Momentum Investing

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

In response to a prior analysis (updated here), a subscriber asked whether adding a simple moving average (SMA) filter to “Simple Asset Class ETF Momentum Strategy” (SACEMS) assets, either before or after ranking them based on past returns, improves strategy performance. SACEMS each month 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. Since many technical traders use a 10-month SMA (SMA10), we test effectiveness of requiring that each asset pass an SMA10 filter as follows:

1. Baseline – SACEMS as presented at “Momentum Strategy” (no SMA10 filter).
2. Apply an SMA10 filter after asset ranking (SACEMS R-F) – Run Baseline SACEMS and then apply SMA10 filters to dividend-adjusted prices of winners. If a winner is above (below) its SMA10, hold the winner (Cash).
3. Apply an SMA10 filter before asset ranking (SACEMS F-R) – If a SACEMS asset is above (below) its SMA10, apply SACEMS ranking rules to it (exclude it from ranking). If there are not enough ranked assets to populate multi-position SACEMS portfolios, put the positions in Cash.

We focus on compound annual growth rates (CAGR), annual Sharpe ratios and maximum drawdowns (MaxDD) of SACEMS Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios. To calculate Sharpe ratios, we use average monthly 3-month U.S. Treasury bill (T-bill) yield during a year as the risk-free rate for that year. Using monthly dividend-adjusted closing prices for the asset class proxies and the (T-bill) yield for Cash over the period February 2006 through November 2022, we find that: Keep Reading

Does use of price data other than the first and last within a lookback interval improve performance of a stock momentum strategy? In their November 2022 paper entitled “Momentum Without Crashes”, Soros Chitsiripanich, Marc Paolella, Pawel Polak and Patrick Walker construct a momentum strategy that ranks stocks based on a weighting scheme using prices throughout the lookback interval, in effect combining reversal and momentum patterns in returns. Specifically, they apply fractional differencing to stock price series differencing parameter d ranging from 0 to 1. When d is 1 (0), the result is a conventional momentum (reversal) strategy. A value of d between 0 and 1 combines momentum and reversal signals. Each week they sort stocks into fifths, or quintiles, by ascending expected returns based on a specific value of d and a lookback interval of 250 calendar days (one year). They then construct a value-weighted or an equal-weighted portfolio that is long (short) the quintile of stocks with the highest (lowest) expected returns. To avoid any day-of-the-week effects, they construct  such portfolios each weekday and average returns across five weekly-reformed portfolios. They consider a sample of all U.S.-listed common stocks and a subsample that selects only stocks that comprise the top 90% of of market capitalization that week (excluding small stocks). For robustness, they consider smaller/shorter samples from six other countries. Using daily prices for the specified stock samples as available during January 1972 through December 2020, they find that: Keep Reading

Does breadth of equity sector performance predict overall stock market return? To investigate, we relate next-month stock market return to sector breadth (number of sectors with positive past returns) over lookback intervals ranging from 1 to 12 months. We consider the following nine sector exchange-traded funds (ETF) offered as Standard & Poor’s Depository Receipts (SPDR):

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

We use SPDR S&P 500 (SPY) to represent the overall stock market and also relate next-month SPY return to the sign of past SPY return. Using monthly dividend-adjusted returns for SPY and the sector ETFs during December 1998 through October 2022, we find that: Keep Reading

Does combining a sentiment indicator with a trend following indicator improve performance of a stocks-bonds timing strategy? In his October 2022 paper entitled “The Complementarity of Trend Following and Relative Sentiment”, Raymond Micaletti investigates effects of combining the following trend following (TF) and relative sentiment (RS) indicators:

• TF – at the end of each month switch to a broad U.S. stock market index (an aggregate bond index) when the prior-close stock market index crosses above (below) its 10-month simple moving average (SMA) strategy. This strategy is the best of six similar SMA strategies.
• RS – each week update the equity allocation from 0% to 100% based on an equal-weighted combination of three prior-week inputs, two of which are driven by weekly Commitments of Traders reports and one of which is driven by monthly Sentix relative sentiment, with the balance of the portfolio in an aggregate bond index. Update the equity allocation only if it differs from the prior allocation by more than 10%.

The combined strategy (TFRS) is a 50-50 mix of TF and RS. He applies frictions of 0.04% to account for costs of both stock and bond index allocation changes. For interpretation of results, he focuses on nine times the equity index suffers a drawdown of at least 10% from an all-time high. Using daily U.S. equity market total returns and U.S. Treasury bill yields (for Sharpe ratio calculations) from the Kenneth French data library, daily levels of Bloomberg Barclays U.S. Aggregate Bond Total Return Index, weekly Commitments of Traders reports and the monthly Sentix economic outlook survey of institutional and individual investors during November 1994 through August 2022, he finds that: Keep Reading

Is there a best time of the month for measuring momentum within the Simple Asset Class ETF Momentum Strategy (SACEMS)? To investigate, we compare 21 variations of baseline SACEMS by shifting the monthly return calculation cycle from 10 trading days before the end of the month (EOM) to 10 trading days after EOM. For example, an EOM+5 cycle ranks assets based on closing prices five trading days after EOM each 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 daily dividend-adjusted prices for SACEMS assets during mid-February 2006 through mid-October 2022, we find that:

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Subscribers have questioned selecting assets with negative past returns within the “Simple Asset Class ETF Momentum Strategy” (SACEMS). Inclusion of Cash as one of the assets in the SACEMS universe of exchange-traded funds (ETF) prevents the SACEMS Top 1 portfolio from holding an asset with negative past returns. To test full dual momentum versions of SACEMS equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios, we add two more copies of Cash to the universe, thereby preventing both of them from holding assets with negative past returns. We focus on the effects of adding two copies of Cash on the holdings, compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) of SACEMS EW Top 2 and EW Top 3 portfolios. Using monthly dividend adjusted closing prices for the asset class proxies and the yield for Cash during February 2006 through September 2022, we find that:

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