Bonds

Can investors improve retirement glide paths via judicious use of smart beta funds? In their March 2017 paper entitled “Life Cycle Investing and Smart Beta Strategies”, Bill Carson, Sara Shores and Nicholas Nefouse augment a conventional equities-bonds life cycle investing glide path with smart beta strategies. They use a conventional glide path, which gradually decreases the allocation to equities with age to a constant after retirement, to determine target risk levels over the life cycle. When the investor is young, they tilt equities toward the MSCI USA Diversified Multiple-Factor (DMF) Index to boost returns via value, size momentum and quality beta exposures. As the investor approaches retirement, they shift equities to the MSCI USA Minimum Volatility Index, designed to match the market return at lower risk. For bonds, they use the Barclays Constant Weights Index, which has greater diversification and higher Sharpe ratio than a conventional market capitalization-based bond index. They incorporate the specified smart beta indexes into the glide path via a procedure that maximizes Sharpe ratio while matching the risk of the conventional glide path. Specifically, they: (1) deviate no more than 3% from conventional glide path risk; (2) constrain smart beta equities beta relative to the Russell 1000 Index and the MSCI World Index ex U.S. to within 5% of the benchmark equities beta; (3) constrain smart beta bond index duration to within 0.05 years of the benchmark bonds duration; and, (4) require at least 1% allocation to bonds for all target date portfolios. Using monthly data for conventional capitalization-weighted U.S. equity and bond indexes and for the specified smart beta indexes during 2007 through 2016, they find that: Keep Reading

Are anomaly premiums (expected winners minus losers among assets within a class, based on some asset characteristic) more or less predictable than broad market returns? In their April 2017 paper entitled “Predicting Relative Returns”, Valentin Haddad, Serhiy Kozak and Shrihari Santosh apply principal component analysis to assess the predictability of premiums for published asset pricing anomalies spanning stocks, U.S. Treasuries and currencies. For tractability, they simplify asset classes by forming portfolios of assets within them, as follows:

• For stocks, they consider the long and short legs of portfolios reformed monthly into tenths (deciles) based on each of the characteristics associated with 26 published stock return anomalies (monthly data for 1973 through 2015).
• They sort zero-coupon U.S. Treasuries by maturity from one to 15 years to assess term premiums (yield data for 1985 through 2014).
• They sort individual exchange rates into five portfolios reformed daily based on interest rate differentials with the U.S. to assess the carry trade premium (daily data as available for December 1975 through December 2016).

Using the specified data, they find that: Keep Reading

Is there a straightforward way to interpret the state of the yield curve as a manifestation of how efficiently the economy is processing information? In his March 2017 paper entitled “Simple New Method to Predict Bear Markets (The Entropic Linkage between Equity and Bond Market Dynamics)”, Edgar Parker Jr. presents and tests a way to understand interaction between bond and equity markets based on arrival and consumption of economic information. He employs Shannon entropy to model the economy’s implied information processing ratio (R/C), with interpretations as follows:

1. R/C ≈ 1: healthy continuously upward-sloping yield curve when information arrival and consumption rates are approximately equal.
2. R/C >> 1: low end of the yield curve inverts when information is arriving much faster than it can be consumed.
3. R/C << 1: high end of the yield curve inverts when information is arriving much slower than it can be consumed.

Under the latter two conditions, massive information loss (entropy growth) occurs, and firms cannot confidently plan. These conditions delay/depress economic growth and produce equity bear markets. He tests this approach by matching actual yield curve data with standardized (normal) R and C distributions that both have zero mean and standard deviation one (such that standardized R and C may be negative). Using daily yields for U.S. Treasuries across durations and daily S&P 500 Index levels during 1990 through 2016, he finds that: Keep Reading

How do widely studied anomalies relate to representative stocks-bonds portfolio returns (rather than the risk-free rate)? In his March 2017 paper entitled “Understanding Anomalies”, Filip Bekjarovski proposes an approach to asset pricing wherein a representative portfolio of stocks and bonds is the benchmark and stock anomalies are a set of investment opportunities that may enhance the benchmark. He therefore employs benchmark-adjusted returns, rather than excess returns, to determine anomaly significance. Specifically, his benchmark portfolio captures the equity, term and default premiums. He considers 10 potentially enhancing anomalies: size, value, profitability, investment, momentum, idiosyncratic volatility, quality, betting against beta, accruals and net share issuance. He estimates each anomaly premium as returns to a portfolio that is each month long (short) the value-weighted tenth, or decile, of stocks with the highest (lowest) expected returns for that anomaly. He assesses the potential of each anomaly in three ways: (1) alphas from time series regressions that control for equity, term and default premiums; (2) performances during economic recessions; and, (3) crash proneness. He measures the attractiveness of adding anomaly premiums to the benchmark portfolio by comparing Sharpe ratios, Sortino ratios and performances during recessions of five portfolios: (1) a traditional portfolio (TP) that equally weights equity, term and default premiums; (2) an equal weighting of size, value and momentum premiums (SVM) as a basic anomaly portfolio; (3) a factor portfolio (FP) that equally weights all 10 anomaly premiums; (4) a mixed portfolio (MP) that equally weights all 13 premiums; and, (5) a balanced portfolio (BP) that equally weights TP and FP. Using monthly returns for the 13 premiums specified above from a broad sample of U.S. stocks and NBER recession dates during July 1963 through December 2014, he finds that: Keep Reading

What is a safe portfolio withdrawal rate for early retirees who expect more than 30 years of retirement? In their February 2017 paper entitled “Safe Withdrawal Rates: A Guide for Early Retirees”, ERN tests effects of several variables on retirement portfolio success:

• Retirement horizons of 30, 40, 50 and 60 years.
• Annual inflation-adjusted withdrawal rates of 3% to 5% in increments of 0.25%.
• Terminal values of 0% to 100% of initial portfolio value in increments of 25%.
• Implications of different starting levels of Shiller’s Cyclically Adjusted Price-to-Earnings ratio (CAPE or P/E10).
• Implications of Social Security payments coming into play after retirement.
• Effects of reducing withdrawal rate over time (planning a gradual decline in consumption during retirement).

They assume 6.6% average real annual return for U.S. stocks with zero volatility. For 10-year U.S. Treasury notes (T-note), they assume 0% real return for the first 10 years and 2.6% thereafter (zero volatility except for one jump). They assume monthly withdrawal of one-twelfth the annual rate at the prior-month market close, with monthly portfolio rebalancing to target stocks and T-note allocations. They assume annual portfolio costs of 0.05% for low-cost mutual fund fees. Based on the stated assumptions, they find that: Keep Reading

“Equity Market and Treasuries Variance Risk Premiums as Return Predictors” reports a finding, among others, that the variance risk premium for 10-year U.S. Treasury notes (T-note) predicts near-term returns for those notes (as manifested via futures). However, the methods used to calculate the variance risk premium are complex. Is there a simple way to exploit the predictive power found? To investigate, we test whether a simple measure of the volatility risk premium (VRP) for T-notes predicts returns for the iShares 7-10 Year Treasury Bond (IEF) exchange-traded fund. Specifically we:

• Calculate daily realized volatility of IEF as the standard deviation of daily total returns over the past 21 trading days, multiplied by the square root of 252 to annualize.
• Use daily closes of CBOE/CBOT 10-year U.S. Treasury Note Volatility Index (TYVIX) as annualized implied volatility.
• Calculate the daily T-note VRP as TYVIX minus IEF realized volatility.

VRP here differs from that in the referenced research in three ways: (1) it is a volatility premium rather than a variance premium based on standard deviation rather than the square of standard deviation; (2) it is implied volatility minus expected realized volatility, rather than the reverse, and so should be mostly positive; and, (3) estimation of expected realized volatility is much simpler. When TYVIX has daily closes on non-market days, we ignore those closes. When TYVIX does not have daily closes on market days, we reuse the most recent value of TYVIX. These exceptions are rare. Using daily IEF dividend-adjusted prices since December 2002 and daily closes of TYVIX since January 2003 (earliest available), both through January 2017, we find that: Keep Reading

Do bonds, like equity markets, offer a variance risk premium (VRP)? If so, does the bond VRP predict bond returns? In their January 2017 paper entitled “Variance Risk Premia on Stocks and Bonds”, Philippe Mueller, Petar Sabtchevsky, Andrea Vedolin and Paul Whelan examine and compare the equity VRP (EVRP) via the S&P 500 Index and U.S. Treasuries VRP (TVRP) via 5-year, 10-year and 30-year U.S. Treasuries. They specify VRP generally as the difference between the variance indicated by past values of variance (realized) and that indicated by current option prices (implied). Their VRP calculation involves:

• To forecast daily realized variances at a one-month horizon, they first calculate high-frequency returns from intra-day price data of rolling futures series for each of 5-year, 10-year and 30-year Treasury notes and bonds and for the S&P 500 Index. They then apply a fairly complex regression model that manipulates squared inception-to-date returns (at least one year) and accounts for the effect of return jumps. 
• To calculate daily implied variances for Treasuries at a one-month horizon, they employ end-of-day prices on cross-sections of options on Treasury futures. For the S&P 500 Index, they use the square of VIX.
• To calculate daily EVRP and TVRPs with one-month horizons, subtract respective implied variances from forecasted realized variances.

When relating VRPs to future returns for both Treasuries and the S&P 500 Index, they calculate returns from fully collateralized futures positions. Using the specified futures, index and options data during July 1990 through December 2014, they find that: Keep Reading

“Cross-asset Class Intrinsic Momentum” summarizes research finding that past country stock index (government bond index) returns relate positively (positively) to future country stock market index returns and negatively (positively) to future country government bond index returns. Is this finding useful for specifying a simple strategy using exchange-traded fund (ETF) proxies for the U.S. stock market and U.S. government bonds? To investigate we test the following five strategies:

1. Buy and hold.
2. TSMOM Long Only – Each month, hold the asset (cash) if its own 12-month past return is positive (negative).
3. TSMOM Long or Short – Each month, hold (short) the asset if its own 12-month past return is positive (negative).
4. XTSMOM Long Only – Each month hold stocks if 12-month past returns for stocks and government bonds are both positive, and otherwise hold cash. Each month hold bonds if 12-month past returns are negative for stocks and positive for government bonds, and otherwise hold cash. 
5. XTSMOM L-S-N (Long, Short or Neutral) – Each month hold (short) stocks if 12-month past returns for both are positive (negative), and otherwise hold cash. Each month hold (short) bonds if 12-month past returns are negative (positive) for stocks and positive (negative) for bonds, and otherwise hold cash.

We use SPDR S&P 500 (SPY) and iShares 7-10 Year Treasury Bond (IEF) as proxies for the U.S. stock market and U.S. government bonds. We use the 3-month U.S. Treasury bill (T-bill) yield as the return on cash. We apply the five strategies separately to SPY and IEF, and to an equally weighted, monthly rebalanced combination of the two for a total of 15 scenarios. Using monthly total returns for SPY and IEF and monthly T-bill yield during July 2002 (inception of IEF) through December 2016, we find that: Keep Reading

Are stock and bond markets mutually reinforcing with respect to time series (intrinsic or absolute return) momentum? In their December 2016 paper entitled “Cross-Asset Signals and Time Series Momentum”, Aleksi Pitkajarvi, Matti Suominen and Lauri Vaittinen examine a strategy that times each of country stock and government bond (constant 5-year maturity) indexes based on past returns for both. Specifically:

• For stocks, they each month take a long (short) position in a country stock index if past returns for both the country stock and government bond indexes are positive (negative). If past stock and bond index returns have different signs, they take no position.
• For bonds, they each month take a long (short) position in a country government bond index if past return for bonds is positive (negative) and past return for stocks is negative (positive). If past stock and bond index returns have the same sign, they take no position.

They call this strategy cross-asset time series momentum (XTSMOM). For initial strategy tests, they consider past return measurement (lookback) and holding intervals of 1, 3, 6, 9, 12, 24, 36 or 48 months. For holding intervals longer than one month, they average monthly returns for overlapping positions. For most analyses, they focus on lookback interval 12 months and holding interval 1 month. For a given lookback and holding interval combination, they form a diversified XTSMOM portfolio by averaging all positions for all countries. They measure excess returns relative to one-month U.S. Treasury bills. They employ the MSCI World Index and the Barclays Capital Aggregate Bond Index as benchmarks. Using monthly stock and government bond total return indexes for 20 developed countries as available during January 1980 through December 2015, they find that: Keep Reading

Does the credit premium, measured by the difference in returns between U.S. corporate bonds and duration-matched U.S. Treasuries, exhibit momentum? In his December 2016 paper entitled “Momentum in the Cross-Section of Corporate Bond Returns”, Jeroen van Zundert tests for momentum of the volatility-adjusted credit premium among U.S. corporate bonds via the following methodology:

1. Acquire the monthly total credit premium of each corporate bond as the difference in total (coupon-reinvested) returns between the bond and a duration-matched U.S. Treasury instrument.
2. For each bond, divide cumulative total credit premium over the last six months by standard deviation of monthly credit premiums over the last 12 months (something like a Sharpe ratio).
3. After inserting a skip-month, sort all bonds on this metric into tenths (decile portfolios), with each bond weighted by the inverse of its volatility.
4. Hold each portfolio for six months, computing an overall monthly return as the average for portfolios formed within the last six months.
5. Calculate volatility-adjusted credit premium momentum as the gross difference in performance between the top (winner) and bottom (loser) decile portfolios.

To estimate portfolio alphas, he adjusts for six factors (equity market, equity size, equity value, equity momentum, bond term and default risk). In robustness tests, he considers past return measurement and holding intervals of one, three, nine and 12 months. Using total credit premiums, trading volumes and characteristics for a broad sample of U.S. investment grade and high yield corporate bonds during January 1994 through December 2015, he finds that: Keep Reading