Calendar Effects

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

“Seasonal Strategy for QQQ?” finds an interesting even year-odd year effect in Invesco QQQ Trust (QQQ) annual returns. The Trading Calendar and “Monthly Returns During Presidential and Congressional Election Years” find notable differences in S&P 500 Index performances for even years and odd years. A plausible culprit is federal elections. Is this effect growing over time? To investigate, we look at four indexes over their full histories:

1. Shiller’s S&P Composite Index during 1871 through 2023 (152 annual returns).
2. The S&P 500 Index during 1927 through 2023 (96 returns).
3. The NASDAQ 100 Index during 1985 through 2023 (38 returns).
4. The Russell 200 Index during 1987 through 2023 (36 returns).

For each index, we calculate annual returns for even years and odd years and look at the separate trends in these returns over time. Using the selected end-of-year index levels, we find that: Keep Reading

The full-year Trading Calendar indicates that the U.S. stock market has three phases over the calendar year, corresponding to calendar year trading days 1-84 (January-April), 85-210 (May-October) and 211-252 (November-December). What are typical stock market returns and return variabilities for these phases? Using daily S&P 500 Index closes from the end of December 1927 through December 2023, we find that: Keep Reading

Does the New Year’s Day holiday, a time of replanning and income tax positioning, systematically affect investors in a way that translates into U.S. stock market returns? To investigate, we analyze the historical behavior of the S&P 500 Index during the five trading days before and the five trading days after the holiday. Using daily closing levels of the S&P 500 Index around New Year’s Day for 1951-2023 (73 observations), we find that: Keep Reading

Does the Christmas holiday, a time of putative good will toward all, give U.S. stock investors a sense of optimism that translates into stock returns? To investigate, we analyze the historical behavior of the S&P 500 Index during five trading days before through five trading days after the holiday. Using daily closing levels of the S&P 500 Index for 1950-2022 (73 events), we find that: Keep Reading

Does the Thanksgiving holiday, a time of families celebrating plenty, give U.S. stock investors a sense of optimism that translates into stock returns? To investigate, we analyze the historical behavior of the S&P 500 Index during the three trading days before and the three trading days after the holiday. Using daily closing levels of the S&P 500 Index for 1950-2022 (73 events), 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