Momentum Investing

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 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. Benchmark is iShares Russell 3000 (IWV).
• Invesco DWA SmallCap Momentum (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 (IWM).
• iShares Edge MSCI USA Momentum Factor (MTUM) – holds U.S. large-capitalization and mid-capitalization stocks with relatively high momentum. Benchmark is iShares Russell 1000 (IWB).
• 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. Benchmark is IWV.
• iShares MSCI Intl Momentum Factor (IMTM) – holds international developed market large-capitalization and mid-capitalization stocks with relatively high momentum. Benchmark is iShares MSCI ACWI ex U.S. (ACWX).
• SPDR Russell 1000 Momentum Focus (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 (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 (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 (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 2023, we find that: Keep Reading

A subscriber asked about the performance of a variation of the monthly reformed 50-50  Simple Asset Class ETF Value Strategy (SACEVS) Best Value-Simple Asset Class ETF Momentum Strategy (SACEMS) Equal-Weighted (EW) Top 2 combination that substitutes 100% SACEMS EW Top 2 whenever both:

1. SPDR S&P 500 ETF Trust (SPY) is the selection for SACEVS Best Value at the end of the prior month.
2. SPY is below its 10-month simple moving average at the end of the prior month.

The objective of the variation is to suppress SACEVS Best Value drawdowns. To investigate, we compare performance results for this variation (“Filtered”) with those for baseline 50-50 SACEVS Best Value-SACEMS EW Top 2. Using monthly returns for SACEVS Best Value and SACEMS EW Top 2 since July 2006 (limited by SACEMS) and monthly dividend-adjusted prices for SPY since September 2005, all through March 2023, we find that: Keep Reading

Is timing of U.S. equity factors broadly and reliably attractive? In their March 2023 paper entitled “Timing the Factor Zoo”, Andreas Neuhierl, Otto Randl, Christoph Reschenhofer and Josef Zechner analyze effectiveness of 39 timing signals applied to 318 known factors. Factors include such categories as intangibles, investment, momentum, profitability, trading frictions and value/growth. Timing signals encompass momentum, volatility, valuation spread, characteristics spread, issuer-purchaser spread and reversal. Specifically, they:

• Forecast monthly returns for each factor and each signal (12,402 timed factors).
• Aggregate timing signals using partial least squares regression.
• Construct multi-factor portfolios that are each month long (short) the fifth, or quintile, of factors with the highest (lowest) predicted returns.
• Investigate composition of optimal factor timing portfolios, considering such properties such as turnover and style tilt.

Using monthly factor and signal data as available (different start dates) during 1926 through 2020, they find that: Keep Reading

A subscriber asked about boosting the performance of the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS), and thereby the Combined Value-Momentum Strategy (SACEVS-SACEMS), by substituting ProShares Ultra S&P500 (SSO) for SPDR S&P 500 ETF Trust (SPY) in these strategies whenever:

1. SPY is above its 200-day simple moving average (SMA200); and,
2. The CBOE Volatility Index (VIX) SMA200 is below 18.

Substitution of SSO for SPY applies to portfolio holdings, but not SACEMS asset ranking calculations. To investigate, we test all versions of SACEVS, SACEMS and monthly rebalanced 50% SACEVS-50% SACEMS (50-50) combinations. We limit SPY SMA200 and VIX SMA200 conditions to month ends as signals for next-month actions (no intra-month changes). We consider baseline SACEVS and SACEMS (holding SPY as indicated) and versions of SACEVS and SACEMS that always hold SSO instead of SPY as benchmarks. We look at average gross monthly return, standard deviation of monthly returns, monthly gross reward/risk (average monthly return divided by standard deviation), gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio as key performance metrics. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using daily unadjusted SPY and VIX values for SMA200 calculations since early September 2005 and monthly total returns for SSO since inception in June 2006 to modify SACEVS and SACEMS inputs, all through February 2023, we find that: Keep Reading

A subscriber suggested that the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS) may each exhibit return momentum at the strategy level, such that an investor holding both as in Combined Value-Momentum Strategy (SACEVS-SACEMS) may want to tilt each month toward the one with stronger recent returns. To investigate, we test a SACEVS Best Value-SACEMS Equal-Weighted (EW) Top 2 combination strategy that each month assigns 60% weight to the strategy with the higher return over a specified lookback interval and 40% to the one with the lower return (60-40). We consider lookback intervals of 1 to 12 months. We also look at a “full tilt” version for a selected lookback interval. We use standalone SACEVS Best Value, standalone SACEMS EW Top 2 and monthly rebalanced 50% SACEVS Best Value-50% SACEMS EW Top 2 (50-50) as benchmarks. We look at average gross monthly return, standard deviation of monthly returns, monthly gross reward/risk (average monthly return divided by standard deviation), gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio as key performance metrics. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using SACEVS Best Value and SACEMS EW Top 2 gross monthly returns during July 2006 (limited by SACEMS) through February 2023, we find that:

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Do factor investing (smart beta) mutual funds capture for investors the premiums found in academic factor research? In their November 2022 paper entitled “Factor Investing Funds: Replicability of Academic Factors and After-Cost Performance”, Martijn Cremers, Yuekun Liu and Timothy Riley analyze the performance of funds seeking to capture of published (long-side) factor premiums. They group factor investing funds into four styles: dividend, volatility, momentum and q-factor (profitability and investment). They separately measure how closely fund holdings adhere to the long sides of academic factor specifications. They measure fund outperformance (alpha) relative to the market factor via the Capital Asset Pricing Model (CAPM) and via a multi-factor model (CPZ6) that accounts for the market factor and for granular size/value interactions. Using monthly returns for 233 hand-selected factor investing mutual funds and for the academic research factors during January 2006 (16 funds available) through September 2020 (207 funds available), they find that:

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Can investors achieve attractive asset class momentum strategy performance by applying mixed-lookback interval momentum to different risk-on (offensive) and risk-off (defensive) sets of exchange-traded funds (ETF), and to a separate risk mode identification ETF? In their February 2023 paper entitled “Dual and Canary Momentum with Rising Yields/Inflation: Hybrid Asset Allocation (HAA)”, Wouter Keller and Jan Willem Keuning present a simplification of the prior Bold Asset Allocation strategy. This Hybrid Asset Allocation strategy consists of the following baseline asset universes and rules, with a single asset momentum metric (equal-weighted average return over the past 1, 3, 6 and 12 months):

• When TIP momentum is positive (negative), use the offensive (defensive) mode.
• When in offensive mode, hold the equal-weighted four of SPY, IWM, VWO, VEA, VNQ, DBC, IEF and TLT with the strongest momentum, except replace any of the top four with non-positive momentum by the one of BIL and IEF with the strongest momentum for crash protection.
• When in defensive mode, hold the one of BIL and IEF with the strongest momentum.

They reform the portfolio monthly, assuming constant 0.1% 1-way trading frictions. Using modeled monthly total returns prior to ETF inception and actual monthly total returns after inception for each specified ETF during December 1970 through December 2022, they find that: Keep Reading

Can investors make the stock return momentum effect stronger/more reliable by isolating stocks for which many similar stocks exhibit very strong or very weak past returns? In his December 2022 paper entitled “Neighbouring Assets”, Sina Seyfi explores this question by sorting stocks based on average past returns of other stocks with the most similar sets of 94 characteristics (neighbor stocks). He measures similarity between two stocks as the aggregate distance of their normalized and winsorized (excluding top and bottom 1% of values) characteristics over a baseline rolling 10-year history. His baseline “neighborhood” is 1,000 stocks. His baseline past return metric is average monthly value-weighted return of neighbor stocks over the past year. He considers three stock universes, consisting of all NYSE/AMEX/NASDAQ stocks: (1) excluding the 5% with the smallest market capitalizations; (2) excluding those below the 20% breakpoint of NYSE market capitalizations; and, (3) excluding those below the median of NYSE market capitalizations. He each month sorts stocks into tenths (deciles) of average past return of neighborhood stocks and reforms a value-weighted portfolio that is long (short) those in the decile with the highest (lowest) neighbor-stock average past return. Using monthly characteristics and returns for the specified stocks during January 1970 (with portfolio formation commencing January 1980) through December 2021, he finds that: Keep Reading

Do the widely used U.S. stock return factors exhibit long-term trend changes and shorter-term cyclic behaviors? In his November 2022 paper entitled “Trends and Cycles of Style Factors in the 20th and 21st Centuries”, Andrew Ang applies various methods to compare trends and cycles for equity value, size, quality, momentum and low volatility factors, with focus on a breakpoint at the end of 2000. He measures size using market capitalization, value using book-to-market ratio, quality using operating profitability, momentum using return from 12 months ago to one month ago and low volatility using idiosyncratic volatility relative to the Fama-French 3-factor (market, size, book-to-market) model of stock returns. He each month for each factor sorts stocks into tenths, or deciles, and computes gross monthly factor return from a portfolio that is long (short) the average return of the two deciles with the highest (lowest) expected returns. As a benchmark, he uses the value-weighted market return in excess of the U.S. Treasury bill yield. Using market and factor return data from the Kenneth French data library during July 1963 through August 2022, he finds that:

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Do stocks that are winners or losers over multiple lookback intervals generate stronger future returns because they attract wider audiences of momentum investors? In their June 2022 paper entitled “Overlapping Momentum Portfolios”, Iván Blanco, Miguel De Jesus and Alvaro Remesal explore this question by comparing performances of three portfolios:

1. MOM (benchmark): long (short) the value-weighted tenth, or decile, of stocks with the highest (lowest) returns from 12 months ago to one month ago.
2. OMOM (overlapping): long (short) the value-weighted stocks in the MOM highest-return (lowest-return) decile that are also in the top (bottom) decile of stocks sorted by returns from six months ago to one month ago.
3. Non-OMOM (non-overlapping): long (short) the value-weighted stocks in the MOM highest-return (lowest-return) decile that are not also in the top (bottom) decile of stocks sorted by return from six months ago to one month ago.

They test portfolio holding intervals ranging from one month to 24 months. They consider such portfolio performance metrics (often annualized) as average monthly return, Sharpe ratio and 1-factor (market), 3-factor (plus size and book-to-market) and 5-factor (plus profitability and investment) alphas. Using monthly returns for a broad sample of U.S. stocks priced over $5 during December 1926 through December 2018, they find that: Keep Reading