Value Premium

Why do factor timing strategies that shine in research papers disappoint in real life? In his May 2025 paper entitled “Caveats of Simple Factor Timing Strategies”, David Blitz discusses the following  simple factor timing strategies with material and statistically significant outperformance per published studies:

• Short-term factor momentum – each month allocates 40%, 30%, 20%, 10% and 0% to the five factors based on prior-month highest to lowest returns.
• Medium-term factor momentum – each month allocates 40%, 30%, 20%, 10% and 0% to the five factors based on past 12-month highest to lowest returns.
• Structurally overweighting momentum – each month gives double weight to the momentum factor and zero weight to size factor.
• Volatility scaling of the momentum factor – each month scales the momentum factor allocation between 40% and 0% based on the ratio of its 20-year volatility to its 12-month volatility, with remaining funds allocated equally to the other four factors.
• Seasonal momentum – each month allocates 40%, 30%, 20%, 10% and 0% to the five factors based on their average historical returns for the same calendar month over the last 20 years.
• Positioning based on investor sentiment – each month takes 200% (0%) exposure to an equal-weighted factor portfolio when last-month Baker-Wurgler investor sentiment is positive (negative).
• Exploiting long-term factor decay – takes an initial 200% exposure to an equal-weighted factor portfolio and linearly reduces exposure to 0% at the end of the sample.

He applies these strategies to five widely accepted U.S. stock market factors: size, value, profitability, investment and momentum. His benchmark is the monthly rebalanced equal-weighted portfolio of these five factors. For each strategy, he addresses general concerns such as portfolio maintenance frictions and recent performance decay, and he identifies strategy-specific concerns. He concludes with minimum standards for future factor timing studies (see the table below). Using monthly returns for the selected factors during July 1963 until December 2024, he finds that:

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Should a long-only equity investor seeking exposure to the size factor (stocks with small market capitalizations tend to outperform) and the value factor (stocks that are cheap with respect to fundamentals tend to outperform) choose two distinct funds or a combined small-value fund? In his March 2025 paper entitled “Small, Value, or Small/Value?”, Javier Estrada tests six annually rebalanced allocations for an investor seeking to enhance a 60% core exposure to the broad U.S. stock market with 40% total exposure to small and value factors:

• 60-40: 40% exposure to one combined small-value fund.
• 60-8-32: 8% exposure to a small fund and 32% exposure to a value fund.
• 60-16-24: 16% to small and 24% to value.
• 60-20-20: 20% to small and 20% to value.
• 60-24-16: 24% to small and 16% to value.
• 60-32-8: 32% to small and 8% to value.

He considers two samples of monthly returns: (1) Fama-French small-cap, value, small-value and market portfolios during July 1926 through December 2024; and, (2) to account for portfolio maintenance frictions and management fees, iShares small-cap (IJR), value (IUSV), small-value (IJS) and market (IVV) exchange-traded funds during August 2000 through December 2024. Using the specified allocations and samples, he finds that: Keep Reading

Do popular style-based exchange-traded funds (ETF) offer a reliable way to exploit the value premium? To investigate, we compare differences in returns (value-minus-growth) for each of the following three matched pairs of value-growth ETFs:

• iShares Russell 2000 Growth ETF (IWO)
• iShares Russell 2000 Value ETF (IWN)
• iShares Russell Mid-Cap Growth ETF (IWP)
• iShares Russell Mid-Cap Value ETF (IWS)
• iShares Russell 1000 Growth ETF (IWF)
• iShares Russell 1000 Value ETF (IWD)

To aggregate, we define monthly value return as the equal-weighted average monthly return of IWN, IWS and IWD and monthly growth return as the equal-weighted average monthly return of IWO, IWP and IWF. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly dividend-adjusted closing prices for these ETFs during August 2001 (limited by IWP and IWS) through March 2025, we find that: Keep Reading

The performance of conventional factor portfolios, long and short extreme quantiles of assets sorted on the factor metric, faces considerable skepticism (see “Compendium of Live ETF Factor/Niche Premium Capture Tests”). Is their some more surgical way to capture theoretical factor premiums? In their March 2025 paper entitled “Investment Base Pairs”, Christian Goulding and Campbell Harvey offer a factor portfolio construction approach that confines portfolio long-short selections to pairs that most strongly exhibit value, momentum and carry premiums (base pairs). The approach identifies enduring pair relationships, not short-lived price gaps. Base pair identification derives from a combination of five variables:

1. The correlation between an asset’s factor signal and its own subsequent return.
2. The correlation between an asset’s factor signal and the paired asset’s subsequent return.
3. The correlation between factor signals between paired assets.
4. Differences in factor signal volatilities between paired assets.
5. Differences in average signal levels between paired assets.

They apply this base pair identification approach by each month reforming long-short, leveraged portfolios of futures and forwards base pairs to generate 20-year backtests of 12 strategies: Equity Value, Bond Value, Currency Value, Commodity Value, Equity Momentum, Bond Momentum, Currency Momentum, Commodity Momentum, Equity Carry, Bond Carry, Currency Carry and Commodity Carry. They also look at strategy averages by class and factor, and overall (All). Benchmarks are comparable conventional strategies that rank assets only on a factor signal. Using monthly data for 64 liquid futures and forwards series (15 equities, 13 bonds, 9 currencies and 27 commodities) during January 1985 through September 2023, they find that: Keep Reading

Can fund managers, and thereby individual investors, reliably exploit academic research on equity factors? In his January 2025 paper entitled “Do Factor Strategies Beat the Market?”, Edward McQuarrie reviews differences between the results of academic factor studies and fund manager/investor experience, with focus on the size effect (small stocks outperform) and the value premium (value stocks outperform). Based on decades of academic research and actual long-only funds, he concludes that: Keep Reading

Is momentum a rational firm earnings growth proxy rather than a manifestation of investor underreaction/overreaction to news? In their August 2024 paper entitled “A Unified Framework for Value and Momentum”, Jacob Boudoukh, Tobias Moskowitz, Matthew Richardson and Lei Xie present an asset pricing model that treats value and momentum as complementary inputs to a present value of earnings estimate. They view momentum, return from 12 months ago to one month ago, as a noisy proxy for earnings growth. They test this view by relating momentum retrospectively to actual earnings growth. They further construct an asset pricing model based on a single growth-adjusted value factor and compare its effectiveness to that of the widely used 4-factor (market, size, book-to-market, momentum) model. They calculate growth-adjusted value factor returns via monthly, 5-year smoothed bivariate value-growth regressions, with three alternatives for earnings growth adjustment: (1) momentum as a proxy for growth; (2) a combination of momentum and analyst earnings forecasts as a proxy for growth; and, (3) retrospective actual earnings. They focus on individual U.S. stocks, but also look at U.S. industries, stocks across 23 developed equity markets and Japanese stocks. Using monthly book-to-market ratios, stock returns, next-year earnings growth forecasts and actual annual earnings as available for Russell 3000 stocks since the end of March 1984, for stocks in 23 developed country markets since the end of January 1989 and for stocks in the MSCI Japan Index since the end of August 1988, all through December 2019, they find that:

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Are equity multifactor strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider eight multifactor ETFs, all currently available:

• iShares Edge MSCI Multifactor USA (LRGF) – holds large and mid-cap U.S. stocks with focus on quality, value, size and momentum, while maintaining a level of risk similar to that of the market. The benchmark is iShares Russell 1000 (IWB).
• iShares Edge MSCI Multifactor International (INTF) – holds global developed market ex U.S. large and mid-cap stocks based on quality, value, size and momentum, while maintaining a level of risk similar to that of the market. The benchmark is iShares MSCI ACWI ex US (ACWX).
• Goldman Sachs ActiveBeta U.S. Large Cap Equity (GSLC) – holds large U.S. stocks based on good value, strong momentum, high quality and low volatility. The benchmark is SPDR S&P 500 (SPY).
• John Hancock Multifactor Large Cap (JHML) – holds large U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns. The benchmark is SPY.
• John Hancock Multifactor Mid Cap (JHMM) – holds mid-cap U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns. The benchmark is SPDR S&P MidCap 400 (MDY).
• JPMorgan Diversified Return U.S. Equity (JPUS) – holds U.S. stocks based on value, quality and momentum via a risk-weighting process that lowers exposure to historically volatile sectors and stocks. The benchmark is SPY.
• Xtrackers Russell 1000 Comprehensive Factor (DEUS) – seeks to track, before fees and expenses, the Russell 1000 Comprehensive Factor Index, which seeks exposure to quality, value, momentum, low volatility and size factors. The benchmark is IWB.
• Vanguard U.S. Multifactor (VFMF) – uses a rules-based quantitative model to evaluate U.S. common stocks and construct a U.S. equity portfolio that seeks to achieve exposure to multiple factors across market capitalizations (large, mid and small). The benchmark is iShares Russell 3000 (IWV).

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the seven equity multifactor ETFs and benchmarks as available through August 2024, we find that: Keep Reading

A subscriber requested evaluation of versions of the Simple Asset Class ETF Value Strategy (SACEVS), the Simple Asset Class ETF Momentum Strategy (SACEMS) and a combination of the two, as follows:

1. For SACEVS Best Value, which chooses the asset proxy for the term, credit or equity premium that is most undervalued (if any), use only every third signal rather than every monthly signal. We start these quarterly signals with the first available signal in July 2002.
2. For SACEMS Equal-Weighted (EW) Top 2, use only every third signal rather than every monthly signal, rebalancing to equal weights only with these quarterly signals. We align quarterly signals with those for SACEVS.
3. For the SACEVS-SACEMS combination, use 30%-70% weights (per “SACEVS-SACEMS for Value-Momentum Diversification”) and rebalance only quarterly. We align quarterly rebalancings with those for SACEVS.

The overall approach is to suppress trading by limiting portfolio reformations/rebalancings to quarterly, while retaining the informativeness of monthly inputs. We apply these changes and compare results to those for the tracked (baseline) versions of SACEVS Best Value, SACEMS EW Top 2 and their 50%-50% combination. Using monthly SACEMS and SACEVS data during July 2006 (limited by availability of a commodities proxy in SACEMS) through June 2024, we find that: Keep Reading

“Beat the Market with Hot-Anomaly Switching?” concludes that “a trader who periodically switches to the hottest known anomaly based on a rolling window of past performance may be able to beat the market. Anomalies appear to have their own kind of momentum.” Does momentum therefore work for style-based exchange-traded funds (ETF)? To investigate, we apply a simple momentum strategy to the following six ETFs that cut across market capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

We test a simple Top 1 strategy that allocates all funds each month to the one style ETF with the highest total return over a specified momentum ranking (lookback) interval. We focus on a 6-month ranking interval as often used in prior research, but test sensitivity of findings to ranking intervals ranging from one to 12 months. As benchmarks, we consider an equal-weighted and monthly rebalanced combination of all six style ETFs (EW All), and buying and holding SPDR S&P 500 (SPY). As an enhancement we consider holding the Top 1 style ETF (3-month U.S. Treasury bills, T-bills) when the S&P 500 Index is above (below) its 10-month simple moving average at the end of the prior month (Top 1:SMA10), with a benchmark substituting SPY for Top 1 (SPY:SMA10). We employ the performance metrics used for SACEMS. Using monthly dividend-adjusted closing prices for the six style ETFs and SPY, monthly levels of the S&P 500 Index and monthly yields for T-bills during August 2001 (limited by IWS and IWP) through June 2024, we find that:

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Does the Fama-French 5-factor model of stock returns stand up to causality analyses? Do the factors cause the returns? In their December 2023 paper entitled “Re-Examination of Fama-French Factor Investing with Causal Inference Method”, Lingyi Gu, Ellen Zhang, Andrew Heinz, Jingxuan Liu, Tianyue Yao, Mohamed AlRemeithi and Zelei Luo construct causal graphs to analyze the relationship between future (next-month) stock return and each of the five factors in the model, which are:

1. Market – value-weighted market return minus the risk-free rate.
2. Size – return on small stocks minus the return on big stocks.
3. Value –  return on high book-to-market ratio stocks minus the return on low book-to-market ratio stocks.
4. Profitability – return on robust profitability stocks minus the return on weak profitability stocks.
5. Investment – return on conservative investment stocks minus the return on aggressive investment stocks.

They consider a constraint-based algorithm, a score-based algorithm and a functional model to estimate causality. For each approach, they evaluate the stability and strength of the causal relationships across different conditions by explore robustness to data loss or alterations. Their goal is to replicate initial conditions and datasets used in the 2015 paper that introduced the 5-factor model. Using monthly returns for a broad sample of U.S. common stocks and the five specified factors during July 1963 through December 2013, they find that:

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