Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for November 2021 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for November 2021 (Final)
1st ETF 2nd ETF 3rd ETF

Strategic Allocation

Is there a best way to select and weight asset classes for long-term diversification benefits? These blog entries address this strategic allocation question.

Combining the Smart Money Indicator with SACEMS and SACEVS

“Verification Tests of the Smart Money Indicator” reports performance results for a specific version of the Smart Money Indicator (SMI) stocks-bonds timing strategy, which 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). Do these sentiment-based results diversify those for the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS)? To investigate, we look at correlations of annual returns between variations of SMI (no lag between signal and execution, 1-week lag and 2-week lag) and each of SACEMS equal-weighted (EW) Top 3 and SACEVS Best Value. We then look at average gross annual returns, standard deviations of annual returns and gross annual Sharpe ratios for the individual strategies and for equal-weighted, monthly rebalanced portfolios of the three strategies. Using gross annual returns for the strategies during 2008 through 2019, we find that: Keep Reading

Effects of Execution Delay on SACEVS

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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Reducing Downside Risk of Trend Following Strategies

How can investors suppress the downside of trend following strategies? In their July 2019 paper entitled “Protecting the Downside of Trend When It Is Not Your Friend”, flagged by a subscriber, Kun Yan, Edward Qian and Bryan Belton test ways to reduce downside risk of simple trend following strategies without upside sacrifice. To do so, they: (1) add an entry/exit breakout rule to a past return signal to filter out assets that are not clearly trending; and, (2) apply risk parity weights to assets, accounting for both their volatilities and correlations of their different trends. Specifically, they each month:

  • Enter a long (short) position in an asset only if the sign of its past 12-month return is positive (negative), and the latest price is above (below) its recent n-day minimum (maximum). Baseline value for n is 200.
  • Exit a long (short) position in an asset only if the latest price trades below (above) its recent n/2-day minimum (maximum), or the 12-month past return goes negative (positive).
  • Assign weights to assets that equalize respective risk contributions to the portfolio based on both asset volatility and correlation structure, wherein covariances among assets adapt to whether an asset is trending up or down. They calculate covariances based on monthly returns from an expanding (inception-to-date) window with baseline 2-year half-life exponential decay.
  • Impose a 10% annual portfolio volatility target.

Their benchmark is a simpler strategy that uses only past 12-month return for trend signals and inverse volatility weighting with annual volatility target 40% for each asset. Their asset universe consists of 66 futures/forwards. They roll futures to next nearest contracts on the first day of the expiration month. They calculate returns to currency forwards using spot exchange rates adjusted for carry. Using daily prices for 23 commodity futures, 13 equity index futures, 11 government bond futures and 19 developed and emerging markets currency forwards as available during August 1959 through December 2017, they find that: Keep Reading

Optimizing the Combination of Economic Growth and Price Trends

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

Stick to the Plan, or Adjust?

When a retirement portfolio veers from its planned path, is it better to count on reversion-to-path or adjust the plan? In his March 2019 paper entitled “Managing to Target: Dynamic Adjustments for Accumulation Strategies”, Javier Estrada employs a simple retirement portfolio model to compare outcomes for sticking to the plan (S2P) with 13 dynamic strategies of three types:

  1. Five effective but impractical (EBI) dynamic contribution strategies. EBI1 at the end of each year contributes to or withdraws from the portfolio so that it stays exactly on track. EBI2, EBI3, EBI4 and EBI5 are similar but limit annual adjustments to no more than 5%, 10%, 15% and 20% above or below the prior-year contribution, respectively.
  2. Five feasible but limited (FBL) dynamic contribution strategies. FBL1, FBL2, FBL3, FBL4 and FBL5 also at end of each year contribute to or withdraw from the portfolio to help keep it on track, but limit changes to no more than 5%, 10%, 15%, 20% and 50% (FBL5) above or below the initial plan contribution, respectively.
  3. Three dynamic asset allocation (AA) strategies that every five years make portfolio asset allocations more aggressive (conservative) when the portfolio is below (above) plan. AA1, AA2 and AA3 limit changes in asset class allocations to 10%, 20% and 30%, respectively, compared to allocations five years ago.

His model portfolio consists of 39 annual contributions over 40 years, with 5% annualized real return (the historical average for 60% stocks and 40% bonds) and target value $1 million at retirement. He evaluates portfolio performance over 80 possible 40-year periods over 118 years. Using annual real (based on the U.S. Consumer Price Index) total returns for the S&P 500 Index and 10-year U.S. Treasury notes during 1900 through 2017, he finds that:

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SACEMS Optimization in Depth

The Simple Asset Class ETF Momentum Strategy (SACEMS) each month picks the one, two or three of nine asset class proxies with the highest cumulative total returns over a specified lookback interval. A subscriber proposed instead using the optimal intrinsic (time series or absolute) momentum lookback interval for each asset rather than a common lookback interval for all assets. SACEMS and the proposed approach represent different beliefs (which could both be somewhat true), as follows:

  • Many investors adjust asset class allocations with some regularity, such that behaviors of classes are important and coordinated.
  • Many investors switch between specific asset classes and cash with some regularity, such that each class may exhibit distinct times series behavior. 

To investigate, we consider two ways to measure intrinsic momentum for each asset class proxy:

  1. Correlation between next-month return and average monthly return over the past one to 12 months. The lookback interval with the highest correlation has the strongest (linear) relationship between past and future returns and is optimal.
  2. Intrinsic momentum, measured as compound annual growth rate (CAGR) for a strategy that is in the asset (cash) when its total return over the past one to 12 months is positive (zero or negative). The lookback interval with the highest CAGR is optimal.

We use the two sets of optimal lookback intervals (optimization-in-depth) to calculate momentum for each asset class proxy as its average monthly return over its optimal lookback interval. We then compare performance statistics for these two alternatives to those for base SACEMS, focusing on: gross CAGR for several intervals; average gross annual return; standard deviation of annual returns; gross annual Sharpe ratio; and, gross maximum drawdown (MaxDD). Using monthly dividend-adjusted prices for SACEMS asset class proxies during February 2006 through September 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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SACEMS vs. Luck

How lucky would a asset class picker with no skill have to be to match the performance of the Simple Asset Class Momentum Strategy (SACEMS), which each month picks winners from a set of eight exchange-traded funds (ETF) plus cash based on total returns over a specified lookback interval. To investigate, we run 1,000 trials of a “strategy” that each month allocates funds to one, the equally weighted two or the equally weighted three of these nine assets picked at random. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics. Using monthly total (dividend-adjusted) returns and for the specified assets during February 2006 (limited by DBC) through June 2019, we find that:

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Adjust the SACEMS Asset Universe?

The Simple Asset Class ETF Momentum Strategy (SACEMS) each month picks winners based on total return over a specified ranking (lookback) interval from the following eight asset class exchange-traded funds (ETF), plus cash:

  1. PowerShares DB Commodity Index Tracking (DBC)
  2. iShares MSCI Emerging Markets Index (EEM)
  3. iShares MSCI EAFE Index (EFA)
  4. SPDR Gold Shares (GLD)
  5. iShares Russell 2000 Index (IWM)
  6. SPDR S&P 500 (SPY)
  7. iShares Barclays 20+ Year Treasury Bond (TLT)
  8. Vanguard REIT ETF (VNQ)
  9. 3-month Treasury bills (Cash)

Based on findings in “SACEMS Portfolio-Asset Addition Testing”, a subscriber proposed adding iShares JPMorgan Emerging Market Bond Fund (EMB) to this set. To investigate, we revisit relevant analyses and conduct robustness tests, with focus on the equal-weighted (EW) Top 3 SACEMS portfolio. Using monthly dividend-adjusted closing prices for asset class proxies and the yield for Cash during February 2006 (when all ETFs in the baseline universe are first available) through June 2019, we find that: Keep Reading

Mimicking Portfolios of Five ETFs Beat Most Active Mutual Funds?

Can investors beat a typical active U.S. equity mutual fund via a small portfolio of periodically re-weighted equity exchange-traded funds (ETF)? In their February 2019 paper entitled “Are Passive Funds Really Superior Investments: An Investor Perspective”, flagged by a subscriber, Edwin Elton, Martin Gruber and Andre de Souza:

  1. Determine via cluster analysis a small set of ETFs that captures most of the variation in 69 broad U.S. equity indexes.
  2. Explore use of this set to mimic past performances of many active U.S. equity mutual funds via 24-month linear regressions with ETF coefficients scaled to sum to one.
  3. Compare next-year (close of first trading day of the year after coefficient calculation to close of first trading day next year) returns of mimicking ETF portfolios and active mutual fund counterparts.

Their target set of 883 active U.S. equity mutual funds are those with at least: three years of data as of January 2003; $15 million in assets; and, 90% of assets allocated broadly to stocks. Using monthly returns for 69 U.S. equity indexes, the small set of passive equity ETFs that capture variation in these indexes and 883 active U.S. equity mutual funds during January 2003 through December 2018, they find that:

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