Momentum Investing

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 23 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)
PowerShares DB G10 Currency Harvest (DBV)
iShares JPMorgan Emerging Market Bond Fund (EMB)
First Trust US IPO Index (FPX)
Guggenheim Frontier Markets (FRN)
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)
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)
iPath S&P 500 VIX Short-Term Futures (VXX)
iPath S&P 500 VIX Medium-Term Futures (VXZ)

The base set consists of:

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)

Each month, we rank the base set plus one of the additional ETFs/ETNs based on past return and reform the SACEMS portfolios. The sample starts with the first month all base set ETFs are available (February 2006), but inceptions for most of the additional ETFs/ETNs are after this month. 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 total (dividend-adjusted) returns for the specified 32 assets as available during February 2006 through May 2019, 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 following 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:

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)

The test starts with the first month all ETFs are available (February 2006). 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 total (dividend-adjusted) returns for the specified nine assets during February 2006 through May 2019, we find that: Keep Reading

Do simple factor models help explain future return variations across different cryptocurrencies, as they do for stocks? In their April 2019 paper entitled “Common Risk Factors in Cryptocurrency”, Yukun Liu, Aleh Tsyvinski and Xi Wu examine performances of cryptocurrency (coin) counterparts for 25 price-related and market-related stock market factors, broadly categorized as size, momentum, volume and volatility factors. They first construct a coin market index based on capitalization-weighted returns of all coins in their sample. They then each week sort coins into fifths based on each factor and calculate average excess return for a portfolio that is long (short) coins in the highest (lowest) quintile. Finally, they investigate whether any small group of factors accounts for returns of all significant factors. Using daily prices in U.S. dollars and non-return variables (excluding top and bottom 1% values as potential errors/outliers) for all coins with market capitalizations over $1 million dollars from Coinmarketcap.com during January 2014 through December 2018 (a total of 1,707 coins, growing from 109 in 2014 to 1,583 in 2018), they find that:

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What steps should investors consider to mitigate impact of inevitable large U.S. stock market corrections? In their May 2019 paper entitled “The Best of Strategies for the Worst of Times: Can Portfolios be Crisis Proofed?”, Campbell Harvey, Edward Hoyle, Sandy Rattray, Matthew Sargaison, Dan Taylor and Otto Van Hemert compare performances of an array of defensive strategies with focus on the eight worst drawdowns (deeper than -15%) and three NBER recessions during 1985 through 2018, including:

1. Rolling near S&P 500 Index put options, measured via the CBOE S&P 500 PutWrite Index.
2. Credit protection portfolio that is each day long (short) beta-adjusted returns of duration-matched U.S. Treasury futures (BofAML US Corp Master Total Return Index), scaled retrospectively to 10% full-sample volatility.
3. 10-year U.S. Treasury notes (T-notes).
4. Gold futures.
5. Multi-class time-series (intrinsic or absolute) momentum portfolios applied to 50 futures contract series and reformed monthly, with:
   • Momentum measured for 1-month, 3-month and 12-month lookback intervals.
   • Risk adjustment by dividing momentum score by the standard deviation of security returns.
   • Risk allocations of 25% to currencies, 25% to equity indexes, 25% to bonds and 8.3% to each of agricultural products, energies and metals. Within each group, markets have equal risk allocations.
   • Overall scaling retrospectively to 10% full-sample volatility.
   • With or without long equity positions.
6. Beta-neutral factor portfolios that are each day long (short) stocks of the highest (lowest) quality large-capitalization and mid-capitalization U.S. firms, based on profitability, growth, balance sheet safety and/or payout ratios.

They further test crash protection of varying allocations to the S&P 500 Index and a daily reformed hedge consisting of equal weights to: (1) a 3-month time series momentum component with no long equity positions and 0.7% annual trading frictions; and, (2) a quality factor component with 1.5% annual trading frictions. For this test, they scale retrospectively to 15% full-sample volatility. Throughout the paper, they assume cost of leverage is the risk-free rate. Using daily returns for the S&P 500 Index and inputs for the specified defensive strategies during 1985 through 2018, they find that:

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Do alternative “Cash” (deemed risk-free) instruments materially affect performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS)? Changing the proxy for Cash can affect how often the model selects Cash, as well as the return on Cash when selected. To investigate, we test separately each of the following yield and exchange-traded funds (ETF) as the risk-free asset:

3-month Treasury bills (Cash), a proxy for the money market as in base SACEMS
SPDR Bloomberg Barclays 1-3 Month T-Bill (BIL)
iShares 1-3 Year Treasury Bond (SHY)
iShares 7-10 Year Treasury Bond (IEF)
iShares TIPS Bond (TIP)

In other words, we add one of the five risk-free assets to the following base set of eight ETFs:

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)

We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics and consider Top 1, equally weighted (EW) EW Top 2 and EW Top 3 SACEMS portfolios. Using end-of-month total (dividend-adjusted) returns for the specified assets during February 2006 (except May 2007 for BIL) through May 2019, we find that:

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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 decreasing assets):

• 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.
• 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.
• First Trust Dorsey Wright Dynamic Focus 5 ETF (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.
• SPDR Russell 1000 Momentum Focus (ONEO) – tracks the Russell 1000 Momentum Focused Factor Index, picking U.S. stocks that have recently outperformed.

Because some sample periods are very short, we focus on daily return statistics, but also consider cumulative returns and maximum drawdowns (MaxDD). We use two benchmark ETFs, iShares Russell 1000 (IWB) and iShares Russell 3000 (IWV), according to momentum fund descriptions. Using daily returns for the five momentum funds and the two benchmarks as available through mid-May 2019, we find that: Keep Reading

Do all kinds of assets and long-short equity factor premiums exhibit exploitable time series (intrinsic or absolute momentum)? In their September 2018 paper entitled “Trends Everywhere”, Abhilash Babu, Ari Levine, Yao Hua Ooi, Lasse Pedersen and Erik Stamelos test intrinsic momentum on 58 traditional (studied in prior research) assets, 82 alternative (futures, forwards, and swaps not previously studied) assets and 16 long-short equity factors. They include only reasonably liquid (investable) assets and strategies. For equity factors, they each month: (1) classify over 4,000 U.S. common stocks as big or small according to NYSE median market capitalization; (2) within each size group, reform for each factor a value-weighted hedge portfolio that is long (short) the 30% of stocks with the highest (lowest) expected returns; and, (3) for each factor, average big and small hedge portfolio returns. They focus on a 12-month lookback interval for calculating momentum, taking a long (short) position in an asset/factor with positive (negative) return over this interval. For comparability of assets, they scale each position to an estimated 40% annualized volatility based on exponentially-weighted squared past daily returns. They assess diversification potentials by looking at pairwise correlations between momentum series, and between portfolios of momentum series and benchmark indexes (S&P 500 Index, MSCI World Index, Barclays Aggregate Bond Index and S&P GSCI Index). Using daily excess returns for the selected assets, factors and benchmarks as available during January 1985 through December 2017, they find that:

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

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)

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 measurement interval N corresponds to SMA measurement interval N+1. For example, a 6-month IM measurement uses the same start and stop points as a 7-month SMA measurement. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key metrics for comparing different IM and SMA measurement intervals since earliest ETF data availabilities based on the longest IM measurement 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 2019, we find that:

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A subscriber asked whether more granularity in international equity choices for the “Simple Asset Class ETF Momentum Strategy” (SACEMS), as considered by Decision Moose, would improve performance. To investigate, we replace the iShares MSCI Emerging Markets Index (EEM) and the iShares MSCI EAFE Index (EFA) with four regional international equity exchange-traded funds (ETF). The universe of assets becomes:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Pacific ex Japan (EPP)
iShares MSCI Japan (EWJ)
SPDR Gold Shares (GLD)
iShares Europe (IEV)
iShares Latin America 40 (ILF)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

We compare original (SACEMS Base) and modified (SACEMS Granular), each month picking winners from their respective sets of ETFs based on total returns over a fixed lookback interval. We focus on gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD) and rough gross annual Sharpe ratio (average annual return divided by standard deviation of annual returns) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using daily and monthly total (dividend-adjusted) returns for the specified assets during February 2006 (limited by DBC) through April 2019, we find that: Keep Reading

Does return persistence of individual stocks predict associated option returns? In their March 2019 paper entitled “Stock Return Autocorrelations and the Cross Section of Option Returns”, Yoontae Jeon, Raymond Kan and Gang Li investigate relationships between equity option returns and return autocorrelations of underlying stocks. They consider call options, put options and straddles (long both a call and a put with the same strike price). Each month on standard option expiration date, they:

• Measure one-step monthly stock return autocorrelations using a 36-month rolling window of monthly returns for U.S. stocks with over 20 monthly observations.
• Rank stocks (and respective options) by autocorrelation into fifths (quintiles).
• Construct a hedge portfolio that is long (short) the equal-weighted or market capitalization-weighted stocks in the top (bottom) quintile of autocorrelations, to calculate stock portfolio return as a control variable.
• Construct corresponding hedge portfolios of call options, put options or straddles, limiting choices to reasonably liquid options with moneyness closest to 1.0 and time to expiration closest to 30 days. 
• Hold these portfolios until the next standard option expiration date.

They further explore out-of-sample use of results via modified mean-variance optimization of a portfolio consisting of the S&P 500 Index, the risk-free asset and equity options with bid-ask spreads no greater than 10% of price. They size individual option positions as a function of underlying stock volatility, variance risk premium and stock return autocorrelation. They assume investor utility derives from constant relative risk aversion level 3. For the frictionless case, they base option returns on the bid-ask midpoint. For the case with frictions, they assume buys (sells) occur at the ask (bid). Using specified stock and options data during January 1996 through December 2017, they find that: Keep Reading