Momentum Investing

“Beat the Market with Hot-Anomaly Switching?” concludes that “a trader who periodically switches to the hottest known anomaly based on a rolling window of past performance may be able to beat the market. Anomalies appear to have their own kind of momentum.” Does momentum therefore work for style-based exchange-traded funds (ETF)? To investigate, we apply a simple momentum strategy to the following six ETFs that cut across market capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

We test a simple Top 1 strategy that allocates all funds each month to the one style ETF with the highest total return over a specified momentum ranking (lookback) interval. We focus on the baseline ranking interval from the Simple Asset Class ETF Momentum Strategy (SACEMS), but test sensitivity of findings to ranking intervals ranging from one to 12 months. As benchmarks, we consider an equally weighted and monthly rebalanced combination of all six style ETFs (EW All), and buying and holding SPDR S&P 500 (SPY). As an enhancement we consider holding the Top 1 style ETF (3-month U.S. Treasury bills, T-bills) when the S&P 500 Index is above (below) its 10-month simple moving average at the end of the prior month (Top 1:SMA10), with a benchmark substituting SPY for Top 1 (SPY:SMA10). We employ the performance metrics used for SACEMS. Using monthly dividend-adjusted closing prices for the six style ETFs and SPY, monthly levels of the S&P 500 Index and monthly yields for T-bills during August 2001 (limited by IWS and IWP) through October 2021, 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), such as considered by Decision Moose, would improve performance. To investigate, we augment/replace international developed and emerging equity market exchange-traded funds (ETF) such that the universe of assets becomes:

• SPDR S&P 500 (SPY)
• iShares Russell 2000 Index (IWM)
• iShares Europe (IEV)
• iShares MSCI Japan (EWJ)
• iShares MSCI Pacific ex Japan (EPP)
• iShares MSCI Emerging Markets Index (EEM)
• iShares JPMorgan Emerging Markets Bond Fund (EMB)
• iShares Latin America 40 (ILF)
• iShares Barclays 20+ Year Treasury Bond (TLT)
• Vanguard REIT ETF (VNQ)
• SPDR Gold Shares (GLD)
• PowerShares DB Commodity Index Tracking (DBC)
• 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 gross annual Sharpe ratio (average annual excess return divided by standard deviation of annual excess returns, using average monthly T-bill yield during a year to calculate excess 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 through October 2021, we find that: Keep Reading

Does adding a position take-profit (stop-gain) rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by harvesting some upside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-gains, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return rises above a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month maximum returns for the specified assets during February 2006 through September 2021, we find that: Keep Reading

Does adding a position stop-loss rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by avoiding some downside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-losses, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return falls below a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month drawdowns for the specified assets during February 2006 through September 2021, we find that: Keep Reading

What is the best way to understand and anticipate variations in equity factor returns? Past research emphasizes factor return connections to business cycle variables or measures of investor sentiment (with little success). In his September 2021 paper entitled “The Quant Cycle”, David Blitz analyzes factor returns themselves to understand their variations, arguing that behavioral rather than economic forces drive them. He determines the quant cycle (bull and bear trends in factor returns) by qualitatively identifying peaks and troughs. He focuses on U.S. versions of four conventionally defined long-short factors frequently targeted by investors (value, quality, momentum and low-risk), emphasizing the most volatile (value and momentum). He also considers some alternative factors. Using monthly data for factors from the online data libraries of Kenneth French, Robeco and AQR spanning July 1963 through December 2020 (and for a reduced set of factors spanning January 1929 through June 1963), he finds that:

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What drives 12-month (with skip-month) momentum and 1-month reversal effects among U.S. common stock returns?  In their July 2021 paper entitled “Mapping out Momentum”, Yimou Li and David Turkington decompose momentum and reversal effects into distinct industry/sector, factor (size, value, profitability, investment) and stock-specific contributions. In addition to full-sample results, they look at:

• High and low volatility states, as defined by a threshold of 25 for average daily CBOE Volatility Index (VIX) during the month of stock return measurement.
• Contributions of past winners versus past losers.
• Two subsamples with breakpoint December 2009.

They focus on S&P 500 stocks to avoid concerns that any anomalies are due to market frictions or are not exploitable on a large scale. They assume a 3-day implementation lag in computing next-month returns. They examine statistical significance (t-statistic) rather than magnitude of anomaly returns. Using S&P 500 stock, sector/industry and factor data and daily VIX levels during January 1995 through December 2020, they find that:

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A subscriber asked about the stability of the momentum measurement (lookback) interval used for strategies like the Simple Asset Class ETF Momentum Strategy (SACEMS). To investigate, we run two tests on each of top one (Top 1),  equal-weighted top two (EW Top 2) and equal-weighted top three (EW Top 3) versions of SACEMS:

1. Identify the SACEMS lookback interval with the highest gross compound annual growth rate (CAGR) for a sample starting February 2006 when Invesco DB Commodity Index Tracking Fund (DBC) becomes available and ending each of May 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 and 2021. We consider lookback intervals of one to 12 months, meaning that earliest allocations are for February 2007 to accommodate the longest interval. The shortest sample period is therefore 5.3 years. This test takes the perspective of an investor who devises SACEMS in May 2012 and each year adds 12 months of data and checks whether the optimal lookback interval has changed.
2. Identify the SACEMS lookback interval with the highest gross CAGR for a sample ending May 2021 and starting each of February 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 and 2016. The shortest sample period is again 5.3 years. This test takes perspectives of different investors who devise SACEMS at the end of February in different years.

Using monthly SACEMS inputs and the SACEMS model as currently specified for February 2006 through May 2021, we find that: Keep Reading

Can investors detect when commodity futures momentum, value and carry (basis) strategies are crowded and therefore likely to generate relatively weak returns? In the March 2021 version of their paper entitled “Crowding and Factor Returns”, Wenjin Kang, Geert Rouwenhorst and Ke Tang examine how crowding by commodity futures traders affects expected returns for momentum, value and basis strategies. They define commodity-level crowding based on excess speculative pressure, measured for each commodity as the deviation of non-commercial trader net position (long minus short) from its 3-year average, scaled by open interest. They calculate crowding for a long-short strategy portfolio as the average of commodity-level crowding metrics of long positions minus the average of commodity-level crowding metrics for short positions, divided by two. They specify strategy portfolios as follows:

• Momentum – each week long (short) the equally weighted 13 commodities with the highest (lowest) past 1-year returns as of the prior week.
• Value – each week long (short) the equally weighted 13 commodities with the highest (lowest) ratios of last-week nearest futures price to nearest futures price three years ago.
• Basis – each week long (short) the equally weighted 13 commodities with the highest (lowest) basis, measured as percentage price difference between nearest and next maturity contracts as of the prior week.

For each strategy, they measure effects of crowding by measuring returns separately when strategy crowding is above or below its rolling 3-year average. Using weekly (Tuesday close) investor position data published by the Commodity Futures Trading Commission (CFTC) for 26 commodities traded on North American exchanges during January 1993 through December 2019, they find that:

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A subscriber inquired whether a longer test of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) is feasible using mutual funds rather than exchange-traded funds (ETF) as asset class proxies. To investigate, we consider the following set of mutual funds (partly adapted from the paper summarized in “Asset Allocation Combining Momentum, Volatility, Correlation and Crash Protection”):

1. Vanguard Total Stock Market Index Investor Shares (VTSMX)
2. Vanguard Small Capitalization Index Investor Shares  (NAESX)
3. Fidelity Diversified International (FDIVX)
4. Vanguard Long-Term Treasury Investor Shares (VUSTX)
5. Fidelity New Markets Income Fund (FNMIX)
6. Vanguard REIT Index Investor Shares (VGSIX)
7. First Eagle Gold A (SGGDX)
8. Oppenheimer Commodity Strategy Total Return A (QRAAX) until in October 2011, and BlackRock Commodity Strategies Portfolio Institutional Shares (BICSX) thereafter
9. 3-month U.S. Treasury bills (Cash)

We rank mutual funds based on total (dividend-adjusted) returns over past (lookback) intervals of one to 12 months. We consider portfolios of past mutual fund winners based on Top 1 and on equally weighted (EW) Top 2 through Top 5. We consider as benchmarks: an equally weighted portfolio of all mutual funds, rebalanced monthly (EW All); buying and holding VTSMX; and, holding VTSMX when the S&P 500 Index is above its 10-month simple moving average (SMA10) and Cash when the index is below its SMA10 (VTSMX:SMA10). Using monthly dividend-adjusted closing prices for the above mutual funds and the yield for Cash during March 1997 through April 2021, we find that: Keep Reading

A subscriber suggested review of the “SPY-TLT Universal Investment Strategy”, which each day allocates 100% of funds to SPDR S&P 500 (SPY) and/or iShares 20+ Year Treasury Bond (TLT) with SPY-TLT allocations equal to that with the best risk-adjusted daily performance over the past few months. There are 11 SPY-TLT allocation percentage choices: 100-0, 90-10, 80-20, 70-30, 60-40, 50-50, 40-60, 30-70, 20-80, 10-90 and 0-100. We test a simplified version of the strategy as follows:

1. Each trading day, calculate dividend-adjusted close-to-close SPY and TLT returns.
2. As soon as enough days are available, calculate the ratio of average daily return to standard deviation of daily returns over the past 63 trading days (about three months) for each of the 11 allocation choices. This lookback interval is common for such analyses and is within the lookback interval range of 50-80 days suggested by the author.
3. For each day thereafter, maintain a portfolio with SPY-TLT allocations equal to those of the winning allocation choice over the specified lookback interval. We consider both same-close (requiring slight anticipation of the winning allocation choice) and next-open rebalancing executions (because such anticipation appears problematic).

We ignore small rebalancing frictions incurred daily when the allocation does not change. We initially ignore rebalancing frictions when the allocation does change, but then perform a frictions sensitivity test. Using daily dividend-adjusted opening and closing prices for SPY and TLT during July 30, 2002 (limited by TLT) through April 20, 2021, we find that: Keep Reading