Calendar Effects

Does the broad U.S. stock market, as represented by SPDR S&P 500 (SPY), exhibit reliable day-of-the-week and/or overnight return anomalies? To check, we consider three returns:

• Close-Open: measured from prior close to open. (For example, the Monday Close-Open return is from the close on the prior trading day, usually Friday, to the open on Monday.)
• Open-Close: measured from open to close.
• Close-Close: measured from prior close to close.

We calculate these returns overall, by day of the week and by the number of calendar days since the prior close (for example, three days for a normal weekend). Using daily opening and closing prices for SPY during end of January 1993 through most of August 2017 (6,188 days), we find that: Keep Reading

Do the returns of iPath S&P 500 VIX Short-term Futures ETN (VXX) and VelocityShares Daily Inverse VIX Short-term ETN (XIV) vary systematically across days of the week? To investigate, we look at daily close-to-open, open-to-close and close-to-close returns for both. Using daily split-adjusted opening and closing prices for VXX during February 2009 through July 2017 and for XIV during December 2010 through July 2017, we find that:

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Is there a preferred frequency and are there preferred months for rebalancing conventional asset class portfolio holdings? To investigate we consider annual, semiannual and quarterly rebalancing of a simple portfolio targeting a 60-40 stocks-bonds mix. We consider all possible combinations of calendar month ends as rebalancing points. We ignore rebalancing (and dividend-reinvestment) frictions and tax implications, thereby giving an advantage to frequent rebalancing. We focus on compound annual growth rate (CAGR) as the critical portfolio performance metric. Using dividend-adjusted monthly closes for SPDR S&P 500 (SPY) to represent stocks and Vanguard Total Bond Market Index (VBMFX) to represent bonds over the period January 1993 (SPY inception) through June 2017 (about 24 years), we find that: Keep Reading

A reader suggested looking at the strategy described in “Kaeppel’s Corner: Sector Seasonality” (from November 2005, link no longer in place) and updated in “Kaeppel’s Corner: Get Me Back, Clarence” (from October 2007, link no longer in place). The steps of this calendar-based sector strategy are:

1. Buy Fidelity Select Technology (FSPTX) at the October close.
2. Switch from FSPTX to Fidelity Select Energy (FSENX) at the January close.
3. Switch from FSENX to cash at the May close.
4. Switch from cash to Fidelity Select Gold (FSAGX) at the August close.
5. Switch from FSAGX to cash at the September close.
6. Repeat by switching from cash to FSPTX at the October close.

Does this strategy materially and persistently outperform? To investigate, we compare results for three alternative strategies: (1) Kaeppel’s Sector Seasonality strategy (Sector Seasonality); (2) buy and hold Vanguard 500 Index Investor (VFINX) as an investable broad index benchmark (VFINX); and, (3) a simplified seasonal strategy using only VFINX from the October close through the May close and cash otherwise (VFINX/Cash). Using monthly dividend-adjusted closing levels for FSPTX, FSENX, FSAGX, the 3-month Treasury bill (T-bill) yield as the return on cash and VFINX over the period December 1985 through May 2017 (about 31.5 years), we find that: Keep Reading

Are there opportunities to trade S&P 500 Index additions in the current market environment? In her May 2017 paper entitled “The Diminished Effect of Index Rebalances”, Konstantina Kappou examines returns for S&P 500 Index additions before and after the 2008 financial crisis. She focuses on additions because deletions generally involve confounding information such as restructuring, bankruptcy or merger. Current index management practices are to announce changes after market hours about five days in advance (announcement date – AD) and to implement changes at the specified close (event date – ED). She investigates returns during an event window from 15 trading days before AD through 252 trading days after ED. She calculates abnormal returns as differences between returns for added stocks and contemporaneous market returns. She considers 276 index additions during January 2002 through November 2013, with October 2008 separately pre-crisis from post-crisis. She excludes 48 of the additions due to lack of data or confounding information. Using daily returns for the remaining 228 S&P 500 Index additions during the specified sample period, she finds that: Keep Reading

Does “sell-in-May” interact with the U.S. election cycle? In the April 2017 update of their paper entitled “Buy Equities in Winter and Sell in May in Pre-Election Years: Market Premiums and Political Uncertainty in the Presidential Cycle”, Kam Fong Chan and Terry Marsh examine interactions between seasonal (May-October versus April-November) and U.S. election cycle effects on U.S. Stock market returns. They focus on variations in the equity premium, defined as market return minus risk-free rate. Using monthly returns for U.S. equities since January 1927 and for U.S. Treasury bonds since January 1942, and contemporaneous 1-month U.S. Treasury bill yields as the risk-free rate, all through December 2015 (89 years and 22 presidential election cycles), they find that: Keep Reading

What are the most common strategies for trading commodity futures? In their brief January 2017 article entitled “Commodity Futures Trading Strategies: Trend-Following and Calendar Spreads”, Hilary Till and Joseph Eagleeye describe the two most common strategies among commodity futures traders: (1) trend-following, wherein non-discretionary traders automatically screen markets based on technical factors to detect beginnings and ends of trends across different timeframes; and, (2) calendar-spread trading, wherein traders exploit commercial/institutional supply and demand mismatches that affect price spreads between commodity futures contract delivery months. Examples of the latter are seasonal inventory build and draw cycles (as for natural gas) and precise roll cycles for expiring contracts included in commodity futures indexes. Based on the body of research and examples, they conclude that: Keep Reading

A reader commented and asked: “Ned Davis Research calculates a time cycle composite. How good is an equal weighting of the annual seasonal cycle, the Presidential term cycle and the decennial cycle at predicting the direction of the market?” To check, we forecast return for a given month by averaging: (1) the average return for the calendar month up through the previous year; (2) the average monthly return for the Presidential term year up through the previous Presidential term; and, (3) the average monthly return for the year of a decade up through the previous decade. Using monthly levels of the Dow Jones Industrial Average (DJIA) since October 1928, the S&P 500 Index since January 1950 and Shiller’s S&P Composite Index since January 1871, all through December 2016, we find that: Keep Reading

Do individual stocks react differently and persistently to aggregate investor mood changes? In their December 2016 paper entitled “Mood Beta and Seasonalities in Stock Returns”, David Hirshleifer, Danling Jiang and Yuting Meng investigate whether some stocks have higher sensitivities to investor mood changes (higher mood betas) than others, thereby inducing calendar effects in the cross-section of returns. They specify mood based on three calendar-based U.S. stock market return anomalies:

1. January (highest average excess return of all months) represents good mood, while October (lowest average excess return of all months) represents bad mood.
2. Friday (highest average excess return of all days) represents good mood, while Monday (lowest average excess return of all days) represents bad mood.
3. The two days before holidays (abnormally high average excess return) represent good mood, while the two days after holidays (abnormally low average excess return) represent bad mood.

They structure their investigation via a factor model of stock returns, with mood as a factor. They measure a stock’s mood beta by regressing its returns during high and low mood intervals versus contemporaneous equal-weighted market returns over a rolling historical window. Each year, they regress a stock’s monthly January and October returns versus monthly equal-weighted market returns for those months over the last 10 years. Each week, they regress a stock’s daily Friday and Monday returns versus contemporaneous equal-weighted market returns for those days over the last ten weeks. Each holiday, they regress a stocks pre-holiday and post-holiday daily returns versus versus equal-weighted market returns for those days over the last year (including the same holiday the previous year. They then use the stock’s mood betas to predict its returns during subsequent times of good and bad mood. Using daily and monthly stock returns for a broad sample of U.S. common stocks during January 1963 through December 2015, they find that: Keep Reading

Does the S&P 500 implied volatility index (VIX) exhibit predictable behaviors around holidays? If so, is the predictability exploitable? To check, we look at percentage changes in VIX from three trading days before to three trading days after the following annual holidays: New Year’s Day, Super Bowl, Good Friday, Memorial Day, 4th of July, Labor Day, Thanksgiving and Christmas. To test exploitability, we employ iPath S&P 500 VIX ST Futures ETN (VXX), exchange-traded notes that hold short-term VIX futures. Using daily closes of VIX and VXX from their respective inceptions (January 1990 and February 2009) through November 2016 (214 and 62 holidays), we find that: Keep Reading