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Add Position Stop-gain to SACEMS?

| | Posted in: Momentum Investing, Strategic Allocation, Technical Trading

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 months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. 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 through March 2020, we find that:

Specific trading assumptions are:

  • At the end of each month, reform SACEMS portfolios using ETFs with the highest total returns over the past four months.
  • If intra-month gain for any current position rises above the stop-gain threshold, sell that position at the threshold and put proceeds in Cash. Otherwise, hold all positions to the end of the month.
  • Ignore trading frictions for monthly portfolio reformations and stop-outs.
  • Ignore return on Cash while stopped out (close to accurate during the available sample period).

The following chart summarizes numbers of stop-outs for first, second and third ranks of ETFs for intra-month stop-gain thresholds ranging from 1% to 20% over the available sample period. Numbers of stop-outs decrease rapidly as stop-gain threshold increases. For example the total number of stop-outs for threshold 5% (10%) is 128 (31) over the sample period.

Results suggest that third-ranked ETFs are less likely to stop out than those ranked second or third, perhaps because they tend to be less volatile assets.

How do these stop-outs translate to SACEMS portfolio performance?

The next chart summarizes CAGRs for SACEMS Top 1, EW Top 2 and EW Top 3 portfolios for intra-month stop-gain thresholds ranging from 1% to 20% over the available sample period. The Baseline portfolio CAGRs are for no stop-outs. Stop-gain portfolios mostly outperform Baseline portfolios for thresholds in the range 8-%-19% and higher. For 16%-19%, there is only one stop-out over the available sample period (there are no stop-outs at 20%).

How do stop-gains affect MaxDDs?

The final chart summarizes MaxDDs for SACEMS Top 1, EW Top 2 and EW Top 3 portfolios for intra-month stop-gain thresholds ranging from 1% to 20% over the available sample period. Baseline portfolio MaxDDs are for no stop-outs. Stop-gain portfolios have MaxDDs mostly shallower than or the same as those for the Baseline portfolios. For the range of stop-gain portfolios with outperforming CAGRs, MaxDDs are close to or the same as Baseline.

The following two tables provide data used to construct the preceding two charts.

In summary, evidence from tests on available data generally supports belief that a simple position stop-gain rule with threshold around 10%-15% improves SACEMS performance (at the cost of some additional trades).

In other words, stop-gains appear to miss downside reversions more than miss further upsides. Investors in SACEMS-like strategies may want to consider including a monthly stop-gain position rule.

Cautions regarding findings include:

  • For high thresholds, numbers of stop-gains are small, undermining confidence in results. Said differently, the available sample is very short for testing high thresholds.
  • As noted, analyses ignore trading frictions. Frictions are generally higher with than without stop-gains, working against any improvements they offer.
  • Different ETFs have different volatilities, so different stop-gain thresholds may be optimal across ETFs.
  • Brute force testing of different stop-gain thresholds introduces data snooping bias, such that the best-performing thresholds overstate expectations. A small number of stop-outs amplifies this bias.
  • Some other stop-gain rule (such as different thresholds across ETFs, thresholds adaptive to recent volatility, a portfolio-level threshold or intra-month re-entry) may work better, but brute force experimentation would increase snooping bias, again amplified by small sample size.
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