Momentum Investing

Are all of the potentially trending/diversifying asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) necessary? Might one or more of them actually be harmful to performance? To investigate, we each month rank the nine SACEMS assets based on past return with one excluded (nine separate test series) and reform the Top 1, equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics, ignoring monthly portfolio reformation costs. Using end-of-month, dividend-adjusted returns for SACEMS assets during February 2006 through June 2022, we find that: Keep Reading

Multifactor funds offer rules-based, diversified exposures to firm/stock factors found to beat the market in academic studies. Do the funds beat the market in real life? In his June 2022 paper entitled “Multifactor Funds: An Early (Bearish) Assessment”, Javier Estrada assesses performance of such funds across U.S., global and emerging markets relative to that of corresponding broad capitalization-weighted indexes and associated exchange-traded funds (ETF). He focuses on multifactor funds with exposure to at least three factors that are explicitly marketed as multifactor funds. Using monthly total returns for 56 U.S.-based equity multifactor funds with at least three years of data and $10 million in assets from respective inceptions (earliest June 2014) through March 2022, and total returns for matched broad market indexes and ETFs, he finds that:

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What determines whether a stock will exhibit short-term momentum or short-term reversal? In their May 2022 paper entitled “Short-term Relative-Strength Strategies, Turnover, and the Connection between Winner Returns and the 52-week High”, building upon prior research, Chen Chen, Chris Stivers and Licheng Sun investigate interactions among proximity to 52-week high, share turnover and 1-month return momentum/reversal behaviors for U.S. stocks.  Specifically, at the end of each month t, they form 125 portfolios by:

1. Sorting stocks into fifths (quintiles) based on return during month t.
2. Further sorting these quintiles stocks into sub-quintiles based on ratio of price at the end of month t-1 to highest price over the preceding 52 weeks.
3. Further sorting the sub-quintiles into sub-sub-quintiles based on share turnover during month t.

They then use month t+1 value-weighted returns of the resulting 125 portfolios to evaluate short-term momentum/reversal strategies in multiple ways: buying winners and shorting losers (momentum); buying losers and shorting winners (reversal); and, winners-only or losers-only strategies based on 52-week high proximity. Using the specified trading data for a broad sample of U.S. common stocks priced at least $1 during July 1963 to December 2020, they find that:

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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 May 2022, 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 May 2022, 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 May 2022, we find that:

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A subscriber suggested review of a Follow the Leader (FTL) strategy that, in simplest form, each month holds iShares MSCI Emerging Markets ETF (EEM) when prior-month SPDR S&P 500 ETF Trust (SPY) return is positive and iShares 20+ Year Treasury Bond ETF (TLT) when prior-month SPY return is negative. To investigate, we run this simplest FTL strategy over the available history of EEM. We use buy-and-hold SPY as a benchmark. Using monthly dividend-adjusted returns for SPY since March 2003 and for EEM and TLT since April 2003, all through April 2022, we find that: Keep Reading

Do intraday (open-to-close, trading-driven/technical) and overnight (close-to-open, news-driven) stock returns play different roles in signaling short-term reversal, intermediate-term momentum and long-term reversion? In their March 2022 paper entitled “What Drives Momentum and Reversal? Evidence from Day and Night Signals”, Yashar Barardehi, Vincent Bogousslavsky and Dmitriy Muravyev investigate by relating past cumulative daily, intraday and dividend-adjusted overnight returns over various lookback intervals to future returns. The lookback intervals they consider are:

• For reversal: last month.
• For momentum: from seven or 12 months ago to one month ago.
• For reversion: from 36, 48 or 60 months ago to 12 months ago.

They measure reversal, momentum and reversion effects via monthly gross 3-factor (market, size, book-to-market) alphas of hedge portfolios that are each month long (short) the extreme tenth, or decile, of stocks with the highest (lowest) past returns. Using the specified inputs for a narrow sample of U.S. common stocks during January 1926 through December 1962, and for a broad sample of U.S. common stocks during January 1963 through December 2019, they find that: Keep Reading

A subscriber proposed substituting SPDR S&P Global Natural Resources ETF (GNR) for Invesco DB Commodity Index Tracking Fund (DBC) as a proxy for commodities in the Simple Asset Class ETF Momentum Strategy (SACEMS). GNR holds stocks of relatively large firms engaged in natural resources and commodities businesses. DBC holds a range of commodity futures contracts. To investigate, we run SACEMS since June 2006 but substitute GNR for DBC as soon as GNR becomes available in September 2010 (dovetailing with older data). Using dividend-adjusted closing prices for SACEMS asset class proxies and GNR and the yield for Cash during February 2006 (per tracked SACEMS) through April 2022, we find that: Keep Reading

Can investors improve the performance of stock momentum portfolios by isolating stocks that “hold” their momentum? In their April 2022 paper entitled “Enduring Momentum”, Hui Zeng, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti exploit firm characteristics to identify stocks that continue to be winners or losers after selection as momentum stocks (stocks with enduring momentum). They measure momentum by each month ranking stocks into equal-weighted tenths, or deciles, based on past 6-month returns, with the top (bottom) decile designated winners (losers). They then develop a model that uses information from 37 firm characteristics to estimate each month the probability that each winner or loser stock will continue as a winner or loser during each of the next six months. They verify that the model reasonably predicts momentum persistence and proceed to test the economic value of the predictions by each month reforming an enduring momentum hedge portfolio that is long (short) the 10 equal-weighted winner (loser) stocks with the highest probabilities of remaining winners (losers) and holding the portfolio for six months. They compare the performance of this portfolio to that of a conventional momentum portfolio that is each month long the entire winner decile and short the entire loser decile, also held for six months. Using returns for a broad sample of U.S. common stocks priced over $1.00 and 37 associated firm characteristics during January 1980 through December 2018, they find that: Keep Reading