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.

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Stock Returns Around Thanksgiving

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 1950-2013 (64 events), we find that: Keep Reading

“Sell in May” Over the Long Run

Does the conventional wisdom to “Sell in May” (and “Buy in November”, hence also termed the “Halloween Effect”) work over the long run, perhaps due to biological/psychological effects of seasons (such as Seasonal Affective Disorder)? To check, we turn to the long run data set of Robert Shiller. This data set includes monthly levels of the S&P Composite Index, calculated as average of daily closes during the month. This method of calculation deviates from that most often used for return calculations, but arguably suppresses noise in daily data. We split the investing year into two half-years (seasons): May through October, and November through April. Using S&P Composite Index levels, associated dividend yields and contemporaneous long-term interest rates (comparable to yields on 10-year U.S. Treasury notes) from the Shiller data set spanning April 1871 through October 2014 (287 six-month returns), we find that: Keep Reading

Momentum-driven Turn-of-the-month Effect in Commodity Futures

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

Smart Beta Interactions with Tax-loss Harvesting

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

Models, Trading Calendar and Momentum Strategy Updates

We have updated the S&P 500 Market Models summary as follows:

  • Extended Market Models regressions/rolled projections by one month based on data available through October 2014.
  • Updated Market Models backtest charts and the market valuation metrics map based on data available through October 2014.

We have updated the Trading Calendar to incorporate data for October 2014.

We have updated the the monthly asset class momentum winners and associated performance data at Momentum Strategy.

End-of-Quarter Effect

Does the U.S. stock market offer a predictable pattern of returns around the ends of calendar quarters? Do funds deploy cash to bid stocks up at quarter ends to boost portfolio values at the end of reporting periods (with subsequent reversals)? Or, do they sell stocks to raise cash for fund redemptions? Is the end-of-quarter effect the same as the Turn-of-the-Month (TOTM) effect? To investigate, we examine average daily stock market returns from 10 trading days before to 10 trading days after the ends of calendar quarters. We compare these returns to those for turns of calendar months. Using daily closes for the S&P 500 Index for January 1950 through September 2014 (259 quarters), we find that: Keep Reading

Simple Tests of Sy Harding’s Seasonal Timing Strategy

Several readers have inquired about the performance of Sy Harding’s Street Smart Report Online, which includes the Seasonal Timing Strategy. This strategy combines “the market’s best average calendar entry [October 16] and exit [April 20] days with a technical indicator, the Moving Average Convergence Divergence (MACD).” According to Street Smart Report Online, applying this strategy to a Dow Jones Industrial Average (DJIA) index fund generated a cumulative return of 213% during 1999 through 2012, compared to 93% for the DJIA itself. As a robustness test, we apply this strategy to the SPDR S&P 500 (SPY) exchange-traded fund since its inception. Using daily dividend-adjusted closing prices for SPY and daily 13-week Treasury bill (T-bill) yields during 1/29/93 (inception of SPY) through 9/30/14, we find that: Keep Reading

Kaeppel’s Sector Seasonality Strategy

A reader suggested looking at the strategy described in “Kaeppel’s Corner: Sector Seasonality” (from November 2005) and updated in “Kaeppel’s Corner: Get Me Back, Clarence” (from October 2007). 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 13-week Treasury bill (T-bill) yield as the return on cash and VFINX over the period December 1985 through September 2014 (almost 29 years), we find that: Keep Reading

Recent Intraday U.S. Stock Market Behavior

“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

Turn-of-the-Month Effect in Stock Markets Around the World

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

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