have a correlation of 1. This is a strong assumption. In this case, the risk contribution of each asset is considered to be equal to the weight of the asset in the portfolio times asset’s volatility alone. If the goal is to equally distribute the naive risk of assets in the portfolio, we weight each asset in proportion to the inverse of its volatility.

Perhaps the most formal and widely recognized approach to robust risk parity is Equal Risk Contribution (ERC) (Maillard et. al., 2008). Maillard described a process for allocating to assets with the goal of equally distributing assets’ Total Risk Contribution (TRC). When optimizing for ERC, portfolio weights are derived exclusively using estimates of the variance/covariance matrix such that we define an asset’s Total Risk Contribution in terms of the asset’s weight in the portfolio times the covariance of the asset with the portfolio itself.

Unfortunately, the process to derive portfolio weights that formally equalize Total Risk Contribution (i.e. form the ERC portfolio) are mathematically and computationally complex, so a demonstration of this method is beyond the scope of this article. However, we thought it might be instructive to contrast Figure 3 and Figure 4 in the naive risk parity article, which showed the contribution of each asset’s naive risk (volatility) through time and resulting weights, with their ERC analogs. We have reproduced all four figures below for easy comparison.

Figure 2. Naive risk contribution transition map

Data source: Bloomgerg

Figure 3. Total Risk Contribution transition map

Data source: Bloomberg

It is instructive to focus on IEF in the charts because the intermediate term Treasury ETF has a low correlation with the other assets in the portfolio (except TLT), and it also has a relatively low volatility. Note how the orange area corresponding to IEF’s naive risk contribution in Figure 1 is larger than the area corresponding to its Total Risk Contribution in Figure 2. This highlights the fact that IEF’s volatility contribution is attenuated by the fact that it also has a very low correlation with the other assets, and therefore acts as a portfolio diversifier.

If we flip it around and look at the weights (which are proportional to the inverse of risk contribution), you will observe that indeed IEF has a higher weight in the ERC framework than it does in the naive risk parity framework. The average weight of IEF in the naive risk parity approach is 20% while the average weight in the ERC framework is 27%. Given that the difference between the naive risk parity weight and the ERC weight is the result of diversification impact, we might say that the diversification impact of IEF reduces its total risk contribution to the portfolio by (27% – 20%)/20% =  35% relative to its estimated contribution under naive risk parity assumptions.

Figure 4. Naive risk parity weights transition map

Data source: Bloomberg

Figure 5. Equal Risk Contribution transition map

Data source: Bloomberg

As mentioned above, while ERC is often considered the most formal method of deriving the robust risk parity portfolio, it is quite involved mathematically, and perhaps not very intuitive for the uninitiated. Another valid method of deriving a robust risk parity portfolio involves the use of ‘clusters.  The seminal work in this area (to my knowledge) was introduced by David Varadi in partnership with Michael Kapler (2013). I strongly urge you to surf on over to the above links to explore the concept in more detail; Michael Kapler in particular has shared a great deal of R code for those who want to attempt to do it themselves, and his Systematic Investor Toolbox is a treasure trove for nascent (and more experienced!) quants.

Clusters

It is useful to think about correlation as a measure of similarity, such that strong correlation implies a high degree of similarity. Conversely, weak or negative correlation implies a high degree of dissimilarity. In this way, dissimilarity is analogous to diversification.

$\large dissimilarity\sim -correlation \sim diversification$

Therefore we can create a dissimilarity matrix by imposing the negative of the correlation matrix. A cursory inspection of the dissimilarity matrix makes it clear which assets are quantitatively quite similar to each other, and which assets are dissimilar. We can formalize this analysis mathematically by applying techniques that organize the assets in the portfolio into clusters of assets, where assets within a cluster are very similar to each other while the clusters themselves are quite dissimilar from one another. The mathematical derivation of this process (kmeans) is again beyond the scope of this article, but Michael Kapler has graciously provided all of the R code needed to perform the analysis yourself (here).

The following charts illustrate the quantitatively derived asset clusters currently in force from asset correlations year-to-date in 2013. You can see how assets in close proximity to each other in ‘correlation space’ are grouped together into clusters. Currently 5 clusters explain about 95% of the total variance of our basket of 10 assets.

Figure 6.. Current asset class clusters

Data source: Bloomberg

It’s comforting to note that most of the assets fall into clusters that confirm, for the most part, our understanding of the fundamental asset classes: high quality fixed income (TLT,IEF,TIP,LQD) form a cluster, as do equities and real estate (VTI,EFA,EEM,ICF,RWX). Gold and commodities are behaving quite different from each other of late, and so each earns its own cluster this year.  Interestingly, the real estate assets are split up in terms of clusters despite being fairly proximate on the chart. The real estate ETFs are lumped in with Japanese stocks.

It is perhaps more intuitive to view the clusters in terms of how they might look in a portfolio. The following pie charts compare a traditional equal weight portfolio with a portfolio where each asset within a cluster receives equal weight, and each individual cluster also receives equal weight. We’ve demarcated the boundaries between clusters with thick black lines.

Figure 7. Equal weight (top) vs. Equal Cluster Weight (bottom) portfolios

Data source: Bloomberg

Closer observation of the equal cluster weight portfolio above reveals that the low volatility fixed income cluster consisting of both IEF and TLT receives the same nominal allocation as the equity cluster (VTI,IEV,EEM), while gold and commodities each receive the same weight in the portfolio as all the fixed income assets together. In fact IEF and TLT receive the same allocation in the equal cluster wieght portfolio as they do in a traditional equal weight portfolio. This is obviously problematic from the perspective of risk contribution.

Above we analyzed the challenges with equally allocating between assets with very different volatility profiles: higher risk assets come to dominate the aggregate risk of the portfolio. The same concept applies to clusters. Furthermore, low volatility assets which might otherwise have an opportunity to act as strong diversifiers in the portfolio – like bonds – are marginalized by their low relative risk.

We can apply naive risk parity concepts to obviate this challenge within the cluster framework above by distributing risk or volatility equally between clusters, and between assets within each cluster. David Varadi and Michael Kapler call this concept ‘Cluster Risk Parity’, and in our opinion it is a perfectly coherent way to distribute risk in a portfolio. If we apply this concept to the assets and clusters above we observe the following portfolio weights:

Figure 8. Naive risk parity and Cluster risk parity

Data source: Bloomberg

We would note that, in addition to the cluster methods described above, Varadi and Kapler also contributed some less mathematically rigorous, but quite prospective, heuristic methods under the broad heading of ‘Minimum Correlation Algorithms‘ or MinCorr. The MinCorr methods achieve diversification by reducing the dimensionality of the correlation matrix via averaging and simple transforms. For example, the Total Risk Contribution for each asset is approximated as a function of the average of its respective column (not including the unit diagonal row) in the correlation matrix, and the asset’s individual variance. The data is transformed in some simple ways (rank, Gaussian) to ensure all weights are positive.

Alternative Methods

Equal Risk Contribution and Cluster Risk Parity are excellent frameworks to consider for a robust risk parity approach, but they are not the only ones. In our article on ‘structural diversification’ we described a process to diversify a portfolio across two major drivers of asset returns, namely changes in expectations related to economic growth and inflation. However, as discussed above, the concept of structural diversification is predicated on three strong assumptions:

• our understanding of the financial theory about each asset is thorough and consistent through time;
• our assumptions about the relative risk contribution by each asset are stationary through time, and;
• the growth and inflation dynamics we applied to create our four economic regimes represent the only drivers of asset prices

Let’s examine the third assumption in particular, which asserts that asset prices are driven by just two factors: changes in growth and inflation expectations. Can this really be true? Is it not possible, for example, that assets are sensitive to drivers such as monetary policy, sentiment, political risk, herding, or a host of other factors that emerge and dissipate through time? Why should we restrict our diversification framework to just the growth and inflation dimensions when assets are surely driven by other factors as well over time?

Meucci (2007, 2009) proposed  a concept of distributing risk equally across the true empirical factors that drive portfolio volatility. Under Meucci’s framework, it is not necessary to make the strong assumptions above because the true independent drivers of portfolio returns can be derived mathematically from the assets’ variance/covariance matrix. The process used to extract these factors – principal component analysis – serves to create several new portfolios made up of the original assets, but where the return series for each new portfolio describes the impact of the independent factors that are actually driving portfolio returns. Meucci called these new portfolios ‘Principal Portfolios’.

Importantly, the factors described by principal portfolios do not necessarily map directly to traditional portfolio drivers; you can’t be sure that Principal Portfolio 1 (or Factor 1) is analogous to a growth or inflation factor, for example. However, it is often possible to approximate some traditional meaning from a principal portfolio factor by inspecting its composition.

For example, Lohre et. al. (2013) analyzed the factor portfolios for a simple universe consisting of government bonds, developed market stocks, emerging market stocks, commodities and credit, based on monthly return data from 1987 – 2011. The numbers in each column correspond to each asset’s ‘loading’ within the principal portfolio, or in other words the principal portfolio’s sensitivity to that asset class. All of the loadings are standardized between -1 and 1.

Figure 9. Factor portfolios

Source: Lohre et. al. (2013)

Note that Principal Portfolio 1 (PP1) has large positive exposures or loadings on emerging and developed equity markets, so Lohre makes the logical leap that PP1 tracks the global equity beta factor. An equally plausible explanation might be that PP1 tracks global growth; emerging markets are obviously more sensitive to this dynamic than developed equities. PP2 meanwhile is very heavily loaded on commodities with small negative loadings on equities and fixed income, so Lohre speculates PP2 relates to the diversification potential of commodities relative to equities. One might alternatively speculate that PP2 relates to inflation. PP3 is long emerging equities and short developed equities, suggesting that PP3 tracks the return spread between emerging and developed equities. PP4 loads meaningfully on the two fixed income assets, implying an interest rate analog, while PP% loads positively on credit and negatively on government bonds, indicating a credit spread factor.

It is instructive to observe the difference between Maillard’s Equal Risk Contribution portfolio and Meucci’s Diversified Risk Parity portfolio in terms of their distribution of asset weights, asset total risk contribution, and principal portfolio risk contribution. Lohre provides superb visualizations of these concepts in his paper, which we have extracted into Figure 10. Note how traditional ERC (labeled RP in Figure 10) does an excellent job of equally distributing asset total risk contribution in the portfolio, as represented by the near perfect rainbow of colours in column 2 of the diagram. However, ERC does a relatively poor job of diversifying risk across principal portfolios, as can clearly be observed in the chart showing the volatility contribution by principal portfolio. Note ERC’s very concentrated exposures to Principal Portfolios 1 and 4, with virtually no exposure to factors 2, 3 and 5. In other words, ERC is ineffective at ensuring that the portfolio is equally distributed across the latent drivers of asset prices over time.

Figure 10. Visualizing asset risk contribution vs. principal portfolio risk contribution

Source: Lohre et. al. (2013)

In contrast, Meucci’s Diversified Risk Parity (DRP) approach is unconcerned with diversifying marginal risk across individual assets, as can can clearly be seen by inspecting the middle image under DRP. Rather, DRP is concerned with equally distributing risk across the latent portfolio factors – principal portfolios – to ensure equal exposure to all of the major sources of portfolio risk rather than equal exposure to individual asset risk. Note that the principal portfolio risk chart in Figure 1 is not totally balanced at all times because of the no shorting constraint; otherwise the right most chart in the second row would closely resemble the middle chart in the first row.

It is further interesting to note that, while DRP does a more effective job of diversifying across the latent drivers of portfolio variance, it results in a more concentrated portfolio from the perspective of asset weights. In particular, DRP results in very large and persistent exposure to global bonds and more limited but still persistent exposure to commodities, but only limited, intermittent rotation between developed and emerging equities. However, despite the low and sporadic allocation to equities in the DRP portfolio according to Lohre’s 5 asset class simulations, it still managed to outperform ERC, equal weight and other optimization methods from 1993 – 2011 (Figure 11). DRP (including the constrained long-only version) exhibited outperformance in terms of both total returns and Sharpe ratio, and maximum drawdowns were competitive with the Minimum Variance portfolio.

Figure 11. Lohre paper performance comparison across different risk parity methods

Results

Figures 11 and 12 summarize the results of simulations using each of the methods described above on the same broad asset class universe we used in the Naive Risk Parity (NRP) simulations. The NRP tests revealed a high degree of sensitivity to the asset universe chosen for optimization. While some of the NRP methods did a good job of reducing portfolio volatility and improving Sharpe ratios when applied to our well specified 10 asset universe, we observed virtually no improvement in these metrics in simulations on a larger, equity biased, noisier universe which we’ve named ‘Dog’s Breakfast’.

We are interested in discovering how well robust risk parity techniques perform on these same universes, so we ran simulations using each of the methods described above over the period 1995 – 2013, with quarterly rebalancing.

Our 10 asset universe:

 Commodities (DB Liquid Commoties Index) Gold U.S. Stocks (Fama French top 30% by market capitalization) European Stocks (Stoxx 350 Index) Japanese Stocks (MSCI Japan) Emerging Market Stocks (MSCI EM) U.S. REITs (Dow Jones U.S. Real Estate Index) International REITs (Dow Jones Int’l Real Estate Index) Intermediate Treasuries (Barclays 7-10 Year Treasury Index) Long Treasuries (Barclays 20+ Year Treasury Index)

The universe above is very well specified by many important measures, so it does not do a very good job of demonstrating the inherent weaknesses of risk parity approaches. In contrast, the universe below, which consists of 35 different equity universes along with REITs, gold, commodities, and 1 intermediate-term Treasury index clearly exposes the limitations of  risk parity, as the massive overweight in equities swamps the diversification of alternative asset classes. Where possible we have extended the horizon of ETFs back through time using their respective total return  indices. We will call this universe our ‘Dog’s Breakfast’ universe for lack of a better name.

Dog’s Breakfast Universe (non-equity assets highlighted in gold)

 VTI – Total U.S. Stock Market EIRL – Ireland TUR – Turkey ECH – Chile THD – Thailand EEM – Emerging Markets QQQ – Nasdaq DBC – Commodities IFN – India ACWI – All Cap World Index IDX – Indonesia VGK – Europe GLD – Gold RSX – Russia GREK – Greece RWX – Int’l RE EWZ – Brazil IYR – U.S. Real Estate EZA – South Africa IEF – 7-10 Yr Treasuries EWW – Mexico EWM – Malaysia EWY – South Korea EWK – Belgium EWT – Taiwan EWL – Switzerland EWU – United Kingdom EWJ – Japan EWQ – France EWH – Hong Kong EWS – Singapore EWI – Italy EWO – Austria EWD – Sweden EWP – Spain EWG – Germany EWN – Netherlands EWA – Australia EFA – EAFE EWC – Canada

Note that all Sharpe ratios in the performance tables below are net of the 3 month T-bill rate.

Figure 12. Robust risk parity simulations on 10 asset universe

Data source: Bloomberg

Figure 13. Robust risk parity simulations on 40 asset ‘Dog’s Breakfast’ universe

Data Source: Bloomberg

Conclusions

The purpose of this article was to extend the concepts first proposed in our article on Structural Diversification into a dynamic framework. We described the strong assumptions embedded in passive methods of diversification, and offered reasons to question those assumptions. We then explored a variety of methods for dynamically engineering optimal portfolio diversification through time using evolving estimates of asset correlations and variances.

We examined the Equal Risk Contribution framework proposed by Maillard and extended by Roncalli; cluster risk parity and heuristic minimum correlation methods proposed by Varadi and Kapler (2012), and; Meucci’s diversified risk parity framework involving principal portfolios. The principal portfolio framework allowed us to examine the true number of diversified ‘bets’ in different types of risk parity portfolios. We discovered that while the Equal Risk Contribution method definitionally equalizes each individual assets‘ contribution to total portfolio volatility, it can result in portfolios that have concentrated exposures to a small number of latent portfolio drivers. In contrast, the objective of Diversified Risk Parity is to equalize exposures to the underlying drivers of portfolio variance, and the method effectively achieves this objective even under long-only constraints.

In scrutinizing results of simulations across our two asset universes, we observe that the risk profile of the Diversified Risk Parity approach is consistently lower than all other methods across both universes. Further, DRP exhibits a higher Sharpe ratio than ERC in both universes, though a combination of ERC and Clusters delivered competitive Sharpe ratios and higher returns than DRP for the 10 asset universe. Further, applying ERC with clusters results in about 2/3 less turnover than DRP. Thus we conclude that DRP and the application of Equal Risk Contribution within and across clusters are both highly robust methods to achieve true risk parity, but that the cluster ERC method may be preferable because turnover is less onerous.

Our next instalment in this series will deal with minimum risk algorithms, including minimum variance, minimum VaR, minimum CVaR and other methods.

Don’t Fear the (Alpha) Reaper

“[People] occasionally stumble over the truth, but most of them pick themselves up and hurry off as if nothing ever happened.” – Winston Churchill

Non Timetis Messor (Latin – Don’t Fear the Reaper)

Preface

Let us preface this article by saying that we can’t for the life of us figure out why any investor cares about beating the market in the first place. To us, the whole concept of beating the market is a red herring. The only people who should be concerned with beating the market are investment managers themselves, because their compensation is directly tied to this specific objective. For the rest of us, our financial goals have almost nothing to do with beating the market. We care about consistent results with few major twists and turns that allow us to grow our savings at or above the rate of inflation.

Secondly, we have nothing against active management. From an economic perspective, active management is critical to the establishment of equilibrium price signals for a wide variety of transactions. From an investment standpoint, a select few areas of active management offer substantial opportunities for sustainable strong returns with manageable risk. For example, we are big believers in trend following and global macro strategies. Further, we acknowledge that structural market dislocations present infrequent, fleeting, but highly lucrative opportunities in many markets from time to time. The dislocation in the Canadian asset backed commercial paper space, or the U.S. leveraged loan space after the credit crisis, are easy to recall examples.

Rather, our beef is specifically with traditional, fundamentally based stock-picking investment mandates, of which traditional mutual funds are a very representative sample. As such, the following analysis, which focuses on these stock-picking mutual fund managers’ inability to beat their respective equity indices, draws no conclusions about active management in general. I hope we’re clear on this point.

We’re going to say it again: beating ‘the market’ is a mug’s game. We’ve repeated this ad nauseum on this blog, but the evidence keeps pouring in. Look no further than the latest SPIVA®  (S&P DOW JONES INDICES VERSUS ACTIVE FUNDS) report, which by the way echoes the exact same themes as all previous SPIVA® reports. The SPIVA® authors summarize thusly: “The only consistent data point we have observed over a five- year horizon is that a majority of active equity managers in most categories lag comparable benchmark indices.”

The SPIVA® reports are worth paying attention to because they cover all mutual funds – in market cap weight and equal weight – in every major category. Further, the authors assiduously adjust for the many biases that infect many analyses of mutual fund performance. For example, the SPIVA® report accounts for the fact that 30%-40% (depending on category) of all mutual funds were delisted over the past 5 years. Fund companies don’t delist top funds, so an analysis conducted only on those funds that survived would naturally only cover the best performing funds. SPIVA® counts delisted funds in their analysis to offer an unbiased comparison.

We should note before we present the grisly details that this article is NOT a condemnation of mutual fund managers. Most managers are exceptionally well qualified with MBAs, CAs and CFAs. Further, they are passionate about investing, have an informational edge, and are possessed of very high levels of integrity. Also, the mutual fund structure tends to have some advantages over other investment vehicles like the now ubiquitous “Separately Managed Accounts” structure, such as economies of scale and low trading costs.

But perhaps most importantly, these managers spend all their time thinking about the investment process, while many other investment professionals are distracted by sales, marketing and customer service efforts. If anyone should be able to ‘beat the index’ it’s these guys and gals.Unfortunately, very few succeed, as the table below illustrates with painful clarity.

What gives? If these guys and gals are so smart, qualified, and passionate, why do so few beat the market? The answer is simple and twofold. First, the index that they are measured against has no costs and no fees. That one’s easy. The other reason is a little more esoteric: these guys are all competing against each other because excess returns over the benchmark are a zero sum game. And by virtue of the fact that there are so many smart, passionate, qualified guys competing for the same golden egg, they’ve effectively killed the golden goose.

Source: SPIVA® 2012

What exactly is the takeaway for YOU? First of all, the likelihood that your Advisor is going to ‘beat the market’ with his stock picks – or by choosing mutual fund or SMA managers to pick stocks – is extremely small. Over any given year, if you were to pick randomly, your chances range between 1 in 40 (for U.S. equity mandates) and 1 in 8 for Canadian Focused Equity mandates. [Canadian Focused Equity allows a manager latitude to range allocations between Canadian, U.S. and global stocks, but with a bias toward Canadian securities].

Another interesting observation is that dividend fund managers were about 50% less competitive than traditional Canadian Equity fund managers, in that only 5.6% of dividend focused managers beat the passive dividend benchmark. Translation: if you want exposure to dividend stocks (and by the way we think this is a bad bet with every Tom, Dick and Harry chasing dividend stocks into stratospheric valuations), fire your active dividend manager and buy a dividend ETF for less than 0.5% per year in management fees. No brainer.

Non Timetis Messor [Don't fear the reaper]

But again, the broader – and we feel much more important – point is that the whole concept of ‘beating the market’ is insanely misguided. ‘Beating the market’ won’t get anyone to retirement, or improve anyone’s standard of living in retirement, and ‘beating the market’ won’t help any institution sustainably fund its long-term obligations. These objectives rely on strong, consistent returns with no major ‘whammies’ along the way.

In our experience, most investors would be much better served by pursuing structurally diversified strategies of passive ETFs which are constructed to track the major global asset classes with the lowest possible fees. Remember, achieving index returns puts you ahead of 84% – 97% of stock pickers!

We mentioned above that smart, qualified stockpickers can’t succeed because their intense competition has killed their golden goose. Consider this: would you rather compete in an arena where many skilled players are competing for a small pie, or would it be better to compete in a space where there are few competitors competing for a really large pie? If you’d prefer the latter, you might wish to consider strategies that seek extra returns and lower risk by moving capital among a variety of liquid, low cost ETFs. This space has very few competitors, and many structural impediments to arbitrage, which presents substantial opportunities. For more information on what’s possible in the space please see our article or longer whitepaper on Adaptive Asset Allocation.