Momentum Investing

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

Does crowding of factor investing strategies reliably predict returns for those strategies? In his March 2019 paper entitled “The Impact of Crowding in Alternative Risk Premia Investing”, Nick Baltas explores mechanics of alternative risk (factor) premium crowding and implications of crowding for future performance. He classifies factor premiums as: divergent (such as momentum), inherently destabilizing due to positive feedback loops and lack of fundamental anchors; or, convergent (such as value), having self-correcting negative feedback loops and fundamental anchors. To test crowding effects, he considers the following premiums: equity value (book-to-market), size (market capitalization), momentum (from regression of return from 12 months ago to one month ago versus volatility), quality (return on assets) and low beta (versus the MSCI World Index); commodities momentum (12-month return); and, currencies value (purchasing power parity) and momentum (12-month return). Each premium consists of returns from a hedge portfolio that is each week long (short) the equal-weighted assets with the highest (lowest) expected returns. For equities, he uses top and bottom tenths. For commodities and currencies, he uses top and bottom thirds. His crowding metric (CoMetric) is average pairwise correlation of factor-adjusted returns of assets within the long or short sides of premium portfolios over the last 52 weeks (except 260 weeks for value). He defines the 20% of weeks with the highest (lowest) CoMetrics as most (least) crowded. Using the specified factor and return data for liquid developed market stocks since September 2004, 24 constituents of the S&P GSCI Commodity Index since January 1999, and 26 developed and emerging markets currency pairs versus the U.S. dollar since January 2000, all through May 2018, he finds that:

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Do returns for equity factors (long stocks with high expected returns and short stocks with low expected returns based on some firm/stock trading characteristic) broadly and reliably exhibit momentum? In other words, do factors with strong (weak) returns in recent months have strong (weak) returns next month? In the February 2019 revision of their paper entitled “Factor Momentum Everywhere”, Tarun Gupta and Bryan Kelly test return momentum among 65 widely studied long-short equity factors for the U.S. and 62 factors globally that have underlying data available since the mid-1960s, including: valuation ratios (such as earnings-to-price and book-to-market); size, investment and profitability metrics (such as market capitalization, sales growth and return on equity); idiosyncratic risk metrics (such as betting against beta, stock volatility and skewness); and, liquidity metrics (such as Amihud illiquidity, share volume and bid-ask spread). For each factor, they each month:

• Exclude as outliers the top and bottom 1% of stocks with the most extreme factor characteristic values.
• Split residual stocks into big and small size segments based on median NYSE market capitalization for U.S. stocks and 80th percentile of market capitalizations for international stocks.
• Within size segments, sort stocks into low/medium/high characteristic bins based on 30/40/30 percentile splits and form value-weighted sub-portfolios that are long (short) high (low) bins.
• Form an overall factor portfolio with long side 0.5 * (Large High + Small High) and short side 0.5 * (Large Low + Small Low).

They consider both time series factor momentum (TSFM, intrinsic or absolute momentum) and cross-sectional factor momentum (CSFM, relative momentum). As benchmarks, they consider the equal-weighted average return for all factors and a conventional stock momentum factor based on returns from 12 months to one month ago. Using monthly U.S. and global data required to construct the factor portfolios and their returns from 1965 through 2017, they find that: Keep Reading

What are optimal allocations during retirement years for a portfolio of stocks and bonds, without and with a trend following overlay? In their March 2019 paper entitled “Absolute Momentum, Sustainable Withdrawal Rates and Glidepath Investing in US Retirement Portfolios from 1925”, Andrew Clare, James Seaton, Peter Smith and Steve Thomas compare outcomes across two sets of U.S. retirement portfolios since 1925:

1. Standard – allocations to the S&P 500 Index and a bond index ranging from all stocks to all bonds in increments of 10%, rebalanced at the end of each month.
2. Trend following – the same portfolios with a trend following overlay that shifts stock index and bond index allocations to U.S. Treasury bills (T-bills) when below respective 10-month simple moving averages at the end of the preceding month.

They consider investment horizons of 2 to 30 years to assess glidepath effects. They consider both U.S. Treasury bonds and U.S. corporate bonds to assess credit effects. For comparison of portfolio outcomes, they use real (inflation-adjusted) returns and focus on Perfect Withdrawal Rate (PWR), the maximum annual withdrawal rate that results in zero terminal value (requiring perfect foresight). Using monthly data for the S&P 500 Index, U.S. government and corporate bond indexes and U.S. inflation during 1926 through 2016, they find that: Keep Reading

How can investors confidently assess risk of strategy crashes (tail events) when there are so few crashes even in long samples? In their March 2019 paper entitled “Time-Series Momentum: A Monte-Carlo Approach”, Clemens Struck and Enoch Cheng present a Monte-Carlo simulation procedure for strategy backtesting that both preserves time series and cross-sectional return characteristics while diversifying time series simulation inputs. They use this procedure to test intrinsic (absolute or time series) momentum on S&P 500 Index futures and on an equal-weighted multi-class portfolio of 27 futures series. They consider long-short and long-only (long-cash) versions of time series momentum (TSM), with or without volatility adjustment. For testing actual histories, they consider lookback intervals of 1, 3, 6, 9 and 12 months to measure momentum. For simulations, they focus on optimal lookbacks from actual histories and consider multiple time series models. Their in-sample subperiods are 1985-2009 for the S&P 500 Index and February 1989-2009 for the multi-class portfolio. Their out-of-sample subperiod is 2010-2018. They roll each futures series at the end of each month into the next front contract, using spot indexes prior to the availability of some futures. They use buy-and-hold portfolios (with rolling) as benchmarks. Using monthly prices for nine equity indexes, four government bonds, eight commodities and six currencies futures/spot series in U.S. dollars over the specified sample period, they find that:

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A subscriber asked whether the optimal momentum ranking (lookback) interval for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) shrinks during bear markets for U.S. stocks. 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 SACEMS monthly performance statistics when the S&P 500 Index at the previous monthly close is above (bull market) or below (bear market) its 10-month simple moving average. We consider Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners for the baseline SACEMS lookback interval. In a robustness test for the EW Top 3 portfolio, we consider lookback intervals ranging from one to 12 months. Using monthly total (dividend-adjusted) returns for the specified assets since February 2006 (limited by DBC) and the monthly level of the S&P 500 Index since September 2005, all through February 2019, we find that:

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