Fundamental Valuation

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

Do S&P 500 earnings growth rates predict S&P 500 Index (SP500) returns? To investigate, we relate actual 12-month SP500 operating earnings growth rate and as-reported earnings growth rate measured quarterly to SP500 quarterly return. We use 12-month earnings growth rates to avoid confounding calendar effects. Actual earnings releases for a quarter occur throughout the next quarter. Using quarterly S&P 500 earnings and index levels during March 1988 through June 2025, we find that:

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To determine whether the stock market is expensive or cheap, some experts use aggregate valuation ratios, either trailing or forward-looking, such as earnings-price ratio (E/P) and dividend yield. Under belief that such ratios are mean-reverting, most imminently due to movement of stock prices, these experts expect high (low) future stock market returns when these ratios are high (low). Where are the ratios now and how are they changing during recent months? Using recent actual and forecasted earnings and dividend data from Standard & Poor’s and associated S&P 500 Index levels as available through June 2025, we find that: Keep Reading

A subscriber commented and asked:

“I have been wondering whether Morningstar’s estimate of ‘fair value’ for stocks has any relationship to actual subsequent returns. For instance, when the fair value is more than double the market price, is the return over the next year substantially greater than for stocks where the fair value is less than half the market price? I haven’t found anything like this on your site, only an assessment of mutual fund ratings which is a very different matter.”

As an alternative, tractable investigation, we hand collect end-of-month Morningstar price relative to Fair Market Value (P/FMV) for the overall stock market from their 5-year historical chart. We then relate those P/FMVs to monthly returns for SPDR S&P 500 ETF (SPY) over the same period. Using monthly Morningstar P/FMVs and SPY returns during May 2020 through April 2025, we find that: Keep Reading

A subscriber suggested looking at the S&P 500 price-to-sales ratio (P/S) as an indicator for timing the U.S. stock market. To investigate, we relate P/S and change in P/S to S&P 500 Index (SP500) quarterly returns, as follows:

1. Conduct lead-lag analyses for quarterly P/S versus quarterly SP500 returns, and for quarterly change in P/S versus quarterly SP500 returns.
2. Calculate average next-quarter SP500 returns by range of quarterly values of P/S and quarterly changes in P/S.

Using quarterly S&P 500 P/S data since the end of December 2000 and quarterly closing SP500 levels since September 2000, both through Mar 2025, we find that: Keep Reading

Are all stocks except U.S. growth cheap? In his brief February 2025 paper entitled “Decomposing Equity Returns: Earnings Growth vs. Multiple Expansion”, David Blitz decomposes equity returns of different global equity markets and styles (size, low-volatility, value) into dividend yield, earnings growth and multiple expansion. The decomposition consists of:

• Subtracting price return from total return to derive the contribution from dividends.
• Calculating the part of price return due to earnings growth.
• Attributing the part of price return not due to earnings growth to multiple expansion/contraction based on change in price-to-earnings ratio (P/E).

The breakdown exposes different reasons for underperformance of markets and styles relative to the U.S. equity market. Using the specified data for 2015 through 2024, he finds that: Keep Reading

Do stock-by-stock return forecasts from deep learning produce an exploitable aggregate equity risk premium (ERP) forecast? In the January 2025 revision of their paper entitled “The Aggregated Equity Risk Premium”, Vitor Azevedo, Christoph Riedersberger and Mihail Velikov predict ERP by first applying deep learning to predict returns for individual U.S. stocks and then aggregating these returns at the market level. The firm-level forecasts come from combined outputs of several neural networks of varying complexity applied to 290 firm-level characteristics, 14 U.S. economic variables and 49 industry classification indicators. They iterate these forecasts annually using an expanding training window and a rolling six-year validation window. For comparison, they consider some conventional ERP forecasting approaches. They quantify the economic value of aggregate ERP forecasts via a stock market timing strategy that each month allocates to stocks or U.S. Treasury bills with a 50% leverage limit and conservative 0.5% portfolio rebalancing frictions. Using the specified inputs during March 1957 through December 2021 (with out-of-sample testing commencing January 2000), they find that:

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What do expectations for Bitcoin supply and demand imply for the future trajectory of its price? In the January 2025 revision of their paper entitled “A Supply and Demand Framework for Bitcoin Price Forecasting”, Murray Rudd and Dennis Porter construct a supply-and-demand model to forecast Bitcoin price trajectory. Their model combines a fixed, inelastic supply with demand drivers consisting of accumulation for strategic reserves, institutional adoption and other long-term holders. They consider a conservative scenario calibrated to April 2024 and December 2024 Bitcoin market snapshots and a bullish scenario with more aggressive institutional adoption. Based on an array of supply and demand assumptions and the market snapshots, they find that:

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Which price-to-earnings ratio is best for screening stocks? In the November 2024 first version of his paper entitled “Forward Price-Earnings Ratio”, Luca Conrads compares the practical abilities of seven price-to-earnings ratios to predict S&P 500 returns (see the chart below for four of these seven):

1. Conventional price-to-earnings (PE) – current price divided by prior-year actual earnings.
2. Cyclically Adjusted Price-to-Earnings (CAPE) – current price divided by average annual inflation-adjusted earnings over the last 10 years.
3. Cyclically Adjusted Price-to-Earnings (CAPE5) – current price divided by average annual inflation-adjusted earnings over the last five years.
4. Forward Price-to-Earnings Analysts (FPE^A) – current price divided by next-year annual earnings as forecasted by analyst consensus.
5. Average Forward Price-to-Earnings Analysts (AFPE^A) – current price divided by average annual earnings over the next five years as forecasted by analyst consensus.
6. Forward Price-to-Earnings Mechanical (FPE^M) – current price divided by next-year annual earnings as mechanically forecasted via ordinary least squares (OLS).
7. Average Forward Price-to-Earnings Mechanical (AFPE^M) – current price divided by average annual earnings over the next five years as mechanically forecasted via OLS.

He each quarter for each ratio ranks all S&P 500 stocks with positive ratios into equal-weighted fifths (quintiles) and applies four separate portfolio strategies:

1.  Assign weights of 30%, 20%, 20% 20% and 10% to quintiles 1, 2, 3, 4 and 5, respectively.
2. Invest only in quintile 1.
3. Modify Strategy 1 by taking a 130% position in quintile 1 and a -30% (short) position in quintile 5.
4. Modify Strategy 1 by taking a 200% position in quintile 1 and a -100% in quintile 5.

He then estimates and deducts quarterly portfolio reformation transaction costs for each ratio-strategy combination. His benchmark is the equal-weighted portfolio of all S&P 500 stocks. Using a broad sample of all listed U.S. stocks and firm fundamentals to evaluate earnings forecasts and a narrower sample of S&P 500 data for strategy tests during 1981 through 2023 (with tests commencing in 1991), he finds that:

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“Predicting Stock Market Return with Stocks-TIPS Yield Delta” summarizes results of a study finding that deviations of the S&P 500 earnings yield from the real government bond yield, as measured by the 10-year Treasury Inflation-Protected Securities (TIPS) coupon yield, has statistical power to predict future stock market returns. To corroborate this finding from an investing perspective, we:

• Reuse monthly estimates of the S&P 500 operating earnings yield from the Simple Asset Class ETF Value Strategy (SACEVS).
• Obtain the monthly series of market yields on 10-year constant maturity TIPS.
• Relate monthly differences between these two series to future monthly S&P 500 Index (SP500) returns via linear lead-lag regressions and ranked sorting.

Using monthly data as specified during January 2003 (limited by the TIPS series) through November 2024, we find that: Keep Reading