Tuesday, May 12, 2015

The Case Against High Yield

Following up on my post The Relationship Between Stocks and Bonds, which outlined why it is probable that stocks will outperform Treasury Bonds over the next 10 years, let's take a look at another expensive area of the bond universe... high yield U.S. corporate bonds.

High yield bonds: Where's the high yield?

As the right-hand chart below highlights, the 5.95% yield to worst of the Barclays U.S. High Yield Index (as of 4/30/15) is near its all-time low. Due to defaults, this yield should be thought of as the best case return that can be expected over a 5-10 year period, rather than the likely forward return.

Why? Over ten year periods since 1986 (the furthest back I could gather yield to worst data):

  • The forward ten-year return of the Barclays U.S. High Yield Index has under-performed its yield 93% of the time
  • The forward ten-year return of the Barclays U.S. High Yield Index has under-performed its yield by an average of 3.5% / year (with a 3.5% standard deviation) 

While there are certainly no guarantees, given the 5.95% yield, history "implies" a ten year forward annualized return of 2.5% with a one-standard deviation range of -1% to 6%.

High yield vs. stocks: The story gets worse

While U.S. stocks appear expensive by most measures, they have nothing on high yield. Using the same format as above, the below compares the excess yield of high yield relative to stocks vs. the excess return of high yield relative to stocks.

The first thing you may notice in the right-hand chart below is that high yield's excess yield vs. stocks is only slightly above 2%, well shy of the 7% average from 1987 through 2005 and not even in the same ball park as the 15% level reached during the early 1990's and financial crisis. Note... the below uses the CAPE yield (more details here); using the S&P 500's current earnings yield gets you to a sub 1% figure, slightly above the negative level that occurred in 2013.

Relative performance results look even worse than high yield in isolation:
  • High yield's forward ten-year relative performance vs. stocks has under-performed its excess yield 100% of the time
  • High yield's forward ten-year relative performance vs. stocks has under-performed its excess yield by an average of 8.3% / year, with a 5.2% standard deviation 

Given the current 2.2% excess yield of high yield bonds vs. stocks (as of 4/30/15), history "implies" a -6% ten-year annualized underperformance vs. stocks with a one-standard deviation range of annualized underperformance of -11% to -1%.

"History doesn't repeat itself, but it does rhyme" - Mark Twain

Looking back at the performance of high yield corporate bonds since the crisis, we see remarkably strong performance among the lowest credit quality segments of the market. It wasn't long ago that we found ourselves in a similar situation of lower quality outperformance; the chart below highlights the almost five year run from late 2002 through mid-2007 that looks awfully similar to the returns we've experienced since the end of 2008. In between these two time frames was a massive flight to quality / junk sell-off that took place during the financial crisis, which created the opportunity for the most recent period of junk outperformance (and NOBODY wanted to allocate to high yield in early 2009).

There are times when high yield bonds present an attractive opportunity in absolute or relative terms. Today does not appear to be one of them.

Thursday, May 7, 2015

A Guide to Creating Your Own Hedge Fund

As a follow-up to A Guide to Creating Your Own Smart Beta Fund, let's dive into the high flying paying world of hedge fund management..

 Per Investopedia:

Hedge funds are alternative investments using pooled funds that may use a number of different strategies in order to earn active return, or alpha, for their investors. Hedge funds may be aggressively managed or make use of derivatives and leverage in both domestic and international markets with the goal of generating high returns (either in an absolute sense or over a specified market benchmark). Because hedge funds may have low correlations with a traditional portfolio of stocks and bonds, allocating an exposure to hedge funds can be a good diversifier. 
Hedge funds shouldn't be thought of as an asset class, rather (in aggregate) they are simply go anywhere investment vehicles that attempt to provide absolute returns with low correlation to traditional asset classes. As an investment vehicle, they just happen to charge an absurdly high fee (a 2% management fee + 20% of excess returns above some threshold is generally accepted as a reasonable starting point).

As an aside, there is a case for managers that can produce consistent, excess returns (i.e. high Sharpe ratios) that are uncorrelated to traditional asset classes to be paid at least 2% / 20%, but there are far and few managers that come close to producing these type of returns legally. For those managers that can, access is limited to the brightest and/or best connected capital allocators. In other words, if you are a retail investor or financial advisor with assets less than $10 billion without a Rolodex that needs its own warehouse... don't bother. 

What is an investor to do? I'll show you.

In this case I'll create a "solution" using funds from the world of liquid alternatives. Actually... I'll go one step further and use the two worst performing funds from Morningstar's multialternative category for the ten years ending 3/31/15.

Turning Lemons into Lemonade

I won't pick on the Dunham Distribution Fund or Hatteras Alpha Hedged Strategies Fund too much... I'll simply show the objectives and present the ten-year equity curve for each fund before outlining how we'll use these funds to create an improved risk-adjusted, uncorrelated, return stream.

Fund Objectives:
  • Dunham Monthly Distribution: Seeks to provide positive returns in rising and falling market environments.
  • Hatteras Alpha Hedged Strategies: Seeks to achieve consistent returns with low correlation to traditional financial market indices while maintaining a high correlation to the Hedge Fund Research, Inc.(“HFRI”) Fund of Funds Composite Index.

Performance Results (3/31/2005-3/31/2015)

Okay, I need to pick on them a bit more as expenses for both funds were higher than their ten year annualized returns by a significant margin, meaning the investment manager received more money than any buy and hold investor (and this ignores the 5.75% front-end load on the Dunham fund). In addition to producing a negative Sharpe over this time frame with returns that had a very high correlation to equities (go here for details outlining the importance of a high Sharpe ratio and/or low correlation within a broader asset allocation), they have experienced material drawdowns. Despite these returns, the Dunham Fund has grown their asset base to almost $300 million (from less than $100 million in 2010), while the Hatteras fund is slightly above $500 million in assets. If you're one of those investors... would love to know why.

Momentum... Momentum... Momentum... Momentum...

As outlined in my post The Case for Hussman Strategic Growth, a dual momentum approach as introduced in Gary Antonacci's white paper Risk Premia Harvesting Through Dual Momentum (follow Gary at Optimal Momentum), provides the possibility of capturing the absolute performance of an asset (or fund), while avoiding extended periods of underperformance. In this instance, I allocated to either the Dunham Distribution Fund, Hatteras Alpha Hedged Strategies Fund, or short-term bond (ETF IEI) based on whichever was the most above it's 9-month moving average. The resulting time series is then compared with the Barclay Hedge Fund Index, which shows 'the average return of all hedge funds (excepting Funds of Funds) in the Barclay database'.

The result... a dual momentum approach to the two worst performing funds in the multialternative universe has provided all of the absolute return of the average hedge fund, with lower standard deviation, and materially lower drawdown / correlation / beta, all with better liquidity.

What's next? Applying the same framework on a segment of the market that is likely to lead to similar levels of correlation, but with materially higher levels of performance.

Monday, May 4, 2015

No... Investors Haven't Underperformed Every Asset Class

The following chart has been floating around for more than a year, supposedly showing investors have not only performed poorly, but even worse than almost any asset class. As Richard Bernstein stated:
The average investor underperformed nearly every asset class. They could have improved performance by simply buying and holding any asset class other than Asian emerging market or Japanese equities.

Let's start with my conclusion. This chart is highly misleading in that it compares apples (geometric time-weighted returns of assets classes) to an orange (dollar weighted returns of the average investor) since 1993, resulting in asset class performance that is materially higher than any investor buying into the market would have received.

This post walks through the error and shows how to get to returns that are a more appropriate comparison. The conclusion is that the discrepancy between the geometric time-weighted asset class returns and investor dollar-weighted returns has not been driven by poor investor behavior, but rather by the path of asset class returns. A huge and important distinction.

Starting Point: What are Geometric Returns?

The blue bars shown in the Richard Bernstein chart are geometric weighted returns (i.e. time weighted returns), which unlike average returns take into account the effects of compounding (a good thing), but do not account for dollar flows.

A quick example....

Let's assume an investor returns 100% in year 1 and is down 50% in year 2. While the average return is 25% / year = (100% - 50%) / 2 = 25%, geometric returns were 0% (as follows):
  • Year 1: the $1 turns into $2 (100% return)
  • Year 2: the $2 turns into $1 (50% loss)
  • The $1 invested at time 0 = $1 at time 2 = 0% geometric return
As Investopedia points out
Investment returns are not independent of each other, so they require a geometric average to represent their mean.
As an aside... beware any manager only showing average returns. At a minimum they are reporting returns too high (average returns are always > geometric returns).

THE ISSUE: Geometric Returns vs. Dollar Weighted Return: Comparing Apples to an Orange

Now to the major issue with Richard Bernstein's chart... it contains two different types of return calculations; (1) time weighted geometric average returns for the various asset classes and (2) dollar weighted for investor returns (these two should NEVER be compared).

dailyVest explains dollar-weighted returns (bold and bullet format mine):
In contrast with a time-weighted approach, the dollar-weighted rate of return calculation method does measure the size and timing of cash flows, as well as the investment performance. Thus,
  1. Periods in which more monies are invested contribute more heavily to the overall return – hence the term “dollar-weighted” 
  2. In this case, investors are rewarded more for larger investments made during periods of greater price appreciation 
To reiterate... dollar weighted returns put more emphasis on recent performance than time weighted returns when market flows are positive. For example, going back to the investor with a 100% return in year 1 and a -50% return in year 2, but this time showing dollar-weighted returns for an investor that dollar cost averages $1 each year:
  • Year 1: the $1 turns into $2 (100% return); the investor then adds another $1 to their $2 investment, so they have $3 total investments heading into year 2
  • Year 2: the $3 turns into $1.50 (50% loss)
  • The $2 total investment is now worth only $1.50 
A -13.3% annualized dollar-weighted loss, despite the same 0% geometric return.

The Path of Returns Over the 20-year Period Mattered

Returns were MUCH higher at the beginning of the 20-year period ending 12/31/2013 than the end due to (1) the 1990's equity bubble and (2) two material drawdowns that took place between 2000 and 2009. The chart below shows annualized geometric returns of the S&P 500 at various points in time using 12/31/1993 as the starting point for all those periods. For example, as of 1998, the annualized geometric returns of the S&P 500 from 12/31/1993 through 1998 was almost 25% annualized. By comparison, by the end of 2013 the S&P 500 returned ~9% annualized from 12/31/1993 - 12/31/2013 (as shown in Richard Bernstein's chart and below).

Dollar-Weighted Returns Have Been Similar to Investor Returns

Since dollar weighted returns put more emphasis on recent performance, we would EXPECT dollar-weighted returns to be lower. For the 20-year period ending 12/31/2013, the S&P 500's geometric returns were 9% annualized, while dollar weighted returns (assuming the same amount investment each month from 1993-2013) were a full 4% lower. Again, this 4% discrepancy assumes completely unemotional dollar cost averaging and is another 1% lower if you assume the contribution rate grew by the rate of inflation over that time frame. Performance dispersion for asset classes where recent performance has been relatively worse (think developed international and emerging markets), was further amplified by the time-weighted / dollar-weighted comparison.

This does work both ways; the chart above was recreated below using data from June 2007 through April 2015, a period when equity returns were much HIGHER in the more recent portion of that time frame.

As a result, dollar-weighted equity returns have actually been higher than geometric returns and my guess is that investor performance would appear "much improved" vs the original Richard Bernstein piece. This, despite what has likely been a period highlighted by poor investment behavior through and since the crisis.

Thursday, April 30, 2015

Implications from the Rise of Passive Investing

ThinkAdvisor shares Jeff Gundlach's (DoubleLine) thoughts from the Milken Institute Global Conference, outlining the potential impact of the shift towards passive investing within Robo Advisors.

The financial planner industry has very wide range of delivery. A lot of … asset gathers do nothing; others are very creative. With robo-advisors, the costs are low. So the draw is the lack of overall success by human advisors at three or four times the cost of an algorithm, or spreadsheet advisor. 
The impact being...
One-size-fits-all solutions channel people into the same investments, which then introduces systemic risk.
In a conversation I had with Ben Carlson (of the must read A Wealth of Common Sense), I shared that while it is odd he focused on robo-advisors given they are such a small percent of the market, his point is relevant in terms of the huge shift we've seen over the last 20+ years towards indexing. While I won't get into the details of my own view on passive vs. active investing (high level... in my view there are parallels to the prisoner's dilemma in that it is a rational choice for most individual taxable investors, but poor for investment returns / the economy as a whole), I do think it increases systematic risk due to the concentration of assets into so few products.

How much has passive taken over?

The shift to passive has been monumental; 20 years ago passive investments made up a bit more than 20% of all assets within small, mid, and large core / blend Morningstar categories, the majority of which was allocated by institutional investors with long-term investment horizons. Now, we're looking at a passive market share of 50-70% within core U.S. equities, with the bulk of net flows coming from retail investors with much shorter investment horizons.

As Ben pointed out:
performance chase by weak hands will probably always cause more volatility in any product type.
I agree and in the case of indexing, the product is the entire market. My hypothesis that systematic risk has increased due to the concentration of assets is supported by the strong historical relationship between market performance, market volatility, and passive outperformance. Money has piled into indexing (a product that is composed of the entire market) when markets have performed well, periods when active managers have underperformed and volatility has been low. Passive market shares gains have slowed when markets have underperformed, periods when active managers have had much stronger relative performance and market volatility has been amplified.

Relative performance of active vs passive (in this case the percent of managers outperforming on the y-axis and market performance on the x-axis)

This raises the question of whether the relative performance of the average active manager is due to stock selection or is instead a function of flows into the market that have been allocated irrespective of underlying fundamentals. If the latter, when flows leave the market (as they always do at some point), it won't be individual stocks that underperform, it will likely be the entire market.

Tuesday, April 28, 2015

The Uncertain Future of Robo-Advice

I had a Twitter back and forth with the founder of Wealthfront regarding the tax efficiency of their platform vs. using the same asset allocation model within an ETF (see here). I thought it might be of interest to provide additional details, as well as some thoughts on robo-advice more broadly.

Investopedia defines a robo-advisor as:

A robo-advisor is an online wealth management service that provides automated, algorithm-based portfolio management advice without the use human financial planners. Robo-advisors (or robo-advisers) use the same software as traditional advisors, but usually only offer portfolio management and do not get involved in more personal aspects of wealth management, such as taxes and retirement or estate planning.  
Let me be clear... I am absolutely THRILLED that robo-advisors exist. If nothing else, they pressure fees traditional advisors and ETFs charge, while an allocation made to a robo-advisor (by a hands-off investor) is likely to result in materially better performance than could be expected from many brokers that charge exorbitant fees. In addition, I've seen first hand from a "techie" brother in-law that a small, (but real and growing) community in the Silicon Valley has been drawn to the automation robo-advisors provide, which hopefully leads to a better financially educated community for all involved. BUT... I really don't understand where the 'actual' service they provide sits on the advice / allocation spectrum that consists of a traditional advisors that act as fiduciary on one side (higher fee, higher interaction, higher education benefits to their clients) and the multi-asset / target date ETFs on the other (lower fee, lower interaction, lower education).

Where's the Advice?

While I think robo-advisors can create a decent portfolio based on an initial survey and may be able to provide decent adjustments over time (none of this is all that difficult... most robo-advisors and ETFs get to the same place), over the years I've realized that 90%+ of the value a traditional advisor provides is the “advice”. Advice does not only consist of the beginning "how much risk are you comfortable with" phase or the year-to-year "has anything changed" phase, but a real thorough understanding of changes that have taken place in their client's life. As important is the "advisor as therapist", talking their clients down from the ledge when investment behaviors ebb and flow with the market.

I question whether robo-advisors can manage investor emotion. Investors generally ignore advice when it goes against their view and/or when they have real fear (of missing out or of losing money). There are times when an investor benefits immensely from a simple "slap across the face" from their advisor (i.e. when markets are off 50% and they want to sell). What will happen to allocations on robo-platforms when markets (eventually) turn lower? Wealthfront has already outlined that their investor base has changed their risk tolerance as markets have moved and this took place in what has generally been an up-and-to-the-right market.

Tax Efficiency: Self-Contained ETFs Rule the Land

Ignoring investor behavior for a moment, robo-advisors platforms are simply inferior to an existing alternative option available to all retail investors in an area they claim superiority... tax efficiency. Without getting into how ETF sausages are made, target-date and multi-asset ETFs can avoid capital gains most of the time (high level details here) even when the ETF rebalances within the wrapper from an outperforming holding to an underperforming holding. This is HUGE for tax efficiency, as an investor may not have to pay capital gains taxes from the day they buy into the ETF, until the day they eventually sell, allowing returns to compound to a greater extent over time.

On the other hand, a robo-advisor can do everything right to optimize tax efficiency (tax loss harvesting / use dividend payments and contributions to limit the capital gains associated with rebalancing), but may still force an investor to realize capital gains as there is a limit to how much they can offset as the asset base grows relative to the contributions made by the investor.

For example... the charts below outline the level of rebalancing (as a percent) that can be supported by flows and dividend / interest payments for a portfolio over time assuming a $10,000 annual contribution, 8% annual returns, and a 3% dividend + interest income (slightly higher than the current "yield" of a 60/40 portfolio). The left hand chart shows the level as a percent over time, while the right hand chart shows the level as a percent in dollar terms using the same assumptions.

Using this simply for illustrative purposes, the key is that over time, the level of rebalancing that can be supported by contributions and distributions narrows materially towards the 3% distribution rate as contributions become smaller relative to the asset base. This also ignores the fact the contributions eventually turn to withdrawals, thus the level of rebalancing supported by flows will turn negative.

Compare the above 3-4% figure to the rebalancing required to maintain a simple 60/40 portfolio over time. The chart below outlines the shift from stocks to bonds (or bonds to stocks) required over rolling 12-month periods to maintain the 60/40 weights. Note the levels are materially higher when rebalancing is needed most (i.e. after sharp moves in stocks) and completely ignores the rebalancing associated with an asset allocation “glidepath” towards more bonds as an investor approaches retirement; rebalancing that is typically made when an investors has an asset base materially higher. This level only goes higher if an investor delays the rebalancing process (the benefit of doing so outlined here).

In my opinion, robo-advice is more of a feature than a solution. Robo-advisors lack the advice / hand-holding of a traditional advisor and the tax efficiency of ETFs.  If you desire the latter, why not just allocate to a multi-asset ETF and have some sort of "lock box" feature that requires a 30 minute conversation with a financial advisor, a full week to think, then another 30 minute conversation if you ever want to trade / sell the ETF? To me, that may result in all the behavior correction investors need at a reduced cost.

Friday, April 24, 2015

The Case for Hussman Strategic Growth

Well... maybe not the "case for". Rather, a "kinda / sorta case". If you're contemplating making a long-term allocation to the fund (you're not)... don't.

While John Hussman is without question a remarkable writer (and in my view a remarkable analyst - just too smart for his own good when it comes to allocating capital), the Hussman Strategic Growth Fund has produced some pretty horrific performance.


  • Performance percentile ranks of 97th, 100th, 100th, and 100th over 1, 3, 5, and 10 years
  • Negative absolute performance over 3 months, 1 year, 3 years, 5 years, and 10 years
  • A current drawdown of 36% since the 9/15/08 Lehman Brothers collapse 

Worse... the first page of the prospectus outlines:
The Fund seeks to achieve long-term capital appreciation, with added emphasis on the protection of capital during unfavorable market conditions.

While I won't remotely defend that performance, especially given its objective, I will point to its history of outperforming during the two market downturns since the fund's inception. This provides a potential opportunity for an investor willing to look past recent performance.

Potential ways to utilize the fund...

Absolute Momentum
Applying a very simple momentum model as introduced in Meb Faber's A Quantitative Approach to Tactical Asset Allocation (follow Meb on his blog mebfaber.com) would have provided an investor with the majority of excess performance, without the subsequent 6 1/2 year downturn. In this case we are simply allocating to the Hussman Strategic Growth Fund when the fund is > 9-month moving average, otherwise allocating to cash.

Dual Momentum
Rather than sit in cash if there are better opportunities, another way to utilize the Hussman Strategic Growth Fund is through a dual momentum approach as introduced in Gary Antonacci's white paper Risk Premia Harvesting Through Dual Momentum (follow Gary at optimalmomentum.com). In this instance, I tweaked his formula slightly and allocated to Hussman Strategic Growth, the S&P 500, or cash based on whichever is the most above it's 9-month moving average.

The results...

So, if/when markets eventually roll over and you think Hussman is capable of outperforming in a bear market environment (he certainly has spent much of the last 15 years contemplating how to structure a bearish portfolio), the strategy might actually make for an interesting allocation.

Tuesday, April 21, 2015

Looking Back at Risk Parity's Golden Age

My initial goal of this post was to share why risk parity was less likely to be a free lunch going forward using historical data back to the 1950's (the last time we saw rates at current levels), but it became more of a risk parity 101 piece. I'll save much of those comments for another day.

What is Risk Parity?

Risk parity was a relatively unknown strategy until Bridgewater's All Weather Fund powered its way through the financial crisis ("risk parity" doesn't even show up on Google Trends until 2009). Since that time, the success has spawned numerous imitators. But first... what is Risk Parity?

Per Investopedia:

A portfolio allocation strategy based on targeting risk levels across the various components of an investment portfolio. The risk parity approach to asset allocation allows investors to target specific levels of risk and to divide that risk equally across the entire investment portfolio in order to achieve optimal portfolio diversification for each individual investor. 
The objective of allocating equally to 2+ asset classes by risk is to receive the benefit of diversification and materially improve the Sharpe ratio of a blended allocation.

Backdrop: The Importance of Sharpe Ratios and Correlation

There are two main factors that drive the relative performance of a risk parity allocation vs stocks; (1) the relative Sharpe Ratio of the new asset class introduced (the higher, the better) and (2) the correlation between the new asset class introduced and stocks (the lower, the better).

As a reminder, per Investopedia:
The Sharpe ratio is the average return earned in excess of the risk-free rate per unit of volatility or total risk. 
For example…. if cash returns are 2.5%, then stocks returning 10% with a 15% standard deviation has the same 0.50 Sharpe ratio as bonds returning 5% with a 5% standard deviation: 
  • Stocks: (10% - 2.5%) / 15% = 0.50 Sharpe ratio 
  • Bonds: (5% - 2.5%) / 5% = 0.50 Sharpe ratio 
An identical Sharpe ratio means an investor would be indifferent as to whether they held either stocks or bonds (in isolation), irrespective of their risk appetite. If an investor prefers a 7.5% risk target, a 50% stock / 50% cash allocation cuts the initial 15% risk in half, while returning 6.125% (50% x 10% + 50% x 2.5% = 6.125%). Similarly, a bond allocation gets to the same 6.125% return at a 7.5% risk by leveraging up bonds by 50% (150% x 5% - 50% x 2.5% = 6.125%).

If viewed in isolation (if you can only allocate to one asset class), an investor should allocate to the asset class with the highest expected Sharpe ratio, then lever up / down that asset class to match their desired risk target (an either / or question of stocks vs. bonds). Looking at the rolling three-year Sharpe ratio of stocks and bond since 1976, we see that from 1976 through the mid-1990's stocks had consistent positive performance relative to cash (the Sharpe was consistently above zero) and bonds moved largely in sync with stocks. Since the mid 1990's, stocks have had two material periods of negative excess performance, while bonds have provided consistent positive excess return.

Few investors are restricted to holding only stocks and bonds, but many are restricted from applying leverage. For those with the flexibility to allocate to both stock and bonds, as well as apply leverage, correlation between asset classes plays an even greater factor in determining whether the additional asset class should be added, broadening the decision from an initial 'stocks or bonds' question to 'stocks and/or/no bonds' question.

As a reminder per Investopedia:
Correlation is computed into what is known as the correlation coefficient, which ranges between -1 and +1. Perfect positive correlation (a correlation co-efficient of +1) implies that as one security moves, either up or down, the other security will move in lockstep, in the same direction. Alternatively, perfect negative correlation means that if one security moves in either direction the security that is perfectly negatively correlated will move in the opposite direction.
The huge shift in the relationship between stocks and bonds (hugely positive correlation to hugely negative) starting in the late 1990's can be seen in the chart above (the Sharpe ratio of bonds spiked when stocks crashed) and below (a shift that has been hugely beneficial for investors allocating to levered bonds to balance their stock allocation).

Taken together, the mid 1990's started what has been the Golden Age for risk parity. Instead of requiring a high Sharpe ratio in order for bonds to be added a stock allocation, the negative correlation has meant bonds have largely benefited a risk parity allocation even in periods when bonds have materially underperformed stocks.

As a reminder for those that took the CFA and have blacked out all memories, the formula for determining whether a new allocation improves a Sharpe ratio is as follows:

New asset class Sharpe ratio > Current Sharpe ratio x correlation with new asset class

This means that an allocation to bonds makes sense if the Sharpe ratio of bonds > the Sharpe ratio of Stocks x correlation. When correlations are sharply negative, there are much fewer instances when an allocation to bonds won't improve the Sharpe ratio relative to a stock only allocation (something to keep in mind for commodities even if your expected excess return to cash is 0%).

If the figure in the below chart is > 0, an allocation to bonds (at some level) improves the Sharpe ratio. Note that any notional size allocation to bonds may not improve the Sharpe (i.e. a 10% allocation may improve the Sharpe, a 50% allocation may not - see comment section for more of this discussion).

Given the added benefit of materially positive excess performance of bonds relative to cash since the mid-1990's, risk parity has been a home run (in the example below, risk parity is defined as 25% stocks and 75% bonds). These strong results since 1996 coincided with the year Bridgewater launched their All Weather iteration (hats off to Ray Dalio and his timing). As a comparison, predating the Golden Age was a ten year period from 1974-1984 when risk parity underperformed a simple stock allocation, despite an annualized bond return north of 8% (they key being cash yielded even more). Note the slope of the lines below is equal to the Sharpe ratio over each period.

And the rolling results (just look at the widening gap since 1996). Note that the Sharpe ratio for the risk parity portfolio would not change if you lever it (i.e. a 25% stock / 75% treasury allocation has the same Sharpe ratio as a 50% / 150% portfolio).

What now?

On one hand, it isn't all that difficult to outperform interest rates when they're zero. On the other hand, when interest rates do (eventually) move higher, excess returns of bonds will be impacted (treasury rates are currently VERY low per unit of duration). When interest rates do eventually back up, the relative attractiveness of stocks vs. bonds becomes smaller (that case is outlined here), resulting in a higher correlation between stocks and bonds going forward.

This would equate to a one-two punch against the 20-year Golden Age run that risk-parity has enjoyed.

Source: S&P, Barclays

Thursday, April 16, 2015

P/E Multiples vs (Past and Future) Returns and Volatility

As I outlined in my previous post The Relationship Between Stocks and Bonds, the S&P 500 yields 3.7% at the current 27 CAPE (cyclically adjusted P/E), attractive from a relative basis to the sub 2% yield of the ten-year treasury. That said, a 3.7% yield is quite low by historical standards. Below is a framework for thinking about why returns should be expected to be lower AND more volatile than their long-term average given these low yields.  

Thinking about stocks in terms of CAPE duration

While bonds, without embedded options, have a pretty well-known duration, there is much less certainty regarding the duration of stocks. That said, a framework for thinking about stocks in terms of their sensitivity to changes in their yield is informative. Stock valuations are highly sensitive to their required yield, with materially higher "stock duration" in the form of P/E multiple expansion at a lower earnings yield than at a high earnings yield. 
  • Low yield = higher "stock duration": For example... at a CAPE of 40, the "required yield" is 2.5% (1 / 2.5% = 40). This means (ignoring convexity), valuations change 40% for each 1% change in the required yield.
  • Higher yield = lower "stock duration": On the other hand at a CAPE of 10, the "required yield" is 10% (1 / 10% = 10). This means (ignoring convexity), valuations change just 10% for each 1% change in the required yield.
Like bonds, the higher the duration (in the form of CAPE), means greater price sensitivity to a move in yield, which should be expected to result in higher forward volatility as well.

Historical returns drive the required yield and CAPE duration

The chart below looks at historical 5-year annualized performance of the stock market going back 50 years, bucketed into 10 distinct groups of ending CAPE values (<4% means the ending CAPE yield was less than 4%, which aligns itself to any ending periods with a CAPE above 25). 

As the chart highlights, low stock yields (and high CAPE) have historically been the result of strong stock performance, as investors are lulled into forecasting low volatility and high returns given their recent experience, despite the poor valuations a low yield means.

The historical result of a low CAPE yield / high CAPE duration

Given the framework outlined at the beginning of this post, one would expect:
  • Low yields = low returns
  • High CAPE duration = high volatility

So... it should come as no surprise that forward returns based on a starting CAPE yield were in fact lower and risk was higher. In fact, when CAPE yields have been less than 4%, the forward average return has been only slightly above 0% or the next five years with materially higher volatility. At the current 3.7% earnings yield, investors in the S&P 500 should not only anticipate lower than normal returns, but higher volatility. Another reason to be diversified to higher "yielding" stocks abroad.

Source: Shiller, S&P

Monday, April 13, 2015

The Relationship Between Stocks and Bonds

A Wealth of Common Sense has a recent post 'Stock Market Losses with Low Interest Rates' that outlines:

  • Just because interest rates are low doesn’t mean stocks can’t or won’t fall. Interest rates are a very important factor in the markets but they’re not everything. 
  • Stocks are risky. To make money you have to be willing to accept occasional losses. Get used to it if you’re invested in risky assets. 
  • Even in a volatile market environment laced with bear markets, stocks can still make money for patient investors.
His points are extremely important and investors should be prepared for this volatility, but there is a relationship between stocks and bonds that I've found helpful when making allocation decisions.

Backdrop: The relationship between stock and bond yields hasn't always been strong

Before the 1960's, there was hardly any relationship between stock and bond yields. The late great Peter Bernstein wrote about a period in the 1950's when dividend yields moved lower than treasury rates for the first time, a phenomenon most investors thought was temporary:
When this inversion occurred, my two older partners assured me it was an anomaly. The markets would soon be set to rights, with dividends once again yielding more than bonds. That was the relationship ordained by Heaven, after all, because stocks were riskier than bonds and should have the higher yield. Well, as I always tell this story, I am still waiting for the anomaly to be corrected.
That differential has finally "corrected" on and off since the crisis, but an investor waiting for it would have missed the 10% annualized stock returns since it first flipped in 1957.

Starting at roughly that same time, the relationship between stock and bond yields grew tight, but instead of a connection through dividends (and the corresponding yield), the connection was through earnings. From the early 1960's through the late 1990's, the Fed Model was viewed as the "new normal". Per Wikipedia:
The model is often used as a simple tool to measure attractiveness of equity, and to help allocating funds between equity and bonds. When for example the equity earnings yield is above the government bond yield, investors should shift funds from bonds into equity. 
The 0.83 correlation from 1965 - 1999 between the CAPE yield (earnings yield based on the cyclical adjusted price to earnings ratio) and 10-year treasury rate provided support for this theory. Since 2000, the correlation between the CAPE yield and 10-year treasury rate has become unhinged at -0.64, highlighting a world where stocks have broadly underperformed in periods of declining rates i.e. flight to quality.

The connection between stocks and bonds... valuations have always mattered

Yet... despite the three stock/bond yield regimes outlined above and in the chart below (low correlation, high correlation, negative correlation), valuations have always driven performance. When the relationship was non-existent, with CAPE yields high relative to bond rates, stocks materially outperformed. When the late 1990's stock bubble pushed CAPE to record highs (and CAPE yields to record lows relative to bond yields), stocks underperformed.

Where do we currently stand? 

Ignoring my view on whether the CAPE is artificially high at 27, we have a CAPE yield of 3.7% relative to a sub 2% treasury rate. Based on history, stocks "should" outperform bonds over the next 10 years (over the past 50 years, they've never underperformed bonds over 10 years when yielding more) and have provided a return in the 2-5% range above the yield on bonds (call it 4-7% nominal at today's levels).

So while stocks absolutely can (and will) sell-off despite low rates at some point, low rates do appear supportive of an allocation to stocks "relative" to bonds here in the U.S. and especially abroad.

Source: Shiller

Thursday, April 9, 2015

A Guide to Creating Your Own Smart Beta Fund

FT tries to define smart beta:

Smart beta is a rather elusive term in modern finance. It lacks a strict definition and is also sometimes known as advanced beta, alternative beta or strategy indices. It can be understood as an umbrella term for rules based investment strategies that do not use the conventional market capitalisation weights that have been criticised for delivering sub-optimal returns by overweighting overvalued stocks and, conversely, underweighting undervalued ones.
A pretty good definition, which is just broad enough to allow for anything and say it's a cure for almost anything. As a result, there has been a proliferation of smart beta ETFs and mutual funds that have, in many instances, gathered an absurd amount of assets from investors, often times with only backtested performance.

While no concept will work over every time frame, the more concerning aspect to me is that interesting concepts that backtested well in one category are being applied to other areas of the market irrespective of supporting analysis and/or whether there is proper liquidity. One highly regarded "fundamental" index provider now has a fundamental index not only within all major equity categories, but also across multiple areas of fixed income, including US high yield corporate bonds. While the 1.07% annualized underperformance of the "smart" index vs the Barclays US High Yield Index over the last 3 years is in itself an issue, the real concern is the incremental 1.10% of annualized underperformance the ETF has to its custom index due to fees + trading in an illiquid part of the credit market. This almost 2.2% annualized underperformance hasn't stopped this almost $700 million fund from taking in another $86mm in assets the first three months of the year!

So... lots of flows, based in large part on back-tested results, with actual results of only limited value. Let’s create our own…

Step 1: Find a Marketable Idea

Leveraging the work from my previous post Is there a Relationship Between the Economy and Stock Market?, let's create a systematic asset allocation index* and call it GDP Growth "Smart Beta". To sell a product, you need a concept that is unique, yet resonates with the masses:

I don't know of any ETFs that are systematically tied to short-term GDP growth as the main factor driving the stock / bond allocation decision

Despite the fact that economists (even good economists) have no idea what's going on, everyone thinks they are an economist (including me... case in point - I started a blog called EconomPic Data)

Step 2: Create a Pitch
Using an exclusive signal, the strategy shifts capital to assets capturing the equity risk premium when deemed attractive and de-risks when valuations are deemed poor, while a proprietary macro overlay identifies periods of relative economic weakness. The resulting portfolio provides equity-like returns during strong market environments, with bond-like characteristics during periods of heightened market uncertainty.

Step 3: Show Backtested Results

Show performance in the best format possible. If total returns are similar, but a time series highlights long periods of underperformance vs the traditional asset class... then only show the time series of the "smart beta" index (and show a total returns comparison vs. the traditional beta separately in a table).

Step 4: Add Leverage

Show how much better returns would have been at similar levels of risk as the stock market, while ignoring the fact that there is no way to know in advance how much leverage you would need to have matched that standard deviation.

Step 5: Profit

Actually, I missed a step. I need a good ETF ticker. Perhaps Goodrich Petroleum would be willing to sell theirs?


Despite what I outline above, I think there is a huge opportunity for systematic / rules based ETFs. That said, I have a hard time understanding why many of these products, sponsors, and providers get a free pass (or in many cases are put on a pedestal) from the same guys that are railing against active management. Also... just because something has worked well in the past and there is academic support that explains (or tries to explain) why it has worked, it doesn't mean it will work in all markets or across all asset classes.

*Each quarter, if the latest one year nominal GDP > the yield to worst on the Barclays US Aggregate Bond Index... allocate the next quarter to stocks (S&P 500); otherwise to bonds (Barclays Aggregate Index). 

Monday, April 6, 2015

Is there a Relationship Between the Economy and Stock Market?

Long story short... yes, there appears to be a relationship between economic growth and stock market performance within the U.S. (and developed world), but that relationship holds only over longer periods of time and does not hold for all countries (less developed countries often "divert" growth to the political elite).

Also, for those interested in the relationship between long-term economic growth and returns across countries, Dimson, Marsh, and Staunton provide a ton of interesting analysis going back 100+ years within the Credit Suisse Investment Returns Yearbook (Figure 13 on page 28). The high level takeaway: "Though difficult for investors to capture in portfolio returns, strong GDP growth is generally good for investors."

Below are two pieces of analysis that outline what I feel drive the relationship between underlying economic growth and the U.S. stock market;

  1. The economy's impact on valuation (economic growth provides a base for corporate earnings, which drives valuations, which drives returns)
  2. Economic contractions that severely impact stock market performance

U.S. Economy vs. U.S. Stock Market - The Valuation Story

The best part about taking an almost 3 year hiatus from blogging is that I can recycle about 90% of my previous ideas and they will seem new. Here is one I initially ran in May 2010, then revised to the format below in February 2012. The background of this is that over the long run, equity valuations and earnings have both grown at roughly the same pace as nominal GDP. This makes intuitive sense... while earnings can absolutely grow slower than the economy (especially in emerging markets with less developed investor protection), if they consistently grew faster than the economy, then earnings would eventually become larger than the entire economy (not possible).

With that in mind, the below chart shows:
  • Blue: the S&P index
  • Red: the ending 2014 value is set to the 2014 year-end value of the S&P 500 index, then brought back in time by the nominal GDP growth rate (GDP data is available at the Bureau of Economic Analysis) - 1929 is the first year the BEA produces annual GDP growth rate

This is an attempt to compare the S&P's historical valuation relative to the size of the US economy, relative to the current level of that relationship. When the S&P 500 (blue) is below the nominal GDP line (red), then the S&P 500 was cheaper on this relative measure than it is now (when the lines cross valuation levels were equal to those in place today).

The chart below shows the relative valuation for each year from 1929 through 2004 (relative to its current value of 0; again if less than 0, the S&P was cheaper by this measure), along with the subsequent 10 year forward change in the S&P 500 total return (annualized). This chart outlines that historically there has been a significant relationship between the underlying economy and stock market. When the S&P composite has grown at a slower rate than the U.S. economy, making it cheap, this has led to historical outperformance.

Of note... there have been many more periods when the stock market was less expensive than the current level, yet the trend-line goes through 0% (the current valuation) on the x-axis at roughly 7.5% annualized (noisy data, but it makes current valuations less stretched than some would think).

U.S. Economy vs. Stock Market - Avoid Economic Contractions if Possible

While the above analysis outlines that the relationship between the economy and stock market generally holds over longer time frames, there has been a relationship worth mentioning over shorter time frames. Going back to the first quarter of 1947 (as far back as the BEA releases quarterly real GDP) and separating quarterly S&P 500 performance when real GDP was either positive or negative, we see a pretty material difference in performance. Not only were returns much better when GDP was expanding vs. contracting (real returns have actually been negative during economic contractions), but returns were much more volatile when the economy was contracting.

While there is no real way for an investor to know in advance a contraction is coming (though a lot of investors think they can), the key is that if the underlying economy has strength, history points to stock markets as being supported.

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