Simple Sector ETF Momentum Strategy Performance
Posted in Momentum Investing
July 6, 2010
Do simple momentum trading strategies applied to the major U.S. stock market sectors outperform the overall U.S. market? To investigate, we apply three simple momentum strategies to the nine sector exchange-traded funds (ETF) defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR), all of which have trading data back to December 1998:
Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)
The three strategies are: (1) at the end of each month, allocate all funds to the sector ETF with the highest total return over the past six months (6-1); (2) at the end of each month, allocate all funds to the sector ETF with the highest total return over the six months ending the prior month (6-1-1); and, (3) at the end of each month, allocate all funds either to the sector ETF with the highest total return over the past six months or to cash depending on whether the S&P 500 Index is above or below its 10-month simple moving average (6-1-SMA10). A six-month ranking period is intuitively large enough to gauge sector momentum but small enough to react to changes in business conditions that might favor one sector over others. Using monthly adjusted closing prices for the sector ETFs, the S&P 500 index, 3-month Treasury bills (T-bills) and S&P Depository Receipts (SPY) over the period 12/98-6/10 (139 months), we find that:
The following chart shows the distribution of sector ETF winners based on past six-month total return over the entire sample period. The energy sector comprises 40 of the 133 monthly winners (30%).
How does applying the above three strategies to these top-ranked ETFs translate into cumulative returns?
The next chart compares the cumulative values of $10,000 initial investments in the three sector ETF momentum strategies and in SPY over the sample period. Calculations derive from the following assumptions:
- Reallocate at the close on the last trading day of each month (assume for the 6-1 variation that total six-month past returns for the ETFs can be calculated just before the close).
- Trading (switching) friction is 0.25% of the balance whenever there is a change in holdings (somewhat unfavorable to the 6-1-SMA10 variation).
- Return on cash for the 6-1-SMA10 variation is equal to the T-bill yield at the time of allocation.
- Ignore any tax implications of trading.
At the assumed level of switching friction, all three momentum strategy variations outperform buying and holding SPY. Both the basic strategy (6-1) and the SMA-moderated strategy (6-1-SMA10) outperform the skip-month strategy (6-1-1). The 6-1-SMA10 variation is in cash 41% of the time over the sample period.
How do average monthly returns, as alternative measures of strategy performance, compare?
The next chart depicts the average monthly net returns (with 0.25% switching frictions) and the standard deviations of monthly returns for the three sector ETF momentum strategies and for buying and holding SPY over the sample period based on the assumptions listed above. The 6-1-SMA10 variation has not only the highest average monthly return but also the lowest volatility (because of frequent allocation to cash).
How sensitive are these statistics to the level of switching friction?
The next chart compares the average monthly net returns for the three sector ETF momentum strategies as switching frictions range from 0.25% to 4% of the balance over the sample period. All three momentum strategies are attractive for switching frictions 1% or lower, and all three become unattractive at switching frictions above 4%. Actual switching friction depends on ETF bid-ask spreads, account size and specific broker fees.
Is the momentum effect consistent across momentum ranking?
The final chart shows the average monthly gross returns by sector momentum ranks 1-9 for the basic (6-1) and skip-month (6-1-1) strategies over the sample period. Rank 1 (9) represents the past winners (losers). The progression across ranks is not highly systematic for either strategy, but it is more regular for the 6-1 strategy than for the 6-1-1 strategy. In general, irregularity undermines belief in reliable asset price momentum. The extreme irregularity for the 6-1-1 strategy undermines belief that a skip-month avoids predictable short-term reversion against trend.
The small sample size (just 22 independent six-month momentum ranking intervals and even fewer independent 10-month SMA intervals) and non-normalities in ETF monthly return distributions limit confidence in the above results.
Including ETFs representing other asset classes (such as bonds, commodities, equity styles and international stocks) may enhance results.
In summary, simple sector ETF momentum strategies have generally outperformed the broad stock market over the past decade for reasonably low trading frictions.
Ranking intervals other than six months and holding periods other than one month may produce different results. Academic studies of momentum most often use 12 months as the ranking period, with six months probably second most common. Many of these studies also test holding periods longer than one month. Some impose a skip-month (as for the 6-1-1 strategy above) to avoid a short-term reversion tendency, and some do not. For the above tests, using longer ranking and hold periods would effectively reduce the already-small sample size. Moreover, data snooping bias is especially pernicious for small samples. In other words, isolating an optimal ranking period from this small sample may well lean toward luck rather than reliable inference.
