Calendar Effects

Are there any seasonal, technical or fundamental strategies that reliably time the U.S. stock market as proxied by the S&P 500 Total Return Index? In the February 2018 version of his paper entitled “Investing In The S&P 500 Index: Can Anything Beat the Buy-And-Hold Strategy?”, Hubert Dichtl compares excess returns (relative to the U.S. Treasury bill [T-bill] yield) and Sharpe ratios for investment strategies that time the S&P 500 Index monthly based on each of:

• 4,096 seasonality strategies.
• 24 technical strategies (10 slow-fast moving average crossover rules; 8 intrinsic [time series or absolute] momentum rules; and, 6 on-balance volume rules).
• 18 fundamental variable strategies based on a rolling 180-month regression, with 1950-1965 used to generate initial predictions.

In all cases, when not in stocks, the strategies hold T-bills as a proxy for cash. His main out-of-sample test period is 1966-2014, with emphasis on a “crisis” subsample of 2000-2014. He includes extended tests on seasonality and some technical strategies using 1931-2014. He assumes constant stock index-cash switching frictions of 0.25%. He addresses data snooping bias from testing multiple strategies on the same sample by applying Hansen’s test for superior predictive ability. Using monthly S&P 500 Index levels/total returns and U.S. Treasury bill yields since 1931 and values of fundamental variables since January 1950, all through December 2014, he finds that:

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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 2019, we find that: Keep Reading

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: 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, equally weighted “(EW) Top 2” and “EW Top 3” versions of SACEMS, which each month equally weights 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 equally 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 October 2019, we find that:

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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 1951-2019 (69 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 1950-2018 (69 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 1950-2018 (69 events), we find that: Keep Reading

Does the market react most strongly to the earliest quarterly earnings announcements? In their October 2019 paper entitled “Calendar Rotations: A New Approach for Studying the Impact of Timing using Earnings Announcements”, Suzie Noh, Eric So and Rodrigo Verdi study effects of the relative order of U.S. firm quarterly earnings announcements, which vary systematically for some firms according to the day of the week of the first day of a month. Specifically, they qualify firms by identifying those firms that exhibit systematic earnings announcement schedules (such as Friday of the fourth week after quarter ends, sometimes set in firm bylaws) for at least four consecutive same fiscal quarters. They then for each firm each fiscal quarter:

• Calculate EA Order, ranking of earnings announcement date divided by number of firms with the same fiscal quarter-end.
• Compute change in EA Order compared to the same fiscal quarter last year, indicating a calendar acceleration or delay in announcement. Positive (negative) change in EA Order indicates delay (acceleration)
• Examine effects of change in EA Order on media coverage (number of articles), stock trading volume and stock return from one trading day before to one trading day after earnings announcement.

Using sample of 76,622 firm-quarters during 2004 through 2017, they find that: Keep Reading

Do quarterly allocation updates for the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS) work as well as monthly updates? These strategies allocate funds to the following asset class exchange-traded funds (ETF) according to valuations of term, credit and equity risk premiums, or to cash if no premiums are undervalued:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

Changing from monthly to quarterly allocation updates does not sacrifice information about lagged quarterly S&P 500 Index earnings, but it does sacrifice currency of term and credit premiums. To assess alternatives, we compare cumulative performances and the following key metrics for quarterly and monthly allocation updates: gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD), annual gross returns and volatilities and annual gross Sharpe ratios. Using monthly dividend-adjusted closes for the above ETFs during September 2002 (earliest alignment of months and quarters) through September 2019, we find that:

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“Stock Market and the National Election Cycle” examines the behavior of the U.S. stock market across the U.S. presidential term cycle (years 1, 2, 3 or 4) starting in 1950. Is a longer sample informative? To extend the sample period, we use the long run S&P Composite Index of Robert Shiller. The value of this index each month is the average daily level during that month. It is therefore “blurry” compared to a month-end series, but the blurriness is not of much concern over a 4-year cycle. Using monthly S&P Composite Index levels from the end of December 1872 through August 2019 (about 37.5 presidential terms), we find that:

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