Momentum Investing

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)
• Invesco 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 monthly total (dividend-adjusted) returns for the specified assets during February 2006 through December 2023, we find that: Keep Reading

What are optimal intrinsic/absolute/time series momentum (IM) and simple moving average (SMA) lookback intervals for different asset class proxies? To investigate, we use data for the following eight asset class exchange-traded funds (ETF), plus Cash:

• Invesco DB Commodity Index Tracking (DBC)
• iShares JPMorgan Emerging Markets Bond Fund (EMB)
• 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 at the ends of the last two to 12 months. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key metrics for comparing different IM and SMA lookback intervals since earliest ETF data availabilities based on the longest IM lookback 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 December 2023, we find that:

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Academic studies of stock portfolio optimization often use an equal-weighted (EW) strategy as benchmark. Are there simple EW enhancements that researchers ought to consider instead? In their December 2023 paper entitled “Outperforming Equal Weighting”, Antonello Cirulli and Patrick Walker test three sets of enhanced long-only EW portfolios relying solely on past returns:

1. Momentum-enhanced EW – sort stocks into tenths (deciles) from lowest to highest average weekly return over the last 12 months.
2. Volatility-enhanced EW – sort stocks into deciles from highest t0 lowest standard deviation of weekly returns over the last five years.
3. Sharpe ratio-enhanced EW – sort stocks into deciles from lowest to highest Sharpe ratio calculated with weekly returns over the last years.

For each set, they then exclude the bottom 1, 2, 3, 4 or 5 deciles and weight stocks in retained deciles equally for a total of 15 enhanced EW portfolios. They reform all portfolios on the first Wednesday of each month. They then compare net performances of these portfolios to those of simple EW and capitalization-weighted portfolios of all stocks in the universe after debiting 0.1% frictions for turnover. They focus on large-capitalization/liquid stocks and check robustness of findings to subperiods, lookback intervals, level of frictions and rebalancing frequency. Using weekly returns in U.S. dollars, adjusted for splits and dividends, of MSCI USA, Europe, Emerging Markets and Developed Markets stocks starting five years before the test period of April 2002 through March 2022, they find that:

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“SACEMS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS). “SACEVS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Value Strategy” (SACEVS). In response, a subscriber requested a sensitivity test of 1.25X, 1.50X and 1.75X leverage targets. To investigate effects of these leverage targets, we separately augment SACEVS Best Value, SACEMS EW Top 2 and the equally weighted combination of these two strategies by: (1) initially applying target leverage via margin; (2) for each month with a positive portfolio return, adding margin at the end of the month to restore target leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore target leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratio, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate. Using monthly total (dividend-adjusted) returns for the specified assets since July 2002 for SACEVS and since July 2006 for SACEMS, both through October 2023, we find that:

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Is leveraging with margin a good way to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS)? To investigate effects of margin, we augment SACEMS by: (1) initially applying 2X leverage via margin (limited by Federal Reserve Regulation T); (2) for each month with a positive portfolio return, adding margin at the end of the month to restore 2X leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore 2X leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratio, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate and consider a range of margin interest rates as increments to this yield. Using monthly gross total returns for SACEMS and monthly T-bill yields during July 2006 through October 2023, we find that:

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Can investors reliably time the market, size, value and profitability long-short equity factor premiums? In their October 2023 paper entitled “Another Look at Timing the Equity Premiums”, Wei Dai and Audrey Dong test strategies that time these four premiums in U.S., developed ex-U.S. and emerging equity markets. They define the premiums as:

1. Market – the capitalization-weighted market return minus the U.S. Treasury bill yield.
2. Size – average return on small-capitalization stocks minus average return on large-capitalization stocks.
3. Value – average return on value stocks minus average return on growth stocks.
4. Profitability – average return on stocks of high-profitability firms minus average return on stocks of low-profitability firms.

They time each premium separately based on each of:

1. Valuation ratio – When the difference in aggregate price-to-book ratio between the long and short sides of a premium becomes high (low) relative to its historical distribution, switch to the short (long) side.
2. Mean reversion – When the premium itself becomes high (low) relative to its historical distribution, switch to the short (long) side  of the premium.
3. Momentum – When the premium over the last year becomes relatively high (low), switch to the long (short) side of the premium.

To measure historical premium distributions, they consider an expanding window of initial length 10 years or a rolling 10-year window. For switching to the short side of premiums, they consider historical distribution thresholds of top 10%, 20% or 50% (bottom 10%, 20% or 50%) for valuation ratio and mean reversion (momentum). For switching to the long side of premiums, they consider thresholds of bottom 10%, 20% or 50% (top 50%) for valuation ratio and mean reversion (momentum). They consider  monthly or annual portfolio rebalancing. The number of timing strategies tested is thus 720. For the U.S. sample, monthly returns start in July 1963 for profitability and July 1927 for the other three premiums. For the developed ex-U.S. (emerging markets) sample, premium returns start in July 1990 (July 1994). Benchmarks are returns to strategies that continuously hold just the long side of each premium portfolio. Using monthly data as specified through December 2022, they find that: Keep Reading

Are there ways to revive the fading performance of the short-term reversal (STR) strategy, which is long stocks with the lowest returns last month and short stocks with the highest? In their September 2023 paper entitled “Reversing the Trend of Short-Term Reversal”, David Blitz, Bart van der Grient and Iman Honarvar investigate revival of the strategy by suppressing its conflicts with either industry momentum or general momentum. Specifically, at the end of each month, they sort stocks into fifths (quintiles) in three ways:

1. Generic STR – sorting on simple last-month returns.
2. Industry-adjusted STR – sorting on last-month returns minus respective last-month industry returns.
3. Residual STR – sorting on 3-factor alphas (adjusting for market, size and book-to-market factors over rolling 36-month intervals), scaled for volatility over the past 36 months.

For each approach each month, they form a hedge portfolio that is long (short) the quintile with the lowest (highest) past performances. For all three approaches, they impose regional neutrality by sorting stocks separately within North America, Europe and the Pacific region. They also consider developed and emerging markets segmentation. Using end-of-month data for all stocks in the MSCI World index during December 1985 through December 2022 (an average of 1,745 stocks per year), they find that: Keep Reading

Does a simple relative momentum strategy applied to tradable U.S. Treasury term structure proxies produce attractive results by picking the best duration for exploiting the current interest rate trend? To investigate, we run short-term and long-term tests. The short-term test employs five exchange-traded funds (ETF) to represent the term structure:

SPDR Barclays 1-3 Month T-Bill (BIL)
iShares 1-3 Year Treasury Bond (SHY)
iShares Barclays 3-7 Year Treasury Bond (IEI)
iShares Barclays 7-10 Year Treasury Bond (IEF)
iShares Barclays 20+ Year Treasury Bond (TLT)

The second test employs three Vanguard mutual funds to represent the term structure:

Vanguard Short-Term Treasury Fund (VFISX)
Vanguard Intermediate-Term Treasury Fund (VFITX)
Vanguard Long-Term Treasury Fund (VUSTX)

For each test, we allocate all funds at the end of each month to the fund with the highest total return over a specified ranking (lookback) interval, ranging from one month to 12 months. To accommodate the longest lookback interval, portfolio formation commences 12 months after the start of the sample. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. Using monthly dividend-adjusted closing prices for BIL since May 2007, for IEI since January 2007, for SHY, IEF and TLT since July 2002 and for VFISX, VFITX and VUSTX since October 1991, all through September 2023, we find that: Keep Reading

Do Relative Rotation Graphs (RRG), which visually segregate assets into leading, weakening, lagging or improving quadrants by relative performance, effectively identify equity sectors with relatively strong future returns? In his September 2023 paper entitled “Dynamic Sector Rotation”, John Rothe tests an RRG-based sector relative momentum strategy with stop-loss risk management based on volatility. Specifically, he:

• Selects a universe of 31 sector sector/subsector exchange-traded funds (ETFs) based on daily trading volume, years in existence, overlap with other sector/subsectors, assets under management and liquidity.
• Each week, holds the equal-weighted top 5 ETFs crossing into the RRG improving quadrant.
• Manages the risk of each holding continuously via a Wilder Volatility Stop with a 5-day range.
• Assumes a 2% annual management fee.

His benchmark is the S&P 500 Momentum Index. Using weekly returns for the selected ETF universe during a test period spanning January 2013 through mid-2023, he finds that: Keep Reading

Can network analysis discover useful momentum spillover across asset classes? In their August 2023 paper entitled “Network Momentum across Asset Classes”, Xingyue (Stacy) Pu, Stephen Roberts, Xiaowen Dong and Stefan Zohren employ a graph machine learning model to discover cross-class momentum connections and devise a network momentum strategy across 64 series of commodities, equities, bonds and currencies future contracts. They train the model on an expanding window of at least 10 years of history for eight momentum features, including volatility-scaled returns and normalized moving average crossover divergences (MACD) over different lookback intervals. They they then apply multiple linear regressions over different lookback intervals (seeking to avoid reversals) to devise a network momentum strategy for out-of-sample testing. Every five years, they retrain the graph model. Using daily prices of the 64 futures contract series during 1990 through 2022, such that out-of-sample testing commences in 2000, they find that:

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