Momentum Investing

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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Do exchange-traded funds (ETF) that track major currencies support a relative momentum strategy? To investigate, we consider the following four ETFs:

Invesco DB US Dollar Bullish (UUP)
Invesco CurrencyShares Euro Currency (FXE)
Invesco CurrencyShares Japanese Yen (FXY)
WisdomTree Chinese Yuan Strategy (CYB)

We each month rank these ETFs based on past return over lookback intervals ranging from one to 12 months. We consider portfolios of past winners reformed monthly based on Top 1 and on equal-weighted (EW) Top 2 and Top 3 ETFs. The benchmark portfolio is the equally weighted combination of all four ETFs. We present findings in formats similar to those used for the Simple Asset Class ETF Momentum Strategy and the Simple Asset Class ETF Value Strategy. Using monthly adjusted closing prices for the currency ETFs during March 2007 (when three become available) through August 2022, we find that: Keep Reading

Do morning and afternoon stock returns convey different meanings due to gradual dissipation of information asymmetry among traders during the trading day (as the market digests overnight news)? In their August 2022 paper entitled “A Tale of One Day: Morning Momentum, Afternoon Reversal”, Haoyu Xu and Xiaoneng Zhu investigate differences in implications for reversal and momentum strategies among morning (9:30AM – 11:30AM), midday (11:30AM – 2:00PM) and afternoon  (2:00PM – 4:00PM). Specifically, they:

• For each stock each month, cumulate returns over these three intervals.
• Sort stocks into tenths, or deciles, based either on cumulative returns over the most recent month (for reversal testing) or compounded cumulative returns from 12 months ago to one month ago (for momentum testing) for different combinations of these three intervals.
• Reform various long-short portfolios using extreme deciles to explore the different predictive powers of past morning and afternoon returns.

For reversal tests, they apply equal weighting. For momentum tests, they consider both value and equal weightings. They calculate raw returns, 3-factor (market, size, book-to-market) alphas and 4-factor (adding momentum) alphas as essential performance statistics. They use conventional strategies using full daily returns as benchmarks. Using intraday and daily return data for a broad sample of U.S. common stocks priced at least $5 during 1993 through 2018, they find that:

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A subscriber, referring to a March 2016 commentary stating that “microcap stocks offer investors one of the best opportunities for consistent, long-term excess returns,” inquired about the performance of quality-value-momentum microcap strategy described therein. To assessment this strategy, we compare the self-reported annual performance of the O’Shaughnessy Micro Cap strategy (OSMC) as of June 2022 (now maintained by Franklin Templeton) to that of simply buying and holding SPDR S&P 500 ETF Trust (SPY). Using annual self-reported OSMC net returns and matched dividend-adjusted SPY returns during August 2007 through June 2022, we find that: Keep Reading

Can investors achieve attractive asset class momentum strategy performance by applying slow relative momentum to different risk-on (offensive) and risk-off (defensive) sets of exchange-traded funds (ETF), and fast absolute momentum to a separate risk mode identification set of ETFs? In his July 2022 paper entitled “Relative and Absolute Momentum in Times of Rising/Low Yields: Bold Asset Allocation (BAA)”, Wouter Keller presents an aggressive asset allocation strategy that combines features of his previous models (Protective Asset Allocation, Vigilant Asset Allocation and Defensive Asset Allocation). This Bold Asset Allocation strategy consists of the following baseline asset universes and rules:

1. When none (any) of SPY, VWO, VEA and BND have negative weighted returns over the past 1, 3, 6 and 12 months, use the offensive (defensive) mode. Weights for past 1, 3, 6 and 12 months returns are 12, 4, 2 and 1, respectively.
2. When in offensive mode, hold the equal-weighted six of SPY, QQQ, IWM, VGK, EWJ, VWO, VNQ, DBC, GLD, TLT, HYG and LQD with the highest ratios of current monthly price to average of the last 13 prices (including current price).
3. When in defensive mode, hold the equal-weighted three of TIP, DBC, BIL, IEF, TLT, LQD and BND with the highest ratios of current monthly price to average of the last 13 prices (including current price), except replace with BIL any of these top three with past price ratio less than that of BIL.

He reforms the BAA 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 Jun 2022, he finds that:

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Do individual investors who chase stocks with high recent returns benefit from momentum or suffer from reversal? In their June 2022 paper entitled “Who Chases Returns? Evidence from the Chinese Stock Market”, Weihua Chen, Shushu Liang and Donghui Shi investigate the characteristics, performance and market impact of retail stock investors who exhibit return-chasing behavior. Each month, they measure:

1. Each retail investor’s return chasing propensity (RCP) as the average of returns during the 12 months prior to purchase across the stocks in the investor’s portfolio. For robustness they also consider past return intervals of one, two, three and six months.
2. Each stock’s return chasing ownership (RCO) by wealth-weighting the RCPs of its retail holders (excluding this stock from holder RCP calculations).

Using monthly stock holdings, trading records and investor demographics, plus associated monthly stock prices, for 18 million Shanghai Stock Exchange retail investors during January 2011 through December 2019, they find that:

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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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