Calendar Effects

Are there anomalies for U.S. stock market returns around equity option expiration (OE) days (normally the third Friday of each month, but the preceding Thursday when the market is closed on the third Friday)? To investigate, we examine close-to-close S&P 500 Index returns from five trading days before through five trading days after a moderately large sample of OE days. Using daily closing prices for the index during January 1990 through February 2022 (386 OE days), we find that:

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Does the overnight return effect found pervasively among equity markets, as summarized in “Persistence of Overnight/Intraday Equity Market Return Patterns”, also hold for other asset classes? To investigate, we compare open-to-close (O-C) and close-to-open (C-O) average returns, standard deviations of returns and cumulative performances for the exchange-traded funds (ETF) used as asset class proxies in the Simple Asset Class ETF Momentum Strategy (SACEMS). Using daily dividend-adjusted opening and closing prices of these ETFs during mid-December 2007 (inception of the youngest ETF) through early March 2022, we find that: Keep Reading

With reference to “Turn-of-the-Month Effect Persistence and Robustness” and “Persistence of Overnight/Intraday Equity Market Return Patterns”, a subscriber asked about a strategy that is long SPDR S&P 500 ETF Trust (SPY) only from market close to market open during the turn-of-the-month (TOTM). To investigate, we consider three strategies:

1. TOTM Close-to-Open: long SPY from close-to-open during TOTM, and otherwise in cash (eight round-trip trades per month).
2. TOTM Close-to-Close: long SPY continuously during TOTM, and otherwise in cash (one round-trip trade per month).
3. B&H: buy and hold SPY.

We initially ignore trading frictions, but then look at breakeven frictions for the first strategy. Because of trading frequency, we ignore return on cash. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) based on daily data as key performance statistics. Using daily dividend-adjusted opens and closes for SPY during February 1993 (inception) through February 2022, we find that: Keep Reading

What best explains the decades-long pattern of strong overnight and weak intraday returns in most equity markets? In his January 2022 paper entitled “They Still Haven’t Told You”, Bruce Knuteson reviews possible explanations for this pattern and identifies the most likely. His theoretical equity index benchmark is a random walk with slight upward drift (due to general economic expansion and survivorship bias), with intraday return on average larger than overnight return due to higher intraday risk. Using close-to-open and open-to-close levels of 21 major stock market indexes as available during January 1990 through December 2021, he finds that: Keep Reading

Trading Calendar presents full-year and monthly cumulative performance profiles for the overall U.S. stock market (proxied by the S&P 500 Index) based on average daily behavior. Do monthly behaviors of U.S. stock market sectors deviate from the overall market profile? To investigate, we consider the nine Select Sector Standard & Poor’s Depository Receipts (SPDR) exchange-traded funds (ETF), all of which originate in 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)

Using monthly dividend-adjusted closing prices for these ETFs, along with contemporaneous data for SPDR S&P 500 (SPY) as a benchmark, during December 1998 through December 2021, we find that: Keep Reading

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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