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Asset Class Momentum Faster During Bear Markets?

Posted in Equity Premium, Momentum Investing, Strategic Allocation

A subscriber asked whether the optimal momentum ranking (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 September 2005, all through February 2019, we find that:

Trading assumptions for the baseline SACEMS strategy are:

  • At the close of each month, reform SACEMS portfolios using ETFs with the highest total returns over the past four months.
  • Ignore trading (switching) frictions.
  • Ignore tax implications of trading.

The following chart summarizes average gross monthly returns and standard deviations of monthly returns for SACEMS Top 1, EW Top 2 and EW Top 3 portfolios and for SPY during U.S. stock market bull and bear months separately. Notable points are:

  • For Top 1 and EW Top 3, returns are lower and more volatile during bear months. Top 1 returns are especially volatile during bear months.
  • Average SPY return during bear months is very low, and returns are very volatile.
  • Diversifying SACEMs via EW Top 2 and EW Top 3 progressively suppresses volatilities of both bull and bear months.

For a risk-adjusted perspective, we compare ratios of average monthly gross returns to standard deviations of monthly returns (monthly reward/risk).

The next chart summarizes monthly reward/risk for SACEMS Top 1, EW Top 2 and EW Top 3 portfolios and for SPY during U.S. stock market bull and bear months separately. Findings parallel those above.

Are findings robust to different momentum measurement lookback intervals?

The final chart compares SACEMS EW Top 3 portfolio monthly reward/risk across lookback intervals ranging from one month (1) to 12 months (12), separately for U.S. equity market bull and bear months. Return calculations start with March 2007 to accommodate the longest (12-month) lookback interval. Notable points are:

  • Optimal lookback intervals during bull months are in the range four to nine months.
  • Optimal lookback intervals during bear months are in the range one to six months. All longer lookback intervals perform poorly. Progression of performance by lookback interval is not systematic.

Findings are likely sensitive to the time series shapes of U.S. equity bull and bear markets within the sample period (especially that of the 2008-2009 crash) and therefore may not be representative of future bull and bear markets.

In summary, evidence from the available sample support belief that short asset class momentum lookback intervals provide better protection from U.S. equity bear markets than long lookback intervals.

Cautions regarding findings include:

  • As noted, performance data are gross, not net. Accounting for costs of monthly portfolio reformation would reduce performance.
  • Findings are weak regarding modification of the baseline SACEMS strategy because:
    • As noted, results may be sensitive to the time series shapes of bull and bear markets, and sample size is extremely small in terms of number of bull and bear regimes.
    • Lookback interval optimization introduces data snooping bias (lucky and unlucky exploitation of randomness), which is elevated for small samples.
  • Changing the universe of SACEMS assets may affect findings (depending on the mix of equities versus other asset classes).
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