Fundamental Valuation

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

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

Can investors reliably capture illiquidity-driven stock price noise, short-term deviations in price from some measurable fair value? In their February 2024 paper entitled “Intraday Residual Reversal in the U.S. Stock Market”, Jonathan Brogaard, Jaehee Han and Hanjun Kim investigate returns to a strategy that exploits reversals of short-lived noise in stock prices by buying (selling) stocks with positive (negative) price noise. Specifically, at intervals of 30 minutes, they:

• Regress stock returns cross-sectionally versus 15 standardized/normalized stock return anomalies to predict next-interval return for each stock, with the difference between predicted and actual returns designated as noise (residual). The first daily noise measurement is at 10:00AM and the last (for overnight) at 4:00PM.
• Sort stocks into tenths (deciles) based on noise from most negative to most positive.
• Reform a hedge portfolio, either value-weighted or equal-weighted, that is long (short) the stocks in the bottom (top) noise decile.

They limit their stock universe to the S&P 500 for depth and liquidity, so the hedge portfolio has positions in about 100 stocks. They consider impacts of trading frictions ranging from 0.03% to 0.07%. Using daily returns for each of the 15 anomalies and 30-minute bid-ask midpoints for S&P 500 index stocks during July 1996 through December 2022, they find that:

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Prior to 2015, we tracked performance of an equity market timing model based on real earnings yield (REY). The Simple Asset Class ETF Value Strategy (SACEVS) subsumed that model in 2015. Earnings yield is aggregate corporate earnings divided by corresponding stock index level. The REY model adjusts this earnings yield by subtracting the inflation rate for the same period. Does the REY concept still hold value for equity market timing? Using quarterly S&P 500 operating and as-reported earnings, S&P 500 Index (SP500) level, quarterly inflation as calculated from the U.S. Consumer Price Index, dividend-adjusted SPDR S&P 500 ETF Trust (SPY) and 3-month U.S. Treasury bill (T-bill) yield as available during March 1988 through December 2023, we find that: Keep Reading

Should qualified investors count on global macro (GM) and managed futures (MF, or alternatively CTA for commodity trading advisors) hedge funds to beat the market? In their November 2023 paper entitled “Global Macro and Managed Futures Hedge Fund Strategies: Portfolio Differentiators?”, Rodney Sullivan and Matthew Wey assess the performances of GM and MF hedge fund categories, defined as:

• GM – try to anticipate how political trends and global economic activity will affect valuations of global equities, bonds, currencies and commodities.
• MF – rely systematic trading programs based on historical prices/market trends across stocks, bonds, currencies and commodities.

For comparison, they also look at the long-short equity (LSE) hedge fund category. They decompose category returns into components driven by exposures to U.S. stock and bond market return factors, other factor premiums and unexplained alpha. They focus on how fund categories have changed since the 2008 financial crisis, emphasizing performances during market downtowns. Using index returns from Hedge Fund Research (equal-weighted) and Credit Suisse (asset-weighted) during January 1994 through December 2022, they find that:

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