Momentum Investing

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:

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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:

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What characteristics of U.S. equity factor return series are most relevant to respective factor performance? In his May 2023 paper entitled “The Cross-Section of Factor Returns” David Blitz explores long-term average returns and market alphas, 60-month market betas and factor performance cyclicality for U.S. equity factors. He also assesses potentials of three factor rotation strategies: low-beta, seasonal and return momentum. Using monthly returns for 153 published U.S. equity market factors, classified statistically into 13 groups, during July 1963 through December 2021, he finds that:

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Why is trend following so persistently popular among investors? In their March 2022 paper entitled “A Guide to Trend Following Strategies”, Stuart Broadfoot and Daniel Leveau describe popular trend identification methods and provide an example of how to build/test a multi-asset class trend following strategy in four steps. Using trend following index data during January 2000 through May 2022 and prices for 52 futures contract series during January 2000 through January 2022, they find that: Keep Reading

A subscriber asked about the performance of a variation of the monthly reformed 50-50  Simple Asset Class ETF Value Strategy (SACEVS) Best Value-Simple Asset Class ETF Momentum Strategy (SACEMS) Equal-Weighted (EW) Top 2 combination that substitutes 100% SACEMS EW Top 2 whenever both:

1. SPDR S&P 500 ETF Trust (SPY) is the selection for SACEVS Best Value at the end of the prior month.
2. SPY is below its 10-month simple moving average at the end of the prior month.

The objective of the variation is to suppress SACEVS Best Value drawdowns. To investigate, we compare performance results for this variation (“Filtered”) with those for baseline 50-50 SACEVS Best Value-SACEMS EW Top 2. Using monthly returns for SACEVS Best Value and SACEMS EW Top 2 since July 2006 (limited by SACEMS) and monthly dividend-adjusted prices for SPY since September 2005, all through March 2023, we find that: Keep Reading

Is timing of U.S. equity factors broadly and reliably attractive? In their March 2023 paper entitled “Timing the Factor Zoo”, Andreas Neuhierl, Otto Randl, Christoph Reschenhofer and Josef Zechner analyze effectiveness of 39 timing signals applied to 318 known factors. Factors include such categories as intangibles, investment, momentum, profitability, trading frictions and value/growth. Timing signals encompass momentum, volatility, valuation spread, characteristics spread, issuer-purchaser spread and reversal. Specifically, they:

• Forecast monthly returns for each factor and each signal (12,402 timed factors).
• Aggregate timing signals using partial least squares regression.
• Construct multi-factor portfolios that are each month long (short) the fifth, or quintile, of factors with the highest (lowest) predicted returns.
• Investigate composition of optimal factor timing portfolios, considering such properties such as turnover and style tilt.

Using monthly factor and signal data as available (different start dates) during 1926 through 2020, they find that: Keep Reading

A subscriber asked about boosting the performance of the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS), and thereby the Combined Value-Momentum Strategy (SACEVS-SACEMS), by substituting ProShares Ultra S&P500 (SSO) for SPDR S&P 500 ETF Trust (SPY) in these strategies whenever:

1. SPY is above its 200-day simple moving average (SMA200); and,
2. The CBOE Volatility Index (VIX) SMA200 is below 18.

Substitution of SSO for SPY applies to portfolio holdings, but not SACEMS asset ranking calculations. To investigate, we test all versions of SACEVS, SACEMS and monthly rebalanced 50% SACEVS-50% SACEMS (50-50) combinations. We limit SPY SMA200 and VIX SMA200 conditions to month ends as signals for next-month actions (no intra-month changes). We consider baseline SACEVS and SACEMS (holding SPY as indicated) and versions of SACEVS and SACEMS that always hold SSO instead of SPY as benchmarks. We look at average gross monthly return, standard deviation of monthly returns, monthly gross reward/risk (average monthly return divided by standard deviation), gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio as key performance metrics. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using daily unadjusted SPY and VIX values for SMA200 calculations since early September 2005 and monthly total returns for SSO since inception in June 2006 to modify SACEVS and SACEMS inputs, all through February 2023, we find that: Keep Reading

Do factor investing (smart beta) mutual funds capture for investors the premiums found in academic factor research? In their November 2022 paper entitled “Factor Investing Funds: Replicability of Academic Factors and After-Cost Performance”, Martijn Cremers, Yuekun Liu and Timothy Riley analyze the performance of funds seeking to capture of published (long-side) factor premiums. They group factor investing funds into four styles: dividend, volatility, momentum and q-factor (profitability and investment). They separately measure how closely fund holdings adhere to the long sides of academic factor specifications. They measure fund outperformance (alpha) relative to the market factor via the Capital Asset Pricing Model (CAPM) and via a multi-factor model (CPZ6) that accounts for the market factor and for granular size/value interactions. Using monthly returns for 233 hand-selected factor investing mutual funds and for the academic research factors during January 2006 (16 funds available) through September 2020 (207 funds available), they find that:

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Can investors achieve attractive asset class momentum strategy performance by applying mixed-lookback interval momentum to different risk-on (offensive) and risk-off (defensive) sets of exchange-traded funds (ETF), and to a separate risk mode identification ETF? In their February 2023 paper entitled “Dual and Canary Momentum with Rising Yields/Inflation: Hybrid Asset Allocation (HAA)”, Wouter Keller and Jan Willem Keuning present a simplification of the prior Bold Asset Allocation strategy. This Hybrid Asset Allocation strategy consists of the following baseline asset universes and rules, with a single asset momentum metric (equal-weighted average return over the past 1, 3, 6 and 12 months):

• When TIP momentum is positive (negative), use the offensive (defensive) mode.
• When in offensive mode, hold the equal-weighted four of SPY, IWM, VWO, VEA, VNQ, DBC, IEF and TLT with the strongest momentum, except replace any of the top four with non-positive momentum by the one of BIL and IEF with the strongest momentum for crash protection.
• When in defensive mode, hold the one of BIL and IEF with the strongest momentum.

They reform the 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 December 2022, they find that: Keep Reading

Can investors make the stock return momentum effect stronger/more reliable by isolating stocks for which many similar stocks exhibit very strong or very weak past returns? In his December 2022 paper entitled “Neighbouring Assets”, Sina Seyfi explores this question by sorting stocks based on average past returns of other stocks with the most similar sets of 94 characteristics (neighbor stocks). He measures similarity between two stocks as the aggregate distance of their normalized and winsorized (excluding top and bottom 1% of values) characteristics over a baseline rolling 10-year history. His baseline “neighborhood” is 1,000 stocks. His baseline past return metric is average monthly value-weighted return of neighbor stocks over the past year. He considers three stock universes, consisting of all NYSE/AMEX/NASDAQ stocks: (1) excluding the 5% with the smallest market capitalizations; (2) excluding those below the 20% breakpoint of NYSE market capitalizations; and, (3) excluding those below the median of NYSE market capitalizations. He each month sorts stocks into tenths (deciles) of average past return of neighborhood stocks and reforms a value-weighted portfolio that is long (short) those in the decile with the highest (lowest) neighbor-stock average past return. Using monthly characteristics and returns for the specified stocks during January 1970 (with portfolio formation commencing January 1980) through December 2021, he finds that: Keep Reading