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Momentum Investing Strategy (Strategy Overview)

Allocations for July 2024 (Final)
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Momentum Investing

Do financial market prices reliably exhibit momentum? If so, why, and how can traders best exploit it? These blog entries relate to momentum investing/trading.

Enhancing Momentum with Multi-lookback Winners/Losers

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

SACEMS with SMA Filter

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

Stock Momentum Exploiting All Price Data in a Lookback Interval

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

Sector Breadth as Market Return Indicator

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

Trend Following Plus Relative Sentiment for Stocks-Bonds Allocation

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

Optimal Monthly Cycle for SACEMS?

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:

Keep Reading

SACEMS with Three Copies of Cash

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:

Keep Reading

Simple Currency ETF Momentum Strategy

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

Morning Momentum and Afternoon Reversal for Stock Returns

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:

Keep Reading

O’Shaughnessy Micro Cap Strategy?

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

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