Fundamental Valuation

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

Do deviations of the aggregate stock market earnings yield from the real government bond yield, as measured by the 10-year Treasury Inflation-Protected Securities (TIPS) coupon yield, predict future stock market returns? In the December 2024 draft of their paper entitled “An Investigation into the Causes of Stock Market Return Deviations from Real Earnings Yields”, flagged by a subscriber, Austin Murphy, Zeina Alsalman and Ioannis Souropanis examine the ability of the difference between current S&P 500 earnings yield and 10-year TIPS real (coupon) yield to predict S&P 500 Index excess returns (relative to 1-year U.S. Treasury notes) at horizons of 1, 5 and 10 years. They also investigate whether any of 25 variables, including inflation rate, drive stock market earnings yield and TIPS real yield apart. Using monthly data for the specified variables during January 1997 (limited by TIPS data) through December 2022, they find that: Keep Reading

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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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 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 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

Are large language models such as GPT-4 as effective as professional human analysts in interpreting numerical financial statements? In their May 2024 paper entitled “Financial Statement Analysis with Large Language Models”, Alex Kim, Maximilian Muhn and Valeri Nikolaev investigate whether GPT-4 can analyze standardized, anonymized financial statements to forecast direction and magnitude (large, moderate or small) of changes in future firm earnings and provide the level of confidence in its answer. They withhold management discussions that accompany financial statements, choosing to evaluate the ability of GPT-4 to analyze only numerical data. They anonymize statements by omitting firm names and replacing years with labels (t, t − 1, …) so that GPT-4 cannot use its training data to find actual future earnings. They consider both a simple query and a series of prompts designed to make GPT-4 think like an ideal human analyst by focusing on changes in certain financial statement items, computing financial ratios and generating economic interpretations of these ratios. They compare GPT-4 forecasts to: (1) consensus (median) human earnings forecasts issued during the month after financial statement release; and, (2) forecasts from other benchmarks, including that of a highly focused state-of-the-art artificial neural net (ANN) model. To test economic value of forecasts, they each year on June 30 form portfolios using GPT-4 forecasts based on annual financial statements from the preceding calendar year end, as follows: 

• Sort stocks based on GPT earnings forecasts.
• Select stocks expected to have moderate/large increases or decreases in earnings and separately resort these two groups based on forecast confidence.
• Form an equal-weighted or value-weighted long (short) portfolio of the tenth, or decile, of these stocks with highest confidence in earnings increases (decreases).

Using financial statements for 15,401 firms during  1968 through 2023 (with 2022 and 2023 out-of-sample with respect to the GPT-4 training period), annual returns of associated stocks and consensus human analyst earnings forecasts for 3,152 firms during 1983 through 2021, they find that: Keep Reading