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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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SACEVS Input Risk Premiums and EFFR

The “Simple Asset Class ETF Value Strategy” seeks diversification across a small set of asset class exchanged-traded funds (ETF), plus a monthly tactical edge from potential undervaluation of three risk premiums:

  1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
  2. Credit – monthly difference between the Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
  3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

Premium valuations are relative to historical averages. How might this strategy react to increases in the Effective Federal Funds Rate (EFFR)? Using monthly values of the three risk premiums, EFFR, total 12-month U.S. inflation and core 12-month U.S. inflation during July 2000 (limited by availability of EFFR) through May 2018 (215 months), we find that: Keep Reading

Benefits of Volatility Targeting Across Asset Classes

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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Testing a Countercyclical Asset Allocation Strategy

“Countercyclical Asset Allocation Strategy” summarizes research on a simple countercyclical asset allocation strategy that systematically raises (lowers) the allocation to an asset class when its current aggregate allocation is relatively low (high). The underlying research is not specific on calculating portfolio allocations and returns. To corroborate findings, we use annual mutual fund and exchange-traded fund (ETF) allocations to stocks and bonds worldwide from the 2018 Investment Company Fact Book, Data Tables 3 and 11 to determine annual countercyclical allocations for stocks and bonds (ignoring allocations to money market funds). Specifically:

  • If actual aggregate mutual fund/ETF allocation to stocks in a given year is above (below) 60%, we set next-year portfolio allocation below (above) 60% by the same percentage.
  • If actual aggregate mutual fund/ETF allocation to bonds in a given year is above (below) 40%, we set next-year portfolio allocation below (above) 40% by the same percentage.

We then apply next-year allocations to stock (Fidelity Fund, FFIDX) and bond (Fidelity Investment Grade Bond Fund, FBNDX) mutual funds that have long histories. Based on Fact Book annual publication dates, we rebalance at the end of April each year. Using the specified actual fund allocations for 1984 through 2017 and FFIDX and FBNDX May through April total returns and April 1-year U.S. Treasury note (T-note) yields for 1985 through 2018, we find that: Keep Reading

Ziemba Party Holding Presidency Strategy Update

“Exploiting the Presidential Cycle and Party in Power” summarizes strategies that hold small stocks (large stock or bonds) when Democrats (Republicans) hold the U.S. presidency. How has this strategy performed in recent years? To investigate, we consider three strategy alternatives using exchange-traded funds (ETF):

  1. D-IWM:R-SPY: hold iShares Russell 2000 (IWM) when Democrats hold the presidency and SPDR S&P 500 (SPY) when Republicans hold it.
  2. D-IWM:R-LQD: hold IWM when Democrats hold the presidency and iShares iBoxx Investment Grade Corporate Bond (LQD) when Republicans hold it.
  3. D-IWM:R-IEF: hold IWM when Democrats hold the presidency and iShares 7-10 Year Treasury Bond (IEF) when Republicans hold it.

We use calendar years to determine party holding the presidency. As benchmarks, we consider buying and holding each of SPY, IWM, LQD or IEF and annually rebalanced portfolios of 60% SPY and 40% LQD (60 SPY-40 LQD) or 60% SPY and 40% IEF (60 SPY-40 IEF). We consider as performance metrics: average annual excess return (relative to the yield on 1-year U.S. Treasury notes at the beginning of each year); standard deviation of annual excess returns; annual Sharpe ratio; compound annual growth rate (CAGR); and, maximum annual drawdown (annual MaxDD). We assume portfolio switching/rebalancing frictions are negligible. Except for CAGR, computations are for full calendar years only. Using monthly dividend-adjusted closing prices for the specified ETFs during July 2002 (limited by LQD and IEF) through April 2018, we find that:

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Worldwide Long-run Returns on Housing, Equities, Bonds and Bills

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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Bond and Stock ETFs Lead-lag

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 LQDHYG and SPY during mid-April 2007 (HYG inception) through March 2018, we find 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 nine alternatives, five of which are bond-like mutual funds and two of which are gold-related:

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)
Spot Gold

Specifically, we compare monthly return statistics, cumulative performances and maximum (peak-to-trough) drawdowns of these nine alternatives for months during which SPY is below its SMA10. Using monthly T-bill yield and monthly dividend-adjusted closing prices for the above assets during January 1993 (as limited by SPY) through Mar 2018, we find that: Keep Reading

Federal Funds Rate-Stock Market Interactions

A subscriber wondered whether U.S. stock market movements predict Federal Funds Rate (FFR) actions taken by the Federal Reserve open market operations committee. To investigate and evaluate usefulness of findings, we relate three series:

  1. FFR actions per the above source, along with recent and historical committee meeting dates.
  2. S&P 500 Index returns.
  3. Changes in yield for the 10-Year U.S. Constant Maturity Treasury note (T-note).

In constructing the first series, for Federal Reserve open market operations committee meeting dates which do not produce FFR changes, we quantify committee actions as 0%. We ignore committee conference calls that result in no changes in FFR. We calculate the second and third series between committee meeting dates because that irregular interval represents new information to the committee and potential exploitation points for investors. Using data for the three series during January 1990 through March 2018, we find that:

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Bonds Lead Stocks?

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

Rise and Fall of the Fed Model?

What is the historical relationship between U.S. stock market earnings yield (E/P) and U.S. government bond yield (Y)? In their February 2018 paper entitled “Stock Earnings and Bond Yields in the US 1871 – 2016: The Story of a Changing Relationship”, Valeriy Zakamulin and Arngrim Hunnes examine the relationship between E/P Y over the long run, with focus on structural breaks, causes of breaks and direction of causality. They employ a vector error correction model that allows multiple structural breaks. In assessing causes of breaks, they consider inflation, income taxes and Federal Reserve Bank monetary policy. Using quarterly S&P Composite Index level, index earnings, long-term government bond yield and inflation data during 1871 through 2016, along with contemporaneous income tax rates and Federal Reserve monetary actions, they find that:

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