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Sector Performance by Calendar Month

| | Posted in: Calendar Effects

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 2019, we find that:

The following chart summarizes average (equally weighted) sector returns and standard deviations of average sector returns by calendar month over the available sample period. Sectors are on average strongest in early spring and late fall and weakest in late winter and summer. Sector activity is relatively dispersed in January, February, April, September, October and November and relatively compressed in March, May, July and August.

How do average returns break down by sector?

The next three charts summarized average returns by calendar month, in groups of three, for the nine sector ETFs over the available sample period. Each chart also shows the average return by month for SPY as a broad market benchmark.

The following table lists the two sectors with highest average returns and the two sectors with lowest average returns for each calendar month.

In summary, evidence suggests that average returns and return dispersion for U.S. sector ETFs differ somewhat across calendar months.

Cautions regarding findings include:

  • Given the variabilities in annual returns, sample size (21 years) is small for reliable inference. One or two years of additional data could change the winners and losers by month.
  • Returns do not account for any trading frictions involved in calendar-based sector rotation.
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