Fundamental Valuation

Is U.S. equity market valuation outrunning its productive value? For perspective, we compare the trajectories of S&P 500 (SP500) index, earnings and dividends over recent decades and look at some potential explanations for divergences. Using quarterly SP500 data and 10-year U.S. Treasury note (T-note) yield during March 1988 through September 2024 and Shiller data as available through October 2024, we find that: Keep Reading

What variables best explain increases and decreases in Cyclically Adjusted Price-to-Earnings ratio (CAPE or P/E10)? In their August 2024 paper entitled “Analyzing Changing ‘Investor Exuberance’: The Determinants of S&P Composite Index Total Return CAPE Changes”, C. Krishnan, Jiemin Yang and Xiyao Tan apply the following three techniques to investigate which of 42 potentially explanatory variables relate most strongly to changes in CAPE:

1. Linear regression with principal component analysis.
2. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, which shrinks some regression coefficients to zero, thereby identifying the most important independent variables.
3. Elastic net, which combine approaches of LASSO and Ridge regression to distill the most important independent variables.

Using monthly values for CAPE and the 42  potentially explanatory variables during February 2000 through December 2019, they find that: Keep Reading

Does an elaborate firm valuation model outperform the blunt instrument of a simple ratio? In their July 2024 paper entitled “Intrinsic Value: A Solution to the Declining Performance of Value Strategies”, Derek Bergen, Francesco Franzoni, Daniel Obrycki and Rafael Resendes model the intrinsic value of a stock, defined as book value of equity plus the discounted sum of estimated future profits with a firm-specific discount rate. Their intrinsic value model includes for each firm:

• A profit forecast based on ultimate decay to zero, with the the path to zero guided by assumptions derived from the historical profit series for the firm, such as:
  • High profits may attract competitors that accelerate decay.
  • Low profits may persist.
  • Stable profits may persist due to a reliable competitive advantage.
  • Volatile profits have rapid decay potential.
  • Large firms have barriers to entry or economies of scale that support profitability.
• A forecast for reinvestment of firm operating cash flow after interest expense, dividends and replacement capital, assuming a consistent capital structure.
• A discount rate estimated by adjusting the median internal rate of return across all firms according to individual firm size and financial leverage.

They apply firm intrinsic value-to-market ratios (IVM) to forecast stock returns, comparing their accuracy to those of the conventional book-to-market ratios (BM). Using monthly inputs as specified for Russell 1000 and Russell 2000 stocks during July 1999 through December 2023, they find that:

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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 2024, we find that:

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Is stock price-to-earnings ratio (P/E), in aggregate or by individual stock, truly predictive of returns? In their April 2024 paper entitled “Valuing Stocks With Earnings”, Sebastian Hillenbrand and Odhrain McCarthy examine relationships between P/E and future returns at both stock index and individual stock levels. They compare generally accepted accounting principles (GAAP) earnings and an alternative earnings used by Wall Street analysts and therefore designated “Street” earnings. Street earnings, constructed from Institutional Brokers’ Estimate System (I/B/E/S) data, are smoother than GAAP earnings and emphasize future fundamentals by excluding transitory items. They consider as potentially predictive metrics: GAAP P/E; GAAP P/E10 (or GAAP CAPE), based on a 10-year moving average of GAAP earnings; Street P/E; and, Street P/E3 (or Street CAPE), based on a 3-year moving average of Street earnings. They test whether: (1) aggregate GAAP and Street earnings metrics predict stock market returns; and, (2) stock-level GAAP and Street earnings yields (E/P) support profitable long-short hedge portfolios. Using quarterly GAAP and Street earnings data, S&P 500 Index levels and individual stock prices during 1988 through 2021, extended back to 1965 for some aggregate earnings tests and forward through 2023 for some portfolio tests, they find that:

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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 2024, we find that:

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Do analysts systematically ignore the connection between future firm earnings and current economic conditions? In their July 2024 paper entitled “Predicting Analysts’ S&P 500 Earnings Forecast Errors and Stock Market Returns Using Macroeconomic Data and Nowcasts”, Steven Sharpe and Antonio Gil de Rubio Cruz examine the quality of bottom-up forecasts of near-term S&P 500 earnings aggregated from analyst forecasts across individual firms. Specifically, they:

• Model expected aggregate S&P 500 quarterly earnings growth as a function of GDP growth, output and wage inflation and change in dollar exchange rate. They also consider a simplified model based only on real GDP growth and change in the dollar exchange rate.
• Calculate the gap between modeled S&P 500 earnings growth and analyst-forecasted growth.
• Estimate how well this forecast gap predicts analyst forecast errors.
• Test the extent to which the forecast gap predicts S&P 500 Index total returns.

Using quarterly actual and forecasted S&P 500 earnings, S&P 500 Index total return and values for the specified economic variables during 1993 through 2023, they find that: Keep Reading

Are stock quality strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider five 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).
• 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 June 2024, 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 mid-July 2024, we find that: Keep Reading

Are the industry membership of a firm, as designated by Standard Industrial Classification (SIC) code, and the position of the firm within its industry good predictors of the performance of its stock? In their May 2024 paper entitled “Decoding Cross-Stock Predictability: Peer Strength versus Firm-Peer Disparities”, Doron Avramov, Shuyi Ge, Shaoran Li and Oliver Linton devise the following two industry related stock metrics and test their abilities to predict stock returns:

1. Peer Index (PI) – calculated for each firm via a multi-input, inception-to-date regression to predict next-month stock return, replacing firm characteristics by the contemporaneous average values for all firms in its industry as inputs.
2. Peer-Deviation Index (PDI) – calculated for each firm via a multi-input, inception-to-date regression to predict next-month stock return using firm characteristics minus the contemporaneous average values of these characteristics for all firms in its industry as inputs (indicating the standing of the firm within its industry).

Inputs consist of 94 firm-specific characteristics and 8 industry-related characteristics, organized into six groups: momentum, value versus growth, investment, profitability, trading frictions and intangibles. Using monthly values for the selected 102 firm/industry characteristics and monthly returns for common stocks in the top 80% of AMEX/NYSE/NASDAQ  market capitalizations during January 1980 through March 2022, they find that: Keep Reading