Fundamental Valuation

A subscriber asked about the performance of a 50-50 combination of a basket of momentum stock exchange-traded funds (ETF) and a basket of quality stock ETFs, specifically with comparison to a 50-50 combination of the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS). To investigate, we employ results from:

• The EW portfolio of momentum ETFs in “Are Equity Momentum ETFs Working?” as the basket of momentum ETFs.
• The equal-weighted (EW) portfolio of quality ETFs in “Are Stock Quality ETFs Working?” as the basket of quality ETFs.
• The 50-50 SACEVS Best Value-SACEMS EW Top 2 portfolio.

We assume monthly rebalancing of the 50-50 momentum-quality portfolio. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We also use SPDR S&P 500 ETF (SPY) to assess effectiveness of the factor portfolios. Using monthly total returns from the above three sources and SPY during April 2007 (limited by momentum ETF data) through October 2025, we find that: Keep Reading

Are stock quality strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider six ETFs, all currently available (from oldest to youngest):

• Invesco S&P 500 Quality ETF (SPHQ) – seeks to track performance of S&P 500 stocks with the highest quality scores based on firm return on equity, accruals ratio and financial leverage ratio, reformed semi-annually. The benchmark is SPDR S&P 500 (SPY).
• iShares Edge MSCI USA Quality Factor ETF (QUAL) – seeks to track performance of U.S. large-capitalization and mid-capitalization stocks selected based return on firm equity, earnings variability and debt-to-equity. The benchmark is SPY.
• iShares Edge MSCI Intl Quality Factor ETF (IQLT) – seeks to track performance of large-capitalization and mid-capitalization developed international stocks screened for attractive return-on-equity, earnings variability and debt-to-equity. The benchmark is iShares MSCI ACWI ex U.S. ETF (ACWX).
• Fidelity Quality Factor ETF (FQAL) – seeks to track performance of U.S. large-capitalization and mid-capitalization stocks with a higher firm quality profile than the broader market. The benchmark is Vanguard Russell 1000 Index Fund ETF (VONE).
• JPMorgan U.S. Quality Factor ETF (JQUA) – designed to provide domestic equity exposure with a focus on companies with strong quality and profitability characteristics and the potential to enhance returns. The benchmark is VONE.
• Vanguard U.S. Quality Factor ETF (VFQY) – applies a rules-based quantitative model to select U.S. common stocks with strong fundamentals (strong profitability and healthy balance sheets) across market capitalizations, sectors and industry groups. The benchmark is iShares Russell 3000 ETF (IWV).

We calculate monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the stock quality ETFs and benchmarks as available through October 2025, we find that:

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Does P/E10 (or Cyclically Adjusted Price-Earnings ratio, CAPE) usefully predict U.S. stock market returns? Per Robert Shiller’s data, P/E10 is inflation-adjusted S&P Composite Index level divided by average monthly inflation-adjusted 12-month trailing earnings of index companies over the last ten years. To investigate its usefulness, we consider in-sample regression/ranking tests and out-of-sample cumulative performance tests. Using monthly values of P/E10, S&P Composite Index levels (calculated as average of daily closes during the month), associated dividends (smoothed), 12-month trailing real earnings (smoothed) and interest rates as available during January 1871 through September 2025, we find that: Keep Reading

Can machine learning exploit many stock similarity factors to produce exceptional statistical arbitrage (pairs trading) performance? In their August 2025 paper entitled “Attention Factors for Statistical Arbitrage”, Elliot Epstein, Rose Wang, Jaewon Choi and Markus Pelger present the Attention Factor Model, which employs machine learning to:

1. Identify similar stocks based on both past returns and firm fundamentals (similarity factors).
2. Generate signals for temporary price divergences between similar stocks.
3. Set weighting/trading rules to exploit such price divergences.

Their model considers many similarity factors and the time series behaviors of these factors to maximize portfolio Sharpe ratio after transaction costs. They retrain the model each year on a rolling window of eight years of data, using the last two years of the first set of training data to select tuning parameters. We consider model variations that identify 1, 3, 5, 8, 10, 15, 30 or 100 similarity factors. They assume total costs of 0.05% one-way trading frictions and 0.01% shorting costs. They consider results of prior research as a benchmark. Using daily returns and 39 firm characteristics for the 500 largest U.S. stocks by month during January 1990 through December 2021, with model testing during January 1998 through December 2022, they find that: Keep Reading

Is the part of the return/alpha of a equity factor that is not associated with simple changes in factor valuation (ratio of long side average price-to-book value ratio to short side average price-to-book value ratio) especially predictive of future factor returns? In their September 2025 paper entitled “Revaluation Alpha”, Robert Arnott, Sina Ehsani, Campbell Harvey and Omid Shakernia define and examine:

• Revaluation return – the portion of historical factor return that comes from changes in factor valuation.
• Structural return – the historical factor return minus the revaluation return.

They investigate whether historical return and structural return are equally useful in predicting future factor performance. They further compare usefulness of such predictions compared to those of factor timing based on factor momentum (1-month and 12-month) and factor valuation (current average price-to-book ratio minus its historical average). Using historical monthly data for 14 equity factors widely studied in past research during July 1973 through December 2022, they find that:

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What variables drive differences in price-to-earnings ratios (P/E) across U.S. stocks? In the September 2025 revision of their paper entitled “The Cross-section of Subjective Expectations: Understanding Prices and Anomalies”, Ricardo Delao, Xiao Han and Sean Myers analyze cross-sectional P/E differences of U.S. common stocks by comparing:

• Professional forecasts of earnings growth, return and P/E over the next four years.
• Actual (realized) earnings growth, returns and P/E over the next four years.

Using the specified forecasted and actual data during 1999 (1982 for some inputs) through 2020, they find that: Keep Reading

A subscriber asked about the lead-lag relationship between S&P 500 earnings and S&P 500 Index returns. To investigate, we relate actual aggregate S&P 500 operating and as-reported earnings to S&P 500 Index returns at both quarterly and annual frequencies. Earnings forecasts are available well in advance of returns. Actual earnings releases for a quarter occur throughout the next quarter. Using quarterly S&P 500 earnings and index levels during March 1988 through March 2025 and June 2025, respectively, we find that: Keep Reading

A subscriber asked about current evidence that high-dividend stocks outperform the market. To investigate, we compare performances of 10  exchange-traded funds (ETFs) holding high-dividend stocks to that of SPDR S&P 500 (SPY) as a proxy for the U.S. stock market. The  high-dividend stock ETFs, from oldest to newest, are:

• iShares Select Dividend ETF (DVY)
• Invesco Dividend Achievers ETF (PFM)
• SPDR S&P Dividend ETF (SDY)
• Vanguard Dividend Appreciation Index Fund (VIG)
• Vanguard High Dividend Yield ETF (VYM)
• WisdomTree Dividend ex-Financials ETF (DTN), dead since January 2022
• iShares Core High Dividend ETF (HDV)
• Schwab U.S. Dividend Equity ETF (SCHD)
• ProShares S&P 500 Dividend Aristocrats ETF (NOBL)
• iShares Core Dividend Growth ETF (DGRO)

For each of these ETFs, we compare average monthly total (dividend-reinvested) return, standard deviation of monthly returns, monthly return-risk ratio (average monthly return divided by standard deviation), compound annual growth rate (CAGR) and maximum drawdown (MaxDD) to those for SPY over matched sample periods. Using monthly total returns for the 10 high-dividend stock ETFs and SPY over available sample periods through August 2025, we find that:

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How do hedge fund managers think about fundamental analysis versus technical analysis in managing their stock portfolios? In his July 2025 paper entitled “Portfolio Construction: Blending Fundamental and Technical Analysis”, Gregory Blotnick describes the interplay between fundamental and technical analyses in long/short equity portfolio construction from the perspective of a hedge fund with a high velocity of ideas. He includes case studies and technical screening exercises to illustrate the roles of momentum, valuation metrics and relative strength in idea generation, risk management and capital allocation. Based on his experience and examples, he concludes that: Keep Reading

Why not select and weight stocks in a growth portfolio using only firm growth fundamentals rather than variables that depend on stock price? In their July 2025 paper entitled “Fundamental Growth”, Robert Arnott, Chris Brightman, Campbell Harvey, Que Nguyen and Omid Shakernia investigate performance of stocks exhibiting growth in fundamentals such as sales, gross profit and research and development (R&D) spending rather than price-based measures such as valuation ratios and market capitalization. Specifically, at the end of each March, they:

• For each stock in a sample comprising the top 98% of U.S. stock market capitalizations and each fundamental, calculate both per-share growth rate and change in dollar value over the last five years.
• For each stock, compute a composite:
  • Per-share growth rate as the average of z-scores for the three individual growth rates divided by sales.
  • Change in dollar value as the average for the three individual changes in dollar value, each scaled by the respective total for all stocks.
• Select the 1,000 firms with the strongest composite per-share growth rates.
• Reform portfolios of these 1,000 stocks weighted by composite change in dollar values, with zero weight for negative values (FG 1000).

As benchmarks, they use: (1) annually rebalanced top 1,000 U.S. stocks ranked by market capitalization (CW 1000); (2) a traditional capitalization-weighted growth-style portfolio reformed similarly to the Russell growth methodology (CW Growth). Using annual U.S. stock/firm fundamentals data and stock prices during March 1969 through December 2024, they find that: Keep Reading