Momentum Investing

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

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 January 2024, 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)
• 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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How sensitive is performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) to choice of momentum calculation lookback interval, and what interval works best? To investigate, we generate gross compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for SACEMS Top 1, equally weighted (EW) EW Top 2 and EW Top 3 portfolios over lookback intervals ranging from one to 12 months. All calculations start at the end of February 2007 based on inception of the commodities exchange-traded fund and the longest lookback interval. Using end-of-month total (dividend-adjusted) returns for the SACEMS asset universe during February 2006 through November 2023, we find that: Keep Reading

“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