Bonds

Did Bill Gross, the Bond King, generate significantly positive alpha during his May 1987 through September 2014 tenure as manager of PIMCO Total Return Fund (Fund)? In their March 2019 paper entitled “Bill Gross’ Alpha: The King Versus the Oracle”, Richard Dewey and Aaron Brown investigate whether Bill Gross generates excess average return after adjusting for market exposures over this tenure. They further compare evaluation of bond market alpha for Bill Gross to evaluation of equity market alpha for Warren Buffett. Following the explanation given by Bill Gross for his outperformance, their factor model of Fund returns includes three long-only market factors: interest rate (Merrill Lynch 10-year Treasury Index), credit (Barclays U.S. Credit Index) and mortgage (Barclays U.S. MBS Index). It also includes a fourth factor that is long U.S. Treasury 5-year notes and short U.S. Treasury 30-year bonds, with weights set to eliminate coupon and roll-down effects of their different durations. Using monthly returns for the Fund and the four model factors, and monthly 1-month U.S. Treasury bill yield as the risk-free rate during June 1987 (first full month of the Fund) through September 2014 (when Gross left the Fund), they find that: Keep Reading

A subscriber asked about the validity of the assertion in “The Daily Shot” of February 26, 2019 (The Wall Street Journal) that “recent weakness in the ISM [Institute for Supply Management] Manufacturing PMI [Purchasing Managers’ Index] index points to downside risks for high-yield debt.” Such a relationship might support a strategy of switching between high-yield bonds and cash, or high-yield bonds and U.S. Treasuries, based on PMI data. To investigate, we consider the following two pairs of funds:

1. Vanguard High-Yield Corporate (VWEHX) and Vanguard Long-Term Treasury (VUSTX) since May 1986 (limited by VUSTX).
2. iShares iBoxx High Yield Corp Bond (HYG) and iShares 7-10 Year Treasury Bond (IEF) since April 2007 (limited by HYG).

We consider both statistical tests and strategies that each month (per the PMI release frequency) holds high-yield bonds or cash, or high-yield bonds or Treasuries, according to whether the prior-month change in PMI is positive or negative. We use the 3-month U.S. Treasury bill (T-bill) yield as a proxy for return on cash. Using fund monthly total returns as available and monthly seasonally adjusted PMI data for January 1950 through January 2016 from the Federal Reserve Bank of St. Louis (discontinued and removed) and from press releases thereafter, all through February 2019, we find that: Keep Reading

Failure rate, the conventional metric for evaluating retirement portfolios, does not distinguish between: (1) failures early versus late in retirement; or, (2) small and large surpluses (bequests). Is there a better way to evaluate retirement portfolios? In their December 2018 paper entitled “Toward Determining the Optimal Investment Strategy for Retirement”, Javier Estrada and Mark Kritzman propose coverage ratio, plus an asymmetric utility function that penalizes shortfalls more than it rewards surpluses, to evaluate retirement portfolios. They test this approach in 21 countries and the world overall. Coverage ratio is number of years of withdrawals supported by a portfolio during and after retirement, divided by retirement period. The utility function increases at decreasing rate (essentially logarithmic) as coverage ratio rises above one and decreases sharply (linearly with slope 10) as it falls below one. They focus on a 30-year retirement with 4% initial withdrawal rate and annual inflation-adjusted future withdrawals. The portfolio rebalances annually to target stocks and bonds allocations. They consider 11 target stocks-bonds allocations ranging from 100%-0% to 0%-100% in increments of 10%. When analyzing historical returns, the first (last) 30-year period is 1900-1929 (1985-2014), for a total of 86 (overlapping) periods. When using simulations, they draw 25,000 annual real returns for stocks and bonds from two uncorrelated normal distributions. For bonds, all simulation runs assume 2% average real annual return with 3% standard deviation. For stocks, simulation runs vary average real annual return and standard deviation for sensitivity analysis. Using historical annual real returns for stocks and bonds for 21 countries and the world overall during 1900 through 2014 from the Dimson-Marsh-Staunton database, they find that: Keep Reading

Should investors rely on aggregate positions of speculators (large non-commercial traders) as indicators of expected futures market returns? In their November 2018 paper entitled “Speculative Pressure”, John Hua Fan, Adrian Fernandez-Perez, Ana-Maria Fuertes and Joëlle Miffre investigate speculative pressure (net positions of speculators) as a predictor of futures contract prices across four asset classes (commodity, currency, equity index and interest rates/fixed income) both separately and for a multi-class portfolio. They measure speculative pressure as end-of-month net positions of speculators relative to their average weekly net positions over the past year. Positive (negative) speculative pressure indicates backwardation (contango), with speculators net long (short) and futures prices expected to rise (fall) as maturity approaches. They measure expected returns via portfolios that systematically buy (sell) futures with net positive (negative) speculative pressure. They compare speculative pressure strategy performance to those for momentum (average daily futures return over the past year), value (futures price relative to its price 4.5 to 5.5 years ago) and carry (roll yield, difference in log prices of  nearest and second nearest contracts). Using open interests of large non-commercial traders from CFTC weekly legacy Commitments of Traders (COT) reports for 84 futures contracts series (43 commodities, 11 currencies, 19 equity indexes and 11 interest rates/fixed income) from the end of September 1992 through most of May 2018, along with contemporaneous Friday futures settlement prices, they find that: Keep Reading

Is the U.S. equity turn-of-the-month (TOTM) effect exploitable as a diversifier of other assets? In their October 2018 paper entitled “A Seasonality Factor in Asset Allocation”, Frank McGroarty, Emmanouil Platanakis, Athanasios Sakkas and Andrew Urquhart test U.S. asset allocation strategies that include a TOTM portfolio as an asset. The TOTM portfolio buys each stock at the open on the last trading day of each month and sells at the close on the third trading day of the following month, earning zero return the rest of the time. They consider four asset universes with and without the TOTM portfolio:

1. A conventional stocks-bonds mix.
2. The equity market portfolio.
3. The equity market portfolio, a small size portfolio and a value portfolio.
4. The equity market portfolio, a small size portfolio, a value portfolio and a momentum winners portfolio.

They consider six sophisticated asset allocation methods:

1. Mean-variance optimization.
2. Optimization with higher moments and Constant Relative Risk Aversion.
3. Bayes-Stein shrinkage of estimated returns.
4. Bayesian diffuse-prior.
5. Black-Litterman.
6. A combination of allocation methods.

They consider three risk aversion settings and either a 60-month or a 120-month lookback interval for input parameter measurement. To assess exploitability, they set trading frictions at 0.50% of traded value for equities and 0.17% for bonds. Using monthly data as specified above during July 1961 through December 2015, they find that:

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How does use of actuarial estimates of retiree longevity and empirical mean reversion of stock market returns affect estimated retirement portfolio success rates? In the October 2018 revision of his paper entitled “Joint Effect of Random Years of Longevity and Mean Reversion in Equity Returns on the Safe Withdrawal Rate in Retirement”, Donald Rosenthal presents a model of safe inflation-adjusted retirement portfolio withdrawal rates that addresses: (1) uncertainty about the number of years of retirement (rather than the commonly assumed 30 years); and, (2) mean reversion in annual U.S. stock market returns (rather than a random walk). He estimates retirement longevity as a random input based on the Social Security Administration’s 2015 Actuarial Life Table. He estimates stock market real returns and measures their mean reversion using S&P 500 Index inflation-adjusted total annual returns during 1926 through 2017. He models real bond returns using 10-year U.S. Treasury note (T-note) total annual returns during 1928 through 2017. He applies Monte Carlo simulations (3,000 trials for each scenario) to assess retirement portfolio performance by:

• Assuming an initial retirement portfolio either 100% invested in stocks or 60%/40% in stocks/T-notes (rebalanced at each year-end).
• Debiting the portfolio each year-end by a fixed, inflation-adjusted percentage of the initial amount.
• Calculating percentage of simulation trials for which the portfolio is not exhausted before death (success) and average portfolio terminal balance for successful trials.

He considers two benchmarks: (1) no stock market mean reversion (random walk) and fixed 30-year retirement; and, (2) no stock market mean reversion and actuarial estimate of retirement duration. He also runs sensitivity tests to see how changes in assumptions affect success rate. Using the specified data, he finds that:

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Does volatility targeting improve Sharpe ratios and provide crash protection across asset classes? In their May 2018 paper entitled “Working Your Tail Off: The Impact of Volatility Targeting”, Campbell Harvey, Edward Hoyle, Russell Korgaonkar, Sandy Rattray, Matthew Sargaison, and Otto Van Hemert examine return and risk effects of long-only volatility targeting, which scales asset and/or portfolio exposure higher (lower) when its recent volatility is low (high). They consider over 60 assets spanning stocks, bonds, credit, commodities and currencies and two multi-asset portfolios (60-40 stocks-bonds and 25-25-25-25 stocks-bonds-credit-commodities). They focus on excess returns (relative to U.S. Treasury bill yield). They forecast volatility using realized daily volatility with exponentially decaying weights of varying half-lives to assess sensitivity to the recency of inputs. For most analyses, they employ daily return data to forecast volatility. For S&P 500 Index and 10-year U.S. Treasury note (T-note) futures, they also test high-frequency (5-minute) returns transformed to daily returns. They scale asset exposure inversely to forecasted volatility known 24 hours in advance, applying a retroactively determined constant that generates 10% annualized actual volatility to facilitate comparison across assets and sample periods. Using daily returns for U.S. stocks and industries since 1927, for U.S. bonds (estimated from yields) since 1962, for a credit index and an array of futures/forwards since 1988, and high-frequency returns for S&P 500 Index and 10-year U.S. Treasury note futures since 1988, all through 2017, they find that:

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How do housing, equities and government bonds/bills perform worldwide over the long run? In their February 2018 paper entitled “The Rate of Return on Everything, 1870-2015”, Òscar Jordà, Katharina Knoll, Dmitry Kuvshinov, Moritz Schularick and Alan Taylor address the following questions:

1. What is the aggregate real return on investments?
2. Is it higher than economic growth rate and, if so, by how much?
3. Do asset class returns tend to decline over time?
4. Which asset class performs best?

To do so, they compile long-term annual gross returns from market data for housing, equities, government bonds and short-term bills across 16 developed countries (Australia, Belgium, Denmark, Finland, France, Germany, Italy, Japan, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the UK and the U.S.). They decompose housing and equity performances into capital gains, investment incomes (yield) and total returns (sum of the two). For equities, they employ capitalization-weighted indexes to the extent possible. For housing, they model returns based on country-specific benchmark rent-price ratios. Using the specified annual returns for 1870 through 2015, they find that:

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Are there exploitable lead-lag relationships between bonds and stocks, perhaps because bond investors are generally better informed than stock investors or because there is some predictable stocks-bonds rebalancing cycle? To investigate, we examine lead-lag relationships between bond exchange-traded fund (ETF) returns and stock ETF returns. We consider iShares iBoxx $ Investment Grade Corporate Bond (LQD) and  iShares iBoxx $ High-Yield Corporate Bond (HYG) as liquid bond ETFs and SPDR S&P 500 (SPY) as a liquid stock ETF. Using dividend-adjusted daily, weekly and monthly returns for LQD, HYG and SPY during mid-April 2007 (HYG inception) through March 2018, we find that: Keep Reading

Are bond market investors generally shrewder than their stock market counterparts, such that bond yield tops (bottoms) anticipate stock market bottoms (tops)? To investigate, we employ both a monthly lead-lag analysis and a comparison of bond yield and stock market tops and bottoms. We define “top” and “bottom” as the highest (lowest) value in a rolling window that extends from 30 months in the past to 30 months in the future (a total window of five years). Using monthly levels of Moody’s yield on seasoned Aaa corporate bonds and the Dow Jones Industrial Average (DJIA) during October 1928 through February 2018 (about 90 years) and monthly levels of the 10-year government bond interest rate and the stock market from Robert Shiller during January 1871 through February 2018 (about 148 years), we find that: Keep Reading