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Wednesday, January 27, 2016

The Case For High Volatility Strategies

Which investment would you prefer to invest in to diversify your existing stock allocation? 

Asset A with an expected:
  • 3% annualized return
  • 3.5% annualized standard deviation
  • 0.00 correlation with your existing investment
Asset B with an expected:
  • -5% annualized return
  • > 50% annualized standard deviation
  • 0.00 correlation with your existing investment

Easy question right? Perhaps not.

Asset B may actually improve long-term returns and reduce risk at the portfolio level, whereas an investment in Asset A may just be a drag on performance. This post will walk through an example, outline the math behind the results, and hypothesize how an investor may want to think about this phenomenon. Going one step further, I will outline how this may, in part, explain the low volatility anomaly (one example being that low-volatility stocks have produced higher risk-adjusted returns than high-beta stocks in most markets studied).


Example 

The chart below outlines the performance of an investment in the S&P 500 (using actual monthly S&P 500 returns) going back to the SPY ETF inception (I was being lazy), as well as a monthly rebalanced allocation consisting of a 90% weight to the S&P 500 and a 10% weight to:: 
  • Asset A: which reverts monthly from negative to positive performance (-0.75%, +1.26%, -0.75%, +1.26%), compounding to a 3% annualized return at a 3.5% standard deviation; in the chart below this assumes price action of $100 to $99 to $100 to $99... but with an incremental 3% return built in.
  • Asset B: which reverts monthly from negative to positive performance (-15.4%, +17.2%, -15.4%, +17.2%), compounding to a -5% annualized return at a 56.5% standard deviation; in the chart below this assumes price action of $100 to $85 to $100 to $85... but with an incremental -5% negative return built in.

Despite the 8% annualized outperformance of asset class A vs B that compounded to a 100% gain in asset A and a 70% decline in asset B, the 90% stock / 10% allocation to asset B results in a combined portfolio with higher returns, a higher sharpe ratio, and a lower drawdown relative to an allocation to the positive returning asset A (the benefit of the allocation to asset A was the reduced standard deviation as that allocation reduced risk asset exposure by 10%).


We see below how much asset B benefits the portfolio when both both asset A and B are on equal return footing.


What gives?

William Bernstein wrote a great piece on the topic back in 1997 titled The Rebalancing Bonus. The whole article is worth a read, but he shares the following formula that calculates the "bonus" received by rebalancing across asset classes.

RB1,2 = X1X2 {SD1SD2 (1 - CC) + (SD1 – SD2)/ 2}

RB = rebalancing bonus
X1 = allocation weight to asset 1
X2 = allocation weight to asset 2
SD1 = standard deviation to asset 1
SD2 = standard deviation to asset 2
CC = correlation coefficient

Before you complain too much about the math, I'll walk through the equation from left to right (assuming "all else equal") with the applicable points to the example above in bold:
  1. A more balanced blend (i.e. a closer to 50/50 weighting) will provide a greater rebalancing bonus
  2. A higher standard deviation of either asset class will result in a greater rebalancing bonus as an investment gets "more bang for your buck" 
  3. A smaller correlation (or negative correlation) will result in a greater rebalancing bonus 
  4. A larger difference in the standard deviation of the two asset classes will result in a greater rebalancing bonus
In the case of a higher volatility solution, bullets #2 and #4 both result in a higher rebalancing bonus and higher return (all else equal). In my example, the rebalancing bonus went from roughly 10 bps given a 10% allocation to asset A to 150 bps given a similar 10% allocation to asset B.
Asset A: RB = 90% x 10% [~15% x 3% (1-0) + (~15% - 3%)^2/2] = ~10 bps
Asset B: RB = 90% x 10% [~15% x 56% (1-0) + (~15% - 56%)^2/2] = ~150 bps
Given the 10% allocation, the return differential of 8% (the 3% return for asset A less the -5% return for asset B) x the 10% weight is only 80 bps, significantly lower than the benefit of the rebalancing bonus. In this example, in order for the two blended portfolios to have a similar return, the return gap between asset A and B must be closer to 14%.


Implications

From an asset allocation perspective, the above has a number of implications.

For one, it certainly makes the case for an allocation to strategies uncorrelated to an existing portfolio that have higher levels of expected volatility. In my opinion, the most obvious strategy that is largely under-allocated to is managed futures and to a lesser extent certain hedge fund styles and (until the last 10 years when everyone piled in)... commodities. I would also note that return expectations for the traditional diversifier, core bonds, are quite low and volatility of those returns are anticipated to remain low (thus a rebalancing bonus near 0), thus an allocation to a high volatility diversifier only needs low (or potentially negative expected) returns to make sense.

Another potential implication of the positive impact higher volatility strategies have on the rebalancing bonus is that this may partially explain the low volatility anomaly seen within asset classes, such as within stocks and bonds. When viewed in isolation, the outperformance of less volatile asset classes seems like an anomaly, but when viewed within a broader portfolio construct it makes sense that these lower volatility investments may need a higher expected return to draw in investors.

Using the rebalancing bonus formula and the following inputs which go back to the December 1990 inception of the S&P 500 High Beta and S&P 500 Low Volatility indices, we get the following rebalancing bonuses despite the higher correlation of the S&P 500 High Beta index with the S&P 500 index:
High Beta: RB = 50% x 10% [14.4% x 28.2% (1-0.89)) + (14.4% - 28.2%)^2/2] = 35 bps
Low Volatility: RB = 50% x 10% [14.4% x 11.0% (1-0.75)) + (14.4% - 11.0%)^2/2] = 11 bps
Pretty close to the 38 bps and 12 bp rebalancing bonus they provided in reality. In this instance, that 26 bp differential makes up ~25% of the excess performance a 50/50 S&P 500 / low volatility blend over that time frame.

Monday, January 25, 2016

Are Stocks Cheap? Checking in on Current Valuations

I'll leave it to others to chime in whether forward P/E's are useful or not given the fact they typically overstate earnings and I'll ignore that earnings may be at a cyclical peak (more on the latter here). As an aside, technicals in the market are filthy, as most short-term signals I look at are providing caution (example here). BUT, based purely on current forward P/E's relative to their own history, both large growth and large value stocks look awfully attractive if you are of the belief that the recent market noise is just noise vs. a sign of recession.

How attractive?

The below chart plots all quarterly forward P/Es against the forward 5-year annualized returns of both indices going back to the 1979 Russell inception of each. As of January 15th, the forward P/E of large value breached 14x while large growth went sub 17x, both historically great valuations to be buying at for the longer-term.


Highlights include that the average forward 5-year annualized returns of large value / growth were 13.9% / 16.1% when the P/E was below current levels and only 7.6% and 5.5% when above (and growth has never had a negative 5-year return when the forward P/E was this low).


Monday, December 14, 2015

Tweeting High Yield: A Round Trip in Investor Sentiment

With high yield all the rage these days, I thought it might be worthwhile aggregating tweets / posts going back to the beginning of this credit cycle to outline where we've come from and to share some thoughts on where we might be going. Curious if this format is helpful or too disjointed.



Backdrop... how did we get from the distressed 2008 (a 20%+ index yield), to sub-10% yields and a risk on mentality?

In March 2009, corporate bonds appeared to be a screaming buy and the Fed had an outsized impact getting spreads (and yields) much lower - much quicker than I thought was possible.

Once things calmed, why was there a reach for yield? Because it was the only place where yields were high.
Why are Investors are Reaching for Yield?: Because high yield is just about the only place you can get yield... http://t.co/EFGqZMR7
Despite the reach, I didn't mind high yield back in 2012 when rates backed up to 8% given where we seemed to be in the credit cycle (i.e. early).




When did things get frothy? I'd say early 2013 when Yields went sub-5%

Yields went from over 8% to under 5% within 6 months. At that point (and since), I could not get my head around high yield valuations.
Especially when viewed relative to stocks, once the yield on high yield bonds < earnings yield on stocks.
I was far from the only person who saw the froth in high yield
High yield sentiment seemed formed by the strong 5 year performance of the asset class. But perspective on how that return was achieved appeared missing:
Interesting back and forth in comments of this tweet. Some very smart people couldn't see a situation I thought / think has a decent probability. High yield underperformance even without stock underperformance given extreme valuations of high yield.


High Yield Sentiment Flashed Warning Signs in 2014 - Very Briefly

The sentiment shift and my view that high yield investors could be well over their ski's became very apparent when high yield "sold off" just 2% in fall 2014 and investors viewed that as abrupt:
Despite that "sell-off", yields in the lowest quality segment were still absurdly rich, but investors calmed their fears and dove back in, despite crazy yields.


Recent Views: The Sell-off was Expected - It Doesn't Seem to Be the Crisis Others Want to Make It

Which gets us caught up to this year when I brought my blog back after a three year hiatus and I jumped right into an area of the market I felt was misunderstood:
Yet, DESPITE my views of how mispriced things were, until financials within high yield become more stressed, I am less concerned about the recent sell-off's impact on the overall market (though things can / do change quickly):
This is supported by the perspective on where current yields are (yields still aren't all that high) and relatively contained within energy:
In times like this, perspective is much needed (i.e. things haven't been bad by historical standards):
If an allocation to high yield is to be made, note that lower quality high yield has not led to historical outperformance:

Wednesday, December 9, 2015

It's Generally Smart to Avoid Credit Risk

I've previously outlined that high yield credit risk is typically less ideal than simply gaining credit exposure through stocks and rate exposure through bonds. Now Larry Swedroe outlines the case for avoiding investment grade credit risk altogether.

There are many well-documented anomalies in finance. Among them is the surprisingly small return that investors historically have earned for taking credit risk in fixed-income markets—the default premium, as measured by the difference in returns between long-term Treasurys and long-term corporate bonds, has been only about 0.3%—and that stocks with a higher risk of defaulting on debt have produced lower returns.
Going back to 1988, which is as far back as Barclays breaks down the returns of the Long Corporate Bond index into the contribution from credit and rates ex the spread, the return from the credit component has actually been slightly negative at -0.09% annualized vs the 8.12% return for a like duration Treasury bond. A similar story plays out in intermediate corporate bond space, where the credit spread contributes only 0.37% of the 7.24% return for the Barclays Corporate Bond Index since 1988.


The story is more nuanced than "credit always underperforms the yield" as yield is generally a great predictor of future returns, but yield should generally be viewed more as the ceiling for future returns than actual future returns. The issue is when there is stress in the market, such as during the financial crisis when 13 year cumulative performance (the rough duration of the index) of long corporate bonds underperformed the yield's "predicted" return by almost 80% (the 13 year forward performance starting in 1995 ended during the 2008 meltdown).


My general view of credit is to avoid it unless you feel you are being more than fairly compensated. Even if you miss shorter periods of relative outperformance (vs treasuries), allocating only when credit looks like a screaming buy will likely result in a much better long term return profile. In the case of long corporate bonds, allocating only when the spread of long corporate bonds to treasuries was greater than 200 bps (something that occured just 20% of the time), returned 1.1% annualized more since that same 1988 start.


Tuesday, December 1, 2015

The Case for an Allocation to Dollar Based EM Debt

While the underperformance of high yield bonds since my post The Case Against High Yield has certainly made high yield bonds more attractive (yields went from sub 6% to north of 8%), I still prefer the risk/return profile of a stock/bond allocation (more here). For those that are looking for a higher yielding fixed income alternative with limited currency risk and the potential for U.S. interest rate diversification, dollar based emerging market fixed income may be an interesting alternative.


What is it?

The Barclays EM USD Aggregate Index is a flagship hard currency Emerging Markets debt benchmark that includes USD denominated debt from sovereign, quasi-sovereign, and corporate EM issuers. The index is broad-based in its coverage by sector and by country, and reflects the evolution of EM benchmarking from traditional sovereign bond indices to Aggregate-style benchmarks that are more representative of the EM investment choice set. 
At present the index is made up of ~75% sovereign debt and ~25% corporate debt, all denominated in the dollar, while the below table breaks down what it looks like in terms of country composition. Interesting to note that while only 2% of the portfolio is composed of Venezuelan debt, that 2% contributes more than 10% of the overall yield of the index (in other words, the higher yield is certainly not risk-free), but the index does provide a pretty wide breadth of exposure.



The EM Debt Relative Valuation Story

The case for EM USD Aggregate exposure is simply a relative valuation story. Despite periods of heightened turmoil within emerging markets over the last 20+ years, the yield to worst of the portfolio has generally been a pretty good predictor of future returns (note six years is used for the forward return projection given the duration of the index has fluctuated between around 5-7 years going back to the 1993 inception).

At the current yield of almost 6%, forward absolute returns are likely to be favorable.


The higher yield becomes more intriguing when viewed relative to the yield of the U.S. Aggregate Bond index. The below chart is similar to the one above, but is shown with yield to worst and forward returns relative to that of the U.S. Aggregate Bond index. In this case the relative yield advantage has been a pretty consistent precursor to future outperformance. A notable exception is the underperformance in the mid 1990's which coincided with six year returns impacted by the 1998 emerging markets crisis (though the yield advantage was sub 2% then vs almost 4% today).


While emerging market bonds are certainly not risk-free, the US dollar denomination protects an investor from a direct impact should the dollar continue to strengthen (though countries trying to pay back dollar denominated debt with a weakened currency are certainly indirectly impacted), while relative yields that are near all-time wide levels vs the US Aggregate index seem attractive given the "emergence" of emerging market countries on the global economy over the more than 20 years since the index incepted. Add in the potential diversification benefits moving some fixed income away from US monetary policy may provide and you get what is in my view a strong case for a strategic allocation to emerging market fixed income.

Monday, November 23, 2015

Valuations Do Matter (Even Over Shorter Time Frames) / Momentum Driven Valuation Timing

I often read that valuations don't matter over the short-term (a case often cited against market timing). Over very short periods (hours, days, etc...) this certainly may be true, but while there can be a lot of variability around month-to-month or year-to-year performance, I completely disagree with the sentiment that it doesn't matter. That said, there are better ways than just using current valuation levels for an investor to time markets which I will outline below.


Debunking the Case that Valuations Don't Matter
Before getting into my analysis supporting the theory that valuations do in fact matter a LOT, especially when you use the information embedded in momentum as support, I did want to highlight a recent research paper / article outlining valuations don't matter that simply appears to be wrong. 

Larry Swedroe from ETF.com shared research in an article Valuation Metrics In Perspective outlining the research done by Javier Estrada which concluded that one year forward performance of the S&P composite from 1899-2014 was independent of preceding valuations:
when the current P/E was between 10.4 and 13.3, the one-year forward return was 7.3 percent. When it was higher, between 16.4 and 18.9, the one-year forward return averaged 11.7 percent. And when the current P/E was above 19, the one-year forward return averaged 10.0 percent.
While I completely agree that one year returns are noisier than 10 year returns, the above results truly surprised me. While any one year period could have much higher / lower returns (i.e. the range of outcomes should be wider), over long periods of time the average one year forward performance when valuations are cheap shouldn't be that different than the average 10 year forward performance (it should average out to roughly the same figure).

Given my surprise, I decided to recreate the results in the paper using Shiller CAPE for my P/E over that same 1899-2014 time frame. The results in the table below make it likely that the analysis in the paper is either flawed (they do use a weird 3-month lag for earnings) or is simply wrong (my personal view). As the table outlines, the results not only show that high valuations = lower returns over one year, but the average one year return is even more dependent on valuations (on average) than ten year returns. You can also see that the range of outcomes is much more dispersed over one year than ten years.



Back to the Original Point of the Post... Momentum to Time Value
I had run the analysis below prior to my attempt at recreating the above data, so my methodology is a bit different. In the example below, I use data going back to 1881 (which is aligned to the inception of the Shiller CAPE time series) and I separate one month forward returns by whether or not the S&P composite had a one year backward looking return that was positive or negative. Below are the same data points in a table form and in a scatter plot.

What is easily discerned is that regardless of momentum, cheap valuations are generally good for returns (though extremely cheap and negative momentum has had some severe volatility - see standard deviation for periods when CAPE < 10), while high valuations have generally resulted in pretty strong returns when momentum is strong. Valuations seemingly only matter when valuations are high and momentum has turned negative (as seen by the negative absolute performance for the three buckets when CAPE > 15 and momentum is negative).



My takeaway... the underlying trend of the market is often as, or more, important than stock price levels, but performance will eventually catch up with how much you paid. BUT, momentum may help protect an investor when markets are rich and turn, as well as help keep an investor appropriately in the market even when valuations appear stretched.

Tuesday, November 17, 2015

The Mean Reversion Case For (and Against) Strong Future Returns

Bull thesis: 15-year S&P annualized returns ending 9/30/15 came in at just under 4%. The average forward return since 1915 when returns were that level (or lower) was 15.5% annualized over the next 15 years with a standard deviation of only 2%

Bear thesis: the 15-year starting point came when the previous 15 year annualized returns were just under 18% (i.e. we are still working off extreme valuations)




The counter argument to the bear argument can be seen in the chart below which compares the same 15 year historical returns on the x-axis with 30 year forward returns on the y-axis. As outlined in my previous post, returns tend to smooth out over 30 years, thus it matters a lot less what you pay for stocks over 30 years than over 5, 10, or even 15 years because more of the return is composed of fundamentals (i.e. dividends, buybacks, etc...) than multiple expansion / contraction as compared to shorter periods. Thus even extreme valuations have historically delivered 30 year returns I think most investors would find acceptable at the moment.


My take? Right between the two. I am not nearly as scared by current valuations, peak margins, etc.. as bears (especially over longer time frames) and I am not remotely a bull either. That said, I'm also not worried about a short term correction that would likely create a much better buying opportunity in the future.

Monday, November 9, 2015

Making Time (Even More of) an Investor's Best Friend

Ben Carlson of A Wealth of Common Sense blog (and author of a great book by the same name), had a recent post Playing the Probabilities outlining that time has been an investor's best friend (for those investors that have had in some cases quite a bit of time), pointing to the following table.


He also shared some pretty amazing stats, including:
The worst total return over a 20 year period was 54%. But the worst 30 year total return was 854%.
While I certainly agree with everything he outlined, he did ask the following question.
Has anyone figured out a better way of compounding your money in stocks beyond increasing your holding period? Not many.

Challenge accepted!

Simple rules (for more... see Meb Faber's record downloaded white paper):
  • If the S&P composite TR is > 10 month moving average, stay in stocks
  • If the S&P composite TR is < 10 month moving average, move to bonds (in this case 10 year treasuries)
The equity curve.


And the updated table with some additional bells and whistles (note there are some very slight differences with the returns Ben produced, but the message is identical).


While there is no free lunch (in this case, an investor gives up some upside over shorter time frames), using these basic momentum rules resulted in no negative returns over any ten year period and actually increased the long-term returns over this 1926-2015 time frame, making time an even better friend for an investor.

Thursday, November 5, 2015

GMO Flows Turn Negative - An Ominous Sign for Risk Taking

As unnecessary as it may seem, contrarian investment managers need to be even more consultative with their clients than managers more aligned with market sentiment, otherwise clients won't be able to handle the extended periods of relative underperformance a contrarian investor is likely to face from time to time. In the case of GMO, while the long-term performance of many of their strategies is pretty strong (and tend to materially outperform when markets turn), the performance captured by their investor base is typically quite poor.

One example being the GMO Benchmark Free Allocation III, a fund in which the average investor has underperformed the fund by 3-5+% over 3, 5, and 10 years. As a result, despite a 16th percentile Morningstar rank in terms of fund performance over 10 years, investor performance only ranks 74th.


In other words, their investor base historical zigs when they should zag... adding money to the contrarian GMO after markets have tanked (when they should be taking market risk) and piling out of the contrarian GMO after markets perform well (when they should be taking risk off the table). Thus, it was relatively alarming to see that funds flows at GMO have been negative $4.2 billion over the twelve months through 9/30/15, including almost $3 billion of outflows the last two months of the third quarter alone.



How poorly have investors timed GMO? Let's take a look at how well a model doing the exact opposite of GMO flows would have performed going back 20 years.

Rules:
  • If twelve month flows to GMO funds are positive, allocate the next month to stocks (S&P 500)
  • If twelve month flows to GMO funds are negative, allocate the next month to bonds (Barclays Agg Bonds)

The result of which is more than 100% of the S&P 500 with almost half the volatility and drawdown.

I have a ton of respect for the way in which GMO manages money (their guts to be massively contrarian if that is their view) and I think their thought leadership is about as good as it gets in the industry. The challenge is GMO knows they are smarter than their clients, leading to a more-or-less 'take it or leave it attitude'.

But what good are strong long-term returns if an investor is unable to capture them?

Wednesday, October 28, 2015

What Exactly Does the VIX Tell Us?

Most investors know of the VIX Index, but not as many understand what information the VIX provides an investor. Here is my attempt to provide an initial outline of what it is and why the information embedded within the figure is so powerful.

VIX Defined

The CBOE has a white paper that provides a ton of detail into the VIX calculation, but the origin of the index describes what it is at a high level well enough:

In 1993, the Chicago Board Options Exchange® (CBOE®) introduced the CBOE Volatility Index® (VIX® Index), which was originally designed to measure the market’s expectation of 30-day volatility implied by at-the-money S&P 100® Index (OEX® Index) option prices. The VIX Index soon became the premier benchmark for U.S. stock market volatility.
Ten years later in 2003, CBOE together with Goldman Sachs, updated the VIX to reflect a new way to measure expected volatility, one that continues to be widely used by financial theorists, risk managers and volatility traders alike. The new VIX is based on the S&P 500® Index (SPXSM), the core index for U.S. equities, and estimates expected volatility by averaging the weighted prices of SPX puts and calls over a wide range of strike prices.
In summary... the VIX is a reflection of the market's expectation of future market volatility.


The Math Behind What the VIX Level Means

As Eddy Elfenbein of the great Crossing Wall Street blog outlined back in 2012, you can take the current market's expectation of future market volatility (i.e. the VIX), to determine the expected range of future stock market returns.
The 3.46 denominator is simply the square root of 12, which takes the VIX from an annualized figure to a monthly figure given there are 12 months in a year.


You can also vary the blue "confidence bands" in the chart above (in the chart above, the one standard deviation bands "should" capture 68% of outcomes). For example, from statistics we know that 1.96 is the z value of a 95% confidence interval. We can convert this 1.96 level to a monthly value as follows:
1.96 / (square root of 12) = 0.57
We can then take that 0.57 value and multiply it by the VIX to determine the bands that (in theory) contain that 95% percent of outcomes. For example, with a VIX of 20, the equation is:
20 x 0.57 = 11.3 or 95% of all outcomes over the next month should be within a +/- 11.3% return
In reality, the VIX typically overstates the level of market risk and understates the results within the band. As the chart shows below, the confidence bands described by the VIX understate the "capture rate" of the bands, especially at higher confidence levels. In the chart above, one standard deviation bands should capture 68% of outcomes, but instead have captured 85% of outcomes.


Conclusions

Neither of the following conclusions should surprise readers of the blog, but they are:
As a result, while an investor can utilize the VIX to scale equity weights to smooth out returns, an investor should rarely be buying volatility protection.


Wednesday, October 21, 2015

Utilizing the Value of Value to Make Value / Growth Tilts

Back in August I outlined why I thought the plain-vanilla value premium had been compressed to the point growth had and was likely to continue to outperform in my post Death of (Plain Vanilla) Value - Long Live GARP. This post is meant as a follow up and suggests a few frameworks as to how an investor might allocate based on the given "value of value".


Backdrop: Value of Value Matters

Value historically outperforms growth when stocks making up the value indices are beaten down relative to growth. Thus, it should be no surprise that value materially outperformed following the Internet bubble as value stocks were massively cheap relative to growth stocks (see below). The issue over the last decade plus is that many investors have piled into value ignoring the driver of value's historical outperformance, resulting in a "value discount" that is historically narrow.


The next chart outlines in more detail why the discount matters, with the starting "value discount" on the x-axis and the subsequent 7-year excess return on the y-axis. You can see the linear relationship between the "value of value" and future value excess return to growth. When the discount is high, outperformance of value vs growth is likely. When the discount is low (which happens to be where we currently sit), underperformance of value vs. growth is likely.




Putting the Insight into Action

While there are a multitude of ways this insight can be put to use, below are a few simplistic ways that only require making a reallocation at most quarterly and has been more likely to require a reallocation once every 3-5 years. Note this analysis does have a huge data mining issue as we know in advance that a 25% and 30% value discount are thresholds where growth and value have diverged in the past - though less of an issue today as we are near all-time tight levels. As an aside, as the charts below show it wasn't until the late 1990's that the performance of the Russell 3000 Value and Growth indices diverged. It was only when investors initially flocked to growth (and later value) that we have seen distinct differences in the "value discount" and in subsequent performance.


Model 1: Long-Only

Rules (reallocate the portfolio quarterly - ignores transaction costs):
  • If the value discount is narrower than -25% (i.e. growth is cheap), allocate to the Russell 3000 Growth Index
  • If the value discount is between -30% and -25%, allocate to the Russell 3000 Index (i.e. don't tilt growth or value)
  • If the value discount is wider than -30% (i.e. growth is expensive), allocate to the Russell 3000 Value Index



Model 2: Long-Short

Rules (reallocate the portfolio quarterly - ignores transaction costs):
  • If the value discount is narrower than -25% (i.e. growth is cheap), long position in the Russell 3000 Growth Index / short position in the Russell 3000 Value index overlayed on the Russell 3000 Index (i.e. generate alpha on top of the Russell 3000 Index through the long/short)
  • If the value discount is between -30% and -25%, allocate to the Russell 3000 Index (i.e. don't tilt growth or value)
  • If the value discount is wider than -30% (i.e. growth is expensive), long position in the Russell 3000 Value Index / short position in the Russell 3000 Growth index overlayed on the Russell 3000 Index

Monday, October 19, 2015

The Relationship Between High Yields and High Yield / Stock Performance

I've previously posted my broader thoughts on high yield (that there is typically limited to no benefit vs. a stock / bond allocation), but the below chart provides some additional context I thought worth sharing.


Starting Yield: Anchor and Cap for High Yield Returns

The left hand chart breaks out five year forward returns vs. various starting yield-to-worst "YTW" buckets of the Barclays High Yield index (along with what the average starting yield was for the index within that bucket over the time frame). It should be no surprise that the average starting yield anchors what the return will be (when yields are low, returns are low), while the right hand chart outlines the average underperformance vs. the starting yield has been roughly 2.0-2.5% / year, irrespective of the starting yield (high yield isn't called junk for nothing).


Stocks vs High Yield Performance at Various Yields

Both charts show that equity market performance is highly correlated with the credit premium; when the YTW of high yield bonds is high, it is highly likely that the equity premium is high too - leading to higher equity returns. On the other hand, when yields are low and the credit premium (as well as equity premium) is low, stock and high yield returns are more muted. However, the unlimited potential of stocks vs the capped upside of high yield at low rates has lead to consistent equity outperformance when yields are low, while high yield has performed exceptionally after yields were completely blown out (post the 2008/09 financial crisis).


At the current yield to worst of 7.6% and a spread of almost 6% to treasuries, we should expect returns to be no more than 4.5-5.5% over the next five years. Nothing special for the potential risk, but much better than the sub 5% yield we saw a bit more than a year ago.

Friday, October 2, 2015

Using "Normal" Drawdowns as a Timing Signal

The below analysis was purely an accident. I was actually looking into periods the U.S. stock market "suffered" a 10% drawdown for the absolute opposite reason; to show that a buy and hold investor should likely ignore these regularly occurring events. How regular?

The always interesting Ryan Detrick points out:

I looked at every calendar year since 1960 and looked at various correction levels. Turns out 94% of all calendar years see at least a 5% correction, while 53% of all years see a 10% correction. Maybe this recent 12% correction isn’t so alarming?
So... what if an investor were to sell-out of their U.S. stock allocation and shift it into bonds whenever one of these standard 10% drawdowns occurred and were only willing to go back into stocks when they once again were within 10% of its all-time high?

Rules:
  • At month-end if the drawdown from its previous peak was less than -10%, bonds (US Agg)
  • Otherwise, stocks (S&P 500)


Well going back to 1976 (the inception of the Barclays U.S. Agg bond index), the results were surprising. Almost 100% of the return, about 20% less volatility, and less than half of the drawdown. As interesting is how well it kept up with the stock market in all periods except those that followed severe drawdowns. This is largely due to the rarity of which 10% drawdowns become 30, 40, or even 50% drawdowns, so the strategy was at most times fully invested.

Monday, September 28, 2015

Using the VIX Futures Term Structure to Reduce Equity Exposure

The WSJ blog had a recent article The VIX Market Suggests It’s Not Yet Time to Buy the Dips outlining:

Typically, longer-dated VIX futures are more expensive than VIX futures expiring in the current month, as there’s a greater chance of stock swings over a longer time period. That makes for an upward sloping futures curve.
In times of stress, when investors are very fearful about the stock market over the next few weeks, they bid up the prices of short-dated futures more than the prices of the longer-dated futures. That phenomenon is known as “backwardation,” meaning a downward sloping futures curve. 
The article itself pointed to the relatively flat term structure (as of Friday), stating that an elevated VIX for the foreseeable future is a potential outcome. I've written in multiple iterations (here and here are two examples), that the VIX does a great job of predicting future levels of volatility AND risk-adjusted returns. What I'll take a look at now is whether the term structure adds additional insight (sneak peak... it just might).


The VIX Term Structure

I went into greater detail in a recent post about the VIX term structure (that was specific to trading VIX futures), where I introduced the VIX/VXV ratio (i.e. the Implied Volatility Term Structure or "IVTS"):
One way to make an allocation is to simply allocate to a long VIX futures position only when they have a tailwind vs. headwind. Simply calculate the term premium (a simple way is to use the VIX/VXV ratio - details of what that is by the great Bill Luby here) to determine contango or backwardation (in this case when the VIX/VXV is less than or greater than 100) and only allocate to UVXY when it's above 100. To put some numbers behind that statement, the average modeled daily performance of UVXY is -1.1% when the ratio is < 100 (2500 trading days) and 5.0% when the ratio > 100 (378 trading days) since 2004.
So... in addition to simply looking at the level of the VIX (in this case whether the VIX is greater than 20), we also look at the levels of the IVTS (in this case if it's greater than 100).

The table below outlines the results of next day S&P 500 returns given the level and term structure of the VIX.


Some highlights... the geometric returns are broadly the same whether the VIX is above or below 20, but the volatility is MUCH lower at low levels of VIX (resulting in higher levels of risk-adjusted returns at low levels of the VIX). However, the addition of the IVTS signal shows a lot of interesting promise. 

When both signs are in disagreement, returns are by far the best, with returns of  11.9% when the VIX > 20, but the IVTS < 100 and a whopping 105% when the VIX < 20 and the IVTS > 100. When both signals are telling investors to tread carefully (i.e. VIX > 20 and IVTS > 100),  returns have been abysmal, with returns of -8.6% and volatility north of 40%.


Data Mined Model

Using the above insight to create a model that is absolutely data mined (yet may be interesting to look at going forward), we use the following rules:
  • If VIX > 20 and IVTS > 100, go to aggregate bonds
  • Otherwise, S&P 500

Something to keep an eye on as the warning signs are both currently flashing red.

Monday, September 21, 2015

Momentum Applied to Mutual Funds

Back in May, I posted A Guide to Creating Your Own Hedge Fund outlining how the application of momentum to the two worst performing funds within the Morningstar Multialternative category over the previous ten years would have provided an investor with better risk-adjusted returns than the Barclays Hedge Fund index and a lower correlation to equity markets.

Now, I'll share how a similar strategy going back a further ten years would have performed using randomly selected funds from the Morningstar Moderate Allocation / World stock universes. As an aside... this isn't too far removed from how I manage my own retirement money.


Narrowing the Universe for a Few Examples
  • Use the oldest share class within the Morningstar Moderate Allocation or World stock universe (oldest share classes typically have a relatively high fee structure - so I'll deem this conservative)
  • Only funds with a track record going back 20 years, the result of which is 69 funds in the Moderate Allocation universe and 44 funds in the World stock universe (this unfortunately adds to the survivorship bias, which makes the below figures a bit less conservative)
  • Randomly narrow the universe down to five funds; I used Excel's rand function for each iteration (this makes the results much more conservative in my view)

Allocation Rules

Random Momentum
  • If the 9-month return of the fund is > 0, allocate next month to the fund, otherwise to aggregate bonds
  • 20% weight to each fund's "path"
Dual Momentum (Top 3)
  • Pick five funds at random (as done with 'random momentum')
  • Take the best 3 performing funds over the previous 9-months and if > 0, allocate next month to the fund, otherwise to aggregate bonds
  • 1/3rd weight to top 3 at the previous month-end

Results

Moderate Allocation

The below are my first 5 results (random iterations of the 69 funds) vs. an equal weight of all 69 funds with a 20 year track record and the S&P 500.







And the same rules applied to the Morningstar World stock fund universe





The challenge (and likely reason why it works) are the extended periods of underperformance an investor using momentum must deal with relative to a long only stock portfolio (these strategies have all materially underperformed during strong bull markets). I''ll save what I view as a potential fix to that for another day. For now, buyer beware... as of 8/31/15, less than 10% of funds within the Moderate Allocation and World categories had positive returns over the previous 9-months.

Tuesday, September 8, 2015

The Case for Put Writing / Further Improving PutWrite Performance

Jesse Livermore of the always interesting Philosophical Economics outlines the case for writing puts in his recent post The World’s Best Investment For the Next 12 Months. Given this has been an area of focus for me professionally for the better part of the last 5 years (sneak preview... I love the concept), I thought I could add to the conversation. Note that some of the below post duplicates Jesse's efforts, but I would highly recommend reading his piece if you haven't done so.


What is Put Writing?

The CBOE has some great resources on what exactly put writing is here, but in a nutshell:

The CBOE S&P 500® PutWrite Index is a benchmark that measures the performance of a hypothetical portfolio that sells S&P 500® Index put options against collateralized cash reserves held in a money market account.
What this means is that the index sells puts at a 100% notional value (i.e. there is no leverage). For those more familiar with covered call writing (i.e. writing calls on the stocks you own, which gives up a less certain upside for a more certain option premium), the chart below highlights that covered calls and put writing result in the exact same economic exposure (the green on the left and orange on the right are identical), which is downside risk commensurate to the stock's decline with upside capped at the premium collected (the below chart comparing the two ignores that premium).


The historical benefit of put writing has been a long run return close to that of the S&P 500 index, with slight underperformance during bull markets and outperformance during bear markets. The PutWrite index can be thought of as an alternative means of capturing the same equity premium; through premium collection in the case of put writing (i.e. collecting the insurance that others are willing to pay to protect their portfolio) vs. the upside of the equity market.

Of note is the PutWrite index has historically achieved this similar performance with a lower risk profile than the S&P 500 (as defined by both standard deviation and drawdown), the reason being that an investor has the same downside risk (the market), but a PutWrite investor collects a higher level of income (the option premium) vs the lower dividend distributions and the option premium increases as market risk increases (while dividends can decrease), helping cushion losses further.



Improving Upon the Strategy Further - Switching Between Put Writing and the S&P 500

As mentioned above, put writing generally underperforms the S&P 500 during bull markets and outperforms during bear market in part due to cushion the higher option premium provides put writing when market volatility picks up (i.e. insuring against a market downturn is more costly when investors view an increased probability of needing that insurance). As a result, Jesse introduces the following rule:
If the monthly close of the volatility index (VIX), the best proxy for option valuation, is above 20, then for the next month, we’ll invest in the put-write strategy. If the monthly close of the volatility index is below 20, then for the next month, we’ll buy and hold the index.
The above rule does improve upon the strategy's absolute return (see "$PUT Switching" in the below chart). YET, that higher return comes at the cost of reduced risk-adjusted returns (higher standard deviation, higher drawdown, and a lower sharpe than the $PUT index itself).



Another Alternative Utilizing a Surprising Characteristic of PutWrite Performance

While I do think the above is an interesting solution, I think it misses one of the surprising characteristics of put writing... its risk-adjusted returns are actually higher when the VIX is lower. As the table outlines below, the absolute returns have indeed been lower when the VIX < 20 (9.2% vs. the 10.6% returns for the S&P 500), the sharpe ratio of the PutWrite index is a whopping 1.20 during these periods (a huge number for a 25 year time frame as any investor can attest). The reason being the level of VIX (or the amount of option premium received) matters less than the premium collected compared to the puts realized payout (i.e. what the insurance is actually paying out). In other words, it is more profitable to insure a good driver at a low insurance premium than a bad driver at a high premium.


As a result, rather than moving from put writing to the S&P 500 when the VIX is below 20 (a move that has resulted in higher returns, but lower risk-adjusted returns), simply increase your notional allocation to put writing when the volatility is low and decrease it when it is high (for more on this topic, back in June I wrote a post The Case for a Steady Volatility-State Managed Portfolio that utilizes the same concept, but applies it to the S&P 500). 

In the analysis below, my rules are as follows:
If the monthly close of the volatility index (VIX) is above 20, then for the next month invest in the put-write strategy at 67% notional (a 50% reduction) with the balance in 3-month t-bills. If the monthly close of the volatility index is below 20, then for the next month invest in the put-write strategy at 150% notional (a 50% increase) financed at the 3-month t-bill rate.

The end result? Improved absolute return and material improvement in both standard deviation, drawdown, and sharpe ratio.

As an aside... put writing provides a ton of flexibility for an investor. Unlike covered calls, which requires collateral (the stocks you are writing calls against), put writing simply assumes you are holding 100% cash (t-bills). For those that are willing to be creative, there are a lot of interesting things you can do that can provide returns in excess of cash to further improve returns with limited increase in total risk.

Tuesday, September 1, 2015

Utilizing the Money Sucking $UVXY to Improve Risk-Adjusted Returns

Horrific Performance
An initial investment of more than $450,000 to the ProShares Ultra VIX Short-Term Futures ETN (UVXY) at the open of its October 4th, 2011 inception date (the split adjusted opening price) would be worth just $87 at today's close (this after a more than 28% gain today and more than 300% gain over the past few weeks). Modeling what the performance would have been going back to the inception of VIX futures in March 2004, a $100 million initial investment would be worth a bit less than $5 today (you read that correctly).


Investors Haven't Fared Better
Given the above performance figures, it should be no surprise that UVXY has eaten a tremendous amount of the capital investors have sunk into it. Since its launch, the ETN has seen net flows in excess of $1.7 billion, while current AUM stood at less than $400 million as of yesterday's close (simple math tells you investors have lost more than $1.3 billion in the strategy since inception). This figure may improve short-term, but I all-but-guarantee it will get worse over the longer term. As an aside... how in the world is UVXY accessible by mom and pop investors who have zero clue how it works.

What is It / Explaining the Underperformance
The ETNs description:
The Ultra Fund seeks daily results that match (before fees and expenses) two times (2x) the daily performance of the S&P 500 VIX Short-Term Futures Index. The index seeks to offer exposure to market volatility through publicly traded futures markets and is designed to measure the return from a rolling long position in the first and second month VIX futures contracts.
To summarize, the ETN provides exposure to a long position in the front two VIX futures contracts at 200% notional (i.e. it is a 2x levered position). If VIX futures simply followed the VIX, UVXY returns would be extremely mean-reverting as the VIX is one of them most mean-reverting figures in all of finance as it cannot go to zero or infinity. But, while VIX futures do typically move in sync with the VIX index over very short periods (i.e. the exposure most investors in UVXY believe they are getting), it does not over long periods of time.

The issue with VIX futures is the term structure, which during calm markets is typically in contango (i.e. VIX futures are priced higher than spot), while during distressed markets is typically in backwardation (i.e. VIX futures are priced lower than spot). UVXY benefits immensely from backwardation because the upward slope of VIX futures owned provides a tailwind to the futures contracts as they approach maturity (while UVXY faces a severe headwind during contango... which is present much more often).

Examples of VIX futures in contango (bad for UVXY) and backwardation (good for UVXY) is shown in the two below charts using data / charting from the especially helpful VIX Central (follow Eli on Twitter).

Example of VIX in Contango (6/23/15)


Example of VIX in Backwardation (9/1/15)


Using UVXY
All that said, there is absolutely a case for going long VIX futures / VIX ETNs. As the last few weeks have shown, there are very few financial instruments that have a correlation near -1.0 to stocks during periods of market distress.

One way to make an allocation is to simply allocate to a long VIX futures position only when they have a tailwind vs. headwind. Simply calculate the term premium (a simple way is to use the VIX/VXV ratio - details of what that is by the great Bill Luby here) to determine contango or backwardation (in this case when the VIX/VXV is less than or greater than 100) and only allocate to UVXY when it's above 100. To put some numbers behind that statement, the average modeled daily performance of UVXY is -1.1% when the ratio is < 100 (2500 trading days) and 5.0% when the ratio > 100 (378 trading days) since 2004.

As an example of putting this to work within a portfolio, below is an allocation to the S&P 500 compared against an allocation to the S&P 500 when the term structure is in contango (bad for UVXY), or a 83% allocation to the S&P 500 / 17% allocation to UVXY (UVXY is ~5-6x more volatile than the S&P 500 over the long-term) when the term structure is in backwardation (i.e. favorable to UVXY).


The above breaks down to annualized returns of 6.9% for the S&P 500 and 12.4% for the hedged version, while standard deviation moves from 19.6% for the S&P 500 down to 15.5% for the hedged version. In addition, max drawdowns moves from 55% down to 29%.

So, while I certainly feel the average retail investor should not be allowed access to something as volatile as UVXY without proof they have a good understanding of the mechanics that drive performance, I do think there is a benefit in using such vehicles for those that understand how they might tame them.