Bonds have two price components, yield and response of price to prevailing interest rates. How much of a return premium should investors in bonds expect? How can investors enhance this premium? These blog entries examine investing in bonds.

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Four-factor Model of Corporate Bond Returns

Do factor models predict returns for corporate bonds as they do for stocks? In their October 2014 paper entitled “Factor Investing in the Corporate Bond Market”, Patrick Houweling and Jeroen van Zundert develop and test a four-factor (size, low-risk, value and momentum) model of future corporate bond returns. Each month for investment grade and high yield bond market segments separately, they construct an equally-weighted long-only portfolio consisting of the 10% of bonds with the highest exposure to each factor. They hold portfolios for 12 months, resulting in 12 overlapping portfolios for each segment and factor. Specifically, the factor portfolios are:

  1. Size – the 10% of bonds with the smallest company index weights, calculated as the sum of market value weights of all company bonds in the index that month.
  2. Low-risk – a combination of rating and maturity. For investment grade, the portfolio holds the 10% of bonds rated AAA to A- and having the shortest maturities. For high yield, the portfolio holds the 10% of bonds rated BB+ to B- and having the shortest maturities. On average, the maturity threshold is 2.8 (3.7) years for investment grade (high yield).
  3. Value – the 10% of bonds with the largest percentage gaps between actual credit spread and credit spread indicated by monthly regressions of credit spread on rating.
  4. Momentum – the 10% of bonds with the highest return relative to duration-matched U.S. Treasuries from six months ago to one month ago (with a skip-month to avoid reversal).

They evaluate factor portfolio performance based on excess return of constituent corporate bonds versus duration-matched U.S. Treasuries (thereby focusing on the default premium component of corporate bond returns). To estimate trading frictions, they model bid-ask spreads based on maturity and rating (the longer maturity or the lower the rating, the larger the estimated trading friction). Portfolio-level trading frictions are sums of frictions for all bonds traded. Using monthly data for all bonds in the Barclays U.S. Corporate Investment Grade index and the Barclays U.S. Corporate High Yield index during January 1994 through December 2013 (about 800,000 investment grade and 300,000 high yield bond-month observations), they find that: Keep Reading

Optimal Rebalancing Method/Frequency?

How much performance improvement comes from rebalancing a stocks-bonds portfolio, and what specific rebalancing approach works best? In their August 2014 paper entitled “Testing Rebalancing Strategies for Stock-Bond Portfolios Across Different Asset Allocations”, Hubert Dichtl, Wolfgang Drobetz and Martin Wambach investigate the net performance implications of different rebalancing approaches and different rebalancing frequencies on portfolios of stocks and government bonds with different weights and in different markets. With buy-and-hold as a benchmark, they consider three types of rebalancing rules: (1) strict periodic rebalancing to target weights; (2) threshold rebalancing, meaning periodic rebalancing to target weights if out-of-balance by 3% or more; and, (3) range rebalancing, meaning periodic rebalancing to plus (minus) 3% of target weights if above (below) target weights by more than 3%. They consider annual, quarterly and monthly rebalancing frequencies. They use 30 years of broad U.S., UK and German stock market, bond market and risk-free returns to construct simulations with 10-year investment horizons. Their simulation approach preserves most of the asset class time series characteristics, including stocks-bonds correlations. They assume round-trip rebalancing frictions of 0.15% (0.10% for stocks and 0.05% for bonds). Using monthly returns for country stock and bonds markets and risk-free yields during January 1982 through December 2011 to generate 100,000 simulated 10-year return paths, they find that: Keep Reading

Best Safe Haven ETF?

A subscriber asked which exchange-traded fund (ETF) asset class proxies make the best safe havens for the U.S. stock market as proxied by the S&P 500 Index. To investigate, we consider the the following 12 ETFs as potential safe havens:

Utilities Select Sector SPDR ETF (XLU)
iShares 20+ Year Treasury Bond (TLT)
iShares 7-10 Year Treasury Bond (IEF)
iShares 1-3 Year Treasury Bond (SHY)
iShares Core US Aggregate Bond (AGG)
iShares TIPS Bond (TIP)
SPDR Gold Shares (GLD)
PowerShares DB Commodity Tracking ETF (DBC)
United States Oil (USO)
iShares Silver Trust (SLV)
PowerShares DB G10 Currency Harvest ETF (DBV)

We consider three ways of testing these ETFs as safe havens for the U.S. stock market based on daily, weekly and monthly return measurement intervals:

  1. Contemporaneous return correlation with the S&P 500 Index during all market conditions.
  2. Return/performance during S&P 500 Index bear markets as specified by the index being below its 200-day/40-week/10-month simple moving average (SMA) for the prior measurement interval.
  3. Return/performance during S&P 500 Index bear markets as specified by the index being in drawdown from a prior high-water mark by more than some percentage (baseline -10%) for the prior measurement interval.

Using daily, weekly and monthly dividend-adjusted closing prices for the 12 ETFs from their respective inceptions through July 2014, and contemporaneous daily, weekly and monthly levels of the S&P 500 Index from 10 months before the earliest ETF inception through July 2014, we find that: Keep Reading

Real Bond Returns and Inflation

A subscriber asked (more than two years ago): “Everyone says I should not invest in bonds today because the interest rate is so low (and inflation is daunting). But real bond returns over the last 30 years are great, even while interest rates are low. Could you analyze why bonds do well after, but not before, 1981?” To investigate, we consider the U.S. long-run interest rate and the U.S. Consumer Price Index (CPI) series from Robert Shiller. The long-run interest rate is the yield on U.S. government bonds, specifically the 10-year U.S. Treasury note (T-note) after 1953. We apply the formula used by Aswath Damodaran to this yield series to estimate the nominal T-note returns. We use the CPI series to calculate the inflation rate. We subtract the inflation rate from the nominal T-note return to get the real T-note return. Using annual Shiller interest rate and CPI data for 1871 through 2013, we find that: Keep Reading

Credit Spread as a Stock Market Indicator

A reader commented and asked: “A wide credit spread (the difference in yields between Treasury notes or Treasury bonds and investment grade or junk corporate bonds) indicates fear of bankruptcies or other bad events. A narrow credit spread indicates high expectations for the economy and corporate world. Does the credit spread anticipate stock market behavior?” To investigate, we define the credit spread as the difference in yields between and Moody’s seasoned Baa corporate bonds and 10-year Treasury notes (T-note). Using average daily yields for these instruments by calendar month and contemporaneous monthly closes of the S&P 500 Index for April 1953 through June 2014 (735 months), we find that: Keep Reading

New Active Bond ETF Skims the Cream?

Do new funds have the latitude to concentrate in the best opportunities while they remain small? In his June 2014 presentation package entitled “How Long Might An Active Bond ETF’s ‘Best Ideas’ Outperformance Window Last?”, Claude Erb compares the performance of the PIMCO Total Return ETF (BOND), an exchange-traded fund (ETF) introduced in March 2012, to that of its parent mutual fund PIMCO Total Return Institutional Class (PTTRX). Using monthly total returns for BOND and PTTRX during March 2012 through May 2014, he finds that: Keep Reading

Long-term Equity Risk Premium Erosion?

Does the reward for taking the risk of holding stocks exhibit any long-term trend? In his April 2014 presentation package entitled “The Incredible Shrinking ‘Realized’ Equity Risk Premium”, Claude Erb examines the trend in the realized U.S. equity risk premium (ERP) since 1925. He defines this ERP as the retrospective difference in 10-year yield between the broad U.S. stock market and the 10-year yield on safe assets such as U.S. Treasury bills or intermediate-term U.S. Treasury notes. Using 10-year returns for U.S. stocks and various alternative safe assets (bills, notes and bonds) during 1925 through 2013, he finds that: Keep Reading

Net Performance of SMA and Intrinsic Momentum Timing Strategies

Does stock market timing based on simple moving average (SMA) and time-series (intrinsic or absolute) momentum strategies really work? In the November 2013 version of his paper entitled “The Real-Life Performance of Market Timing with Moving Average and Time-Series Momentum Rules”, Valeriy Zakamulin tests realistic long-only implementations of these strategies with estimated trading frictions. The SMA strategy enters (exits) an index when its unadjusted monthly close is above (below) the average over the last 2 to 24 months. The intrinsic momentum strategy enters (exits) an index when its unadjusted return over the last 2 to 24 months is positive (negative). Unadjusted means excluding dividends. He applies the strategies separately to four indexes: the S&P Composite Index, the Dow Jones Industrial Average, long-term U.S. government bonds and intermediate-term U.S. government bonds. When not in an index, both strategies earn the U.S. Treasury bill (T-bill) yield. He considers two test methodologies: (1) straightforward inception-to-date in-sample rule optimization followed by out-of-sample performance measurement, with various break points between in-sample and out-of-sample subperiods; and, (2) average performance across two sets of bootstrap simulations that preserve relevant statistical features of historical data (including serial return correlation for one set)He focuses on Sharpe ratio (including dividends) as the critical performance metric, but also considers terminal value of an initial investment. He assumes the investor is an institutional paying negligible broker fees and trading in small orders that do not move prices, such that one-way trading friction is the average bid-ask half-spread. He ignores tax impacts of trading. With these assumptions, he estimates a constant one-way trading friction of 0.5% (0.1%) for stock (bond) indexes. Using monthly closes and dividends/coupons for the four specified indexes and contemporaneous T-bill yields during January 1926 through December 2012 (87 years), he finds that: Keep Reading

Best Bear Market Asset Class?

A subscriber asked which asset (short stocks, cash, bonds by subclass) is best to hold during equity bear markets, defined simply as intervals when SPDR S&P 500 (SPY) is below its 10-month simple moving average (SMA10). To investigate, we test the following eight alternatives, five of which are bond-like mutual funds and one of which is a gold stocks/gold mutual fund:

Short SPY
Cash, with return estimated as the yield on 13-week U.S. Treasury bills (T-bill)
Vanguard GNMA Securities (VFIIX)
T. Rowe Price International Bonds (RPIBX)
Vanguard Long-Term Treasury Bonds (VUSTX)
Fidelity Convertible Securities (FCVSX)
T. Rowe Price High-Yield Bonds (PRHYX)
Fidelity Select Gold Portfolio (FSAGX)

Specifically, we compare monthly return statistics, cumulative performances and maximum drawdowns of these eight alternatives for months during which SPY is below its SMA10. Using monthly T-bill yield and monthly dividend-adjusted closing prices for the funds above during January 1993 (as limited by SPY) through February 2014, we find that: Keep Reading

Predicting Government Bond Term Premiums with Leading Economic Indicators

Do economic indicators usefully predict government bond returns? In the January 2014 version of their paper entitled “What Drives the International Bond Risk Premia?”, Guofu Zhou and Xiaoneng Zhu examine whether OECD-issued leading economic indicators predict government bond returns at a one-month horizon. They focus on a four-country (U.S., UK, Japan and Germany) aggregate leading economic indicator (LEI4). They test whether LEI4 outperforms historical averages and individual country LEIs in predicting term premiums (relative to a one-year bond) for U.S., UK, Japanese and German government bonds with terms of two, three, four and five years. Their test methodology employs monthly inception-to-date regressions of annual change in LEI4 versus next-month bond return for an out-of-sample test period of 1990 through 2011. Using end-of-month total return data for 1-year, 2-year, 3-year, 4-year and 5-year government bonds since 1962 for the U.S., 1970 for the UK, 1980 for Japan and 1975 for GM, all through 2011, they find that: Keep Reading

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