Bonds

A subscriber requested review of a finding that deviation of 10-year constant maturity U.S. Treasury note (T-note) yield from an intermediate-term linear trend predicts U.S. stock market return. Specifically, when weekly yield is more than one standard deviation of weekly trend divergences below (above) a weekly 70-week linear extrapolation, next-week S&P 500 Index return is on average unusually high (low). To confirm and test usefulness of this finding, we each week:

1. Perform a linear extrapolation of past T-note yields to forecast next-week T-note yield, but using a 52-week rolling window rather than a 70-week window. A 52-week lookback aligns with an annual inflation cycle, while a 70-week lookback seems arbitrary and may be snooped.
2. Calculate the difference between next-week actual and forecasted T-note yields.
3. Calculate the standard deviation of these differences over the 52-week rolling window.

We then segment weekly actual minus forecasted T-note yield differences into: those more than one standard deviation below forecasted yield (Below Lower); those between one standard deviation below and above forecasted yield (Between); and, those more than one standard deviation above forecasted yield (Above Upper). Next, we calculate next-week S&P 500 Index returns for these three segments. Limited by availability of weekly T-note yield data, return calculations commence January 1964. To check robustness of results, we also consider a recent subsample commencing January 2008. To test economic value of findings, we examine a Dynamic Weighted strategy that modifies a benchmark 60% allocation to SPDR S&P 500 (SPY) and 40% allocation to iShares Barclays 7-10 Year Treasuries (IEF), rebalanced weekly, to 80% SPY when T-note condition the prior week is Below Lower and 40% SPY when Above Upper. The strategy backtest commences with inception of IEF at the end of July 2002 and focuses on weekly return statistics, compound annual growth rate (CAGR) and maximum drawdown (MaxDD), ignoring rebalancing/reallocation frictions. Using weekly T-note yields (average of daily values measured on Friday) and contemporaneous S&P 500 Index levels since January 1962, and weekly dividend-adjusted levels of SPY and IEF since July 2002, all through January 2018, we find that: Keep Reading

Is value investing particularly profitable when the price spread between cheap and expensive assets (the value spread) is extremely large (deep value)? In their November 2017 paper entitled “Deep Value”, Clifford Asness, John Liew, Lasse Pedersen and Ashwin Thapar examine how the performance of value investing changes when the value spread is in its largest fifth (quintile). They consider value spreads for seven asset classes: individual stocks within each of four global regions (U.S., UK, continental Europe and Japan); equity index futures globally; currencies globally; and, bond futures globally. Their measures for value are:

• Individual stocks – book value-to-market capitalization ratio (B/P).
• Equity index futures – index-level B/P, aggregated using index weights.
• Currencies – real exchange rate based on purchasing power parity.
• Bonds – real bond yield (nominal bond yield minus forecasted inflation).

For each of the seven broad asset classes, they each month rank assets by value. They then for each class form a hedge portfolio that is long (short) the third of assets that are cheapest (most expensive). For stocks and equity indexes, they weight portfolio assets by market capitalization. For currencies and bond futures, they weight equally. To create more deep value episodes, they construct 515 sub-classes from the seven broad asset classes. For asset sub-classes, they use hedge portfolios when there are many assets (272 strategies) and pairs trading when there are few (243 strategies). They conduct both in-sample and out-of-sample deep value tests, the latter buying value when the value spread is within its top inception-to-date quintile and selling value when the value spread reverts to its inception-to-date median. Using data as specified and as available (starting as early as January 1926 for U.S. stocks and as late as January 1988 for continental Europe stocks) through September 2015, they find that:

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What firm/asset/market conditions signal mispricing? In the November 2017 version of their paper entitled “Bonds, Stocks, and Sources of Mispricing”, Doron Avramov, Tarun Chordia, Gergana Jostova and Alexander Philipov investigate drivers of U.S. corporate stock and bond mispricing based on interactions among asset prices, financial distress of associated firms and investor sentiment. They measure financial distress via Standard & Poor’s long term issuer credit rating downgrades. They measure investor sentiment primarily with the multi-input Baker-Wurgler Sentiment Index, but they also consider the University of Michigan Consumer Sentiment index and the Consumer Confidence Index. They each month measure asset mispricing by:

1. Ranking firms into tenths (deciles) based on each of 12 anomalies: price momentum, earnings momentum, idiosyncratic volatility, analyst forecast dispersion, asset growth, investments, net operating assets, accruals, gross profitability, return on assets and two measures of net share issuance.
2. Computing for each firm the equally weighted average of its anomaly rankings, such that a high (low) average ranking indicates the firms’s assets are relatively overpriced (underpriced).

Using monthly firm, stock and bond data for a sample of U.S. firms with sufficient data and investor sentiment during January 1986 through December 2016, they find that: Keep Reading

Do value strategy returns vary exploitably over time and across asset classes? In their October 2017 paper entitled “Value Timing: Risk and Return Across Asset Classes”, Fahiz Baba Yara, Martijn Boons and Andrea Tamoni examine the power of value spreads to predict returns for individual U.S. equities, global stock indexes, global government bonds, commodities and currencies. They measure value spreads as follows:

• For individual stocks, they each month sort stocks into tenths (deciles) on book-to-market ratio and form a portfolio that is long (short) the value-weighted decile with the highest (lowest) ratios.
• For global developed market equity indexes, they each month form a portfolio that is long (short) the equally weighted indexes with book-to-price ratio above (below) the median.
• For each other asset class, they each month form a portfolio that is long (short) the equally weighted assets with 5-year past returns below (above) the median.

To quantify benefits of timing value spreads, they test monthly time series (in only when undervalued) and rotation (weighted by valuation) strategies across asset classes. To measure sources of value spread variation, they decompose value spreads into asset class-specific and common components. Using monthly data for liquid U.S. stocks during January 1972 through December 2014, spot prices for 28 commodities during January 1972 through December 2014, spot and forward exchange rates for 10 currencies during February 1976 through December 2014, modeled and 1-month futures prices for ten 10-year government bonds during January 1991 through May 2009, and levels and book-to-price ratios for 13 developed equity market indexes during January 1994 through December 2014, they find that:

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Do widely used charts of equity and bond market performance inculcate harmfully false beliefs among investors? In his September 2017 paper entitled “Stock Market Charts You Never Saw”, Edward McQuarrie dissects some of these charts and outlines cautions to investors in interpreting them. Using very long-term data for U.S. stock and bond markets spanning hundreds of years, he concludes that: Keep Reading

How differently does the “Simple Asset Class ETF Value Strategy” (SACEVS) perform when the U.S. stock market rises and falls? This strategy seeks to exploit relative valuation of the term risk premium, the credit (default) risk premium and the equity risk premium via exchange-traded funds (ETF). To investigate, because the sample period available for mutual funds is much longer than that available for ETFs, we use instead data from “SACEVS Applied to Mutual Funds”. Specifically, each month we reform a Best Value portfolio (picking the asset associated with the most undervalued premium, or cash if no premiums are undervalued) and a Weighted portfolio (weighting assets associated with all undervalued premiums according to degree of undervaluation, or cash if no premiums are undervalued) using the following four assets:

• Monthly average 3-Month Treasury Bill (T-bill) Secondary Market Rate as an approximation of the return on Cash.
• Vanguard GNMA Investor Shares (VFIIX).
• Vanguard Long-Term Investment Grade Investor Shares (VWESX).
• Vanguard US Growth Investor Shares (VWUSX).

The benchmark is a monthly rebalanced portfolio of 60% stocks and 40% U.S. Treasuries (60-40 VWUSX-VFIIX). We say that stocks rise (fall) during a month when the return for VWUSX is positive (negative) during the SACEVS holding month. Using monthly risk premium estimates, SR and LR, and Best Value and Weighted returns during June 1980 through June 2017 (444 months), we find that:

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A subscriber asked how the “Simple Asset Class ETF Value Strategy” (SACEVS) performs when interest rates rise. This strategy seeks to exploit relative valuation of the term risk premium, the credit (default) risk premium and the equity risk premium via exchange-traded funds (ETF). To investigate, because the sample period available for mutual funds is much longer than that available for ETFs, we use instead data from “SACEVS Applied to Mutual Funds”. Specifically, each month we reform a Best Value portfolio (picking the asset associated with the most undervalued premium, or cash if no premiums are undervalued) and a Weighted portfolio (weighting assets associated with all undervalued premiums according to degree of undervaluation, or cash if no premiums are undervalued) using the following four assets:

• Monthly average 3-Month Treasury Bill (T-bill) Secondary Market Rate as an approximation of the return on Cash.
• Vanguard GNMA Investor Shares (VFIIX).
• Vanguard Long-Term Investment Grade Investor Shares (VWESX).
• Vanguard US Growth Investor Shares (VWUSX).

The benchmark is a monthly rebalanced portfolio of 60% stocks and 40% U.S. Treasuries (60-40 VWUSX-VFIIX). We use the T-bill yield as the short-term interest rate (SR) and the 10-year Constant Maturity U.S. Treasury note (T-note) yield as the long-term interest rate (LR). We say that each rate rises or falls when the associated average monthly yield increases or decreases during the SACEVS holding month. Using monthly risk premium estimates, SR and LR, and Best Value and Weighted returns during June 1980 through June 2017 (444 months), we find that:

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Does a dual momentum selection/weighting approach applied to the U.S. Treasuries term structure identify a safe haven superior to any one duration? In his February 2015 paper entitled “The Search for Crisis Alpha: Weathering the Storm Using Relative Momentum”, Nathan Faber tests a dual momentum safe haven based on U.S. Treasuries of different durations as proxied by either constant maturity indexes or exchange-traded funds (ETFs). He constructs constant maturity indexes from 1-year, 3-year, 5-year, 7-year, 10-year and 20-year constant maturity U.S. Treasuries yields by each month accruing a coupon and repricing at the new yield. For ETFs, he uses total returns for five iShares U.S. Treasuries ETFs: SHY (1-3 years), IEI (3-5 years), IEF (7-10 years), TLH (10-20 years) and TLT (20+ years). The dual momentum approach consists of the following steps:

1. Calculate the return from 10 months ago to one month ago for each duration.
2. Subtract from the return of each duration that of 1-year U.S. Treasuries (SHY) if using constant maturity indexes (ETFs) to calculate an excess return as a measure of intrinsic (absolute or time series) momentum.
3. Discard any durations with negative excess returns.
4. Rank remaining durations based on risk-adjusted excess returns, with variances used to indicate risk, as a measure of relative momentum and assign weights to these durations based on their ranks. If no durations have positive excess returns, assign 100% weight to 1-year U.S. Treasuries (or SHY if using ETFs).

He then investigates the performance of this dual momentum strategy as a safe haven during S&P 500 crises defined in two ways: (1) drawdowns of at least 20% peak to trough; or, (2) monthly declines of at least 5%. He further tests a specific strategy that is long the S&P 500 Index (or SPY if using ETFs) when above its 10-month SMA (SMA10) and in either the dual momentum safe haven portfolio or in a fixed duration (1-year or 20+ years) when below its SMA10. Using data for the yields/indexes/funds specified above since 1962 for constant maturity index tests and since 2003 for ETF tests, all through 2014, he finds that: Keep Reading

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