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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.

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Multi-class, Multi-factor Investing

What is the best way to tackle multi-class, multi-factor investing? In their August 2017 paper entitled “Investing in a Multi-Asset Multi-Factor World”, Alexandar Cherkezov, Harald Lohre, Sergey Protchenko and Jay Raol investigate the use of factor investing across multiple asset classes. They define several factors for each of four asset classes, as follows:

  1. Equities (individual stocks for developed and emerging markets) – value, momentum, quality and low volatility.
  2. Currencies (forwards for developed and emerging markets) – carry, value, momentum and quality.
  3. Commodities (24 futures series) – carry, term (duration) and momentum.
  4. Interest rates (10-year swaps for developed and emerging markets) – carry, value, momentum and quality.

To integrate the portfolio with high diversification, they employ diversified risk parity by each month: (1) identifying uncorrelated clusters of risk across asset classes and factors based on a rolling 60-month or expanding (inception-to-date) lookback window; and, (2) setting long-only allocations such that each uncorrelated risk cluster contributes equally to overall portfolio risk. For comparison, they also consider equal weight, minimum variance and equal risk contribution (equal contribution to portfolio risk by each individual factor) allocation approaches. Using data needed to form factor portfolios and measure factor returns in U.S. dollars across asset classes from the end of January 2001 through the end of December 2016, they find that:

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Asset Class Momentum Faster During Bear Markets?

A subscriber asked whether the optimal momentum measurement (lookback) interval for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) shrinks during bear markets for U.S. stocks. This strategy each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

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

To investigate, we compare SACEMS monthly performance statistics when the S&P 500 Index at the previous monthly close is above (bull market) or below (bear market) its 10-month simple moving average. We consider Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners for the baseline SACEMS lookback interval. In a robustness test for the EW Top 3 portfolio, we consider lookback intervals ranging from one to 12 months. Using monthly total (dividend-adjusted) returns for the specified assets since February 2006 (limited by DBC) and the monthly level of the S&P 500 Index since October 2005, all through September 2017, we find that:

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Trend-following Managed Futures to Make Retirement Safer?

Should retirement portfolios include an allocation to managed futures? In his October 2017 paper entitled “Using Trend-Following Managed Futures to Increase Expected Withdrawal Rates”, Andrew Miller compares seven 30-year retirement scenarios via backtests and modified backtests. Specifically, he compares maximum annual real withdrawal rates as a percentage of initial assets that do not exhaust any 30-year retirement portfolios starting each year during 1926-2012 (SAFEMAX). The seven scenarios, all rebalanced annually, are:

  1. Historical Returns 50-50: uses actual annual returns for a 50% allocation to large-capitalization U.S. stocks and a 50% allocation to intermediate-term U.S. Treasuries.
  2. Historical Returns 50-40-10: same as Scenario 1, except shifts 10% of the Treasuries allocation to a trend-following managed futures strategy that is long and short 67 stocks, bonds, currencies and commodities futures series based on equally weighted 1-month, 3-month and 12-month past returns with a 10% annual volatility target.
  3. Lower Historical Returns 50-50: same as Scenario 1, but reduces monthly returns for stocks and Treasuries by 0.19%, reflecting end-of-2016 valuations.
  4. Lower Historical Returns 50-40-10: same as Scenario 2, but reduces monthly returns for stocks, Treasuries and managed futures by 0.19%.
  5. Lower Managed Futures Sharpe Ratio 50-40-10: same as Scenario 2, but reduces the Sharpe ratio for managed futures from an historical level to 0.5.
  6. Lower Historical Returns/Lower Managed Futures Sharpe Ratio 50-40-10: same as Scenario 4, but reduces Sharpe ratio for managed futures to 0.5.
  7. Historical Returns 50-50 with Trend Following for Stocks: same as Scenario 1, but each month puts the stocks allocation into stocks (30-day U.S. Treasury bills) when the return on stocks is positive (negative) over the prior 12 months.

He ignores all trading frictions, fees and taxes. Using monthly asset class returns as specified and monthly inflation data during January 1926 through December 2012, he finds that: Keep Reading

SACEMS with Three Copies of Cash

Subscribers have expressed concern about the “Simple Asset Class ETF Momentum Strategy” (SACEMS) selecting assets with negative past returns. Inclusion of Cash as one of the assets in the SACEMS universe of exchange-traded funds (ETF) already prevents the SACEMS Top 1 portfolio from holding an asset with negative past returns. To test full dual momentum versions of SACEMS equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios, we add two more copies of Cash to the universe, thereby preventing both of them from holding assets with negative past returns. The SACEMS universe thus becomes:

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

We focus on the effects of adding two copies of Cash on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) of SACEMS EW Top 2 and EW Top 3 portfolios. Using monthly dividend adjusted closing prices for the asset class proxies and the yield for Cash over the period February 2006 (the earliest all ETFs are available) through September 2017 (140 months), 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 21 variations of each strategy with execution days ranging from end-of-month (EOM) per the baseline strategy to 20 trading days after EOM (EOM+20). For example, an EOM+5 variation computes allocations baed on EOM but delays execution until the close five trading days after EOM. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics. Using daily dividend-adjusted closes for the above ETFs from late July 2002 through mid-September 2017, we find that:

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Optimal Monthly Cycle for SACEMS?

Is there a best time of the month for measuring momentum within the Simple Asset Class ETF Momentum Strategy (SACEMS)? This strategy each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

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

To investigate, we compare 21 variations of the strategy based on shifting the monthly return calculation cycle relative to trading days from the end of the month (EOM). For example, an EOM+5 cycle ranks assets based on closing prices five trading days after EOM each month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns for the specified assets during February 2006 (limited by DBC) through August 2017, we find that: Keep Reading

Add Position Stop-gain to SACEMS?

Does adding a position take-profit (stop-gain) rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by harvesting some upside volatility? SACEMS each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

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

To investigate the value of stop-gains, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return rises above a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month maximum returns for the specified assets during February 2006 (limited by DBC) through August 2017, we find that: Keep Reading

Add Position Stop-loss to SACEMS?

Does adding a position stop-loss rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by avoiding some downside volatility? SACEMS each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

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

To investigate the value of stop-losses, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return falls below a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month drawdowns for the specified assets during February 2006 (limited by DBC) through August 2017, we find that: Keep Reading

Slow Down or Speed Up SACEMS with Volatility?

A subscriber, noting an article on slowing down intrinsic (absolute or time series) momentum for SPDR S&P 500 (SPY) when its return volatility is relatively high, suggested doing the same for the Simple Asset Class ETF Momentum Strategy (SACEMS). The hypothesis is that this dynamic lookback interval approach avoids undesirable whipsaws when asset returns are volatile. SACEMS each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a fixed lookback interval:

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

To investigate the suggested dynamic lookback interval, we each month:

  1. Calculate the average of the standard deviations of daily returns over the last 60 trading days for the individual risky assets (all except Cash).
  2. Calculate the average of these end-of-month averages over the past 12 months.
  3. Divide the current month average standard deviation by the 12-month average of averages to get a lookback interval factor.
  4. Multiply the baseline fixed lookback interval by the current lookback interval factor.
  5. Round the result to the nearest whole number of months as the current dynamic lookback interval.

We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using daily and monthly total (dividend-adjusted) returns for the specified assets during February 2006 (limited by DBC) through August 2017, we find that: Keep Reading

Trend Following to Boost Retirement Income

Does simple asset price trend following based on 10-month simple moving average (SMA10) reliably boost the performance of retirement portfolios? In their July 2017 paper entitled “Can Sustainable Withdrawal Rates Be Enhanced by Trend Following?”, Andrew Clare, James Seaton, Peter Smith and Steve Thomas compare effects of asset class diversification and trend following on safe withdrawal rates from UK retirement portfolios. They consider 60-40 UK stocks-bonds, 30-70 UK stocks-bonds and equally weighted UK stocks, global stocks, bonds, commodities and UK real estate (EW Multi-asset). They further consider risk parity (RP) multi-asset (each class weighted by the inverse of its prior-year volatility) and 100% global stocks (equally weighted across five regions). They focus on a 20-year retirement period (but also consider 30-year), assume annual withdrawals the first day of each year and ignore taxes and rebalancing frictions. They use both in-sequence historical asset returns and Monte Carlo simulations (random draws with replacement from the historical annual returns of each portfolio). They apply trend following separately to each asset by holding the asset (cash) when asset price is above (below) its SMA10. Their key portfolio performance metric is Perfect Withdrawal Rate (PWR), the constant real (inflation-adjusted) withdrawal rate as a percentage of initial portfolio value that exactly exhausts the portfolio at the end of the retirement period. Using monthly total returns in pounds sterling for the selected asset classes and values of the UK consumer price index during 1970 through 2015, they find that: Keep Reading

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