Momentum Investing

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

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:

• PowerShares 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 November 2022, we 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 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

Do alternative “Cash” (deemed risk-free) instruments materially affect performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS)? Changing the proxy for Cash can affect how often the model selects Cash, as well as the return on Cash when selected. To investigate, we test separately each of the following yield and exchange-traded funds (ETF) as the risk-free asset:

• 3-month Treasury bills (Cash), a proxy for the money market as in base SACEMS
• SPDR Bloomberg Barclays 1-3 Month T-Bill (BIL)
• iShares 1-3 Year Treasury Bond (SHY)
• iShares 7-10 Year Treasury Bond (IEF)
• iShares TIPS Bond (TIP)

We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics and consider Top 1, equally weighted (EW) EW Top 2 and EW Top 3 SACEMS portfolios. Using end-of-month total (dividend-adjusted) returns for the specified assets during February 2006 (except May 2007 for BIL) through October 2022, we find that:

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A subscriber asked about an update of “Simplest Asset Class ETF Momentum Strategy?”, which each month holds SPDR S&P 500 ETF Trust (SPY) or iShares 20+ Year Treasury Bond (TLT)  depending on which has the higher total return over the last three months, including a direct comparison to a portfolio that each month allocates 50% to Simple Asset Class ETF Value Strategy (SACEVS) Best Value and 50% to Simple Asset Class ETF Momentum Strategy (SACEMS) equal-weighted (EW) Top 2. We begin the test at the end of June 2006, limited by SACEMS inputs. We ignore monthly switching frictions for both strategies. Using monthly dividend-adjusted prices for SPY and TLT starting March 2006 and monthly gross returns for 50-50 SACEVS Best Value and SACEMS EW Top 2 starting July 2006 (limited by SACEMS inputs), all 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