Calendar Effects

Is the behavior of the U.S. stock market around exchange holidays consistent enough to generate an aggregate pattern? To investigate, we look at daily S&P 500 Index returns from three trading days before a holiday through three trading days after for the following holidays (adding the Super Bowl) as available since 1950:

New Year’s Day (74 observations, including 2024)
Super Bowl (58 observations, including 2024)
Good Friday (74 observations)
Memorial Day (53 observations)
4th of July (74 observations)
Labor Day (74 observations)
Thanksgiving (74 observations)
Christmas (74 observations)

The total number of observations is 555. Using daily closes of the S&P 500 Index during the specified intervals around holidays, we find that: Keep Reading

Does iShares 20+ Year Treasury Bond ETF (TLT) exhibit a predictable monthly pattern due to beginning-of-month dividends and mid-month U.S. government consumer and producer inflation releases? To investigate, we calculate average cumulative return for TLT across the month (from trading day 1 through trading day 23). We also investigate exploitability of findings. Using daily raw and dividend-adjusted levels of TLT from the end of July 2002 (inception) through January 2024 (21.5 years), we find that: Keep Reading

“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:

Keep Reading