Momentum Investing

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 June 2024, we find that: Keep Reading

Is industry trend-following an attractive strategy over the long run? In their June 2024 paper entitled “A Century of Profitable Industry Trends”, Carlo Zarattini and Gary Antonacci evaluate the long-term performance of a long-only industry trend-following (Timing Industry) strategy, modeled as follows:

• Entry – buy an industry when its daily closing price crosses above the upper band of either its 20-day Keltner Channel (with a multiplier of 2 for the high-low price range component) or its 20-day Donchian Channel.
• Sizing – each day for each open position, calculate 14-day past return volatility as an estimate of its future volatility and resize all open positions so that they contribute equally to overall portfolio volatility, limiting overall portfolio leverage to 200%.
• Exit – each day for each open position, close the position if it crosses below a stop loss represented by the lower band of either its 40-day Keltner Channel (again with a multiplier of 2 for the high-low price range component) or its 40-day Donchian Channel. However, do not ever lower the stop loss. When a position closes, reinvest proceeds into 1-month U.S. Treasury bills.

For a long-term test, they apply these rules to nearly 98 years of daily returns for 48 hypothetical annually rebalanced, capitalization-weighted industry portfolios constructed by assigning a Standard Industrial Classification (SIC) Code to each stock traded on NYSE, AMEX and NASDAQ. For a recent and more realistic test, they apply these rules to 31 sector exchange-traded funds (ETF) offered by State Street Global Advisors. Utilizing daily returns for the 48 industry portfolios since July 1926 and for the 31 sector ETFs as available (inceptions January 2005 to June 2018), all through March 2024, they find that:

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Are there interactions between stock return momentum and days of the week? In their March 2024 paper entitled “Same-Weekday Momentum”, Zhi Da and Xiao Zhang investigate how momentum interacts with days of the week. They first perform regression tests to evaluate abilities of same-day and other-day past returns to predict day-of-the-week momentum. They then evaluate economic significance of findings by comparing three trading strategies:

1. Standard Momentum – each month, reform a value-weighted hedge portfolio that is long (short) stocks that are in the top (bottom) tenth, or decile, of stocks with the highest (lowest) average monthly returns from 12 months ago to one month ago.
2. Same-Weekday Momentum – each weekday during a month, reform a value-weighted hedge portfolio that is long (short) the decile of stocks with the highest (lowest) average daily returns on the same day of the week from 12 months ago to one month ago.
3. Other-Weekday Momentum – each weekday during a month, reform a value-weighted hedge portfolio that is long (short) the decile of stocks with highest (lowest) average daily returns on other weekdays from 12 months ago to one month ago.

Using daily data for publicly listed U.S. stocks, excluding those priced less than $5 and those in the bottom tenth of NYSE market capitalizations, during 1963 through 2021 and daily equity fund/institutional trading data as available, they find that: Keep Reading

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, 2021, 2022, 2023 and 2024. 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, 2016, 2017, 2018 and 2019. 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 2024, we find that: Keep Reading

A subscriber requested validation of the performance of a simple momentum strategy that each month selects the best performing debt mutual fund based on total return over the past three months. To investigate, we test a simple strategy on the following 14 mutual funds, most of which have long histories:

T. Rowe Price New Income (PRCIX)
Thrivent Income A (LUBIX)
Vanguard GNMA Securities (VFIIX)
T. Rowe Price High-Yield Bonds (PRHYX)
T. Rowe Price Tax-Free High Yield Bonds (PRFHX)
Vanguard Long-Term Treasury Bonds (VUSTX)
T. Rowe Price International Bonds (RPIBX)
Fidelity Convertible Securities (FCVSX)
PIMCO Short-Term A (PSHAX)
Fidelity New Markets Income (FNMIX)
Eaton Vance Government Obligations C (ECGOX)
Franklin Floating Rate Daily Access A (FAFRX)
Vanguard Total Bond Market Index Adm (VBTLX)
Principal Spectrum Pref&Cptl Scs IncA (PPSAX)

We consider a strategy that allocates funds at the end of each month based on total returns over a specified ranking (lookback) interval to the Top 1, equal-weighted (EW) Top 2, EW Top 3, EW Top 4 or EW Top 5 funds. We determine the first winners in November 1988 so that at least nine funds are available for lookback interval sensitivity testing. As a benchmark, we use the equal-weighted and monthly rebalanced combination of all available funds (EW All). Using monthly dividend-adjusted closing prices for the 14 mutual funds from inceptions through April 2024, we find that: Keep Reading

Is intraday time series momentum of a very liquid exchange-traded fund (ETF) such as SPDR S&P 500 ETF Trust (SPY) exploitable? In their May 2024 paper entitled “Beat the Market: An Effective Intraday Momentum Strategy for S&P500 ETF (SPY)”, Carlo Zarattini, Andrew Aziz and Andrea Barbon iteratively construct and test a SPY intraday time series momentum strategy, with alternatives specified as follows:

• Each minute of each day, compute noise boundaries as daily opening SPY price times one plus and one minus the average daily return up to that minute over the last 14 trading days, adjusting the upper bound up by any gap-down the prior overnight and the lower bound down by any gap-up the prior overnight. When SPY price is between these boundaries, demand and supply are in balance (no trend).
• If at any clock hour or half-hour (HH:00 or HH:30), SPY price has moved above (below) the upper (lower) boundary, open a long (short) position in SPY. Consider either:
  1. 100% allocation of funds to each trade; or.
  2. An allocation to each trade sized to a volatility target of 2% daily volatility based on actual SPY volatility over the past 14 trading days (allowing up to 4X leverage when actual SPY volatility is below 2%).
• Consider as dynamic trailing stop-losses, executed at the next clock hour or half-hour, either:
  1. The lower boundary for long positions and the upper boundary for short positions, opening the opposite trade upon triggering a stop-loss; or,
  2. The higher of the upper boundary or the intraday SPY Volume-Weighted Average Price (VWAP) for long positions and the lower of the lower boundary or the intraday VWAP for short positions.
• Terminate any open positions at each market close to avoid exposure to overnight moves.

They assume trading frictions of $0.0035 per share in commissions plus $0.001 per share in slippage. They explore sensitivity of strategy performance to market volatility (VIX), day of the week, level of trading frictions and recent market direction. They further compare strategy returns to those of well-known technical patterns. Using 1-minute open, high, low and close data for SPY and VIX and 1-minute volume data for SPY during May 2007 through April 2024, they find that: Keep Reading

Are stock and sector momentum strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider nine momentum-oriented equity ETFs, all currently available, in order of longest to shortest available histories:

• Invesco Dorsey Wright Momentum ETF (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. Benchmark is iShares Russell 3000 ETF (IWV).
• Invesco Dorsey Wright SmallCap Momentum ETF (DWAS) – invests at least 90% of assets in approximately 200 small-capitalization U.S. common stocks per a proprietary methodology designed to identify powerful relative strength characteristics, reformed quarterly. Benchmark is iShares Russell 2000 ETF (IWM).
• iShares Edge MSCI USA Momentum Factor ETF (MTUM) – holds U.S. large-capitalization and mid-capitalization stocks with relatively high momentum. Benchmark is iShares Russell 1000 ETF (IWB).
• First Trust Dorsey Wright Focus 5 ETF (FV) – holds five equally weighted sector and industry ETFs selected via a proprietary relative strength methodology, reformed twice a month. Benchmark is IWV.
• iShares MSCI Intl Momentum Factor ETF (IMTM) – holds international developed market large-capitalization and mid-capitalization stocks with relatively high momentum. Benchmark is iShares MSCI ACWI ex U.S. ETF (ACWX).
• SPDR Russell 1000 Momentum Focus ETF (ONEO) – tracks the Russell 1000 Momentum Focused Factor Index, picking U.S. stocks that have recently outperformed. Benchmark is IWB.
• Alpha Architect U.S. Quantitative Momentum ETF (QMOM) – seeks to buy stocks with the highest quality momentum via a portfolio that generally holds 50 stocks. Benchmark is IWV.
• 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. Benchmark is IWV.
• JPMorgan U.S. Momentum Factor ETF (JMOM) – holds U.S. common stocks with strong risk-adjusted momentum. Benchmark is IWB.

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We assign broad market benchmark ETFs according to momentum fund descriptions. Using monthly dividend-adjusted returns for the nine momentum funds and respective benchmarks as available through April 2024, we find that: Keep Reading

Is momentum better measured by a granular fitted line or beginning-to-end return? In their March 2024 paper entitled “Trended Momentum”, Charlie Cai, Peng Li and Kevin Keasey investigate use of an analytically/visually clear linear stock price trend to enhance conventional momentum. They measure price trend clarity (TC) as R-squared for a regression of daily price versus date over the past 12 months. Specifically, they each month:

• Sort stocks into fifths (quintiles) based on conventional momentum, return from 12 months ago to one month ago.
• Further sort the top momentum quintile into finer quintiles based on TC.
• Form  equal-weighted or value-weighted portfolios of resulting sorts and compute their gross returns and 3-factor (market, size, book-to-market) alphas over the next six months.

To confirm use of TC to measure clarity of price trend, they separately conduct an experiment that relates analytical TC to trend clarity perceived by sample of 128 individuals each evaluating 10 pairs of stock charts. Their sample includes daily price data for U.S. common stocks from January 1927 through December 2020. Analyses requiring earnings start in 1964, while those involving investor sentiment span 1967 through 2018. They groom all variables to exclude outliers. In further analyses, they employ global stock price data. Using the specified methodology and data, they find that:

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Is an investment strategy that follows trends in economic fundamentals (rather than asset prices) an attractive alternative to conventional momentum? In their January 2024 paper entitled “Economic Trend”, Jordan Brooks, Noah Feilbogen, Yao Hua Ooi and Adam Akant test a strategy that shifts allocations to equity, bond, currency and commodity futures/forwards series based on trends in five important global economic fundamentals, as follows:

• Growth – 12-month change in GDP growth forecast (increasing growth is good for equities, currencies and commodities, but bad for bonds).
• Inflation – 12-month change in CPI-based inflation forecasts (increasing inflation is good for currencies and commodities, but bad for equities and bonds).
• International trade – 12-month change in local spot currency exchange rate versus an export-weighted basket (increasing international trade is good for equities, but bad for bonds, currencies and commodities).
• Monetary policy – 12-month change in 2-year bond yield (increasing yield is good for currencies, but bad for equities, bonds and commodities).
• Risk aversion – equal-weighted 12-month trailing stock market return and 12-month change in credit spread (increasing risk aversion is good for equities, currencies and commodities, but bad for bonds).

When the above variables are unavailable, they use substitutes. They consider: (1) single-class, equal risk-weighted portfolios based on all five economic fundamental trends; (2) single-fundamental portfolios positioned across all four asset classes; and, (3) an equal risk-weighted composite of all single-class portfolios (the full Economic Trend strategy). For comparison, they form similar portfolios based on equal-weighted 1-month, 3-month and 12-month trailing asset returns. Composite portfolios (both economic trend and price trend) each month target 10% constant volatility based on the last three years of asset class returns. Using economic fundamentals data and monthly prices as available for 15 global equity futures, 9 bond futures, 7 interest rate futures, 8 currency forwards and 20 commodity futures series during January 1970 through December 2022, they find that: Keep Reading

Do published commodity futures factors exhibit exploitable momentum? In their December 2023 paper entitled “Factor Momentum in Commodity Futures Markets”, Yiyan Qian, Xiaoquan Liu and Ying Jiang examine factor momentum in fully collateralized nearest-rolled contracts of various commodity futures. They consider ten factors:

• Market –S&P Goldman Sachs Commodity Index. 
• Basis -slope of futures term structure.
• Momentum – cross-sectional predictability of past performance.
• Basis-momentum – slope and curvature of the term structure of futures returns.
• Hedging pressure – mismatch in hedging and speculating activity.
• Skewness – investor return distribution preferences and selective hedging.
• Open interest – existing price positions.
• Currency beta – changes in the U.S. dollar versus a basket of other currencies.
• Inflation beta – impact from unexpected inflation.
• Liquidity – liquidity risk of commodity futures trading.

They calculate return series for each factor by each month buying (selling) the equal-weighted fifth of commodity futures with the highest (lowest) predicted next-month returns. For each factor return series, they then test the ability of returns over the past 1, 3, 6, 9 or 12 months to predict next-month return. Using daily data for 36 commodity futures contracts from U.S. and UK markets (16 agriculture, 6 energy, 3 livestock and 10 metal) as available during January 1985 through May 2022, they find that: Keep Reading