Momentum Investing

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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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

Do published stock factors exhibit performance streaks exploitable via intrinsic (absolute, or time series) and relative (cross-sectional) momentum? In the March 2021 revision of their paper entitled “Factor Momentum and the Momentum Factor”, Sina Ehsani and Juhani Linnainmaa investigate stock factor portfolio monthly time series and cross-sectional momentum. They consider 15 factors for U.S. stocks (size, value, profitability, investment, momentum, accruals, betting against beta, cash flow-to-price, earnings-to-price, liquidity, long-term reversals, net share issuance, quality minus junk, residual variance and short-term reversals) and seven of these factors for global stocks. Each factor portfolio is long (short) stocks with higher (lower) expected returns based on that factor. They each month measure factor momentum as factor portfolio return from 12 months ago to one month ago. They consider six factor momentum strategies and one benchmark strategy that all exclude the stock momentum factor and are all rebalanced monthly and equal-weighted, as follows:

• Time Series Winners –  long factor portfolios with positive momentum.
• Time Series Losers – long factor portfolios with negative momentum.
• Time Series Hedge– long Time Series Winners and short Time Series Losers.
• Cross-sectional Winners –  long factor portfolios with above-median momentum.
• Cross-sectional Losers – long factor portfolios with below-median momentum.
• Cross-sectional Hedge – long Cross-sectional Winners and short Cross-sectional Losers.
• Benchmark – long all factor portfolios.

Using monthly returns as available for the 15 U.S. stock anomalies since July 1963 and seven of these anomalies applied to global stocks since July 1990, all through December 2019 (mostly Kenneth French data), they find that: Keep Reading

A subscriber asked for an extended test of a very simple momentum strategy that each month holds Vanguard 500 Index Fund Investor Shares (VFINX) or Vanguard Long-Term Treasury Fund Investor Shares VUSTX according to which of these funds has the highest total return over the last three months. To investigate, based on the way mutual funds report prices, we calculate past 3-month total returns using dividend-adjusted prices for month-ends and strategy returns using dividend adjusted prices for first days of the following month. We assume zero fund switching costs and no restrictions on monthly fund switching. We use buying and holding VFINX as a benchmark. Using the specified fund price series and monthly 3-month U.S. Treasury bill (T-bill) yield from the end of May 1986 (limited by VUSTX) through the beginning of March 2021, we find that: Keep Reading

How have value, quality, low-volatility and momentum equity factors, and combinations of these factors, performed in recent years. In their October 2020 paper entitled “Equity Factor Investing: Historical Perspective of Recent Performance”, Benoit Bellone, Thomas Heckel, François Soupé and Raul Leote de Carvalho review and put into context recent performances of these these factors/combinations as applied to medium-capitalization and large-capitalization World, U.S. and European stock universes. They consider both long-short and long-only factor portfolios and further investigate effects of (1) neutralizing beta and sector dependencies, (2) using multiple metrics for each factor and (3) including small stocks. Using firm accounting data and stock returns to support factor portfolio construction during 1995 through early 2020, they find that:

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