Strategic Allocation

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

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

We have updated monthly allocations and performance data for the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS). We have also updated performance data for the Combined Value-Momentum Strategy.

We have updated the Trading Calendar to incorporate data for December 2019. This update includes an extension of the sample period back in time to 1928.

The home page, Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) now show preliminary positions for January 2020. For SACEMS, positions two and three are in close competition, so their order may change by the close. For SACEVS, allocations are unlikely to change.

A subscriber asked whether the Simple Asset Class ETF Momentum Strategy (SACEMS) exhibits monthly calendar effects. In investigating, we also look at the Simple Asset Class ETF Value Strategy (SACEVS)? We consider the “Best Value” (most undervalued asset) and “Weighted” (assets weighted by degree of undervaluation) versions of SACEVS. We consider the Top 1, equally weighted “(EW) Top 2” and “EW Top 3” versions of SACEMS, which each month equally weights the top one, two or three of nine ETFs/cash with the highest total returns over a specified lookback interval. We further compare seasonalities of these strategies to those of their benchmarks: for SACEVS, a monthly rebalanced 60% stocks-40% bonds portfolio (60-40); and, for SACEMS an equally weighted and monthly rebalanced portfolio of the SACEMS universe (EW All). Using monthly gross total returns for SACEVS since August 2002 and for SACEMS since July 2006, both through October 2019, we find that:

Keep Reading

Does  applying a simple moving average (SMA) filter improve performance of the “Simple Asset Class ETF Value Strategy” (SACEVS), which seeks diversification across the following three asset class exchange-traded funds (ETF) plus cash according to the relative valuations of term, credit and equity risk premiums?

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

Since many technical traders use a 10-month SMA (SMA10), we test effectiveness of requiring that each of the ETFs pass an SMA10 filter by comparing performances for three scenarios:

1. Baseline – SACEVS as currently tracked.
2. With SMA10 Filter – Run Baseline SACEVS and then apply SMA10 filters to dividend-adjusted prices of ETF allocations. If an allocated ETF is above (below) its SMA10, hold the allocation as specified (Cash). This rule is inapplicable to any Cash allocation.
3. With Half SMA10 Filter – Same as scenario 2, but, if an allocated ETF is above (below) its SMA10, hold the allocation as specified (half the specified allocation and half cash at the T-bill yield).

We focus on gross compound annual growth rates (CAGR), maximum drawdowns (MaxDD) and annual Sharpe ratios (using average monthly T-bill yield during a year as the risk-free rate for that year) of SACEVS Best Value and SACEVS Weighted portfolios. We also look at how the SMA rule affects a 60%-40% SPY-TLT benchmark (60-40) portfolio. Using SACEVS historical data and monthly dividend-adjusted closing prices for the asset class proxies and yield for Cash during July 2002 (the earliest all ETFs are available) through November 2019, we find that: Keep Reading

Are the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) mutually diversifying. To check, we look at 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).

We also test sensitivity of results to deviating from equal SACEVS-SACEMS weights. Using monthly gross returns for SACEVS and SACEMS portfolios since January 2003 for the first strategy and since June 2006 for the latter two, all through November 2019, we find that: Keep Reading

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:

Keep Reading

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

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:

Keep Reading