Thursday, April 13, 2017

Buy When There's Blood in the Streets? Market Timing with Volatility Triggers

An 18th century British nobleman, Baron Rothschild, was rumored to have made his fortune buying during the panic that followed the Battle of Waterloo against Napoleon. He is behind the often quoted saying "Buy when there's blood in the streets”, which he continued “even if the blood is your own." This post will share a framework that may identify regimes that benefit from buying when there's blood in the streets, as well as when the market is at the greatest risk of underperformance (potentially allowing investors to reduce risk before there is blood in the streets).


MARKET VOLATILITY AND INVESTOR SELF-SACRIFICE 

Market volatility has long been one of the most disruptive aspects to investing and investor behavior. Over the past few decades, U.S. investors have seen large portions of their equity wealth evaporate as market volatility spiked on multiple occasions.

January 31st, 1993* - December 31st, 2016

  • 4 Distinct S&P 500 drawdowns greater than 30%  
  • 15% realized monthly standard deviation of the S&P 500 (annualized) 
  • 5% to 29% range of one year rolling standard deviation of monthly S&P 500 return  
  • 20 average level of the VIX Index
  • 67 Month-ends where the VIX Index was > 30
  • 89.5 peak VIX value during 2008-2009 financial crisis
* The CBOE first started publishing the VIX Index in January 1993


Poor investor reaction to market volatility has contributed to the poor returns investors have captured. Analysis of Morningstar investor returns relative to the returns produced across US equity styles reveals the extent to which investors have undermined their own investment performance over time. Over the last decade the average investor realized returns that were as much as two percentage points lower each year than the relevant Morningstar category.


In the case of the Morningstar large-cap value category, investor behavior reduced compounded dollar returns by 40% over this ten year period relative to what the average fund within the category produced. If / when markets exhibit another period of heightened volatility – which at some point is bound to happen – and if investors continue to undermine their investment performance during these periods – which is likely to happen – a systematic approach that can reduce the impact of these swings in price and volatility may help investors stay on track.


THE VIX CAN HELP IDENTIFY VOLATILITY REGIMES

We’ve all seen the caveat that “past performance is not indicative of future returns”. The same has been true regarding past levels of volatility (high or low) and future returns (i.e. the relationship between the VIX and forward returns is weak). On the other hand, past levels of market volatility has been correlated with future levels of market volatility (i.e. when volatility is high... it tends to stay high). Given returns have been similar irrespective of the VIX, while volatility has been lower when the VIX is low / higher when the VIX is high, risk-adjusted performance (return per unit of risk) has been higher when VIX (and volatility) has been low (the return numerator stays roughly the same, while the standard deviation denominator is higher in high volatility regimes).

To identify high / low volatility regimes, we can use the current level of the VIX (in the examples below, when the VIX is above or below the historical average of 20).
  • High Volatility Regime (VIX > 20): volatility is more likely to remain elevated
  • Low Volatility Regime (VIX < 20): volatility is more likely to remain low


But wait… there’s more.

The returns within high volatility regimes (those when the VIX ended the previous month above 20) can be broken down further, in this case split between periods when the VIX had declined or increased month-over-month. Since the VIX Index inception in 1993, during high volatility regimes when the VIX declined month-over-month (i.e. VIX was above 20, but the VIX was less than the previous month-end), returns have been materially higher than when the VIX was elevated and had increased, while the risk was also greatly reduced when the VIX had declined. In fact, the risk-adjusted returns when the VIX was elevated and declining closely match the high levels of those generated within low volatility regimes.



RISK-MANAGED APPROACH

Given historical risk-adjusted returns have been much more favorable when the VIX ended the previous month below 20 or when the VIX was above 20 and declining, we can test the hypothetical performance of a model rebalancd monthly that has a risk-on allocation (stocks) when the VIX is low or declining and a risk-averse allocation (intermediate bonds) when the VIX is high and increasing.
  • Low volatility regime (VIX < 20 or declining): 100% S&P 500 Index
  • High volatility regime (VIX > 20 and increasing): 100% Bloomberg Barclays US Intermediate Treasury Index
The model’s results are promising. Not only have the returns been similar (in fact slightly higher) than a buy and hold allocation to the S&P 500, risk was greatly reduced resulting in materially higher risk-adjusted returns.



CONCLUSION

Although Baron Rothschild may have had the fortitude to buy when there was blood in the streets, the above framework reveals there may be other, potentially less stressful, ways to capture the opportunity. While this example is simplified – and of course hypothetical – a similar framework may help protect investors from undermining their own financial progress by reducing equity exposure before fear fully materializes and/or increasing equity exposure when fear is high and improving.

Monday, March 20, 2017

Capturing Mean Reversion Via Momentum

Ben from A Wealth of Common Sense recently posted an update of his "favorite chart", which stacks the calendar year performance of a variety of asset classes.


As Ben points out:
There’s little rhyme or reason for how these things play out from year-to-year so it provides a good reminder for investors to understand that any single year’s performance in the markets is fairly meaningless.
While the year to year performance is rather random, this post will weigh the benefit of mean reversion (allocating to risk assets that have underperformed and stack low on the quilt) vs momentum (allocating to risk assets that have worked well and rank high on the quilt).


Asset Class Performance Over Longer Time Frames

The chart below shows the same asset classes that Ben highlighted, but rather than rank the asset classes by calendar year performance, it ranks them by rolling five year returns as of the end of February for each year (I picked end of February simply because that was the last data point).


There is a lot of interesting information here. One of the more interesting aspects is how mean reversion AND momentum can be seen over various time frames. Asset classes appear to be mean-reverting over longer periods (note the strong relative performance of US equities at the beginning of the 2000's, the poor relative performance through the mid to late 2000's, and the strong relative performance we are currently experiencing - while EM and international stocks were the opposite) and asset classes that have done well continue to do well (momentum) over shorter periods (note that if something did well the previous five years, it tended to stick around in the years to follow).


Allocating by Mean Reversion and Momentum

Using the February 1997 data as a starting point, we can look at the performance over several different time frames to determine whether mean reversion or momentum makes more sense. In this example I narrowed the universe down to equity-like holdings (US - small, mid, large-, International, EM, and REITs) as I personally don't necessarily believe commodities, cash, or even bonds should always be long-term strategic investment holdings (a conversation for another day).

Five year allocation: In this example, an allocation to the worst two performing asset classes over the last 5 years (mean reversion) and the best two performing asset classes (momentum) are held for the next five years. There is a HUGE caveat in this analysis as since 1997 there have been only 3 periods of rebalancing (so take the exact results with a grain of salt, though this has been verified in past research performed by Meb Faber).

Mean Reversion Momentum
2002-2007 21.10% 10.81%
2007-2012 1.80% 2.30%
2012-2017 8.67% 6.80%
Geometric Return 10.24% 6.58%


One year allocation: The reason I didn't bother to build out the five year allocation analysis further (to remove the issue outlined above) is that it doesn't really matter once you see the shorter-term results. In this example, we allocated to the bottom two / top two performing asset classes from the previous five years, but held on for the following 12-months (more data points than above, but we'll have a lot more below).

Mean Reversion Momentum
2003 -15.3% -19.9%
2004 64.5% 50.9%
2005 13.3% 21.0%
2006 13.9% 21.3%
2007 16.9% 23.5%
2008 -8.1% 4.4%
2009 -43.0% -53.0%
2010 76.9% 73.7%
2011 30.8% 26.2%
2012 -1.1% 1.4%
2013 15.2% 7.8%
2014 7.0% 18.7%
2015 2.9% 17.1%
2016 -19.0% -7.8%
2017 23.1% 22.0%
Geometric Return 8.0% 9.7%

Monthly allocation: In this case we allocated to the bottom two / top two performing asset classes from the previous five years, but held on for the following one month (performance is shown for the 12-months ending February of each year).

Mean Reversion Momentum
2003 -15.2% -14.7%
2004 48.7% 57.4%
2005 13.2% 12.3%
2006 13.9% 34.9%
2007 14.1% 23.9%
2008 -8.2% 5.4%
2009 -42.1% -56.1%
2010 78.8% 73.1%
2011 35.5% 24.4%
2012 -1.2% 1.6%
2013 17.4% 5.7%
2014 4.4% 24.3%
2015 3.0% 13.8%
2016 -18.9% -9.8%
2017 23.2% 23.6%
Geometric Return 7.5% 10.1%

Mean Reversion Captured via Momentum

Asset classes mean revert over longer periods, but this analysis provides a good starting point for the hypothesis that it can can be captured more effectively through momentum than by allocating to a down-an-out area of the market. The chart below shows that the best performing asset class was emerging markets for an extended period roughly 5 years after being the worst ranked asset class in 2002, REITs in 2012 were the best after being the worst ranked asset class during the financial crisis, and US stocks more recently were the best after ranking poorly for much of the period following the financial crisis.


For an investor the takeaway is good news... rather having to allocate to an underperforming asset class over the past x years, simply wait for that underperforming / cheap asset class to start performing well. While you may miss the exact turn, you may be able to capture the longer run success when the asset class starts working without having to deal with the pain that created the opportunity. 

Thursday, February 23, 2017

The Potential Return-Free Risk of Bonds

I've read too many posts / articles that outline why a rise in rates is good for long-term bond investors (as that would allow reinvestment at higher rates). While this can be true depending on the duration of bonds owned and/or for nominal returns over an extended period of time, it is certainly not true over shorter periods of time and absolutely not true for an investor in most real return scenarios... even over very long periods of time.


BACKDROP

I'll take a step back and go to an interesting question posed by George Pearkes the other day (abbreviations removed for clarity):

Anyone care to estimate how big losses would be if you rolled 10 year US Treasuries at constant maturity for next 10 years w/ 25 bps of rate rise per quarter?
My response (completely translated from Twitter speak for clarity) was:
  • A 25 bp move per quarter is roughly a 2% loss per move given the current duration of around 8 years (0.25% x 8 = 2%).
  • So an investment would lose money each quarter until the yield (currently 2.4%) is greater than 8% (8% / 4 quarters in a year = 2%, which would offset the loss from the rate hike). 
  • Given an 8% yield would happen during year 6 (6 years x 4 quarters x 0.25% = 6% hike + current 2.4% = 8.4% at the end of year 6).
  • Year 6 is around midway of the 10 year horizon, so total return would be close to 0% cumulative over the ten years.
This was pretty close to being correct. The chart on the right shows the path of rates assuming a 0.25% rise per quarter, while the chart on the left shows the cumulative return for an investor (slightly above 0% over this period).


In the above example, a 0.25% rise per quarter (1% per year) is pretty extreme, but even a smaller 50 bp / year rise would result in lower returns (~10%) than no move (1.024^10-1 = ~27%).



YOU CAN'T EAT NOMINAL RETURNS

Another problem for investors is that a rise in nominal rates does not occur in isolation. A rise is typically a function of a credit concern (much more likely with corporate / muni debt than treasuries), supply / demand imbalance, or inflation. For this exercise, I'll focus on the impact of inflation.

Nominal rates moved relatively closely with inflation from the late 1980's until the global financial crisis as investors demanded a real rate (nominal rate less inflation) of ~2% over that period (the recent period of QE pushed them much lower). It's the 1970's that highlights the real risk of inflation in a rising rate scenario; inflation overshot expectations, which created an environment in which inflation pushed real rates into negative territory (bond investors lost from rising rates and negative real carry).


Back to the scenarios... taking the same 0.25% rise in rates per quarter (1% / year) shown above and applying two alternative inflation paths, the left hand chart below shows the return profile if real returns were a constant 5% (i.e. inflation was consistently 5% below nominal treasury yields - in itself very optimistic for investors), while the right hand chart shows the return profile if real returns were a constant 2% (i.e. 3% higher inflation on the right hand side than left). In either scenario, the returns are decimated (not surprisingly... when inflation is higher, they are decimated more).



If you think the nominal return paths are too pessimistic (likely), let's take a look at a few scenarios that seem like pretty realistic possibilities based on market expectations for both rates and inflation. On the left hand chart we show a 20 bp rise per year with 1.5% real yields (settling at ~4.5% yields with 3% inflation) and on the right hand chart we show a 15 bp rise per year scenario with 0.5% real yields (settling at ~4% yields with 3.5% inflation). In each of these scenarios there are cumulative losses over ten years in real terms.


My takeaway... if you think rates are poised to rise in the future... think twice about owning them. While the risk-free return of cash is hard to accept at current levels, that return may end up being more attractive than the return-free risk of bonds if rates do rise.

Monday, January 9, 2017

The Asymmetry of Reaching for Yield at Low Spreads

Bloomberg Gadfly's Lisa Abramowicz (follow her on twitter here) outlined in a recent piece The Credit Boom that Just Won't Die the insatiable demand for investment grade credit.

Last month, bankers and investors told Bloomberg's Claire Boston that they expected U.S. investment-grade bond sales to finally slow after six consecutive years of unprecedented issuance. But the exact opposite seems to be happening, at least if the first few days of 2017 are any guide. The debt sales are accelerating, with the biggest volumes of issuance ever for the first week of January, according to data compiled by Bloomberg.
Lisa followed up this morning with a tweet outlining similar demand within high yield pushing the spread to treasuries to 3.83%, the lowest level since September 2014. That 3.83% option adjusted spread is the excess yield a high yield investor demands above a treasury bond of similar duration. Note that I did not say to be paid above a treasury bond of similar duration. The reason is historically high yield bonds have (on average) returned ~3.5% less than their yield going back 30 years due to credit events (the chart below is from a previous post The Case Against High Yield).


As a result, with a current option adjusted spread of 3.83%, if high yield bonds returned what they have returned relative to their spread ON AVERAGE since 1986, high yield bond investors should only expect a forward return that matches that of a treasury bond with similar duration (with a whole lot more risk).


But things can get worse

The next chart compares the option adjusted spread "OAS" of the Barclays High Yield Index relative to the forward excess performance vs treasury bonds of a similar duration since 1995. Note that yield to worst data goes back to the mid 1980's, whereas OAS only goes back to the mid 1990's hence the different time frame than the example above. The chart clearly shows the strong relationship between the two, but note that the upside potential of high yield is much more symmetrical at higher OAS levels, whereas there is more downside when starting OAS is at lower levels. This is driven largely by where in the credit cycle we are when OAS is low (often near the end) vs when OAS is high (often near the beginning).


In fact, we can see in the chart above that when we were at similar levels of OAS as we currently sit, high yield has never provided excess returns to treasuries more than its starting OAS. In fact, the chart below breaks out each of these ~80 starting periods when OAS was less than 4% and we can see that not only did high yield bonds underperform their starting OAS in every instance, the likelihood of underperforming treasuries has been much more prevalent (and with a higher degree of underperformance) than the likelihood of outperforming treasuries (the red line shows that on average high yield bonds underperformed treasuries by 2% at similar levels).


So if you are looking at the low yields of treasury bonds and searching for an alternative or believe that the spread of high yield may help cushion performance from any further rise in treasury rates, I would tread very carefully.