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SACEMS with SMA Filter

A subscriber asked whether applying a simple moving average (SMA) filter to  “Simple Asset Class ETF Momentum Strategy” (SACEMS) winners improves strategy performance. SACEMS each months picks winners from among the following asset class exchange-traded fund (ETF) proxies based on past returns over a specified 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)

Since many technical traders use a 10-month SMA (SMA10), we test effectiveness of requiring that each winner pass an SMA10 filter by comparing performances for three scenarios:

  1. Baseline – SACEMS as presented at “Momentum Strategy”.
  2. With SMA10 Filter – Run Baseline SACEMS and then apply SMA10 filters to dividend-adjusted prices of winners. If a winner is above (below) its SMA10, hold the winner (Cash). This rule is inapplicable to Cash as a winner.
  3. With Half SMA10 Filter – Same as scenario 2, but, if a winner is above (below) its SMA10, hold the winner (half the winner and half cash at the T-bill yield).

We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) of SACEMS Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios. Using monthly dividend-adjusted closing prices for the asset class proxies and the yield for Cash over the period February 2006 (the earliest all ETFs are available) through November 2018, we find that: Keep Reading

Best Safe Haven ETF?

A subscriber asked which exchange-traded fund (ETF) asset class proxies make the best safe havens for the U.S. stock market as proxied by the S&P 500 Index. To investigate, we consider the following 12 ETFs as potential safe havens:

Utilities Select Sector SPDR ETF (XLU)
iShares 20+ Year Treasury Bond (TLT)
iShares 7-10 Year Treasury Bond (IEF)
iShares 1-3 Year Treasury Bond (SHY)
iShares Core US Aggregate Bond (AGG)
iShares TIPS Bond (TIP)
Vanguard REIT ETF (VNQ)
SPDR Gold Shares (GLD)
PowerShares DB Commodity Tracking ETF (DBC)
United States Oil (USO)
iShares Silver Trust (SLV)
PowerShares DB G10 Currency Harvest ETF (DBV)

We consider three ways of testing these ETFs as safe havens for the U.S. stock market based on daily or monthly returns:

  1. Contemporaneous return correlation with the S&P 500 Index during all market conditions at daily and monthly frequencies.
  2. Performance during S&P 500 Index bear markets as defined by the index being below its 10-month simple moving average (SMA10) at the end of the prior month.
  3. Performance during S&P 500 Index bear markets as defined by the index falling -20%, -15% or -10% below its most recent peak at the end of the prior month.

Using daily and monthly dividend-adjusted closing prices for the 12 ETFs since respective inceptions, and contemporaneous daily and monthly levels of the S&P 500 Index since 10 months before the earliest ETF inception, all through November 2018, we find that: Keep Reading

Sell Equity Index OTM Put Options and ATM Straddles?

Does accounting for realistic trading frictions support beliefs that equity index out-of-the money (OTM) put options and at-the-money (ATM) straddles are systematically overpriced? In their October 2018 paper entitled “Index Option Anomalies: How Real Are They?”, Michal Czerwonko and Stylianos Perrakis re-examine assumptions and data used in several high-profile studies finding that OTM put options and ATM straddles for the S&P 500 Index are overpriced, and that shorting these positions is therefore reliably profitable. They focus on the following aspects of option pricing: accounting for realistic trading frictions (bid-ask spreads); differences in pricing of same-strike price puts and calls; and, inconsistency in pricing across maturities. Using groomed intraday prices and quotes for S&P 500 Index (cash-settled) options 28, 14, and seven days to maturity during January 1990 through February 2013 (278 settlement dates), they find that:

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Commodity Futures Momentum and Reversal

Do prices of commodity futures contract series reliably exhibit reversal and/or momentum? In their October 2018 paper entitled “Do Momentum and Reversal Strategies Work in Commodity Futures? A Comprehensive Study”, Andrew Urquhart and Hanxiong Zhang investigate the performance of four momentum/reversal trading strategies as applied to excess return indexes for 29 commodity futures contract series. Excess return indexes invest continuously in nearest S&P GSCI futures, rolling forward during the fifth to ninth business day of each month. The four strategies are:

  1. Pairs reversal trading – At the end of each formation interval, identify the five pairs of indexes (with equal capital commitments) that track most closely based on sum of squared deviations of normalized price differences. During the ensuing trading interval, when the normalized prices of any pairs diverge by at least two standard deviations of formation period differences, go long (short) the member of the pair that is undervalued (overvalued). Close all pair trades when prices re-converge at a daily close or at the end of the trading interval.
  2. Pairs momentum trading – The inverse of pairs reversal trading, wherein the long (short) position is the pair member exhibiting relative strength (weakness) during the trading interval.
  3. Conventional momentum – At the end of each month, rank all indexes by cumulative return over the formation interval. Go long (short) the equal-weighted 30% of assets with the highest (lowest) past returns during the ensuing holding interval.
  4. Nearness to high momentum – At the end of each month, rank all indexes based on nearness to respective formation interval highs. Go long (short) the equal-weighted 30% of assets that are nearest/at (farthest below) past highs during the ensuing holding interval.

They consider nine formation intervals (1, 3, 6, 9, 12, 24, 36, 48 and 60 months) and 21 holding intervals (1, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48, 51, 54, 57 and 60 months).They assume that long-short strategies are about 50% collateralized, with capital therefore available to handle holding interval margin calls. They also test effects of 0.69% per year (0.06% per month) transaction costs. Using daily levels of six energy, 10 metal and 13 agriculture and live stock commodity futures excess return indexes during January 1979 through October 2017, they find that:

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Stock Returns Around Christmas

Does the Christmas holiday, a time of putative good will toward all, 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 five trading days before through five trading days after the holiday. Using daily closing levels of the S&P 500 Index for 1950-2017 (68 events), we find that: Keep Reading

Weekly Summary of Research Findings: 12/10/18 – 12/14/18

Below is a weekly summary of our research findings for 12/10/18 through 12/14/18. 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

Does the Sunspot Cycle Predict Grain Prices?

As a follow-up to “Sunspot Cycle and Stock Market Returns” a reader asked: “Sunspot activity does have a direct relationship to weather. Could one speculate on the agriculture market using the sunspot cycle?” To investigate, we relate sunspot activity to the fairly long U.S. Producer Price Index (PPI) for grains. Using monthly averages of daily sunspot counts and monthly PPI for grains during January 1926 (limited by PPI data) through October 2018, we find that: Keep Reading

Sunspot Cycle and Stock Market Returns

A reader asked whether Charles Nenner, self-described as “the talk of Wall Street since accurately predicting some of the biggest moves in the Markets over the past few years,” accurately forecasts equity and commodity markets. We consider the following:

  • In his July 2007 discussion of the “Nenner Methodology at the Bloomberg Studio”, Charles Nenner cites sunspot activity as a specific key indicator for equity returns. Per this source, he believes that the sunspot cycle correlates strongly with equity markets via the predictable effects of magnetic field disturbances on investors.
  • In “Sunspots Predict ‘Major Crisis’ After 2013: Chartist”, he states: “If there is a high intensity of sunspots, markets rise, if their intensity lowers, markets go down because sunspots affect people’s mood.”

Is there a reliable relationship between sunspot activity and stock market returns? Using monthly averages of daily sunspot counts and monthly levels of Shiller’s S&P Composite Index (also monthly averages of daily levels) during January 1871 (limited by the Shiller data) through October 2018, we find that: Keep Reading

CPI and Stocks Over the Short and Intermediate Terms

Do investors reliably react over short and intermediate terms to changes in the U.S. Consumer Price Index (CPI), a logical measure of the wealth discount rate? Using monthly total and core (excluding food and energy) CPI releases (for all items, not seasonally adjusted) from the Bureau of Labor Statistics (BLS) and contemporaneous S&P 500 Index open and close data for the period mid-January 1994 (the earliest for which CPI release dates are available) through mid-November 2018 (299 releases), we find that: Keep Reading

Pervasive Seasonal Relative Weakness Cycles?

Is there a flip side of cyclic relative weakness to the cyclic relative strength described in “Pervasive 12-Month (and 5-Day) Relative Strength Cycles?”? In their October 2018 paper entitled “Seasonal Reversals in Expected Stock Returns”, Matti Keloharju,Juhani Linnainmaa and Peter Nyberg test whether cyclic weakness (seasonal reversal) balances the cyclic strength (seasonality) effect. For example, if a stock is seasonally strong in March, it may be seasonally weak across other months. They test this hypothesis using actual monthly U.S. stock returns and simulated returns calibrated to actual returns. Specifically, they compute correlations between average historical returns for a stock during one month and the sum of its historical average returns during other months. In robustness tests, they repeat this test for 10-year subperiods and for daily U.S. stock returns, monthly non-U.S. stock returns, monthly country stock indexes, monthly country government bond indexes and monthly commodity returns. Finally, they construct the following three factors for U.S. stocks by first each month sorting stocks into two size groups (small and big market capitalizations) and then:

  1. Seasonality factor – Sorting each size group into three average same-calendar-month past return portfolios. The factor return is the difference in value-weighted returns between the two highest-average portfolios and the two lowest-average portfolios.
  2. Seasonal reversal factor – Sorting each size group into three average other-calendar-month past return portfolios within each size group. The factor return is the difference in value-weighted returns between the two lowest-average and the two highest-average portfolios.
  3. Annual-minus-non-annual factor – Sorting each size group into three portfolios based on the difference between the average same-calendar-month and other-calendar-month returns. The factor return is the difference in value-weighted returns between the two largest-difference and the two smallest-difference portfolios.

Using U.S. monthly and daily stock returns since 1963 and monthly returns for country stocks and stock market indexes, country government bond indexes and commodities since the end of 1974, all through 2016, they find that:

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