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Asian Fixed Income: China and the Global Bond Market

As Volatility Returns to Equities, Corporate Bond Spreads Tighten Near Record Lows

Earnings Revision Strategies in Asia

Energy Stocks Beat Oil Futures In Rising Inflation

Can Realized Volatility Predict Future Volatility for Preferred Securities?

Asian Fixed Income: China and the Global Bond Market

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Michele Leung

Director, Fixed Income Indices

S&P Dow Jones Indices

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In a previous piece, we discussed that China’s lackluster performance had made it the worst-performing country in the Pan Asian bond market in 2017, and that it was the only country that closed the year in negative territory. The one-year total return of the S&P China Bond Index fell 0.29% last year (see Exhibit 1), contrasting with the strong gains observed in previous years.

The S&P China Bond Index also underperformed the broad market. The S&P Global Developed Aggregate Ex-Collateralized Bond Index (USD), which seeks to track the performance of investment-grade debt issued by sovereign, quasi-sovereign, foreign government, and corporate entities in developed countries, delivered a total return of 7.64% in 2017.

Specifically for the corporate bond sector, China lagged its U.S. counterpart. The S&P 500® Bond Index is designed to measure the performance of U.S. corporate bonds issued by the constituents of the iconic S&P 500, and it rose 6.05% last year, compared with the 0.58% gain in the S&P China Corporate Bond Index (see Exhibit 2).

In terms of yield performance, the yield-to-worst of the S&P China Bond Index widened 178 bps to 4.78%, as of Dec. 29, 2017. As a comparison, the yield-to-maturity of the S&P Global Developed Aggregate Ex-Collateralized Bond Index (USD) and the S&P 500 Bond Index were 1.41% and 3.28%, respectively.

Despite their performance, China’s solid economic growth, attractive bond yields, and the underparticipation of foreign investors may serve as positive drivers for the Chinese bond market.

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

As Volatility Returns to Equities, Corporate Bond Spreads Tighten Near Record Lows

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Jason Giordano

Director, Fixed Income, Product Management

S&P Dow Jones Indices

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Broad-based equity markets have been on a rollercoaster ride since Jan. 30, 2018, as market participants appear to be reassessing the impact of inflation and potential consequences from the recent tax reform. While volatility appears to be back, high-grade corporate bond spreads have tightened to levels not seen since 2007. Compared with the last episode of substantive volatility in equities, there is a noticeable difference in how credit markets are reacting (see Exhibit 1).

In 2016, equity indices began the year down double digits, as oil prices plummeted and contagion spread, while multiple energy companies filed for bankruptcy. As a result, investment-grade credit spreads widened by 50 bps, with high-yield spreads jumping over 200 bps.

2018’s volatility is showing markedly different results in the bond market. As of Feb. 5, 2018, investment-grade spreads had tightened 6 bps and were more than 110 bps tighter compared with February 2016, as measured by the S&P 500 Investment Grade Corporate Bond Index.

There were several factors contributing to this.

1) Increased U.S. Treasury Yields: Inflationary pressures from global growth, increasing wages, and quantitative tightening have driven yields higher throughout the curve. The yield on the 10-year U.S. Treasury bond (as measured by the S&P U.S. Treasury Bond Current 10-Year Index) rose 30 bps in January and hit 2.70% for the first time since 2014 (see Exhibit 2).

2) Market Technicals: High-grade issuance was relatively sparse in January, specifically in the non-financial space. Total issuance volume was down over 30% compared with January 2017, with non-financials sectors capped at USD 27 billion—a 45% reduction from 2017. Many market participants anticipate that the repatriation and tax changes in the new laws may potentially affect borrowing patterns with high-quality issuers, and that sentiment was reflected in average new issue spreads (see Exhibit 3).

3) Credit Fundamentals: Investors appear comfortable with current corporate credit fundamentals. The bullish argument asserts that the reduction in corporate tax rates will have a front-loaded impact. Since these rates become effective in 2018, many corporations will have an immediate increase in their level of free cash flow. Additionally, changes in the tax code could also allow companies to use repatriated cash to delever, further reducing credit risk.

 

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

Earnings Revision Strategies in Asia

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Utkarsh Agrawal

Associate Director, Global Research & Design

S&P Dow Jones Indices

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Factor-based strategies have been regularly used by market participants in their portfolio construction process. Apart from the established factors like value, size, volatility, etc., research on alternative factors has remained important to explain sources of alpha. One such alternative factor is consensus analysts’ earnings forecasts. Ample empirical research exists that explains the market’s reaction to analysts’ earnings forecasts. Most of the research focuses on the change in the consensus estimate or the number of upgrades or downgrades in the estimates over short-term periods.

Compared with the U.S. and the European markets, the Asian market is more distributed and each individual market in Asia has its own characteristics. In our research paper Do Earnings Revisions Matter in Asia, we tested earnings revision strategies across seven Pan Asian markets—Australia, China, Hong Kong, India, Japan, South Korea, and Taiwan. We examined the three-month change in the consensus estimate and the three-month diffusion of analysts’ earnings forecasts from Dec. 31, 2005, to Dec. 31, 2016.

The key findings were as follows.

  1. Stock prices tended to move in the same direction as their earnings revisions in the majority of Pan Asian markets. Earnings revision strategies delivered the most significant excess returns in South Korea, India, and Taiwan, but they did not work in Japan (see Exhibit 1).
  2. Market participants generally had stronger reactions to the net percentage of upward and downward revisions in earnings estimates rather than the percentage change of the consensus estimate figures.
  3. Companies with poor earnings revisions tended to have more volatile returns, as market participants reacted negatively to companies with downward revisions in estimates.
  4. Earnings revision strategies tended to generate more alpha in the small-cap universe than in the large-mid-cap universe, although there was no strong sector or size bias.

Like other momentum strategies, the earnings revision strategies also had high turnover. In addition, the market-cap effect disrupted the signal from the earnings revision strategies. Therefore, implementing this strategy in combination with other fundamental factors and alternative weighting schemes would be essential to capture alpha. To find out more, please see the full report.

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

Energy Stocks Beat Oil Futures In Rising Inflation

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Jodie Gunzberg

Managing Director, Head of U.S. Equities

S&P Dow Jones Indices

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There may be a legitimate concern about rising inflation since inflation has been under the Fed’s target for years and there is now very low unemployment with signs of accelerating economic growth.  The low unemployment rate and strong growth are probably positive for the stock market and the tax cuts may provide even more upside.  However, if inflation upticks and the Fed raises rates to slow it, the increased cost of capital and likely drop in consumer spending may put pressure on stocks.

Understanding more about how to position equities in the event of rising inflation may be useful in this uncertain time.  In conjunction with the impacts on stocks from accelerating growth and the falling dollar, a more complete picture of investment strategy inside an equity allocation can be formed.  Small-caps benefit most from accelerating growth, mid-caps do best from a falling dollar, and what will be shown in this analysis is that mid-caps are most sensitive to inflation, and energy is by far the most sensitive sector.  Also what is interesting is that though crude oil itself is more sensitive to inflation than the energy equities, once the roll yield of the futures contracts is considered, all the benefit is lost.

Let’s start with the 10 year correlation using monthly year-over-year data of index levels and CPI (Consumer Price Index.)  Overall, equities are not that correlated with inflation at just over 0.2.  However, the energy sector correlation to inflation of 0.6 is triple the correlation of the broad equities to inflation.  The crude oil futures are even more correlated to inflation, measuring over 0.8, that is considered highly correlated.  This is because energy is the most volatile component of CPI, essentially driving it.

Source: S&P Dow Jones Indices. https://www.bls.gov/cpi/

Another common measure used to evaluate the sensitivity of assets to inflation is called inflation beta.  It means, “if there was a 1% change in CPI, then there was an x% change in the index.”  For example, in the chart below, for every 1% rise in inflation there was a 17% increase on average for small-cap energy stocks.  The S&P MidCap 400 is the most sensitive broad-based index, with an inflation beta of 3.3, only slightly higher than the 3.1 inflation beta of the S&P 600, but markedly higher than the 2.6 inflation beta of the S&P 500.  Again, the crude oil futures have an even higher inflation beta than the equities at about 20.  Since the small-caps are least likely to hedge, they may benefit most with rising oil and inflation.

Source: S&P Dow Jones Indices. https://www.bls.gov/cpi/

One might conclude that for inflation protection, crude oil is the best bet.  Though the problem with getting inflation protection with crude oil futures is that market participants need to pay storage costs, reflected through the roll yield when there is excess inventory.  In a decade plagued with high inventory, this has cost crude oil futures investors an additional 48% beyond the -37% lost in the spot market.  The result has been a negative up market capture ratio of -109.7, meaning for every 1% rise in inflation, the S&P GSCI Crude Oil Excess Return actually fell by 1.1% on average in the past 10 years.

Given equities are better than futures for inflation protection, after the roll yield is included, and that small-caps and mid-caps have done best with accelerating growth, the falling dollar and rising inflation, perhaps the strategy might be to underweight large caps.

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

Can Realized Volatility Predict Future Volatility for Preferred Securities?

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Hong Xie

Senior Director, Global Research & Design

S&P Dow Jones Indices

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The investment community routinely uses historical realized volatility as an indicator of future volatility. One prominent example of this is the construction of ranking-based, low volatility equity strategies where realized volatility is used to form low volatility portfolios. Can the realized volatility of an asset indicate its future volatility? We extend the low volatility analysis to U.S. preferred stocks to see if the low volatility effect is present in other asset classes. For our analysis, the S&P U.S. Preferred Stock Index is used to represent the investment universe of U.S. preferred stocks.

To construct a low volatility factor portfolio, it is common to select securities that had low realized volatility over a pre-specified period and hold the portfolio for the subsequent n months. Accordingly, in our analysis, we measure the relationship between the realized volatility over the previous one-year period and that of the subsequent three months across all preferred securities in the universe for each quarterly rebalance date.

Exhibit 1 shows the volatility for the next three months versus the realized volatility for the past year in scatter plots. The top plot shows a positive relationship between these two sets of realized volatility. Moreover, the relationship seems to be exponentially related, rather than linearly. The bottom graph plots the two sets of volatility in natural logarithm terms and shows a more linear relationship than the top plot.

In Exhibit 2, we show the rolling correlation, where at each time point the correlation is calculated between the realized volatility over the past year and the realized volatility over the next three months for all the preferred stocks in the universe as of each quarterly rebalance date. Since low volatility factor portfolios typically select securities using volatility rank, we also calculated the correlation of volatility ranks based on these two sets of realized volatility.

The top panel shows that the correlation of the logarithmic volatilities seems to be more stable than that of the volatilities themselves for each point in time. The bottom panel shows that the correlation of volatility rank and the logarithmic volatility rank are identical. Since October 2003, the average correlation of the rank of the two sets of volatility is 0.66.

The analysis shows that, on average, a preferred stock that ranks low or high based on its realized volatility over the past year is likely to continue to rank low or high for the volatility exhibited in the next three months. This lends support to arguments for using realized volatility to construct a low volatility factor portfolio for preferred stocks.

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