Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for December 2020 (Preliminary)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for December 2020 (Preliminary)
1st ETF 2nd ETF 3rd ETF

Bonds

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.

Add REITs to SACEVS?

What happens if we extend the “Simple Asset Class ETF Value Strategy” (SACEVS) with a real estate risk premium, derived from the yield on equity Real Estate Investment Trusts (REIT), represented by the FTSE NAREIT Equity REITs Index? To investigate, we apply the SACEVS methodology to the following asset class exchange-traded funds (ETF), plus cash:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR Dow Jones REIT (RWR) through September 2004 dovetailed with Vanguard REIT ETF (VNQ) thereafter
SPDR S&P 500 (SPY)

This set of ETFs relates to four risk premiums, as specified below: (1) term; (2) credit (default); (3) real estate; and, (4) equity. We focus on the effects of adding the real estate risk premium on Compound annual growth rates (CAGR) and Maximum drawdowns (MaxDD) of the Best Value (picking the most undervalued premium) and Weighted (weighting all undervalued premiums according to degree of undervaluation) versions of SACEVS. Using lagged quarterly S&P 500 earnings, monthly S&P 500 Index levels and monthly yields for 3-month U.S. Treasury bill (T-bill), the 10-year Constant Maturity U.S. Treasury note (T-note), Moody’s Seasoned Baa Corporate Bonds and FTSE NAREIT Equity REITs Index during March 1989 through August 2018 (limited by availability of earnings data), and monthly dividend-adjusted closing prices for the above asset class ETFs during July 2002 through September 2019, we find that: Keep Reading

SACEVS with Quarterly Allocation Updates

Do quarterly allocation updates for the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS) work as well as monthly updates? These strategies allocate funds to the following asset class exchange-traded funds (ETF) according to valuations of term, credit and equity risk premiums, or to cash if no premiums are undervalued:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

Changing from monthly to quarterly allocation updates does not sacrifice information about lagged quarterly S&P 500 Index earnings, but it does sacrifice currency of term and credit premiums. To assess alternatives, we compare cumulative performances and the following key metrics for quarterly and monthly allocation updates: gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD), annual gross returns and volatilities and annual gross Sharpe ratios. Using monthly dividend-adjusted closes for the above ETFs during September 2002 (earliest alignment of months and quarters) through September 2019, we find that:

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Bond Returns Over the Very Long Run

Do bonds have a bad rap based on an unfavorable subsample? In the September 2019 revisions of his papers entitled “The US Bond Market Before 1926: Investor Total Return from 1793, Comparing Federal, Municipal, and Corporate Bonds Part I: 1793 to 1857” and “Part II: 1857 to 1926”, Edward McQuarrie revisits analysis of returns to bonds in the U.S. prior to 1926. He focuses on investor holding period returns rather than yields, considering U.S. Treasury, state, city and corporate debt. Specifically, he estimates returns to a 19th century diversified bond portfolio comprised of all long-term investment grade bonds trading in any year (free of contaminating factors such as circulation privileges and tax exemptions). Returns assume:

  1. Weights are proportional to amounts outstanding.
  2. Bonds are far from before maturity.
  3. Calculations use actual bond prices.

In other words, he calculates performance of a diversified index fund tracking actual long-term, investment-grade 19th century U.S. bonds. He also calculates returns to sub-indexes as feasible. He further constructs a new stock index for the period January 1793 to January 1871 and revisits conclusions in Stocks for the Long Run about relative performances of stocks and bonds. Using newly and previously compiled U.S. bond and stock prices extending back to January 1793, he finds that:

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Term Premium End-of-Month Effect

Does the term premium as measured by returns to zero-coupon U.S. Treasury notes (T-notes) concentrate during some part of the monthly cycle? In their August 2019 paper entitled “Predictable End-of-Month Treasury Returns”, Jonathan Hartley and Krista Schwarz examine the monthly cycle of excess returns on 2-year, 5-year and 10-year T-notes. Specifically, they calculate average excess return by trading day before end-of-month (EOM), with excess return measured as raw T-note return minus general collateral repo rate. Using modeled daily prices for the specified T-notes and daily general collateral repo rate during January 1990 through December 2018, they find that: Keep Reading

FFR Actions, Stock Market Returns and Bond Yields

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 early August 2019, we find that:

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SACEMS-SACEVS Diversification with Mutual Funds

“SACEMS-SACEVS for Value-Momentum Diversification” finds that the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) are mutually diversifying. Do longer samples available from “SACEVS Applied to Mutual Funds” and “SACEMS Applied to Mutual Funds” confirm this finding? To check, we look at the following three equal-weighted (50-50) combinations of the two strategies, rebalanced monthly:

  1. SACEVS Best Value paired with SACEMS Top 1 (aggressive value and aggressive momentum).
  2. SACEVS Best Value paired with SACEMS Equally Weighted (EW) Top 3 (aggressive value and diversified momentum).
  3. SACEVS Weighted paired with SACEMS EW Top 3 (diversified value and diversified momentum).

Using monthly gross returns for SACEVS and SACEMS mutual fund portfolios during September 1997 through July 2019, we find that:

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SACEVS Applied to Mutual Funds

“Simple Asset Class ETF Value Strategy” (SACEVS) finds that investors may be able to exploit relative valuation of the term risk premium, the credit (default) risk premium and the equity risk premium via exchange-traded funds (ETF). However, the backtesting period is limited by available histories for ETFs and for series used to estimate risk premiums. To construct a longer test, we make the following substitutions for potential holdings (selected for length of available samples):

To enable estimation of risk premiums over a longer history, we also substitute:

As with ETFs, we consider two alternatives for exploiting premium undervaluation: Best Value, which picks the most undervalued premium; and, Weighted, which weights all undervalued premiums according to degree of undervaluation. Based on the assets considered, the principal benchmark is a monthly rebalanced portfolio of 60% VFINX and 40% VFIIX. Using monthly risk premium calculation data during March 1934 through July 2019 (limited by availability of T-bill data), and monthly dividend-adjusted closing prices for the three asset class mutual funds during June 1980 through July 2019 (39 years, limited by VFIIX), we find that:

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Simple Debt Class Mutual Fund Momentum Strategy

A subscriber requested confirmation of the performance of a simple momentum strategy that each month selects the best performing debt mutual fund based on total return over the past three months. To investigate, we test a simple strategy on the following 12 mutual funds (those with the longest histories from a proposed list of 14 funds):

T. Rowe Price New Income (PRCIX)
Thrivent Income A (LUBIX)
Vanguard GNMA Securities (VFIIX)
T. Rowe Price High-Yield Bonds (PRHYX)
T. Rowe Price Tax-Free High Yield Bonds (PRFHX)
Vanguard Long-Term Treasury Bonds (VUSTX)
T. Rowe Price International Bonds (RPIBX)
Fidelity Convertible Securities (FCVSX)
PIMCO Short-Term A (PSHAX)
Fidelity New Markets Income (FNMIX)
Eaton Vance Government Obligations C (ECGOX)
Vanguard Long-Term Bond Index (VBLTX)

We consider a strategy that allocates funds at the end of each month based on total returns over a specified ranking (lookback) interval to the Top 1, equally weighted (EW) Top 2, EW Top 3, EW Top 4 or EW Top 5 funds. We determine the first winners in November 1988 so that at least nine funds are available for lookback interval sensitivity testing. As a benchmark, we use the equally weighted and monthly rebalanced combination of all available funds (EW All). Using monthly dividend-adjusted closing prices for the 12 mutual funds from inceptions through June 2019, we find that: Keep Reading

Best U.S. Equity Market Hedge Strategy?

What steps should investors consider to mitigate impact of inevitable large U.S. stock market corrections? In their May 2019 paper entitled “The Best of Strategies for the Worst of Times: Can Portfolios be Crisis Proofed?”, Campbell Harvey, Edward Hoyle, Sandy Rattray, Matthew Sargaison, Dan Taylor and Otto Van Hemert compare performances of an array of defensive strategies with focus on the eight worst drawdowns (deeper than -15%) and three NBER recessions during 1985 through 2018, including:

  1. Rolling near S&P 500 Index put options, measured via the CBOE S&P 500 PutWrite Index.
  2. Credit protection portfolio that is each day long (short) beta-adjusted returns of duration-matched U.S. Treasury futures (BofAML US Corp Master Total Return Index), scaled retrospectively to 10% full-sample volatility.
  3. 10-year U.S. Treasury notes (T-notes).
  4. Gold futures.
  5. Multi-class time-series (intrinsic or absolute) momentum portfolios applied to 50 futures contract series and reformed monthly, with:
    • Momentum measured for 1-month, 3-month and 12-month lookback intervals.
    • Risk adjustment by dividing momentum score by the standard deviation of security returns.
    • Risk allocations of 25% to currencies, 25% to equity indexes, 25% to bonds and 8.3% to each of agricultural products, energies and metals. Within each group, markets have equal risk allocations.
    • Overall scaling retrospectively to 10% full-sample volatility.
    • With or without long equity positions.
  6. Beta-neutral factor portfolios that are each day long (short) stocks of the highest (lowest) quality large-capitalization and mid-capitalization U.S. firms, based on profitability, growth, balance sheet safety and/or payout ratios.

They further test crash protection of varying allocations to the S&P 500 Index and a daily reformed hedge consisting of equal weights to: (1) a 3-month time series momentum component with no long equity positions and 0.7% annual trading frictions; and, (2) a quality factor component with 1.5% annual trading frictions. For this test, they scale retrospectively to 15% full-sample volatility. Throughout the paper, they assume cost of leverage is the risk-free rate. Using daily returns for the S&P 500 Index and inputs for the specified defensive strategies during 1985 through 2018, they find that:

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U.S. Corporate Bond Index Return Model

Is there a straightforward way to model the returns on U.S. Corporate bond indexes? In his April 2019 paper entitled “Give Credit Where Credit is Due: What Explains Corporate Bond Returns?”, Roni Israelov models returns on these indexes based on four intuitive factors:

  1. Positive exposure to government bond yields, quantified via duration-matched government bonds.
  2. Negative exposure to rate volatility from bond call provisions (uncertainty in duration), quantified via delta-hedged options on 10-year Treasury note futures.
  3. Positive exposure to firm values due to default risk, quantified via index constituent-weighted equities.
  4. Negative exposure to firm stock volatility due to default risk, quantified via index constituent-weighted delta-hedged single-name equity options.

Exposures 1 and 2 are general (systematic), while exposures 3 and 4 contain both systematic and firms-specific (idiosyncratic) components. He tests this 4-factor model on six Bank of America Merrill Lynch U.S. corporate bond indexes: Investment Grade, High Yield, 1-3 Year Corporate, 3-5 Year Corporate, 5-10 Year Corporate, and 10+ Year Corporate. All duration-specified indexes are investment grade. He also tests two Credit Default Swap (CDS) indexes: investment grade and high yield. He further devises and tests a Risk-Efficient Credit strategy on the six bond indexes that isolates and exploits compensated risk premiums by buying bond index futures, buying equity index futures, selling delta-hedged equity index options and selling delta-hedged options on bond index futures, with allocations sized to match respective historical exposures of each index. Using monthly data for the eight bond/CDS indexes and the four specified factors and their components during January 1997 through December 2017, he finds that:

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