Calendar Effects

“Turn-of-the-Month Effect Persistence and Robustness” indicates that average absolute returns during the turn-of-the-month (TOTM) are strong for both bull and bear markets. Does a strategy of capturing all bull market returns and TOTM returns only during bear markets perform well? To investigate, we apply four strategies to SPDR S&P 500 ETF Trust (SPY) as a tradable proxy for the stock market:

1. SPY – buy and hold SPY.
2. SMA200 – hold SPY (cash) when SPY closes above (below) its 200-day simple moving average (SMA200) the prior day.
3. TOTM – hold SPY from the close five trading days before through the close four trading days after the last trading day of each month and cash at all other times (TOTM).
4. SMA200 or TOTM – hold SPY when SPY closes above its 200-day SMA the prior day and otherwise use the TOTM strategy.

We explore sensitivities of these strategies to a range of one-way SPY-cash switching frictions, with baseline 0.1%. Using daily dividend-adjusted SPY from the end of January 1993 through early January 2024 and contemporaneous 3-month Treasury bill (T-bill) yields as the return on cash, we find that: Keep Reading

Is the Turn-of-the-Month (TOTM) effect, a concentration of relatively strong stock market returns around the turns of calendar months, persistent over time and robust to different market conditions. Does it exist for all calendar months? Does it persist throughout the U.S. political cycle? Does it work for different equity indexes? To investigate, we define TOTM as the interval from the close five trading days before to the close four trading days after the last trading day of the month (a total of eight trading days, centered on the monthly close). Using daily closes for the S&P 500 Index since January 1928 and for the Russell 2000 Index since mid-September 1987, both through early January 2024, we find that: Keep Reading

Does long term data support the belief that “as goes January, so goes the rest of the year” (January is the barometer) for the the U.S. stock market? To investigate, we consider two views of the S&P 500 Index over its full history:

• Correlations between index returns during each calendar month and returns over the next 11 months.
• Index performance during the next 11 months across ranked thirds (terciles) of January returns.

Using monthly closes of the S&P 500 Index from the end of 1927 through 2023 (96 years), we find that: Keep Reading

Are some years of the decade better than others for equity markets? To investigate, we look at average annual returns by year of the decade (xxx0 through xxx9) for the U.S. stock market. Using annual levels of Shiller’s S&P Composite Index for 1871-2023 and the S&P 500 Index for 1928-2023, we find that: Keep Reading

A subscriber requested testing of a strategy that holds a combination of 50% Simple Asset Class ETF Value Strategy (SACEVS) Best Value and 50% Simple Asset Class ETF Momentum Strategy (SACEMS) equal-weighted (EW) Top 2 strategies during November through April and idle cash during May through October. We consider three strategies:

1. Best Value – EW Top 2 – hold Best Value-EW Top 2 during all months.
2. Best Value – EW Top 2 Seasonal (Idle Cash) – hold Best Value-EW Top 2 during November through April and idle cash during May through October, as requested.
3. Best Value – EW Top 2 Seasonal (6-month T-bill) – hold Best Value-EW Top 2 during November through April and 6-month U.S. Treasury bills (T-bill) bought at the beginning May each year during May through October.

We run annual statistics for each variation as in “Combined Value-Momentum Strategy (SACEVS-SACEMS)”. Annualized returns are compound annual growth rates. Maximum drawdown is the deepest peak-to-trough drawdown for these strategies based on monthly measurements over the sample period. For Sharpe ratio, to calculate excess annual return, we use average monthly yield on 3-month Treasury bills during a year as the risk-free rate for that year. Using monthly returns for SACEVS Best Value and SACEMS EW Top 2 and the specified T-bill yield during July 2006 through October 2023, we find that: Keep Reading

A subscriber requested a test of holding Invesco QQQ Trust (QQQ) during November through April and idle cash during May through October. Informed by the Trading Calendar, we consider four strategies:

1. QQQ – buy and hold QQQ.
2. QQQ Seasonal (Idle Cash) – hold QQQ during November through April and idle cash during May through October, as requested.
3. QQQ No-even (Idle Cash) – hold QQQ during odd years and idle cash during even years (avoiding stocks during years with U.S. federal elections).
4. QQQ No-even (1-year T-note) – hold QQQ during odd years and 1-year U.S. Treasury notes (T-note) bought at the beginning of the year during even years.

We consider average monthly return, standard deviation of monthly returns, monthly reward/risk (average return divided by standard deviation), compound annual growth rate (CAGR) and maximum drawdown (MaxDD). We ignore frictions and tax implications of trading once or twice a year. Using monthly dividend-adjusted returns for QQQ during March 1999 (inception) through October 2023, we find that: Keep Reading

Are intraday stock market exchange-traded funds (ETF), stock sector ETFs and individual stock returns exploitably predictable at short horizons? In their June 2023 paper entitled “Intraday Stock Predictability Everywhere”, Fred Liu and Lars Stentoft study intraday U.S. equity return predictability using machine learning methods. Specifically, they:

• Consider the market portfolio represented by SPDR S&P 500 ETF (SPY), sector portfolios represented by the nine Select Sector SPDR ETFs and individual S&P 500 constituent stocks. For portfolios, return predictors are lagged returns of the portfolio itself and its constituents. For individual stocks, return predictors are the lagged returns of SPY and its constituents.
• Consider intraday return horizons of 1, 5, 10, 15 and 30 minutes.
• Train 17 machine learning methods based initially on the first ten months of data, validate on the next month and evaluate out-of-sample predictive power on the ensuing month. Each month, they repeat these steps by rolling all data by one month (142 test months).
• Test statistical significance via the power of predictions to explain actual future stock returns (R-squared).
• Test gross economic value of predictions via portfolios that buy and sell assets according to predicted returns.
• Test net economic value of predictions by trading only when predicted long or short returns exceed trading frictions (estimated as the bid-ask spread) and debiting frictions from actual returns.

Using intraday transaction data for the specified ETFs and S&P 500 stocks during February 2004 through October 2016, they find that: Keep Reading

What characteristics of U.S. equity factor return series are most relevant to respective factor performance? In his May 2023 paper entitled “The Cross-Section of Factor Returns” David Blitz explores long-term average returns and market alphas, 60-month market betas and factor performance cyclicality for U.S. equity factors. He also assesses potentials of three factor rotation strategies: low-beta, seasonal and return momentum. Using monthly returns for 153 published U.S. equity market factors, classified statistically into 13 groups, during July 1963 through December 2021, he finds that:

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Does the Turn-of-the-Month Effect, a concentration of positive stock market returns around the turns of calendar months, work across a broad set of asset classes. To investigate, we measure turn-of-the-month (TOTM) returns for the following nine asset class exchange-traded funds (ETF) used in the “Simple Asset Class ETF Momentum Strategy” and the “Simple Asset Class ETF Value Strategy”:

• Invesco DB Commodity Index Tracking Fund (DBC)
• iShares MSCI Emerging Markets Index (EEM)
• iShares JPMorgan Emerging Markets Bond Fund (EMB) 
• iShares MSCI EAFE Index (EFA)
• SPDR Gold Shares (GLD)
• iShares Russell 2000 Index (IWM)
• iShares iBoxx $ Investment Grade Corporate Bond (LQD)
• SPDR S&P 500 ETF Trust (SPY)
• iShares Barclays 20+ Year Treasury Bond (TLT)
• Vanguard REIT ETF (VNQ)

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 these ETFs from their respective inceptions (ranging from February 1993 for SPY to December 2007 for EMB) through early May 2023, we find that: Keep Reading

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