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Optimal SMA Calculation Interval for Long-term Crossing Signals?

Is a 10-month simple moving average (SMA10) the best SMA for long-term crossing signals? If not, is there some other optimal SMA calculation interval? To check, we compare performance statistics for SMA crossing signals generated by calculation intervals ranging from 2 trailing months (SMA2) to 48 trailing months (SMA48), as applied to the S&P 500 Index. Using monthly S&P 500 Index closes, monthly S&P 500 Composite Index dividend data from Robert Shiller and monthly average yields for 3-month Treasury bills (T-bills) during January 1950 through June 2019, we find that: Keep Reading

GDX and GDXJ vs. GLD

How are behaviors of physically backed gold and gold miner exchange-traded funds (ETF) similar and different? To investigate we consider SPDR Gold Shares (GLD) versus both Market Vectors Gold Miners (GDX) and VanEck Vectors Junior Gold Miners ETF (GDXJ). Using weekly returns for GDX since May 2006 and GDXJ since November 2009, and contemporaneous weekly returns for GLD and the S&P 500 Index (SP500), all through June 2019, we find that:

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Cass Freight Index a Stock Market Return Predictor?

The monthly Cass Freight Index is a “measure of North American freight volumes and expenditures… Data within the Index includes all domestic freight modes and is derived from $28 billion in freight transactions processed by Cass annually on behalf of its client base of hundreds of large shippers. These companies represent a broad sampling of industries including consumer packaged goods, food, automotive, chemical, OEM, retail and heavy equipment… The diversity of shippers and aggregate volume provide a statistically valid representation of North American shipping activity. …Volumes represent the month in which transactions are processed by Cass, not necessarily the month when the corresponding shipments took place. The January 1990 base point is 1.00. …Each month’s volumes are adjusted to provide an average 21-day work month. Adjustments also are made to compensate for business additions/deletions to the volume figures.” Cass typically publishes the index level for a month about the middle of the following month. Does this index usefully anticipate economic trend and thereby U.S. stock market returns? To investigate, we relate index changes to SPDR S&P 500 (SPY) returns. Using monthly Cass Freight Index levels and monthly dividend-adjusted SPY returns during January 1999 (limited by the freight index) through mid-June 2019, we find that: Keep Reading

What Kind of Asset Is Bitcoin?

Does Bitcoin behave like some other asset class? To investigate, we use the easily held, liquid and matched-close Grayscale Bitcoin Trust (GBTC) as a proxy for Bitcoin holdings and calculate daily and monthly return correlations between GBTC and each of 33 exchange-traded products encompassing eight used in “Simple Asset Class ETF Momentum Strategy ” (SACEMS), 22 considered in “SACEMS Portfolio-Asset Addition Testing” plus SPDR Bloomberg Barclays 1-3 Month T-Bill (BIL), iShares 1-3 Year Treasury Bond (SHY) and Powershares DB US Dollar Index Bullish Fund (UUP). These selections represent a broad set of asset classes. We start calculations with inception of GBTC on May 11, 2015. All other assets are available as of that date. Using daily and monthly adjusted (for dividends and splits) prices for GBTC and the 33 exchange-traded products during May 11, 2015 through June 21, 2019, we find that: Keep Reading

Weekly Summary of Research Findings: 7/8/19 – 7/12/19

Below is a weekly summary of our research findings for 7/8/19 through 7/12/19. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Optimal Cycle for Monthly SMA Signals?

A subscriber 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-June 2019, we find that: Keep Reading

Mimicking Portfolios of Five ETFs Beat Most Active Mutual Funds?

Can investors beat a typical active U.S. equity mutual fund via a small portfolio of periodically re-weighted equity exchange-traded funds (ETF)? In their February 2019 paper entitled “Are Passive Funds Really Superior Investments: An Investor Perspective”, flagged by a subscriber, Edwin Elton, Martin Gruber and Andre de Souza:

  1. Determine via cluster analysis a small set of ETFs that captures most of the variation in 69 broad U.S. equity indexes.
  2. Explore use of this set to mimic past performances of many active U.S. equity mutual funds via 24-month linear regressions with ETF coefficients scaled to sum to one.
  3. Compare next-year (close of first trading day of the year after coefficient calculation to close of first trading day next year) returns of mimicking ETF portfolios and active mutual fund counterparts.

Their target set of 883 active U.S. equity mutual funds are those with at least: three years of data as of January 2003; $15 million in assets; and, 90% of assets allocated broadly to stocks. Using monthly returns for 69 U.S. equity indexes, the small set of passive equity ETFs that capture variation in these indexes and 883 active U.S. equity mutual funds during January 2003 through December 2018, they find that:

Keep Reading

Factor Premium Reliability and Timing

How reliable and variable are the most widely accepted long-short factor premiums across asset classes? Can investors time factor premium? In their June 2019 paper entitled “Factor Premia and Factor Timing: A Century of Evidence”, Antti Ilmanen, Ronen Israel, Tobias Moskowitz, Ashwin Thapar and Franklin Wang examine multi-class robustness of and variation in four prominent factor premiums:

  1. Value – book-to-market ratio for individual stocks; value-weighted aggregate cyclically-adjusted price-to-earnings ratio (P/E10) for stock indexes; 10-year real yield for bonds; deviation from purchasing power parity for currencies; and, negative 5-year change in spot price for commodities.
  2. Momentum – past excess (relative to cash) return from 13 months ago to one month ago.
  3. Carry – front-month futures-to-spot ratio for equity indexes since 1990 and excess dividend yield before 1990; difference in short-term interest rates for currencies; 10-year minus 3-month yields for bonds; and, percentage difference in prices between the nearest and next-nearest contracts for commodities.
  4. Defensive – for equity indexes and bonds, betas from 36-month rolling regressions of asset returns versus equal-weighted returns of all countries; and, no defensive strategies for currencies and commodities because market returns are difficult to define.

They each month rank each asset (with a 1-month lag for conservative execution) on each factor and form a portfolio that is long (short) assets with the highest (lowest) expected returns, weighted according to zero-sum rank. When combining factor portfolios across factors or asset classes, they weight them by inverse portfolio standard deviation of returns over the past 36 months. To assess both overfitting and market adaptation, they split each factor sample into pre-discovery subperiod, original discovery subperiod and post-publication subperiod. They consider factor premium interactions with economic variables (business cycles, growth and interest rates), political risk, volatility, downside risk, tail risk, crashes, market liquidity and investment sentiment. Finally, they test factor timing strategies based on 12 timing signals based on 19 methodologies across six asset classes and four factors. Using data as available from as far back as February 1877 for 43 country equity indexes, 26 government bonds, 44 exchange rates and 40 commodities, all through 2017, they find that: Keep Reading

Monthly Returns During Presidential and Congressional Election Years

Do hopes and fears of election outcomes in the U.S. affect the “normal” seasonal variation in monthly stock market returns? To check, we compare average returns and variabilities (standard deviations of returns) by calendar month for the Dow Jones Industrial Average (DJIA) during years with and without quadrennial U.S. presidential elections and biennial congressional elections. Using monthly closes for the DJIA over the period October 1928 through May 2019 (nearly 91 years), we find that: Keep Reading

Optimal Long-Short Stock Momentum Strategies in European Markets

Is there a common optimal set of ranking (lookback) interval, holding interval, weighting scheme and skip-month rule for long-short stock momentum strategies across European country markets? In her May 2019 paper entitled “Are Momentum Strategies Profitable? Recent Evidence from European Markets”, Anastasia Slabchenko identifies optimal parameter sets from 576 long-short stock momentum strategy variations in each of France, Germany, Greece, Italy, Netherlands, Portugal, Spain, Sweden and UK. Variations derive from:

  • Past return ranking (lookback) intervals of 1 to 12 months.
  • Holding intervals of 1 to 12 months.
  • Value weighted (VW) or equal-weighted (EW) momentum portfolios.
  • Skip-month or no skip-month between ranking and holding intervals.

She defines the optimal variation as that generating the highest average gross monthly return. Using end-of-month closing prices for stock samples from each country, excluding financial stocks and stocks priced less than one euro, during December 1989 through January 2018, she finds that: Keep Reading

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