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Calendar Effects

The time of year affects human activities and moods, both through natural variations in the environment and through artificial customs and laws. Do such calendar effects systematically and significantly influence investor/trader attention and mood, and thereby equity prices? These blog entries relate to calendar effects in the stock market.

Combine Long-term SMA, TOTM and Sector Momentum?

Based on results from “Simple Sector ETF Momentum Strategy Performance”, “Does the Turn-of-the-Month Effect Work for Sectors?” and “Long-term SMA and TOTM Combination Strategy”, a subscriber proposed: “Have you ever thought of combining the three? When SPY is above a long term average, buy the best performing sector ETF using the TOTM strategy.” To investigate, we consider the nine sector exchange-traded funds (ETF) defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR), all of which have trading data back to 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)

We determine sector momentum based on total return over the past six months (6-1). We define bull-bear stock market state according to whether SPDR S&P 500 (SPY) is above-below its 200-day simple moving average (SMA). We define the turn-of-the-month (TOTM) as the eight-trading day interval from the close five trading days before the first trading day of a month to the close on the fourth trading day of the month. Using daily dividend-adjusted closes for the sector ETFs and SPY from 12/22/98 through 8/10/12 (164 months), we find that: Keep Reading

“Sell in May” Still Working?

Does the conventional wisdom of avoiding stocks during May through October work in recent years? In their July 2012 paper entitled “‘Sell in May and Go Away’ Just Won’t Go Away”, Sandro Andrade, Vidhi Chhaochharia and Michael Fuerst test the sell-in-May anomaly (or Halloween effect) based on data unambiguously available only after publication of the anomaly. They compute returns in adjacent six-month periods, the beginning of May to end of October and the beginning of November to end of April. They also test a trading strategy that: (1) from the end of April through the end of October, invests a fraction k (for k equals 3/4, 1/2, 1/3 and 0) of the portfolio in the stock market index and the balance in one-month Treasury bills (T-bills); and, (2) from the end of October through the end of April, invests 2-k in the stock market index by borrowing 1-k at the T-bill rate. Using total returns for 37 country stock market index and the MSCI World Index during during May 1970 through October 1998 (replicating prior research) and November 1998 through April 2012 (new data), along with contemporaneous T-bill yields for the latter, they find that: Keep Reading

Deconstructing the Size Effect

What calendar and technical factors drive the size effect? In the June 2012 version of his paper entitled “Predictable Dynamics in the Small Stock Premium”, Valeriy Zakamulin explores the interaction of the size effect with the January effect and both prior-month and prior-year stock market returns. He defines the size effect based on the Small-Minus-Big (SMB) factor of the Fama-French three-factor model of stock returns. A positive (negative) value for the effect means that small (big) stocks outperform big (small) stocks. Using market factor and SMB factor returns from the library of Kenneth French and National Bureau of Economic Research (NBER) business cycle dates during 1927 through 2011 (85 years), he finds that: Keep Reading

VIX Day-of-the-Week Effects

Does the S&P 500 implied volatility index (VIX) exhibit systematic behaviors by day of the week? In their February 2012 paper entitled “Day of the Week Effect on the VIX: A Parsimonious Representation”, Maria Gonzalez-Perez and David Guerrero apply methodologies that minimize sensitivity to outliers to examine VIX day-of-the-week patterns. Using daily closes of VIX and the S&P 500 Index during 2004 through 2008, they find that: Keep Reading

Gold Seasonality Drivers

Does seasonal fear of stock market weakness or demand for jewelry drive gold prices? In his January 2012 paper entitled “The Seasonality of Gold – Jewelery Demand and Investor Behavior”, Dirk Baur examines calendar month seasonality of the price of gold. Using daily gold bullion spot prices (London fixing) and COMEX gold futures prices during 1981 through 2010 (30 years), along with contemporaneous stock market index and gold jewelry demand data, he finds that: Keep Reading

Value Premium Concentration in January

Is the value premium seasonal? In their 2012 paper entitled “Is the Value Effect Seasonal? Evidence from Global Equity Markets”, Praveen Kumar Das and Uma Rao investigate the intersection of the January effect and the value premium in stock market indexes around the world. They consider market capitalization-weighted value and growth stock portfolios for the following indexes: Asia Pacific; Europe, Australasia and Far East (EAFE); Europe, with and without UK; Scandinavian countries; UK; U.S.; and, Japan. They define value (growth) stocks as the 30% with the highest (lowest) book-to-market ratios within their respective market indexes. Using monthly stock prices and lagged annual book-to-market ratios for stocks in these markets during 1975 (or inception if unavailable that early) through 2007, they find that: Keep Reading

Any Stock Market Anomalies Around 3-day Weekends?

Do more traders than usual move to the sidelines before long weekends to avoid the risk of bad news during the extended downtime, thereby depressing prices before the weekend and elevating them after with re-entry? To investigate, we analyze the behavior of the S&P 500 Index during the three trading days before and the three trading days after three-day weekends. Using daily closing levels of the S&P 500 Index for 1950-2011 (62 years and 351 three-day weekends), we find that: Keep Reading

Stock Index Futures Calendar Effects

Do calendar effects found in stock markets also appear in broad stock index futures? In their November 2011 paper entitled “Calendar Anomalies in Stock Index Futures”, Oscar Carchano and Angel Pardo investigate 188 possible cyclical anomalies in S&P 500, DAX and Nikkei index futures contracts (derived from day-of-the-week, month-of-the-year, weekday-of-the-month, week-of-the-month, semi-month, turn-of-the-month, end-of-year, holidays, semi-month-of-the-year, week-of-the-month-of-the-year, Friday the 13th, Halloween effect and quarterly futures expiration). They note that small trading frictions and ease of shorting promote exploitability of anomalies in futures markets. They assume round trip trading frictions of 0.05% for assessing net profitability. Applying tests not dependent on type of return distribution to stock index futures prices from December 1991 through April 2008, they find that: Keep Reading

First and Last Hours of Trading

Do U.S. stock market returns during the first and last hours of normal trading days reliably indicate what comes next? To investigate, we analyze average SPDR S&P 500 (SPY) returns during 9:30-10:30, 9:30-15:00, 9:30-16:00 and 15:00-16:00 for normal trading days during 2007 (bullish year) and 2008 (bearish year). Using a sample of SPY one-minute prices spanning 2007-2008, we find that: Keep Reading

Intraday U.S. Stock Market Behavior

Does the U.S. stock market exhibit predictable return and volatility patterns during the trading day? To investigate, we analyze one-minute prices for SPDR S&P 500 (SPY) over two recent years. Specifically, we calculate average cumulative return, average returns for 15-minute intervals and average standard deviation of one-minute returns during 15-minute intervals over the trading day during each of 2007 (bullish year) and 2008 (bearish year). Using a sample of SPY one-minute prices for 9:30-16:15 spanning 2007-2008 (over 203,000 observations), we find that: Keep Reading

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