Bonds

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

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

Do low-risk bonds, like low-risk stocks, tend to outperform their high-risk counterparts? In their September 2013 paper entitled “Low-Risk Anomalies in Global Fixed Income: Evidence from Major Broad Markets”, Raul Leote de Carvalho, Patrick Dugnolle, Xiao Lu and Pierre Moulin investigate whether low-risk beats high-risk for different measures of risk and different bond segments. They consider only measures of risk that account for the fact that the risk of a bond inherently decreases as it approaches maturity, emphasizing duration-times-yield (yield elasticity). They focus on corporate investment grade bonds denominated in dollars, euros, pounds or yen, but also consider government and high-yield corporate bonds worldwide. Each month, they rank a selected category of bonds by risk into fifths (quintile portfolios). For calculation of monthly quintile returns, they weight individual bond returns by market capitalization. They reinvest coupons the end of the month. They focus on quintile portfolio Sharpe ratios to test the risk-performance relationship. Using monthly risk data and returns for 85,442 individual bonds during January 1997 through December 2012 (192 months), they find that: Keep Reading

What initial retirement portfolio withdrawal rate is sustainable over long horizons when, as currently, bond yields are well below and stock market valuations well above historical averages? In their June 2013 paper entitled “Asset Valuations and Safe Portfolio Withdrawal Rates”, David Blanchett, Michael Finke and Wade Pfau apply predictions of bond yields and stock market returns to estimate whether various initial withdrawal rates succeed over different retirement periods. They define initial withdrawal rate as a percentage of portfolio balance at retirement, escalated by inflation each year thereafter. They simulate future bond yield as a linear function of current bond yield with noise, assuming a long-term average of 5% and bounds of 1% and 10%. They simulate future U.S. stock mark return as a linear function of Cyclically Adjusted Price-to-Earnings ratio (CAPE, or P/E10), the ratio of current stock market level to average earnings over the last ten years, assuming P/E10 has a long-term average of 16.4 with noise (implying average annual return 10% with standard deviation 20%). They simulate inflation as a function of bond yield, change in bond yield, P/E10 and change in P/E10 with noise. They assume an annual portfolio management fee of 0.5%. They run 10,000 Monte Carlo simulations for each of many initial withdrawal rate scenarios, with probability of success defined as the percentage of runs not exhausting the portfolio before the end of a specified retirement period. Using initial conditions of a government bond yield of 2% and a P/E10 of 22 as of mid-April 2013, they find that: Keep Reading

The Federal Reserve states that open market operations regulate “the aggregate level of balances available in the banking system,” thereby keeping the effective Federal Funds Rate close to a target level. The operations are predominantly repurchases, whereby the Federal Reserve provides liquidity. Do Permanent Open Market Operations (POMO) systematically affect the nominal or real yields on 10-year Treasury notes (T-notes)? Using monthly amounts of Treasuries repurchases via POMO during August 2005 through May 2013 (94 months) and contemporaneous monthly T-note yields and 12-month trailing inflation rates, we find that: Keep Reading

A subscriber suggested testing the diversification power of SPDR Barclays International Treasury Bonds (BWX) as a distinct asset class. To check, we add BWX to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for BWX and the average pairwise correlation of BWX monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without BWX. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for BWX and the above nine asset class proxies from November 2007 (first return available for BWX) through April 2013 (66 monthly returns), we find that: Keep Reading

It is plausible that high-yield corporate bonds have return characteristics substantially different from those of other asset classes, and therefore represent a good diversification opprotunity. To check, we add iShares iBoxx $ High-Yield Corporate Bond (HYG) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for HYG and the average pairwise correlation of HYG monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without HYG. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for HYG and the above nine asset class proxies from May 2007 (first return available for HYG) through April 2013 (72 monthly returns), we find that: Keep Reading

Treasury Inflation-Protected Securities (TIPS), offering an explicit inflation hedge, may be an attractive asset for strategic diversification. To check, we add iShares Barclays TIPS Bond Fund (TIP) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for TIP and the average pairwise correlation of TIP monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without TIP. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for TIP and the above nine asset class proxies as available from January 2004 (first available for TIP) through April 2013 (112 monthly returns), we find that: Keep Reading

It is plausible that crude oil as a dominant energy commodity has return characteristics substantially different from those of other commodities and asset classes, and therefore represent a good diversification opprotunity. To check, we add iShares Barclays 7-10 Year Treasury (IEF) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for IEF and the average pairwise correlation of IEF monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without IEF. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for IEF and the above nine asset class proxies as available from January 2003 (the start of the “Simple Asset Class ETF Strategy”) through April 2013 (124 monthly returns), we find that: Keep Reading

What pitfalls face forecasters trying to predict financial markets with economic data series? In their November 2012 preliminary paper entitled “Forecasting through the Rear-View Mirror: Data Revisions and Bond Return Predictability”, Eric Ghysels, Casidhe Horan and Emanuel Moench examine the predictive power of economic data to predict annual returns for U.S. Treasury notes (T-note) with constant maturities of two, three, four and five years. They focus on the effects of publication delays and use of as-released (real-time) versus revised data. They consider a set of 68 monthly economic variables for which initial release (as well as revised) values and release dates are available since the early 1980s. Using annual T-note returns and both as-released and revised data for these 68 economic variables (such as industrial production, employment, housing indicators, personal income, price indexes and money stock) from ArchivaL Federal Reserve Economic Data) during March 1982 through December 2011, they find that: Keep Reading