Momentum Investing

Academic studies of stock portfolio optimization often use an equal-weighted (EW) strategy as benchmark. Are there simple EW enhancements that researchers ought to consider instead? In their December 2023 paper entitled “Outperforming Equal Weighting”, Antonello Cirulli and Patrick Walker test three sets of enhanced long-only EW portfolios relying solely on past returns:

1. Momentum-enhanced EW – sort stocks into tenths (deciles) from lowest to highest average weekly return over the last 12 months.
2. Volatility-enhanced EW – sort stocks into deciles from highest t0 lowest standard deviation of weekly returns over the last five years.
3. Sharpe ratio-enhanced EW – sort stocks into deciles from lowest to highest Sharpe ratio calculated with weekly returns over the last years.

For each set, they then exclude the bottom 1, 2, 3, 4 or 5 deciles and weight stocks in retained deciles equally for a total of 15 enhanced EW portfolios. They reform all portfolios on the first Wednesday of each month. They then compare net performances of these portfolios to those of simple EW and capitalization-weighted portfolios of all stocks in the universe after debiting 0.1% frictions for turnover. They focus on large-capitalization/liquid stocks and check robustness of findings to subperiods, lookback intervals, level of frictions and rebalancing frequency. Using weekly returns in U.S. dollars, adjusted for splits and dividends, of MSCI USA, Europe, Emerging Markets and Developed Markets stocks starting five years before the test period of April 2002 through March 2022, they 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 October 2023, we find that:

Keep Reading

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 October 2023, we find that:

Keep Reading

Can investors reliably time the market, size, value and profitability long-short equity factor premiums? In their October 2023 paper entitled “Another Look at Timing the Equity Premiums”, Wei Dai and Audrey Dong test strategies that time these four premiums in U.S., developed ex-U.S. and emerging equity markets. They define the premiums as:

1. Market – the capitalization-weighted market return minus the U.S. Treasury bill yield.
2. Size – average return on small-capitalization stocks minus average return on large-capitalization stocks.
3. Value – average return on value stocks minus average return on growth stocks.
4. Profitability – average return on stocks of high-profitability firms minus average return on stocks of low-profitability firms.

They time each premium separately based on each of:

1. Valuation ratio – When the difference in aggregate price-to-book ratio between the long and short sides of a premium becomes high (low) relative to its historical distribution, switch to the short (long) side.
2. Mean reversion – When the premium itself becomes high (low) relative to its historical distribution, switch to the short (long) side  of the premium.
3. Momentum – When the premium over the last year becomes relatively high (low), switch to the long (short) side of the premium.

To measure historical premium distributions, they consider an expanding window of initial length 10 years or a rolling 10-year window. For switching to the short side of premiums, they consider historical distribution thresholds of top 10%, 20% or 50% (bottom 10%, 20% or 50%) for valuation ratio and mean reversion (momentum). For switching to the long side of premiums, they consider thresholds of bottom 10%, 20% or 50% (top 50%) for valuation ratio and mean reversion (momentum). They consider  monthly or annual portfolio rebalancing. The number of timing strategies tested is thus 720. For the U.S. sample, monthly returns start in July 1963 for profitability and July 1927 for the other three premiums. For the developed ex-U.S. (emerging markets) sample, premium returns start in July 1990 (July 1994). Benchmarks are returns to strategies that continuously hold just the long side of each premium portfolio. Using monthly data as specified through December 2022, they find that: Keep Reading

Are there ways to revive the fading performance of the short-term reversal (STR) strategy, which is long stocks with the lowest returns last month and short stocks with the highest? In their September 2023 paper entitled “Reversing the Trend of Short-Term Reversal”, David Blitz, Bart van der Grient and Iman Honarvar investigate revival of the strategy by suppressing its conflicts with either industry momentum or general momentum. Specifically, at the end of each month, they sort stocks into fifths (quintiles) in three ways:

1. Generic STR – sorting on simple last-month returns.
2. Industry-adjusted STR – sorting on last-month returns minus respective last-month industry returns.
3. Residual STR – sorting on 3-factor alphas (adjusting for market, size and book-to-market factors over rolling 36-month intervals), scaled for volatility over the past 36 months.

For each approach each month, they form a hedge portfolio that is long (short) the quintile with the lowest (highest) past performances. For all three approaches, they impose regional neutrality by sorting stocks separately within North America, Europe and the Pacific region. They also consider developed and emerging markets segmentation. Using end-of-month data for all stocks in the MSCI World index during December 1985 through December 2022 (an average of 1,745 stocks per year), they find that: Keep Reading

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 September 2023, we find that: Keep Reading

Do Relative Rotation Graphs (RRG), which visually segregate assets into leading, weakening, lagging or improving quadrants by relative performance, effectively identify equity sectors with relatively strong future returns? In his September 2023 paper entitled “Dynamic Sector Rotation”, John Rothe tests an RRG-based sector relative momentum strategy with stop-loss risk management based on volatility. Specifically, he:

• Selects a universe of 31 sector sector/subsector exchange-traded funds (ETFs) based on daily trading volume, years in existence, overlap with other sector/subsectors, assets under management and liquidity.
• Each week, holds the equal-weighted top 5 ETFs crossing into the RRG improving quadrant.
• Manages the risk of each holding continuously via a Wilder Volatility Stop with a 5-day range.
• Assumes a 2% annual management fee.

His benchmark is the S&P 500 Momentum Index. Using weekly returns for the selected ETF universe during a test period spanning January 2013 through mid-2023, he finds that: Keep Reading

Can network analysis discover useful momentum spillover across asset classes? In their August 2023 paper entitled “Network Momentum across Asset Classes”, Xingyue (Stacy) Pu, Stephen Roberts, Xiaowen Dong and Stefan Zohren employ a graph machine learning model to discover cross-class momentum connections and devise a network momentum strategy across 64 series of commodities, equities, bonds and currencies future contracts. They train the model on an expanding window of at least 10 years of history for eight momentum features, including volatility-scaled returns and normalized moving average crossover divergences (MACD) over different lookback intervals. They they then apply multiple linear regressions over different lookback intervals (seeking to avoid reversals) to devise a network momentum strategy for out-of-sample testing. Every five years, they retrain the graph model. Using daily prices of the 64 futures contract series during 1990 through 2022, such that out-of-sample testing commences in 2000, they find that:

Keep Reading

What is the right balance between seeking alpha and avoiding taxes? In their August 2023 paper entitled “Alpha Now, Taxes Later: Tax-Efficient Long-Only Factor Investing”, Yin Chen and Roni Israelov assess trade-offs between rebalancing benefits and tax avoidance from overlapping 10-year backtests of long-only momentum, value, quality and safety factor stock portfolios. They measure momentum as cumulative return from 12 months ago to one month ago, value as book-to-market ratio, quality as operating profitability and safety as winsorized market betas. All portfolios start with the equal-weighted top fifth (300 stocks) as ranked by the factor metric. After initial formation, they consider five monthly portfolio management rules:

1. Fully Rebalanced, each month selling stocks that drop out of the top fifth and buying stocks that enter the top fifth, but not adjusting weights of stocks that remain in the portfolio.
2. Buy-and-Hold (no rebalancing over the 10-year portfolio life).
3. Sell Losers at Losses, each month selling stocks that have migrated to the bottom fifth if they have capital losses.
4. Tax Loss Harvesting, each month selling stocks with more than 5% unrealized losses and not buying them back until at least 30 days later.
5. Tax Loss Harvesting and Sell Losers, selling stocks that have migrated to the bottom fifth even if they have unrealized capital gains so long as the aggregate realized capital gain is zero.

They form the first portfolio for each factor in June 1964 and initiate new portfolios every six months until January 2012, such that the last portfolio is held through December 2021. They focus on 1-factor (market) alpha, averaged across overlapping portfolios, as the key performance metric. To calculate net performance, they assume 0.08% 1-way trading frictions, 23.8% dividend tax rate and 23.8% (40.8%) long-term (short-term) capital gain tax rate. Based on initial findings, they repeat all tests on composite portfolios of value, quality and safety factors constructed by ranking stocks on individual factors and investing equally in the fifth of stocks with the highest combined rankings. Using data as specified for the 1,500 U.S. stocks with the largest market capitalizations at the end of each prior year during 1964 to 2021, they find that:

Keep Reading

Instead of searching among hundreds of firm/stock characteristics to identify those that best predict stock returns, what about first finding the stocks with the highest and lowest past returns and then examining the characteristics of those stocks? In his June 2023 paper entitled “Essence of the Cross Section”, Sina Seyfi identifies the strongest determinants of expected stock returns by:

1. Sorting stocks into fifths (quintiles) at the end of each month during the last 10 years based on monthly returns (120 sets of quintile portfolios).
2. Computing the average monthly value of each of 206 firm/stock characteristics among stocks in each quintile across the last 10 years.
3. Forming each month out-of-sample quintiles that are as similar as possible regarding these 206 average characteristics to the in-sample returns-sorted quintiles.
4. Studying variations of the 206 characteristics across these out-of-sample quintiles to identify the most important drivers of future stock returns.

This method allows for non-linearities and interactions among characteristics, which a conventional linear regression method does not. Using returns and characteristics data for publicly listed U.S. common stocks and the U.S. risk-free rate as available during 1926 through 2021, he finds that:

Keep Reading