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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.

SPY by Day of Week and Overnight

Does the broad U.S. stock market, as represented by SPDR S&P 500 (SPY), exhibit reliable day-of-the-week and/or overnight return anomalies? To check, we consider three returns:

  • Close-Open: measured from prior close to open. (For example, the Monday Close-Open return is from the close on the prior trading day, usually Friday, to the open on Monday.)
  • Open-Close: measured from open to close.
  • Close-Close: measured from prior close to close.

We calculate these returns overall, by day of the week and by the number of calendar days since the prior close (for example, three days for a normal weekend). Using daily opening and closing prices for SPY during end of January 1993 through most of August 2017 (6,188 days), we find that: Keep Reading

VXX and XIV Returns by Day of the Week

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 look at daily close-to-open, open-to-close and close-to-close returns for both. Using daily split-adjusted opening and closing prices for VXX during February 2009 through July 2017 and for XIV during December 2010 through July 2017, we find that:

Keep Reading

Optimal Rebalancing Frequency/Months?

Is there a preferred frequency and are there preferred months for rebalancing conventional asset class portfolio holdings? To investigate we consider annual, semiannual and quarterly rebalancing of a simple portfolio targeting a 60-40 stocks-bonds mix. We consider all possible combinations of calendar month ends as rebalancing points. We ignore rebalancing (and dividend-reinvestment) frictions and tax implications, thereby giving an advantage to frequent rebalancing. We focus on compound annual growth rate (CAGR) as the critical portfolio performance metric. Using dividend-adjusted monthly closes for SPDR S&P 500 (SPY) to represent stocks and Vanguard Total Bond Market Index (VBMFX) to represent bonds over the period January 1993 (SPY inception) through June 2017 (about 24 years), 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, link no longer in place) and updated in “Kaeppel’s Corner: Get Me Back, Clarence” (from October 2007, link no longer in place). 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 3-month Treasury bill (T-bill) yield as the return on cash and VFINX over the period December 1985 through May 2017 (about 31.5 years), we find that: Keep Reading

Alternative U.S. Stock Market Calendar Visualizations

The Trading Calendar presents cumulative return visualizations for the S&P 500 Index across the calendar year and across each calendar month. Here are three alternative visualizations of U.S. stock market performance by calendar month: (1) percentage of positive returns; (2) ratio of average return to standard deviation of returns; and, (3) distribution of returns. Using monthly returns for the S&P 500 Index during January 1950 through April 2017 (67-68 observations per month), we find that: Keep Reading

Stock Index Changes No Longer Meaningful?

Are there opportunities to trade S&P 500 Index additions in the current market environment? In her May 2017 paper entitled “The Diminished Effect of Index Rebalances”, Konstantina Kappou examines returns for S&P 500 Index additions before and after the 2008 financial crisis. She focuses on additions because deletions generally involve confounding information such as restructuring, bankruptcy or merger. Current index management practices are to announce changes after market hours about five days in advance (announcement date – AD) and to implement changes at the specified close (event date – ED). She investigates returns during an event window from 15 trading days before AD through 252 trading days after ED. She calculates abnormal returns as differences between returns for added stocks and contemporaneous market returns. She considers 276 index additions during January 2002 through November 2013, with October 2008 separately pre-crisis from post-crisis. She excludes 48 of the additions due to lack of data or confounding information. Using daily returns for the remaining 228 S&P 500 Index additions during the specified sample period, she finds that: Keep Reading

Optimal Cycle for Monthly SMA Signals?

A reader commented and asked:

“Some have suggested that the end-of-the-month effect benefits monthly simple moving average strategies that trade on the last day of the month. Is there an optimal day of the month for long-term SMA calculation and does the end-of-the-month effect explain the optimal day?”

To investigate, we compare 21 variations of a 10-month simple moving average (SMA10) timing strategy generated by shifting the monthly return calculation cycle relative to trading days from the end of the month (EOM). Specifically, the 21 variations represent calculation cycles ranging from 10 trading days before EOM (EOM-10) to 10 trading days after EOM (EOM+10). We apply the strategy to the S&P 500 Index as a proxy for the U.S. stock market. The strategy holds the S&P 500 Index (cash) whenever the index is above (below) its SMA10 as of the most recent monthly calculation. Using daily S&P 500 Index closes and 3-month Treasury bill (T-bill) yields as the return on cash during January 1990 through mid-May 2017, we find that: Keep Reading

Combined Sell-in-May and Pre-election-year 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

“Sell in May” Over the Long Run

Does the conventional wisdom to “Sell in May” (and “Buy in November”, hence also the term “Halloween Effect”) work over the long run, perhaps due to biological/psychological effects of seasons (Seasonal Affective Disorder)? To check, we turn to the long run dataset of Robert Shiller. This data set includes monthly levels of the S&P Composite Index, calculated as average of daily closes during the month. 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 dataset spanning April 1871 through April 2017 (292 6-month returns), we find that: Keep Reading

Common Commodity Futures Trading Strategies

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

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