Indexology^® Blog

The smaller than expected rise in payrolls in this morning’s January Employment Report disappointed people, but the details were far better than the 151,000 gain in payrolls.  Even though to total of new jobs was about 40,000 lower than the consensus forecast, some details in the number were surprisingly good: manufacturing employment jumped by 29,000.  Hours and earnings also improved. Average weekly hours were 34.6, a new post-recession high and average hourly earnings were 2.5% higher than January 2015.

The more impressive figures were in the other part of the report, the Household Survey.  The unemployment rate dipped to 4.9%, the lowest since the end of the Great Recession.  In some past months the drop in the unemployment rate resulted from people leaving the labor force – people giving up hope of finding a job and dropping out.  Not in these data: the labor force rose faster than the population rose, the employment population gained and the number of unemployed fell.  The number working part time for economic reasons crept down slightly.

The Employment Report is actually two separate surveys combined.  Most securities analysts and economists focus on the number of people working based on company payrolls –the payroll survey.  The total figure was 143,288,000; its monthly increase of 151,000 was weaker than the average of 228,000 per month during 2015.  The chart shows the monthly changes in payrolls since the start of the last recession at the end of 2007.  This report was on the low side, but shouldn’t be seen as a sign of an immediate downturn.  Those who want to watch developments weekly can follow the initial unemployment claims series reported each Thursday morning.  As long as the weekly number is under 300,000, there is not much cause for concern.  The other portion of the Employment Report is based on surveying households and asking people if they’re working. There are some differences in the two surveys – someone who has two jobs will be counted twice in the payrolls numbers but only once in the household data.

Global yields are lower year-to-date in all developed countries except Hong Kong, as measured by the S&P Global Developed Sovereign Bond Index.

Weak economic growth, low inflation, and concern over the situation in the Middle East have led many to invest in safer assets.

The U.S. market took note of the slowing growth, both domestically and internationally, as the S&P/BGCantor Current 10-Year U.S. Treasury Index’s yield-to-worst moved 13 bps lower between the week of Jan. 28, 2016, and Feb. 4, 2016.

Japan’s policy makers switched to negative interest rates in an attempt to keep the economy from going deeper into the malaise that has weighed down its economy for the last two decades.

The European Central Bank is in a sub-zero interest rate environment as well.

The Bank of England board members voted unanimously to hold rates at 0.5%.

The Swedish Riksbank will meet Feb. 11, 2016, possibly to announce even lower interest rates, after adopting negative interest rates a year ago.

Oil prices, as measured by WTI crude oil futures, have moved down 18% since the beginning of the year to USD 31, as of Feb. 4, 2016.

Exhibit 1: Countries of the S&P Global Developed Sovereign Bond Index

Source: S&P Dow Jones Indices LLC.  Data as of Feb. 4, 2016.  Chart is provided for Illustrative purposes.

Factor Definition
The fixed income investment community has long used volatility in analyzing bond valuations and identifying investment opportunities.  We have defined volatility as the standard deviation of bond yield changes for the trailing six-month period.  All else being equal, the more volatile the bond yield is, the higher the yield needs to be in order to compensate for the volatility risk.

For the credit spread factor, we consider the option adjusted spread (OAS), which represents the yield compensation for bearing credit risk, as well as other risks associated with credit.  OAS is a common measure of valuation for corporate bonds.

Factor Identification
To identify the factors that could enhance security selection, we computed the performance statistics of the quintile portfolios ranked by each factor and demonstrated the strong relationship of factor exposure, portfolio return, and return volatility.

The underlying universe for our study was the S&P U.S. Issued Investment Grade Corporate Bond Index (U.S. issuers).  The index seeks to measure the performance of investment-grade corporate bonds issued by U.S.-domiciled corporations denominated in U.S. dollars.  Based on the availability of the constituent data and the yield curve, the period covered in the study was from June 30, 2006, through Aug. 31, 2015.  We derived an investable sub-universe (details in next blog) based on the broad universe and constructed quintile portfolios of the investable sub-universe.

To form the quintile portfolios, we first ranked bonds within the investable sub-universe by each factor (credit spread and low volatility) and divided the universe into five groups, with higher values ranking higher (Quintile 1) for credit spread and lower values ranking higher (Quintile 1) for low volatility.  It should be noted that these two single-factor portfolios did not control for either duration or credit rating.  Exhibit 1 shows the performance statistics of ranked quintile portfolios by single factors.

Exhibit 1 confirms a positive relationship between credit spread, portfolio return, and return volatility; i.e., the wider the credit spread is, the higher the return and return volatility are (Quintile 1).  Contrary to the credit spread factor, we observed a non-linear relationship between risk and return for the low volatility factor.  The Quintile 1 portfolio, containing the least volatile bonds, had the lowest return and the highest level of realized portfolio volatility.

Exhibit 1 also includes performance statistics for the quintile portfolios formed by ranking the low volatility factor within each duration and rating grouping.  These modified quintile portfolios displayed a generally positive relationship between the low volatility factor, return, and return volatility, as expected.

This demonstrates that applying the low volatility factor without taking duration and quality into consideration is not consistent in explaining portfolio return and risk.  This is because simply ranking bonds by yield volatility across the universe can potentially result in highly concentrated portfolios in duration or quality, which in turn can cause greater portfolio volatility.  This can be particularly exacerbated when long-duration bonds exhibit lower yield volatility than short-duration bonds when the market is quiet.

These findings confirm that credit spread and low volatility factors can effectively explain portfolio return and volatility and present the necessity of applying factors while taking duration and quality into consideration.

Despite the fact that many single-factor strategies have empirically delivered positive excess returns in the long run, they have suffered periods of substantial underperformance under certain market conditions due to their cyclicality.  Blending a number of desired factors with low correlations is a potential way to attain more balanced and diversified portfolios.  The obvious questions that arise are: Are they likely to achieve better performance?  How do they behave in different financial environments?

Our stylized portfolios that blend six factors (volatility, value, quality, size, momentum, and dividend yield) with four different strategies (marginal risk contribution, minimum variance, Sharpe-ratio weighted, and equity weighted) demonstrated higher risk-adjusted returns than the S&P 500^®, with a lower tracking error than most single-factor strategies (see Exhibit 1).

Over the entire period, marginal risk contribution and equal-weighted strategies exhibited less cyclicality than other multi-factor and most single-factor strategies.  They achieved more consistent and higher excess return than other strategies across various market cycle phases.  On average, they produced higher information ratios and a higher incidence of outperformance during recovery and bearish periods.

The minimum-variance strategy had a significantly lower information ratio and a lower incidence of outperformance in bullish and recovery markets, similar to, but less defensive than, the single low-volatility strategy.  The Sharpe-ratio-weighted strategy performed well in bear markets, but it significantly lagged the S&P 500 in recovery periods.  This suggests that using recent momentum to tilt factor exposures did not improve the risk-adjusted return of the multi-factor portfolio, even though it had a lower drawdown as a result of the strategy moving into cash during the global financial crisis.

For more information on our research on multi-factor smart beta strategies, please click here, where you will also find details about:

• What is driving risk/return of various smart beta strategies, and
• How each of the smart beta strategies have performed in different macroeconomic and market environments.

Factor-based investing in equities is a well-established concept supported by over four decades of research. However, factor-based investing in fixed income remains in its nascent stages. Our analysis has found that factor-based fixed income strategies implemented in a rules-based, transparent index can represent an alternative tool for fixed income portfolio construction. In the next few blogs, we will detail our approach to and back-tested results of employing credit spread (value) and volatility as factors in order to systematically construct a portfolio of U.S. investment-grade corporate bonds.

Investment Philosophy
In practice, an active portfolio manager for a corporate bond mandate mostly focuses on credit returns, specializing in expressing views on the direction of credit spreads and security selection. We propose a two-factor model to seek to collect an active return from security selection, identifying relative value opportunity in credit returns while keeping portfolio duration and credit duration natural to the underlying universe.

Two Factors: Volatility and Credit Spread
To achieve better security selection, we chose two factors that empirically have demonstrated a strong relationship between factor exposure and performance statistics and that have long been incorporated in investment analysis by corporate bond portfolio managers. Our selection of factors was by no means exhaustive.

The fixed income investment community has long used volatility in analyzing bond valuations and identifying investment opportunities. We have defined volatility as the standard deviation of bond yield changes for the trailing six-month period. All else being equal, the more volatile the bond yield is, the higher the yield needs to be in order to compensate for the volatility risk.

For the credit spread factor, we consider the option-adjusted spread (OAS), which represents the yield compensation for bearing credit risk and other risks associated with credit. OAS is a common measure of valuation for corporate bonds.

Back-Testing Total Return

Exhibit 1 shows the cumulative total return for our two-factor model versus the broad market. In the next post, we will demonstrate how we identify these two factors.