Bonds

“Verification Tests of the Smart Money Indicator” performs tests of ideas and setup features described in “Smart Money Indicator for Stocks vs. Bonds”. The Smart Money Indicator (SMI) is a complicated variable that exploits differences in futures and options positions in the S&P 500 Index, U.S. Treasury bonds and 10-year U.S. Treasury notes between institutional investors (smart money) and retail investors (dumb money) as published in Commodity Futures Trading Commission Commitments of Traders (COT) reports. Since findings for some variations in that test are attractive, we add two further robustness tests:

• Extend the sample period in “Verification Tests of the Smart Money Indicator” through the end of March 2020 to see how SMI performs during the 2019 coronavirus (COVID-19) stock market crash.
• Using the same setup as in “Verification Tests of the Smart Money Indicator”, test alternate definitions of commercial traders as smart money and non-commercial traders as dumb money (from legacy reports), rather than asset manager/institutional traders and non-reportables, respectively (from more granular disaggregated reports).

Using COT report data, dividend-adjusted SPDR S&P 500 (SPY) as a proxy for a stock market total return index, 3-month Treasury bill (T-bill) yield as return on cash (Cash) and dividend-adjusted iShares 20+ Year Treasury Bond (TLT) as a proxy for government bonds during 6/16/06 through 4/3/20, we find that:

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As implied in “Mirror Image Seasonality for Stocks and Treasuries?”, are bonds better than stocks during the “Sell-in-May” months of May through October? Are behaviors of government, corporate investment grade and corporate high-yield bonds over this interval similar? To investigate, we test seasonal behaviors of:

SPDR S&P 500 (SPY)
Vanguard Intermediate-Term Treasury (VFITX)
Fidelity Investment Grade Bond (FBNDX)
Vanguard High-Yield Corporate Bond (VWEHX)

Using dividend-adjusted monthly prices for these funds during January 1993 (limited by SPY) through January 2020, we find that: Keep Reading

How does execution delay affect the performance of the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS)? These strategies each month 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)

To investigate, we compare 22 variations of each strategy with execution days ranging from end-of-month (EOM) per the baseline strategy to 21 trading days after EOM (EOM+21). For example, an EOM+5 variation computes allocations based on EOM but delays execution until the close five trading days after EOM. We include a benchmark that each month allocates 60% to SPY and 40% to TLT (60-40) to see whether variations are unique to SACEVS. We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance statistics. Using daily dividend-adjusted closes for the above ETFs from the end of July 2002 through January 2020, we find that:

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A subscriber requested verification of findings in “Smart Money Indicator for Stocks vs. Bonds”, where the Smart Money Indicator (SMI) is a complicated variable that exploits differences in futures and options positions in the S&P 500 Index, U.S. Treasury bonds and 10-year U.S. Treasury notes between institutional investors (smart money) and retail investors (dumb money). To verify, we simplify somewhat the approach for calculating and testing SMI, as follows:

• Use a “modern” sample of weekly Traders in Financial Futures; Futures-and-Options Combined Reports from CFTC, starting in mid-June 2006 and extending into early February 2020.
• For each asset, take Asset Manager/Institutional positions as the smart money and Non-reporting positions as the dumb money.
• For each asset, calculate weekly net positions of smart money and dumb money as longs minus shorts. 
• For each asset, use a 52-week lookback interval to calculate weekly z-scores of smart and dumb money net positions (how unusual current net positions are). This interval should dampen any seasonality.
• For each asset, calculate weekly relative sentiment as the difference between smart money and dumb money z-scores.
• For each asset, use a 13-week lookback interval to calculate recent maximum/minimum relative sentiments between smart money and dumb money for all three inputs. The original study reports that short intervals work better than long ones, and 13 weeks is a quarterly earnings interval.
• Use a 13-week lookback interval to calculate final SMI as described in “Smart Money Indicator for Stocks vs. Bonds”.

We perform three kinds of tests to verify original study findings, using dividend-adjusted SPDR S&P 500 (SPY) as a proxy for a stock market total return index, 3-month Treasury bill (T-bill) yield as return on cash (Cash) and dividend-adjusted iShares 20+ Year Treasury Bond (TLT) as a proxy for government bonds. We calculate asset returns based on Friday closes (or Monday closes when Friday is a holiday) because source report releases are normally the Friday after the Tuesday report date, just before the stock market close. 

1. Calculate full sample correlations between weekly final SMI and both SPY and TLT total returns for lags of 0 to 13 weeks.
2. Calculate over the full sample average weekly SPY and TLT total returns by ranked tenth (decile) of SMI for each of the next three weeks after SMI ranking.
3. Test a market timing strategy that is in SPY (cash or TLT) when SMI is positive (zero or negative), with 0.1% (0.2%) switching frictions when the alternative asset is cash (TLT). We try execution at the same Friday close as report release date and for lags of one week (as in the original study) and two weeks. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. Buying and holding SPY is the benchmark.

Using inputs as specified above for 6/16/06 through 2/7/20, we find that: Keep Reading

Does combining an economic growth variable trend with an asset price trend improve the power to predict stock market return? What is the best way to use such a combination signal? In his December 2019 paper entitled “Growth-Trend Timing and 60-40 Variations: Lethargic Asset Allocation (LAA)”, Wouter Keller investigates variations in a basic Growth-Trend timing strategy (GT) that is bullish and holds the broad U.S. stock market unless both: (1) the U.S. unemployment rate is below its 12-month simple moving average (SMA12); and, (2) the S&P 500 Index is below its SMA10. When both SMAs trend downward, GT is bearish and holds cash. Specifically, he looks at:

• Basic GT versus a traditional 60-40 stocks-bonds portfolio, rebalanced monthly, with stocks proxied by actual/modeled SPY and bonds/cash proxied by actual/modeled IEF.
• Improving basic GT, especially maximum drawdown (MaxDD), by replacing assets with equal-weighted, monthly rebalanced portfolios with various component selections. His ultimate portfolio is the Lethargic Asset Allocation (LAA), optimized in-sample based on Ulcer Performance Index (UPI) during February 1949 through June 1981 (mostly rising interest rates) and tested out-of-sample during July 1981 through October 2019 (mostly falling interest rates).

He considers two additional benchmarks: GT applied to the Permanent portfolio (25% allocations to each of SPY, GLD, BIL and TLT) and GT applied to the Golden Butterfly portfolio (20% to each of SPY, IWN, GLD, SHY and TLT). He applies 0.1% one-way trading frictions in all tests. Using monthly unemployment rate since January 1948 and actual/modeled monthly returns for ETFs as specified since February 1949, all through October 2019, he finds that: Keep Reading

Do differences in expectations between institutional and individual investors in stocks and bonds, as quantified in weekly legacy Commitments of Traders (COT) reports, offer exploitable timing signals? In the February 2019 revision of his paper entitled “Want Smart Beta? Follow the Smart Money: Market and Factor Timing Using Relative Sentiment”, flagged by a subscriber, Raymond Micaletti tests a U.S. stock market-U.S. bond market timing strategy based on an indicator derived from aggregate equity and Treasuries positions of institutional investors (COT Commercials) relative to individual investors (COT Non-reportables). This Smart Money Indicator (SMI) has three relative sentiment components, each quantified weekly based on differences in z-scores between standalone institutional and individual net COT positions, with z-scores calculated over a specified lookback interval:

1. Maximum weekly relative sentiment for the S&P 500 Index over a second specified lookback interval.
2. Negative weekly minimum relative sentiment in the 30-Year U.S. Treasury bond over this second lookback interval.
3. Difference between weekly maximum relative sentiments in the 10-Year U.S. Treasury note and 30-year U.S. Treasury bond over this second lookback interval.

Final SMI is the sum of these components minus median SMI over the second specified lookback interval. He considers z-score calculation lookback intervals of 39, 52, 65, 78, 91 and 104 weeks and maximum/minimum relative sentiment lookback intervals of one to 13 weeks (78 lookback interval combinations). For baseline results, he splices futures-only COT data through March 14, 1995 with futures-and-options COT starting March 21, 1995. To account for changing COT reporting delays, he imposes a baseline one-week lag for using COT data in predictions. He focuses on the ability of SMI to predict the market factor, but also looks at its ability to enhance: (1) intrinsic (time series or absolute) market factor momentum; and, (2) returns for size, value, momentum, profitability, investment, long-term reversion, short-term reversal, low volatility and quality equity factors. Finally, he compares to several benchmarks the performance of an implementable strategy that invests in the broad U.S. stock market (U.S. Aggregate Bond Total Return Index) when a group of SMI substrategies “vote” positively (negatively). Using weekly legacy COT reports and daily returns for the specified factors/indexes during October 1992 through December 2017, he finds that: Keep Reading

Are Morningstar mutual fund profiles accurate? In their October 2019 paper entitled “Don’t Take Their Word For It: The Misclassification of Bond Mutual Funds”, Huaizhi Chen, Lauren Cohen and Umit Gurun examine whether aggregate credit risks of actual of U.S. fixed income (corporate bond) mutual fund portfolios match those presented by Morningstar in respective fund profiles. They focus on recent data (first quarter of 2017 through second quarter of 2019), during which Morningstar includes percentages of fund holdings by risk category. Using Morningstar profiles, actual holdings as reported to the SEC, detailed credit ratings of holdings and returns for 1,294 U.S. corporate bond funds during January 2003 through June 2019, they find that:

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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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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

“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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