Calendar Effects

In response to “Combining Defensive-in-May and Sector Reversion”, a subscriber requested testing of a strategy combining seasonal effects (cyclical sectors during November through April and defensive sectors during May through October) and sector momentum. Cyclical and defensive choices are:

• Cyclical:
  • Materials Select Sector SPDR (XLB)
  • Industrial Select Sector SPDR (XLI)
  • Technology Select Sector SPDR (XLK)
  • Consumer Discretionary Select SPDR (XLY)
  • Vanguard Small-Cap Value Index Fund (VBR)
• Defensive:
  • Consumer Staples Select Sector SPDR (XLP)
  • Utilities Select Sector SPDR (XLU)
  • Health Care Select Sector SPDR (XLV)
  • Vanguard Dividend Appreciation Index Fund (VIG)
  • iShares 7-10 Year Treasury Bond (IEF)

At the end of each October, the strategy buys the one cyclical fund with the highest return over some past interval (betting on momentum). At the end of each April, the strategy sells the cyclic fund and buys the one defensive fund with the highest return over the past interval (again, betting on momentum). For convenience, we use a 6-month lookback interval to rank funds. We use buy-and-hold SPDR S&P 500 (SPY) as a benchmark. We focus on semiannual return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using semiannual dividend-adjusted prices for the selected funds during October 2006 (limited by availability of VIG) through October 2021 (defining the first and last available semiannual intervals), we find that: Keep Reading

Inspired by “The iM Seasonal ETF Switching Strategy”, a subscriber requested testing of a strategy combining seasonal effects (cyclical sectors during November through April and defensive sectors during May through October) and sector reversion. Cyclical and defensive choices are:

• Cyclical:
  • Materials Select Sector SPDR (XLB)
  • Industrial Select Sector SPDR (XLI)
  • Technology Select Sector SPDR (XLK)
  • Consumer Discretionary Select SPDR (XLY)
  • Vanguard Small-Cap Value Index Fund (VBR)
• Defensive:
  • Consumer Staples Select Sector SPDR (XLP)
  • Utilities Select Sector SPDR (XLU)
  • Health Care Select Sector SPDR (XLV)
  • Vanguard Dividend Appreciation Index Fund (VIG)
  • iShares 7-10 Year Treasury Bond (IEF)

At the end of each October, the strategy buys the one cyclical fund with the lowest return over some past interval (betting on reversion). At the end of each April, the strategy sells the cyclic fund and buys the one defensive fund with the lowest return over the past interval (again, betting on reversion). For convenience, we use a 6-month lookback interval to rank funds. We use buy-and-hold SPDR S&P 500 (SPY) as a benchmark. We focus on semiannual return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using semiannual dividend-adjusted prices for the selected funds during October 2006 (limited by availability of VIG) through October 2021 (defining the first and last available semiannual intervals), we find that: Keep Reading

A subscriber asked about a strategy that holds a portfolio of cyclical sectors and small capitalization stocks during November through April and a portfolio of defensive sectors during May through October, as follows:

• Cyclical:
  • Materials Select Sector SPDR (XLB)
  • Industrial Select Sector SPDR (XLI)
  • Consumer Discretionary Select SPDR (XLY)
  • Vanguard Small Capitalization Index Fund (NAESX)
• Defensive:
  • Consumer Staples Select Sector SPDR (XLP)
  • Utilities Select Sector SPDR (XLU)
  • Health Care Select Sector SPDR (XLV)

We use NAESX for small stocks to obtain a history as long as those for the equity sectors. We weight components of the cyclical and defensive portfolios equally. We use buy-and-hold NAESX and an equal-weighted, semiannually rebalanced portfolio of all seven funds (Sector EW) as benchmarks. We focus on semiannual return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using semiannual dividend-adjusted prices for the selected funds during April 1999 through October 2021 (defining the first and last available semiannual intervals), we find that: Keep Reading

Trading Calendar presents full-year and monthly cumulative performance profiles for the overall stock market (S&P 500 Index) based on its average daily behavior. How much do the corresponding monthly behaviors of the various size and value/growth styles deviate from an overall equity market profile? To investigate, we consider the the following six exchange-traded funds (ETF) that cut across capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

Using monthly dividend-adjusted closing prices for the style ETFs and SPDR S&P 500 (SPY) during August 2001 through October 2021 (limited by data for IWS/IWP), we find that: Keep Reading

Do quarterly allocation updates for the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS) work as well as monthly updates? These strategies 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)

Changing from monthly to quarterly allocation updates does not sacrifice information about lagged quarterly S&P 500 Index earnings, but it does sacrifice currency of term and credit premiums. To assess alternatives, we compare cumulative performances and the following key metrics for quarterly and monthly allocation updates: gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD), annual gross returns and volatilities and annual gross Sharpe ratios. Using monthly dividend-adjusted closes for the above ETFs during September 2002 (earliest alignment of months and quarters) through September 2021, we find that:

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Does the technically adjusted Seasonal Timing Strategy popularized some years ago in Sy Harding’s Street Smart Report Online (now unavailable due to Mr. Harding’s death) generate attractive performance? This strategy combines “the market’s best average calendar entry [October 16] and exit [April 20] days with a technical indicator, the Moving Average Convergence Divergence (MACD).” According to Street Smart Report Online, applying this strategy to a Dow Jones Industrial Average (DJIA) index fund generated a cumulative return of 213% during 1999 through 2012, compared to 93% for the DJIA itself. To check over a longer sample period with an alternative market proxy, we apply the strategy to SPDR S&P 500 (SPY) since its inception and consider several alternatives, as follows:

1. SPY – buy and hold SPY.
2. Seasonal-MACD – seasonal timing per specified dates with MACD refinement, holding cash when not in SPY.
3. Seasonal Only – seasonal timing per the same dates without MACD refinement, again holding cash when not in SPY.
4. SMA200 – hold SPY (cash) when the S&P 500 Index is above (below) its 200-day simple moving average at the prior daily close. 

For all strategies, we use the yield on short-term U.S. Treasury bills (T-bills) as the return on cash. Using daily closes for the S&P 500 Index, dividend-adjusted closes for SPY and T-bill yield during 1/29/93 (SPY inception) through 10/1/21, we find that: Keep Reading

Unlike publicly traded assets generally, investors/speculators can buy and sell bitcoin any day of the week. Do bitcoin returns exhibit anomalies by day of the week, perhaps especially because of weekend trading? To investigate, we calculate (1) average returns and return variabilities for each day of the week; and, (2) gross cumulative returns for holding bitcoin only one specific day of the week. Using daily bitcoin prices from Coindesk during 11/3/2014 (the earliest offered) through September 6, 2021, we find that: Keep Reading

A subscriber requested review of a swing trade strategy that buys and sells Invesco QQQ Trust (QQQ) according to the following rules:

• Buy at the close when it is either Monday or Tuesday and QQQ (Close-Low)/(High-Low) is 0.15 or less.
• Subsequently sell at the close when it is higher than the prior-day high.

To investigate, to simplify portfolio cash management, we assume that there are no overlapping trades (if a position opens on Monday, another position does not open on Tuesday). We further assume that cash earns the 3-month U.S. Treasury bills (T-bill) yield when not in QQQ and that frictions for switching between T-bills and QQQ are 0.10% of trade value. Using daily high, low, close and dividend-adjusted close (to calculate returns) for QQQ and daily T-bill close during March 10, 1999 (QQQ inception) through August 5, 2021, we find that:

A subscriber requested testing of a strategy that buys SPDR S&P 500 (SPY) at the open on the day before each scheduled Federal Open Market Committee (FOMC) meeting and sells at the close. Using daily dividend-adjusted SPY open and close prices and dates of FOMC meetings during January 2016 through June 2021 (43 meetings), we find that: Keep Reading

In view of research indicating that overnight (close-to-open) returns are on average significantly higher than open-to-close returns, a subscriber proposed an enhancement to the Simple Asset Class ETF Momentum Strategy (SACEMS), as follows:

• Instead of ranking SACEMS assets at the market close on the last trading day of each month, rank them at the open.
• Sell any assets leaving SACEMS portfolios at the open.
• Buy any assets entering SACEMS portfolios at the close.

Due to complexity of precisely programming a backtest of this setup, we instead run the following tests:

1. Compare average daily open-to-close and close-to-open returns for each SACEMS non-cash asset over available sample periods since July 2002.
2. Compare SACEMS portfolio performances during July 2006 through May 2021 for: (a) ranking assets at the open on the last trading day of each month and executing all trades at the open; and, (b) ranking assets at the close on the last trading day of each month and executing all trades at the close (baseline SACEMS).
3. Calculate SACEMS portfolio performances during July 2006 through May 2021 for a variation that ranks assets at the open on the last trading day of each month, liquidates SACEMS portfolios at the open and reforms them at the close. This variation is more aggressive in exploiting an overnight return effect than the proposed approach, but is easier to program.

We consider Top 1, equal-weighted (EW) Top 2 and EW Top 3 SACEMS portfolios. We focus on full-sample gross compound annual growth rate, gross annual Sharpe ratio and maximum drawdown based on monthly data for portfolio comparisons. Using dividend-adjusted opening and closing prices for all SACEMS assets during July 2002 through May 2021, we find that: Keep Reading