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.
December 12, 2014 - Calendar Effects, Mutual/Hedge Funds
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
December 5, 2014 - Calendar Effects, Volatility Effects
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 calculate returns by day of the week for:
- VXX over its available sample period.
- VXX over its available sample period excluding the day before and the day after holidays (to remove any distorting effects of expected but unusual market closures).
- XIV over its available sample period.
- VXX over the available sample period of XIV.
Using daily close-to-close returns for VXX during February 2009 through November 2014 and for XIV during December 2010 through November 2014, we find that: Keep Reading
November 28, 2014 - Calendar Effects, Fundamental Valuation, Momentum Investing
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 November 2014.
- Updated Market Models backtest charts and the market valuation metrics map based on data available through November 2014.
We have updated the Trading Calendar to incorporate data for November 2014.
We have updated the the monthly asset class momentum winners and associated performance data at Momentum Strategy.
November 21, 2014 - Calendar Effects
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
November 21, 2014 - Calendar Effects
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
November 19, 2014 - Calendar Effects, Commodity Futures, Momentum Investing
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
November 11, 2014 - Calendar Effects, Momentum Investing, Size Effect, Value Premium, Volatility Effects
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
October 22, 2014 - Calendar Effects
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
October 17, 2014 - Calendar Effects, Technical Trading
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
October 16, 2014 - Calendar Effects
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:
- Buy Fidelity Select Technology (FSPTX) at the October close.
- Switch from FSPTX to Fidelity Select Energy (FSENX) at the January close.
- Switch from FSENX to cash at the May close.
- Switch from cash to Fidelity Select Gold (FSAGX) at the August close.
- Switch from FSAGX to cash at the September close.
- 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