Momentum Investing

How sensitive is performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) to choice of momentum calculation lookback interval, and what interval works best? To investigate, we generate gross compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for SACEMS Top 1, equally weighted (EW) EW Top 2 and EW Top 3 portfolios over lookback intervals ranging from one to 12 months. All calculations start at the end of February 2007 based on inception of the commodities exchange-traded fund and the longest lookback interval. Using end-of-month total (dividend-adjusted) returns for the SACEMS asset universe during February 2006 through June 2020, we find that: Keep Reading

Are widely accepted equity factor exposures available in fact to investors via “smart beta” mutual funds and exchange-traded funds (ETF)? In their May 2020 paper entitled “Smart Beta Made Smart”, Andreas Johansson, Riccardo Sabbatucci and Andrea Tamoni test effectiveness of individual U.S. equity mutual funds and ETFs and combinations of these funds for exploiting several major equity risk factors (value, size, profitability and momentum). After assembling a sample of funds with names that indicate smart beta strategies, they iteratively (annually for size, value and profitability and daily for momentum):

1. Apply a double-regression to each fund to identify those that are actually “closet” market index funds.
2. Refine factor exposures of each true smart beta fund based on actual fund holdings.
3. Construct separately for institutional and retail investors tradable long-side (mutual funds and ETFs) and short-side (ETFs only) risk factors via value-weighted combinations of the 10 funds with the strongest exposures to each factor.

Using daily, monthly, and quarterly data for U.S. equity mutual funds and ETFs with (1) names indicating smart beta strategies, (2) at least one year of returns and (3)assets over $1 billion, data for their individual component U.S. stocks and specified factor returns during January 2003 through May 2019, they find that: Keep Reading

Is there a way to mitigate adverse impact of price trajectory turning points (trend changes) on performance of intrinsic (absolute or time series) momentum strategies? In their May 2020 paper entitled “Breaking Bad Trends”, Ashish Garg, Christian Goulding, Campbell Harvey and Michele Mazzoleni measure impact of turning points on time series momentum strategy performance across asset classes. They define a turning point as a month for which slow (12-month or longer lookback) and fast (3-month or shorter lookback) momentum signals disagree on whether to buy or sell. They test a dynamic strategy to mitigate trend change impact based on turning points defined by disagreement between 12-month (slow) and 2-month (fast) momentum signals. Specifically, their dynamic strategy each month:

1. For each asset, measures slow and fast momentum as averages of monthly excess returns over respective lookback intervals.
2. Specifies the trend condition for each asset as: (1) Bull (slow and fast signals both non-negative); (2) Correction (slow non-negative and fast negative); Bear (slow and fast both negative); and, Rebound (slow negative and fast non-negative). For Bull and Bear (Correction and Rebound) conditions, next-month return is the same (opposite in sign) for slow and fast signals.
3. After trend changes (Corrections and Rebounds separately), empirically determines with at least 48 months of historical data optimal weights for combinations of positions based on slow and fast signals.

They compare performance of this dynamic strategy with several conventional (static) time series momentum strategies, with each competing strategy retrospectively normalized to 10% test-period volatility. They test strategies on 55 futures, forwards and swaps series spanning four asset classes, with returns based on holding the nearest contract and rolling to the next at expiration. Using monthly returns for futures, forwards and swaps for 12 equity indexes, 10 bond indexes, 24 commodities and 9 currency pairs as available during January 1971 through December 2019, they find that:

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Does adding an exchange-traded fund (ETF) or note (ETN) to the Simple Asset Class ETF Momentum Strategy (SACEMS) boost performance via consideration of more trending/diversifying options? To investigate, we add the following 22 ETF/ETN asset class proxies one at a time to the base set and measure effects on the Top 1, equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios:

AlphaClone Alternative Alpha (ALFA)
JPMorgan Alerian MLP Index (AMJ)
UBS ETRACS Wells Fargo Business Development Companies (BDCS)
Vanguard Total Bond Market (BND)
SPDR Barclays International Treasury Bond (BWX)
iShares MSCI Frontier 100 (FM)
First Trust US IPO Index (FPX)
iShares iBoxx High-Yield Corporate Bond (HYG)
iShares 7-10 Year Treasury Bond (IEF)
iShares Latin America 40 (ILF)
iShares National Muni Bond ETF (MUB)
PowerShares Closed-End Fund Income Composite (PCEF)
PowerShares Global Listed Private Equity (PSP)
IQ Hedge Multi-Strategy Tracker (QAI)
PowerShares QQQ Trust (QQQ)
SPDR Dow Jones International Real Estate (RWX)
ProShares UltraShort S&P 500 (SDS)
iShares Short Treasury Bond (SHV)
iShares TIPS Bond (TIP)
United States Oil (USO)
ProShares VIX Short-Term Futures (VIXY)
ProShares VIX Mid-Term Futures (VIXM)

We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics, ignoring monthly reformation costs. Using end-of-month, dividend-adjusted returns for all assets as available during February 2006 through May 2020, we find that: Keep Reading

Are all of the potentially trending/diversifying asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) necessary? Might one or more of them actually be harmful to performance? To investigate, we each month rank the nine SACEMS assets based on past return with one excluded (nine separate test series) and reform the Top 1, equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics, ignoring monthly portfolio reformation costs. Using end-of-month, dividend-adjusted returns for SACEMS assets during February 2006 through May 2020, we find that: Keep Reading

Does the U.S. stock market exhibit reliable and exploitable trends as measured by intrinsic (absolute or time series) momentum? In their April 2020 paper entitled “Time Series Momentum in the US Stock Market: Empirical Evidence and Theoretical Implications”, Valeriy Zakamulin and Javier Giner examine evidence of time series momentum in the excess returns (relative to the risk-free rate) of the S&P Composite Index. Their approach involves autocorrelations of multi-month (not monthly) excess returns. They then use simulations modeled with actual index return statistics to; (1) assess potential profitability of long-only and long-short time series momentum strategies; and, (2) estimate the optimal lookback interval. Using monthly total returns for the S&P Composite Index and the monthly risk-free rate represented by the U.S. Treasury bill (T-bill) yield during January 1857 through December 2018, they find that: Keep Reading

What are the optimal intrinsic/absolute/time series momentum (IM) and simple moving average (SMA) lookback intervals for different asset class proxies? To investigate, we use data for the following eight asset class exchange-traded funds (ETF), plus Cash:

• PowerShares DB Commodity Index Tracking (DBC)
• iShares JPMorgan Emerging Markets Bond Fund (EMB)
• 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)

For IM tests, we invest in each ETF (Cash) when its return over the past one to 12 months is positive (negative). For SMA tests, we invest in each ETF (Cash) when its price is above (below) its average monthly price over the past two to 12 months. Since SMA rules use price levels and IM rules use returns, IM lookback interval N corresponds to SMA lookback interval N+1. For example, a 6-month IM lookback uses the same start and stop points as a 7-month SMA lookback. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key metrics for comparing different IM and SMA lookback intervals since earliest ETF data availabilities based on the longest IM lookback interval. Using monthly dividend-adjusted closing prices for the asset class proxies and the yield for Cash over the period July 2002 (or inception if not available by then) through April 2020, we find that:

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A subscriber asked for an update on whether weekly or biweekly (every two weeks) measurement of asset class momentum works better than monthly measurement as used in “Simple Asset Class ETF Momentum Strategy (SACEMS)” (SACEMS). Do higher measurement frequencies respond more efficiently to market turns? To investigate, we compare performances of strategies based on monthly, weekly and biweekly frequencies with comparable lookback intervals. For this comparison, we align weekly and biweekly results with monthly results, though they differ somewhat due to mismatches between ends of weeks and ends of months. We consider portfolios of past ETF winners based on Top 1 and on equally weighted (EW) Top 2 and Top 3. Using weekly dividend-adjusted closing prices for the asset class proxies per baseline SACEMS and the yield for Cash during February 2006  through April 2020, we find that: Keep Reading

Are U.S. stock and sector momentum strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider five momentum-oriented U.S. equity ETFs with assets over $100 million, all currently available, in order of longest to shortest available histories:

• PowerShares DWA Momentum Portfolio (PDP) – invests at least 90% of assets in approximately 100 U.S. common stocks per a proprietary methodology designed to identify powerful relative strength characteristics, reformed quarterly.
• iShares Edge MSCI USA Momentum Factor (MTUM) – holds U.S. large-capitalization and mid-capitalization stocks with relatively high momentum.
• First Trust Dorsey Wright Focus 5 (FV) – holds five equally weighted sector and industry ETFs selected via a proprietary relative strength methodology, reformed twice a month.
• SPDR Russell 1000 Momentum Focus (ONEO) – tracks the Russell 1000 Momentum Focused Factor Index, picking U.S. stocks that have recently outperformed.
• First Trust Dorsey Wright Dynamic Focus 5 (FVC) – similar to FV but with added risk management via an increasing allocation to cash equivalents when relative strengths of more than one-third of the universe diminish relative to a cash index, reformed twice a month.

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We use two benchmark ETFs, iShares Russell 1000 (IWB) and iShares Russell 3000 (IWV), according to momentum fund descriptions. Using monthly returns for the five momentum funds and the two benchmarks as available through April 2020, we find that: Keep Reading

Are there equity styles that tend to perform relatively well during and after stock market crashes? In their April 2020 paper entitled “Equity Styles and the Spanish Flu”, Guido Baltussen and Pim van Vliet examine equity style returns around the Spanish Flu pandemic of 1918-1919 and five earlier deep U.S. stock market corrections (-20% to -25%) in 1907, 1903, 1893, 1884 and 1873. They construct three factors by:

1. Separating stocks into halves based on market capitalization.
2. Sorting the big half only into thirds based on dividend yield as a value proxy, 36-month past volatility or return from 12 months ago to one month ago. They focus on big stocks to avoid illiquidity concerns for the small half.
3. Forming long-only, capitalization-weighted factor portfolios that hold the third of big stocks with the highest dividends (HighDiv), lowest past volatilities (Lowvol) or highest past returns (Mom).

They also test a multi-style strategy combining Lowvol, Mom and HighDiv criteria (Lowvol+) and a size factor calculated as capitalization-weighted returns for the small group (Small). Using data for all listed U.S. stocks during the selected crashes, they find that: Keep Reading