Calendar Effects

A reader inquired whether the Turn-of-the-Month Effect, a concentration of positive stock market returns around the turns of calendar months, works for U.S. stock market sectors. To investigate, we measure turn-of-the-month (TOTM) returns for the nine sector exchange-traded funds (ETF) defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR), all of which have traded since 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)

We define TOTM as the eight-trading day interval from the close five trading days before the first trading day of a month to the close on the fourth trading day of the month. Using daily dividend-adjusted closes for the sector ETFs and for SPDR S&P 500 ETF Trust (SPY) as a benchmark from December 1998 through early May 2023, we find that: Keep Reading

A subscriber asked about the performance of the 50-50  Simple Asset Class ETF Value Strategy (SACEVS) Best Value-Simple Asset Class ETF Momentum Strategy (SACEMS) Equal-Weighted (EW) Top 2 in combination with “Sell in May”. To investigate, we compare three alternatives:

1. Best Value – EW Top 2 – holds 50-50 SACEVS Best Value-SACEMS EW Top 2 during all months.
2. “Sell in May” – holds 50-50 SACEVS Best Value-SACEMS EW Top 2 during November through April and 3-month U.S. Treasury bills (T-bills) during May through October.
3. “Opposite” – holds 50-50 SACEVS Best Value-SACEMS EW Top 2 during May through October and 3-month U.S. Treasury bills (T-bills) during November through April.

Using monthly returns for SACEVS Best Value and SACEMS EW Top 2 and monthly T-bill yield during July 2006 (limited by SACEMS) through April 2023, we find that: Keep Reading

A subscriber requested review of a strategy that seeks to exploit “Sell in May” by switching between risk-on assets during November-April and risk-off assets during May-October, with assets specified as follows:

• Risk-on – SPDR S&P 500 (SPY), iShares Russell 2000 Index (IWM) and Invesco DWA Technical Leaders (PDP).
• Risk-off – iShares Barclays 20+ Year Treas Bond (TLT) and Invesco S&P 500 Low Volatility (SPLV).

On each portfolio switch date, assets receive equal weight with 0.25% overall penalty for trading frictions. We focus on compound annual growth rate (CAGR), maximum drawdown (MaxDD) measured at 6-month intervals and Sharpe ratio measured at 6-month intervals as key performance statistics. As benchmarks, we consider buying and holding SPY, IWM or TLT and a 60%-40% SPY-TLT portfolio rebalanced frictionlessly at the ends of April and October (60-40). Using April and October dividend-adjusted closes of SPY, IWM, PDP, TLT and SPLV as available during October 2002 (first interval with at least one risk-on and one risk-off asset) through April 2023, and contemporaneous 6-month U.S. Treasury bill (T-bill) yield as the risk-free rate, we find that: Keep Reading

A subscriber asked whether the Simple Asset Class ETF Momentum Strategy (SACEMS) exhibits monthly calendar effects. In investigating, we also look at the Simple Asset Class ETF Value Strategy (SACEVS)? We consider the Best Value (most undervalued asset) and Weighted (assets weighted by degree of undervaluation) versions of SACEVS. We consider the Top 1, equal-weighted (EW) Top 2 and EW Top 3 versions of SACEMS, which each month holds the top one, two or three of nine ETFs/cash with the highest total returns over a specified lookback interval. We further compare seasonalities of these strategies to those of their benchmarks: for SACEVS, a monthly rebalanced 60% stocks-40% bonds portfolio (60-40); and, for SACEMS an equal-weighted and monthly rebalanced portfolio of the SACEMS universe (EW All). Using monthly gross total returns for SACEVS since August 2002 and for SACEMS since July 2006, both through March 2023, we find that:

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Investor mood may affect financial markets. Sports may affect investor mood. The biggest mood-mover among sporting events in the U.S. is likely the National Football League’s Super Bowl. Is the week before the Super Bowl especially distracting and anxiety-producing? Is the week after the Super Bowl focusing and anxiety-relieving? Presumably, post-game elation and depression cancel between respective fan bases. Using past Super Bowl dates since inception and daily/weekly S&P 500 Index levels for 1967 through 2022 (56 events), we find that: Keep Reading

Do gold and gold mining stocks exhibit exploitable seasonality? Using monthly closes for spot gold (various sources) and the S&P 500 Index since December 1974, PHLX Gold/Silver Sector (XAU) since December 1983, AMEX Gold Bugs Index (HUI) since June 1996 and SPDR Gold Shares (GLD) since November 2004, all through December 2022, we find that: Keep Reading

Do annual stock market swing returns swing around their average like a pendulum? In the November update of his 2022 paper entitled “Periodic Structure of Equity Market Annual Returns and Their Predictability”, Daniel Pinelis investigates whether annual returns of the S&P 500 Index and the NASDAQ Composite Index exhibit reliable periodicity. Specifically, he models an oscillator indicator that accumulates directional imbalances in annual stock index returns and applies the indicator, in combination with statistical, graphical and machine learning methods, to estimate extent and timing of further market declines from the current levels. Using annual returns for the S&P 500 Index since the mid-1960s and for the NASDAQ Composite Index since the early 1970s, both through late 2022, he finds that:

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Is there a reliable and material market capitalization (size) effect among U.S. stocks around the turn-of-the-year (TOTY)? To check, we track cumulative returns from 20 trading days before through 20 trading days after the end of the calendar year for the Russell 2000 Index, the S&P 500 Index and the Dow Jones Industrial Average (DJIA) since the inception of the Russell 2000 Index. We also look at full-month December and January returns for these indexes. Using daily and monthly levels of all three indexes during December 1987 through January 2022 (35 December and 35 January observations), we find that: Keep Reading

Is there a best time of the month for measuring momentum within the Simple Asset Class ETF Momentum Strategy (SACEMS)? To investigate, we compare 21 variations of baseline SACEMS by shifting the monthly return calculation cycle from 10 trading days before the end of the month (EOM) to 10 trading days after EOM. For example, an EOM+5 cycle ranks assets based on closing prices five trading days after EOM each 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 daily dividend-adjusted prices for SACEMS assets during mid-February 2006 through mid-October 2022, we find that:

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Do morning and afternoon stock returns convey different meanings due to gradual dissipation of information asymmetry among traders during the trading day (as the market digests overnight news)? In their August 2022 paper entitled “A Tale of One Day: Morning Momentum, Afternoon Reversal”, Haoyu Xu and Xiaoneng Zhu investigate differences in implications for reversal and momentum strategies among morning (9:30AM – 11:30AM), midday (11:30AM – 2:00PM) and afternoon  (2:00PM – 4:00PM). Specifically, they:

• For each stock each month, cumulate returns over these three intervals.
• Sort stocks into tenths, or deciles, based either on cumulative returns over the most recent month (for reversal testing) or compounded cumulative returns from 12 months ago to one month ago (for momentum testing) for different combinations of these three intervals.
• Reform various long-short portfolios using extreme deciles to explore the different predictive powers of past morning and afternoon returns.

For reversal tests, they apply equal weighting. For momentum tests, they consider both value and equal weightings. They calculate raw returns, 3-factor (market, size, book-to-market) alphas and 4-factor (adding momentum) alphas as essential performance statistics. They use conventional strategies using full daily returns as benchmarks. Using intraday and daily return data for a broad sample of U.S. common stocks priced at least $5 during 1993 through 2018, they find that:

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