Calendar Effects

Does fourth quarter global economic data set the stage for asset class returns the next year? In their February 2015 paper entitled “The End-of-the-year Effect: Global Economic Growth and Expected Returns Around the World”, Stig Møller and Jesper Rangvid examine relationships between level of global economic growth and future asset class returns, focusing on growth at the end of the year. Their principle measure of global economic growth is the equally weighted average of quarterly OECD industrial production growth in 12 developed countries. They perform in-sample tests 30 countries and out-of-sample tests for these same 12 countries (for which more data are available). Out-of-sample tests: (1) generate initial parameters from 1970 through 1989 data for testing during 1990 through 2013 period; and, (2) insert a three-month delay between economic growth data and subsequent return calculations to account for publication lag. Using global industrial production growth as specified, annual total returns for 30 country, two regional and world stock indexes, currency spot and one-year forward exchange rates relative to the U.S. dollar, spot prices on 19 commodities, total annual returns for a global government bond index and a U.S. corporate bond index, and country inflation rates as available during 1970 through 2013, they find that: Keep Reading

Do stock return anomalies exhibit January and month-of-quarter (first, second or third, excluding January) effects? In his February 2015 paper entitled “Seasonalities in Anomalies”, Vincent Bogousslavsky investigates whether the following 11 widely cited U.S. stock return anomalies exhibit these effects:

1. Market capitalization (size) – market capitalization last month.
2. Book-to-market – book equity (excluding stocks with negative values) divided by market capitalization last December.
3. Gross profitability – revenue minus cost of goods sold divided by total assets.
4. Asset growth – Annual change in total assets.
5. Accruals – change in working capital minus depreciation, divided by average total assets the last two years.
6. Net stock issuance – growth rate of split-adjusted shares outstanding at fiscal year end.
7. Change in turnover – difference between turnover last month and average turnover the prior six months.
8. Illiquidity – average illiquidity the previous year.
9. Idiosyncratic volatility – standard deviation of residuals from regression of daily excess returns on market, size and book-to-market factors.
10. Momentum – past six-month return, skipping the last month.
11. 12-month effect – average return in month t−k*12, for k = 6, 7, 8, 9, 10.

Each month, he sorts stocks into tenths (deciles) based on each anomaly variable and forms portfolios that are long (short) the decile with the highest (lowest) values of the variable. He updates all accounting inputs annually at the end of June based on data for the previous fiscal year. Using accounting data and monthly returns for a broad sample of U.S. common stocks during January 1964 to December 2013, he finds that: Keep Reading

Are overnight trading motivations systematically different from those that drive trading during normal trading hours? In the January 2015 version of their paper entitled “Tug of War: Overnight Versus Intraday Expected Returns”, flagged by a subscriber, Dong Lou, Christopher Polk and Spyros Skouras (1) decompose abnormal returns associated with well-known stock return predictors into overnight and intraday components and (2) investigate whether differences between institutional and other traders account for differences. Using return, firm characteristic and institutional ownership data for a broad sample of U.S. stocks (excluding low-priced and the smallest fifth of stocks) during 1993 through 2013, they find that: Keep Reading

Do predictable monthly outflows from and inflows to mutual funds drive the Turn-of-the-Month (TOTM) effect, a concentration of positive stock market returns around the turns of calendar months? In their November 2014 paper entitled “Dash for Cash: Month-End Liquidity Needs and the Predictability of Stock Returns”, Kalle Rinne, Matti Suominen and Lauri Vaittinen explore TOTM with focus on the effects of: (1) month-end flows from mutual funds to retirees and dividend-collecting investors; and, (2) beginning-of-month flows from working investors to mutual funds. To account for trade settlement rules, funds must sell stocks at least three trading days before the end of the month to raise cash for expected month-end outflows. The authors therefore define a TOTM interval from three trading days before through three trading days after the last trading day of the month. They also consider intervals of five trading days before TOTM to measure the effect of fund selling and five trading days after TOTM  to measure reversion from fund buying. Using daily value-weighted, (mostly) total return stock market indexes for the U.S. since 1926 and for 24 other developed markets as available during January 1980 through January 2014, and data for individual U.S. stocks and mutual funds during January 1980 through December 2013, they find that: Keep Reading

Is the Commodity Trading Advisor (CTA) segment so crowded that flows of funds into or out of them around the turn of the month materially affect prices? In the October 2014 version of his paper entitled “The MOM-TOM Effect: Detecting the Market Impact of CTA Trading”, Otto Van Hemert explores whether the trend-following or time series momentum (MOM) style employed by many CTAs is so crowded that inflows around the turn of the month (TOM) affect momentum strategy returns. He notes that most CTA-managed funds offer monthly liquidity, thereby concentrating flows at month ends. He defines TOM as the last two days of a month plus the first day of the next month. He tests whether there is an above average return for MOM strategies during TOM (MOM-TOM effect). He uses the Newedge CTA Index (an equal-weighted aggregate of the largest CTAs open to new investments) and the Newedge Trend Index (an equal-weighted aggregate of the MOM style CTAs that are open to new investments) as proxies for the overall market and the MOM style, respectively. Using daily returns for these two indexes during January 2000 through March 2014, he finds that: Keep Reading

Are gains from tax-loss harvesting, the systematic taking of capital losses to offset capital gains, additive to or subtractive from premiums from portfolio tilts toward common factors such as value, size, momentum and volatility (smart beta)? In their October 2014 paper entitled “Factor Tilts after Tax”, Lisa Goldberg and Ran Leshem look at the effects on portfolio performance of combining factor tilts and tax-loss harvesting. They call the incremental return from tax-loss harvesting tax alpha, which (while investor-specific) is typically in the range 1%-2% per year for wealthy investors holding broad capitalization-weighted portfolios. They test six long-only factor tilts based on Barra equity factor models: (1) value (high earnings yield and book-to-market ratio); (2) momentum (high recent past return); (3) value/momentum; (4) small/value; (5) quality (value stocks with low earnings variability, leverage and volatility); and, (6) minimum volatility/value (low volatility with diversification constraint and value tilt). Their overall benchmark is the MSCI All Country World Index (ACWI). Their tax alpha benchmark derives from a strategy that harvests losses in a capitalization-weighted portfolio (no factor tilts) without deviating far from the overall benchmark. The rebalancing interval is monthly for all portfolios. Using monthly returns for stocks in the benchmark index during January 1999 through December 2013, they find that: Keep Reading

“Intraday U.S. Stock Market Behavior” examines behavior of the S&P 500 Index at 15-minute intervals over the trading day during each of 2007 (bullish year) and 2008 (bearish year), finding slight tendencies for market weakness during mid-afternoon and market volatility at the beginning and the end of the trading day. Does recent data confirm these findings? To investigate, we calculate average cumulative returns and standard deviations of returns for both the S&P 500 Index and SPDR S&P 500 (SPY) measured at 5-minute intervals during the trading day over the last six months. Using 5-minute levels/prices for the S&P 500 Index and for SPY during 9:30-16:00 over the period August 2012 through September 2014, we find that: Keep Reading

Is the Turn-of-the-Month (TOTM) effect globally ubiquitous and persistent? In his August 2014 paper entitled “The Turn-of-The-Month-Effect: Evidence from Periodic Generalized Autoregressive Conditional Heteroskedasticity (PGARCH) Model”, Eleftherios Giovanis examines the TOTM effect in 20 country stock markets spanning the Americas, Australia, Europe and Asia. He defines TOTM as the interval including the last trading day of each calendar month through the third trading day of the next calendar month. He applies complex techniques to account for potential autocorrelation, heteroskedasticity and volatility clustering in daily market returns. His samples vary in start date by country, from as early as January 1950 (for the U.S.) to as late as January 2001 (for Australia). He considers full samples from the beginning of each country series through 2013 and two subsamples: (1) from the beginning of each country sample through 2007; and, (2) the financial crisis of 2008 through 2009. Using daily closes for the 20 country stock market indexes as described, he finds that: Keep Reading

Does the stock momentum anomaly interact with the quarterly financial cycle? In his August 2014 paper entitled “Seasonal Patterns in Momentum and Reversal in the U.S. Stock Market: The Consequences of Tax-Loss Sales and Window Dressing”, David Brown examines whether tax-loss selling and window dressing at the ends of calendar quarters affect U.S. stock momentum strategy returns. Each month, he ranks stocks by returns over the last 12 months, skipping the last month to avoid reversal, and then forms a momentum hedge portfolio that is long (short) the capitalization-weighted tenth of stocks with the highest (lowest) past returns, making the long and short sides of the portfolio equal in magnitude. He then measures how this portfolio performs by calendar month to check for end-of-quarter effects. He also investigates whether the level of capital losses among stocks in the portfolio affects performance. Using monthly returns for NYSE, AMEX and NASDAQ common stocks, along with contemporaneous risk-free rates and Fama-French model risk factor returns, during January 1927 through December 2013, he finds that: Keep Reading

In response to “Optimal Monthly Cycle for Simple Asset Class ETF Momentum Strategy?”, a subscriber asked about the optimal monthly cycle for “Simple Sector ETF Momentum Strategy”, which each month allocates all funds to the one of the following nine Select Sector Standard & Poor’s Depository Receipts (SPDR) exchange-traded funds (ETF) with the highest total return over the past six months :

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)

To investigate, we compare 21 variations of the strategy based on shifting the monthly return calculation cycle relative to trading days from the end of the month (EOM). For example, an EOM+5 cycle ranks assets based on closing prices five trading days after EOM each month. Using daily dividend-adjusted closes for the sector ETFs from mid-January 1999 through mid-July 2014 (about 186 months), we find that:

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