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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.

Long-term SMA and TOTM Combination Strategy

“Turn-of-the-Month Effect Persistence and Robustness” indicates that average absolute returns during the turn-of-the-month (TOTM) are strong for both bull and bear markets. Does a strategy of capturing all bull market returns and TOTM returns only during bear markets perform well? To investigate, we apply four strategies to S&P Depository Receipts (SPY) as a tradable proxy for the stock market:

  1. Buy and hold SPY.
  2. Hold SPY (cash) when SPY closes above (below) its 200-day simple moving average (SMA200).
  3. Hold SPY from the close five trading days before through the close four trading days after the last trading day of each month and cash at all other times (TOTM).
  4. Hold SPY when SPY closes above its 200-day SMA and otherwise use the TOTM strategy (SMA200 or TOTM).

We explore sensitivities of these strategies to a range of one-way SPY-cash switching frictions, with baseline 0.1%. Using daily dividend-adjusted closing levels of SPY from inception (January 1993) through early April 2019 and contemporaneous 3-month Treasury bill (T-bill) yields, we find that: Keep Reading

Turn-of-the-Month Effect Persistence and Robustness

Is the Turn-of-the-Month (TOTM) effect, a concentration of relatively strong stock market returns around the turns of calendar months, persistent over time and robust to different market conditions. Does it exist for all calendar months? Does it persist throughout the U.S. political cycle? Does it work for different equity indexes? To investigate, we define TOTM as the interval from the close five trading days before to the close four trading days after the last trading day of the month (a total of eight trading days, centered on the monthly close). Using daily closes for the S&P 500 Index during January 1950 through early March 2019 (831 TOTMs) and for the Russell 2000 Index during September 1987 through March 2019 (379 TOTMs), we find that: Keep Reading

Stock Returns Around Easter

Does the seasonal shift marked by the Easter holiday, with the U.S. stock market closed on the preceding Good Friday, produce anomalous returns? To investigate, we analyze the historical behavior of the S&P 500 Index before and after the holiday. Using daily closing levels of the S&P 500 index for 1950-2018 (69 events), we find that: Keep Reading

Optimal Monthly Cycle for SACEMS?

Is there a best time of the month for measuring momentum within the Simple Asset Class ETF Momentum Strategy (SACEMS)? This strategy each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

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. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns for the specified assets during mid-February 2006 (limited by DBC) through mid-February 2019, we find that: Keep Reading

Any Seasonality for Gold or Gold Miners?

Do gold and gold mining stocks exhibit exploitable seasonality? Using monthly closes for spot gold and the S&P 500 Index since December 1974, PHLX Gold/Silver Sector (XAU) since December 1983, AMEX Gold Bugs Index (HUI) since June 1996 and SPDR Gold Shares (GLD) since November 2004, all through January 2019, we find that: Keep Reading

Effects of Execution Delay on SACEMS

“Optimal Monthly Cycle for SACEMS?” investigates whether using a monthly cycle other than end-of-month (EOM) to pick winning assets improves performance of the Simple Asset Class ETF Momentum Strategy (SACEMS). This strategy each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

In response, a subscriber asked whether sticking with an EOM cycle for determining the winner, but delaying signal execution, affects strategy performance. To investigate, we compare 23 variations of SACEMS portfolios that all use EOM to pick winners but shift execution from the contemporaneous EOM to the next open or to closes over the next 21 trading days (about one month). For example, EOM+5 uses an EOM cycle to determine winners but delays execution until the close five trading days after EOM. We focus on gross compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using daily dividend-adjusted opens and closes for the asset class proxies and the yield for Cash during February 2006 (limited by DBC) through January 2019, we find that: Keep Reading

Tug-of-war Risk and Future Stock Returns

Does persistence in the difference in direction between overnight stock trading and intraday stock trading behaviors (tug of war) predict future returns? In their January 2019 paper entitled “Overnight Returns, Daytime Reversals, and Future Stock Returns: The Risk of Investing in a Tug of War with Noise Traders”, Ferhat Akbas, Ekkehart Boehmer, Chao Jiang and Paul Koch investigate relationships between intensity of the daily tug-of-war between between overnight (noise) and intraday (other) stock traders and future stock returns. They specify tug-of-war intensity as percentage of trading days during a month for which a stock exhibits negative (or positive) daytime reversals divided by average monthly percentage of negative (or positive) reversals over the last 12 months. They then examine whether either negative or positive tug-of-war intensity predicts future stock returns. Using overnight/intraday stock returns for a broad sample of U.S. common stocks, along with monthly returns for widely accepted factors, during May 1993 through December 2017, they find that:

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Global Factor Premiums Over the Very Long Run

Do very old data confirm reliability of widely accepted asset return factor premiums? In their January 2019 paper entitled “Global Factor Premiums”, Guido Baltussen, Laurens Swinkels and Pim van Vliet present replication (1981-2011) and out-of-sample (1800-1908 and 2012-2016) tests of six global factor premiums across four asset classes. The asset classes are equity indexes, government bonds, commodities and currencies. The factors are: time series (intrinsic or absolute) momentum, designated as trend; cross-sectional (relative) momentum, designated as momentum; value; carry (long high yields and short low yields); seasonality (rolling “hot” months); and, betting against beta (BAB). They explicitly account for p-hacking (data snooping bias) and further explore economic explanations of global factor premiums. Using monthly global data as available during 1800 through 2016 to construct the six factors and four asset class return series, they find that:

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Rebalance Timing Noise

Does choice of multi-asset portfolio rebalance date(s) materially affect performance? In their October 2018 paper entitled “Rebalance Timing Luck: The Difference Between Hired and Fired”, Corey Hoffstein, Justin Sibears and Nathan Faber investigate effects of varying portfolio rebalance date on performance. Specifically, they quantify noise (luck) from varying annual rebalance date for a 60% S&P 500 Index-40% 5-year constant maturity U.S. Treasury note (60-40) U.S. market portfolio. Using monthly total returns for these two assets during January 1922 through June 2018, they find that: Keep Reading

Crude Oil Seasonality

Does crude oil exhibit an exploitable price seasonality? To check, we examine three monthly series:

  1. Spot prices for West Texas Intermediate (WTI) Cushing, Oklahoma crude oil since the beginning of 1986 (32 years).
  2. Nearest expiration futures prices for crude oil since April 1983 (35+ years).
  3. Prices for United States Oil (USO), an exchange-traded implementation of short-term crude oil futures since April 2006 (12+ years).

We focus on average monthly returns by calendar month and variabilities of same. Using monthly prices from respective inceptions of these series through December 2018, we find that: Keep Reading

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