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Monetary Cycles and the Fixed Income Market – What Can the Past Tell Us About the Current Cycle?

Avoiding Beta Pollution in the Search for Value

Futures Prices for Europe’s Fear Gauge Jump. U.S. Investors say, “Le Pen? Le Who?”

Commodity February Facts, Bears to Bulls, & Interest Rate Winners

Multi-Factor Indexes: More Bang for Your Buck

Monetary Cycles and the Fixed Income Market – What Can the Past Tell Us About the Current Cycle?

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Dennis Badlyans

Associate Director, Global Research & Design

S&P Dow Jones Indices

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Rising rates are generally seen as bad news by fixed income market participants.  As rates go up, prices of fixed income assets are expected to go down.  However, returns (or losses) can vary depending on characteristics of the cycle, as well as the amount of income or carry available to cushion the decline in price.

Historical analysis of previous policy cycles can help to better understand how fixed income assets may respond through the current cycle.  A change to the federal funds rate transmits to fixed income asset prices through the front end of the risk-free curve.  For example, over the two-year tightening cycle that ended in 2006, the Fed hiked policy rates by 425 bps, causing the One-Year U.S. Treasury Bond yield to increase by 316 bps (see Exhibit 1).  Longer-term rates, however, are heavily influenced by other macroeconomic factors and technical drivers, such as supply and demand of bonds.  Over the same tightening cycle that ended in 2006, the impact on the 10-Year U.S. Treasury Bond yield was 60 bps higher, driving the 1-Year/10-Year slope to flatten by 265 bps (see Exhibit 1).  As shown by the slope changes in the table, flatter curves have been characteristic of tightening cycles and steeper curves have resulted from easing cycles.

The main challenge the committee faces is normalizing from the unprecedented low levels that have been the norm for nearly a decade, while balancing negative effects to a jittery economic recovery.  In spirit, the current tightening cycle is no different from previous ones, in that the FOMC is balancing the tradeoff between inflation and growth.  In the easing cycle that began at the Sep. 18, 2007, FOMC meeting and lasted through the January 2009 meeting, the committee acted swiftly to cut the federal funds rate by 500 bps.  Less traditional policy tools (such as quantitative easing) were used to further augment the accommodative stance of the Fed.  Furthermore, the accommodative stance has been held for nearly a decade (see Exhibit 2).  With two hikes already behind us, FOMC members seem to be uniting around the message of three additional hikes in 2017, for a total of five rate hikes over 20 meetings (a two-year period—or, arguably, a five-year period since the announcement of tapering of quantitative easing in 2013).

In the current protracted tightening cycle, the Treasury yield curve has remained relatively steep.  The yield difference between the S&P U.S. Treasury Bond Current 10-Year Index and the S&P U.S. Treasury Bond Current 2-Year Index was 122 bps as of the close on Feb. 23, 2017, versus about 120 bps pre-taper tantrum (see Exhibit 3).  This may be partly due to technical pressures on the back end of the curve (see Exhibit 4).

In this blog post we provided a glimpse into the characteristics of monetary policy cycles and the impact on yields.  In follow-up posts, we will discuss the implications for total return of treasury portfolios and credit products.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Avoiding Beta Pollution in the Search for Value

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Shaun Wurzbach

Managing Director, Global Head of Financial Advisor Channel

S&P Dow Jones Indices

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In our latest S&P 500® Factor Indices Dashboard, Tim Edwards wrote, “Value’s return to form came after a decade of meagre pickings.  In fact, value recorded in its best annual relative performance since 2000.”

Small wonder then that the S&P 500 Enhanced Value Index and S&P 500 Pure Value were popular topics of financial advisor discussion at the 2017 Inside ETFs Conference in January.

Mike Philbrick, President of ReSolve Asset Management, shared an interesting view: “I like S&P 500 Pure Value because the index isn’t ‘polluted’ with beta.”  I had to follow up on that to see what he meant.  Mike’s point is that since S&P 500 Pure Value’s methodology selects only one-quarter of the benchmark index’s market capitalization (the index has 116 constituents as of the last rebalancing) with the strongest value scores and excludes the rest, it is a better return-enhancing risk premia “mousetrap” than previous generations of value indices.

By taking a look at the factor scoring diagram below of the S&P 500 Value and comparing that to the factor scoring diagram of S&P 500 Pure Value, Mike’s point is made visually.  The S&P 500 Pure Value exhibits a much larger value factor rank than the S&P 500 Value.  In relative terms, the S&P 500 Value does not differ as much from the S&P 500 benchmark (beta) as the S&P 500 Pure Value does.

Speaking of the S&P 500 Pure Value, a question I hear frequently from financial advisors is whether an equal combination of the S&P 500 Pure Value and S&P 500 Pure Growth has outperformed our S&P 500.  Thanks to my friend, Sam Stovall, Chief Strategist at CFRA, one can see that the answer to that question is yes.  Sam calls this “barbell” approach the Mr. Universe Strategy.  Exhibit 3 shows his results after he applied an annual rebalance to the combination of S&P Pure Style Indices.  Based on Sam’s results, Mike Philbrick may have a good point about beta pollution.

At the Inside ETFs event, S&P DJI launched a factor allocator tool designed for us by Optimal Advisors, and we demonstrated that tool to financial advisors who visited our booth.  The tool visually and quantitatively shows how the S&P 500 Pure Value has performed.  Furthermore, the tool allows users to see the effects of combining the value factor with other factors such as quality and momentum.  Powered by more than 15 years of S&P DJI index data, this tool can be found here.  Financial advisors who saw this tool demonstrated at Inside ETFs said it answers some of their questions about how to implement value as a factor and combine value with other factors in a portfolio.  During our webinar for financial advisors on March 22, 2017, we will talk about factor case studies that make use of our factor dashboard and factor allocator.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Futures Prices for Europe’s Fear Gauge Jump. U.S. Investors say, “Le Pen? Le Who?”

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Reid Steadman

Managing Director, Global Head of ESG

S&P Dow Jones Indices

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Futures tied to VSTOXX, the Eurozone’s version of VIX, are signaling risk ahead. The French elections, which take place April 23 (first round) and May 7 (run-off election) and the possibility of Marine Le Pen leading France’s exit from the Eurozone appear to have spooked European options investors. They are now paying higher premiums for put options expiring near the election. As a result, the term structure for VSTOXX futures has assumed an unusual shape.

VSTOXX is usually higher than VIX (on average by 10.6 points over the past five years) but the two benchmarks tend to move in near lockstep.

Should U.S Investors Care?
Here’s a quick quiz for you. What percentage of S&P 500 company sales come from Europe? According to a report issued by S&P Dow Jones Indices last year, 7.8% of S&P 500 sales revenue in 2015 came from Europe.  If France were to leave the Eurozone, this could affect the U.S. in ways beyond those captured in direct sales figures. Still, 7.8% is a good statistic to keep in mind. Leading U.S. companies are less tied to the European economy than many investors would expect.

A Look Back at the Brexit Vote
Traders, policy experts, and journalists are comparing the French elections to the Brexit vote. It’s worth looking back to see how VSTOXX and VIX moved when British voters chose “Leave.”

VSTOXX and VIX are notoriously noisy signals – the “vol of vol” is high – but we can note the following:

  • Though most economists predicted “Remain” would win, VSTOXX traded at elevated levels before the election, indicating greater uncertainty in financial markets about the outcome.
  • When “Leave” prevailed, both VSTOXX and VIX jumped, but U.S. options investors were more surprised. One day after the Brexit vote, the VSTOXX closed only 8.7% higher than the day before (36.4 to 39.6). In comparison, VIX increased 49.3% between closings.
  • Options investors in both markets quickly focused forward and VSTOXX and VIX declined to normal levels in the course of a week.

The French election and Brexit are of course different, but VSTOXX and VIX look like they did leading up to the Brexit vote: VSTOXX is elevated and VIX is low. On April 23 and May 7, we will learn whether U.S. investors have been too complacent.

 

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Commodity February Facts, Bears to Bulls, & Interest Rate Winners

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Jodie Gunzberg

Managing Director, Head of U.S. Equities

S&P Dow Jones Indices

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It was a flattish February for commodities with the S&P GSCI Total Return up 23 basis points for a year-to-date return of -1.2%, and the Dow Jones Commodity Index up 11 basis points bringing its year-to-date return to 78 basis points.  Overall  in the S&P GSCI TR, 3 of 5 sectors were positive and 14 of 24 commodities were positive. The S&P GSCI Precious Metals TR was the best performing sector, gaining 3.7%, and the S&P GSCI Nickel TR was the best performing single commodity up 10.2%.  The worst performing sector was the S&P GSCI Energy TR with a loss of 26 basis points, led by the biggest single commodity drop of 13.1% from the S&P GSCI Natural Gas TR.

Before discussing what a difference one year makes and interest rate impacts on commodities, a few stats from inside the S&P GSCI TR are interesting from this forgettable February:

  • Aluminum had its biggest consecutive 2-month gain of 13.5%, in 5 years when it returned 13.9% in Jan.-Feb. 2012.
  • Cocoa lost for the 6th consecutive month, its longest losing streak since the 9 months ending in May 1999. Only four times in history since 1984 has cocoa been down this long.  Also it is cocoa’s 2nd biggest drop in history, down 34.2% since Sep. 2016.  Cocoa lost most, -48.2%, in Sep. 98 – May 99. Its other big losses occurred from  Jan. – Jun. 1986 (-25.4%) and Dec. 92 – Jun. 93 (-17.9%.)
  • Gold posted its first consecutive 2-month gain, 8.7% since Jun.-Jul. 2016, when it gained 10.9%.
  • Kansas Wheat and Wheat posted the first positive consecutive 3-months ending April 2014 for Kansas Wheat and Dec. 2014 for Wheat.  For the 3 months ending Feb., Kansas Wheat gained 10.0% and Wheat gained 7.3%.
  • Natural gas lost 27% in Jan.-Feb. 2017. its worst 2-month loss since Jan. – Feb. 2016 when it fell 29.2%.
  • Nickel had it best month, up 10.2%, since July 2016 when it gained 12.4%.
  • Unleaded gas is down 10.5% in Jan. – Feb. 2016, the most since Jun. – July 2016 when it lost 19.3%.

While energy has struggled to start the year, losing 4.9%, all other sectors are up, reflected in the year-to-date performance of the S&P GSCI Non-Energy Total Return of 5.1%.  As a reminder of how far commodities have come from this time last year, there were 10 commodities in a bear market that caused both the S&P GSCI TR and energy sector to be down more than 20% for the 12 months ending in Feb. 2016, and every single commodity and sector were negative except lead and gold.  Now for the 12 months ending in Feb. 2017, every one of those bears are positive except for Kansas wheat.   Further, there are 11 bulls plus the sectors of energy and industrial metals are each up over 20% and only cocoa is in a bear market.

Source: S&P Dow Jones Indices. 12-month performance ending Feb. 2016 and Feb. 2017 of S&P GSCI Total Return, singles and sectors within.

If commodities performance going forward depends on interest rates, there may be more of the bulls charging.  Historically rising interest rates are positive for commodities.  On average in rising rate periods, the S&P GSCI TR has gained 43.5%, more than the spot index that has gained on average 31.3%, showing that rising rates may drive carrying costs higher and making it less beneficial to hold inventory.

Source: S&P Dow Jones Indices. https://fred.stlouisfed.org (Mar. 1, 2017)

Which commodities benefit most from the rising rates? Energy and metals. Only cocoa and feeder cattle lose in rising interest rate environments.  Additionally, for natural gas and wheat that are both difficult to store, the total return that includes the storage costs declines with rising rates.

Source: S&P Dow Jones Indices.

The posts on this blog are opinions, not advice. Please read our Disclaimers.

Multi-Factor Indexes: More Bang for Your Buck

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Adam Butler

CEO

ReSolve Asset Management

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Many advisors are unsure whether introducing factor-tilt ‘smart beta’ strategies into portfolios will improve client outcomes. In fact, some factor tilt portfolios appear to provide the equivalent of levered exposures to a diverse set of alternative risk premia. This is because factor tilt portfolios may contain much greater than 100% exposure to several risk factors in aggregate.

Consider the iShares S&P 600 Small-Cap Value Index ETF which is constructed to hold small-cap companies with high book-to-market, earnings-to-price, and sales-to-price ratios. Via simple back-tested regression on the underlying index back to inception in 1997, we find that it offers the following factor exposures, which are all significant at the 0.1% level:

It is useful to think about the factor betas in Table 1 as weights in a synthetic portfolio. Given historical data from Ken French (MKT, SMB, HML and UMD) and AQR (QMJ), and using monthly return data, we calculate the following premia and weighted excess return:

The expected total excess arithmetic return from all factor exposures would be the weighted sum of the factor exposures and the premia, plus the intercept:

1.1*7.6%+0.82*2.7%+.53*4.5%+.26*8.3%+.43*4.2%-4.2% = 12.7%.

Given that the index implies over 300% exposure to risk factors, one might expect the portfolio to exhibit much higher volatility than a market capitalization weighted index. If the factors were all highly correlated, the portfolio would have a volatility approximately equal to the weighted average volatility of all exposures:

1.1*18%+.82*11%+.53*14%+.26*16%+.43*8% = 43.8%.

This would have meaningful implications in terms of compound return, as the implied compound annual return of the portfolio would be the arithmetic mean of 12.7% minus a volatility decay factor equal to half the portfolio variance: 0.127 – 0.5 * 0.438^2 = 3.1%. Compare this to the implied compound return of the market factor alone: 7.6% – 0.5 * 0.18^2 = 6%.

However, the factors are not highly correlated. In fact, the average off-diagonal pairwise correlations of the five factors in Table 1. is just -0.14. As such, we would expect the volatility of the portfolio to be less than the weighted average volatility of the factors. In fact, we can estimate expected volatility of the portfolio (sigma_p) from the following formula:

sigma_p = sqrt(w^T Sigma w),

where w= the factor exposures from Table 1. and Sigma is the covariance matrix of the factors:

Thus, the implied expected portfolio volatility would be about 25%. The compound excess return would be 12.7% – 0.5*.25^2 = 9.6, which now compares quite favorably to the capitalization weighted market factor. Moreover, the Sharpe ratio of the factor-tilted Index is 12.7%/25%=0.51, compared with the market’s Sharpe ratio of .42.

The posts on this blog are opinions, not advice. Please read our Disclaimers.