Calendar Effects

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

Do retail investors tend to underprice equity options in monthly series when the interval between expirations from third Friday to third Friday is five weeks instead of the more frequent (65% versus 35%) four weeks? In their November 2016 paper entitled “Inattention in the Options Market”, Assaf Eisdorfer, Ronnie Sadka and Alexei Zhdanov examine differences in U.S. equity option return behaviors for “months” with five weeks versus four weeks. They focus on stocks and exchange-traded funds (ETFs) with liquid options (relatively large size and high institutional ownership) and exclude options not expiring on the third Friday. Specifically, they each month on the third Friday form equally weighted portfolios of one-month-to-expiration, at-the-money long straddles (call and put with same strike price), delta-hedged calls and delta-hedged puts (both short the underlying stocks) and hold to maturity on the third Friday of the next month. They also run regressions of average weekly returns for these portfolios versus expiration interval and several control variables found in prior research to affect option returns (index option return, gap between implied and historical volatilities, return skewness and kurtosis, firm size, firm book-to-market ratio, past stock return and idiosyncratic volatility. Using daily returns (from closing bid-ask midpoints for options) for the specified options and underlying stocks/ETFs during 1996 through 2014, they find that: Keep Reading

Do government bond returns worldwide exhibit seasonal effects analogous to those of stock market returns? In their August 2016 draft paper entitled “Seasonality in Government Bond Returns and Factor Premia”, Adam Zaremba and Tomasz Schabek investigate seasonal patterns in government bond returns across countries, focusing on regression tests of January and sell-in-May (May-October versus November-April) effects. They also examine whether four bond risk premiums (volatility, credit risk, value and momentum), each specified in multiple ways and measured via long-short portfolios formed from monthly sorts, exhibit these two seasonal effects. Using monthly total return bond indexes hedged against the U.S. dollar spanning 25 countries and allocated to five term ranges (1-3 years, 3-5 years, 5-7 years, 7-10 years and 10+ years) during January 1992 through June 2016, they find that: Keep Reading

Does cyclic information flow from the Federal Open Market Committee (FOMC) drive equity market returns? In the June 2016 update of their paper entitled “Stock Returns Over the FOMC Cycle”, flagged by a subscriber, Anna Cieslak, Adair Morse and Annette Vissing-Jorgensen investigate interaction of the FOMC six-week meeting cycle with excess U.S. and worldwide stock market (relative to the U.S. Treasury bill). Within the FOMC meeting cycle, they look at:

• Meetings of the Federal Reserve Board of Governors and public releases of Federal Reserve book updates, statements and meeting minutes.
• Other potentially influential economic news cycles, including reserve maintenance period, macroeconomic news releases and corporate earnings announcements.
• Evidence on leaks from the Federal Reserve via media and private newsletters (with focus on Wall Street Journal articles on monetary policy).
• Public statements of Federal Reserve officials and conversations with current and former officials.

Using these data, FOMC meeting dates  and daily U.S. and global (overall, developed and emerging) stock market returns, returns for individual U.S. stocks, U.S. Treasury bill (T-bill) yield and 10-year U.S. Treasury note yield during 1994 through 2015, they find that: Keep Reading

Do asset returns exhibit cyclic relative strength? In the December 2015 revision of their paper entitled “Return Seasonalities”, Matti Keloharju, Juhani Linnainmaa and Peter Nyberg examine 12-month relative strength cycles via a strategy that is each month long (short) assets with the highest (lowest) returns during the same calendar month over the past 20 years. They apply this strategy to individual U.S. stocks, factor and anomaly portfolios of U.S. stocks, industry portfolios of U.S. stocks, developed country stock indexes and commodity futures contract series. They also test a 5-day relative strength cycle across individual U.S. stocks. They perform ancillary tests to investigate sources and interactions of relative strength cycles. Using monthly and daily data for a broad sample of U.S. common stocks, industry portfolios and factor/anomaly portfolios mostly since July 1963 and monthly data for 24 commodity futures series and 15 country stock indexes since January 1970, all through December 2011, they find that: Keep Reading

Does the January (turn-of-the-year) stock return anomaly affect value and momentum strategies applied at the country stock market level? In his June 2015 paper entitled “The January Seasonality and the Performance of Country-Level Value and Momentum Strategies”, Adam Zaremba investigates this question using four value and two momentum firm/stock metrics. The four value metrics, each measured over four prior quarters with a one-quarter lag and weighted by company according to the methodology of the associated stock index, are:

1. Earnings-to-price ratio (EP).
2. Earnings before interest, taxes, depreciation and amortization (EBITDA)-to-enterprise value (EV) ratio (EBEV).
3. EBITDA-to-price ratio (EBP).
4. Sales-to-EV ratio (SEV).

The two momentum metrics are:

1. Stock index return from 12 months ago to one month ago (LtMom).
2. Stock index return from 12 months ago to six months ago (IntMom).

He assesses strategy performance via returns in U.S. dollars in excess of one-month U.S. Treasury bill yield from hedge portfolios that are each month long (short) the equally weighted fifth of country stock indexes with the highest (lowest) expected returns based on each metric. He first reviews performances for all months and then focuses on turn-of-the-year (December and January) performances. Using monthly data for 78 existing and discontinued country stock market indexes during June 1995 through May 2015, he finds that: Keep Reading