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Fundamental Valuation

What fundamental measures of business success best indicate the value of individual stocks and the aggregate stock market? How can investors apply these measures to estimate valuations and identify misvaluations? These blog entries address valuation based on accounting fundamentals, including the conventional value premium.

Timing GBTC Based on Its Inferred Premium

“Evolution of Bitcoin as an Investment” suggests a shift toward acceptance of Bitcoin (BTC) as an investment asset, as do recent actions by some large investors. Grayscale Bitcoin Trust (GBTC) offers a way for investors to access BTC via a fund that manages BTC holdings. GBTC price generally carries a premium over its BTC holdings in consideration for this convenience (17% as of the end of 2020). Does variation in this premium indicate good times to buy and sell GBTC? To investigate, we use the ratio GBTC/BTC (with BTC divided by 1,000 because the prices greatly differ in scale) as an easy way to infer the premium. We then look at ways to exploit variation in the ratio to buy and sell GBTC. Because of the rapid evolution of Bitcoin, we limit analysis to recent data. Using daily closing prices of GBTC and BTC during 2019 through 2020, we find that: Keep Reading

CAPE (P/E10) Version of Fed Model?

How does the Cyclically Adjusted Price-to-Earnings ratio (CAPE, or P/E10) behave during the COVID-19 pandemic? What are its current implications? In the November 2020 revision of their paper entitled “CAPE and the COVID-19 Pandemic Effect”, Robert Shiller, Laurence Black and Farouk Jivraj examine behavior of CAPE during 2020 in the U.S., UK, Europe, Japan and China, highlighting the impact of the pandemic. They apply CAPE to generate current 2-year, 5-year and 10-year equity return forecasts based on full-sample regressions. They then extend the CAPE forecasting approach to forecast changes in excess real return of stocks over bonds (see the chart below) to explore why investors strongly prefer equities over bonds during the pandemic. Finally, they look at sector dynamics within each economy. Using Shiller data during January 1871 through September 2020, they find that: Keep Reading

S&P 500 Index Additions Underperform?

Do stocks added to major indexes, such as the S&P 500 Index, exhibit exceptionally strong subsequent returns? In their July 2020 paper entitled “Does Joining the S&P 500 Index Hurt Firms?”, Benjamin Bennett, René Stulz and Zexi Wang investigate effects on firms/stocks of joining the S&P 500 Index and whether these effects change over time. They estimate abnormal stock performance using both market-adjusted returns and alphas from 3-factor (market, size, book-to-market), 4-factor (adding momentum) or 5-factor (adding profitability and investment instead of momentum) models of stock returns. Using monthly and daily fundamentals and price data for 659  firms/stocks added to the S&P 500 Index during 1997 through 2017, they find that:

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FactSet S&P 500 Earnings Growth Estimate Evolutions

A subscriber, citing the weekly record of S&P 500 earnings growth estimates in the “FactSet Earnings Insight” historical series, wondered whether estimate trends/revisions are exploitable. To investigate, we collect S&P 500 quarterly year-over-year earnings growth estimates as recorded in this series. These data are bottom-up (firm by firm) aggregates, whether purely from analyst estimates (before any actual earnings releases), or a blend of actual earnings and estimates (during the relevant earnings season). Using these data and contemporaneous weekly levels of the S&P 500 Index during April 2011 through June 2020, we find that: Keep Reading

COVID-19 Impacts on Stock Valuation

What are the roles of changes in earnings forecasts and the discount rate on stock valuation during the COVID-19 stock market crash? In the May 2020 update of their paper entitled “Earnings Expectations in the COVID Crisis”, Augustin Landier and David Thesmar investigate firm-level analyst earnings forecast revisions and discount rate changes as jointly reflected in stock market behavior during COVID-19 discovery and spread. They further decompose the effect of discount rate changes into impacts of: (1) change in interest rates, (2) change in equity risk premium and (3) the leverage effect (declining stock prices driving an increase in expected equity return). Using analyst earnings forecasts and prices for the top 1000 U.S. stocks by market capitalization as of year-end 2019, and contemporaneous interest rates, during January 2020 through mid-May 2020, they find that:

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Impact of COVID-19 on Markets and Economies

Economic data arrive too slowly to help investors navigate crises such as the 2019 coronavirus (COVID-19) outbreak. Are there data that support quick reactions? In their March 2020 paper entitled “Coronavirus: Impact on Stock Prices and Growth Expectations”, Niels Gormsen and Ralph Koijen employ equity index dividend futures by maturity to understand the evolution of investor reactions to COVID-19 outbreak and subsequent policy actions. They argue that a stock market decline means that expected future dividends fall and/or the discount rate for future dividends rises, differently by maturity. These changes in expectations affect stock market valuation. Using daily dividend futures closing mid-quotes in the U.S. and settlement prices in the EU during January 2006 through March 25, 2020, they find that:

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Evolving Equity Index Earnings-returns Relationship

Why does the coincident relationship between U.S. aggregate corporate earnings growth and stock market return change from negative in older research to positive in recent research? In their January 2020 paper entitled “Assessing the Structural Change in the Aggregate Earnings-Returns Relation”, Asher Curtis, Chang‐Jin Kim and Hyung Il Oh examine when the change in the aggregate earnings growth-market returns relationship occurs. They then examine factors explaining the change based on asset pricing theory (expected cash flow and expected discount rate). They calculate aggregate earnings growth as the value-weighted average of year-over-year change in firm quarterly earnings scaled by beginning-of-quarter stock price. They consider only U.S. firms with accounting years ending in March, June, September or December, and they exclude firms with stock prices less than $1 and firms in the top and bottom 0.5% of quarterly earnings growth. They calculate corresponding quarterly stock market returns from one month prior to two months after fiscal quarter ends to capture earnings announcement effects. Using quarterly earnings and returns data as specified for a broad sample of U.S. public firms from the first quarter of 1970 through the fourth quarter of 2016, they find that:

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Underreaction to Changes in Firm Fundamentals

Do investors systematically and exploitably underreact to deviations in firm fundamentals from recent averages? In their January 2020 paper entitled “Anchoring on Past Fundamentals”, Doron Avramov, Guy Kaplanski and Avanidhar Subrahmanyam investigate how deviations of quarterly firm accounting variables from averages over recent quarters relate to future returns across stocks. They first construct a stock performance deviation index (PDI) based on seven variables: (1) cash and short-term investments, (2) retained earnings, (3) operating income, (4) sales, (5) capital expenditures, (6) invested capital and (7) inventories. They then each month for each stock starting June 1977:

  • Calculate the deviation for each variable as the difference between its most recent quarterly value and its average over the preceding three quarters, scaled by total assets.
  • Rank each deviation (in percentiles) relative to deviations for the same variable for all stocks.
  • Calculate PDI for a stock as the equally weighted average of percentile rankings across all seven variables.

They extend this approach to a more comprehensive fundamental-based deviation index (FDI) that considers deviations of all Compustat accounting variables plus 14 commonly used accounting ratios, with weights of deviation percentile rankings optimized via least absolute shrinkage and selection operator (LASSO) regression starting January 1979. For all variables, if the exact release date is unavailable, they assume a 60-day delay in release. For portfolio tests, they calculate returns to hedge portfolios that are long (short) stocks in the top (bottom) tenth, or decile, of PDIs or FDIs, with holding intervals ranging from one to 24 months. Using monthly data needed to construct PDI, FDI and 30 style, technical, fundamental and liquidity control variables across a broad sample of reasonably liquid U.S. common stocks with positive book values and prices over $5 during January 1976 through October 2017, they find that: Keep Reading

A Better Stock Value Ratio?

Is there a better stock value ratio than commonly used ones such as book-to-market, dividend-to-price, earnings-to-price and cash flow-to-price ratios? In the January 2020 revision of his paper entitled “A New Value Strategy”, Baolian Wang investigates the effectiveness of cash-based operating profitability-to-price (COP/P) as a value ratio. He computes COP as operating profitability minus accruals, with operating profitability defined as revenue minus cost of goods sold and reported selling, general and administrative expenses (not including expenditures on research and development). He each year at the end of June sorts stocks into tenths, or deciles, based on COP/P and then calculates next-month excess returns for a value-weighted or equal-weighted hedge portfolio that is long (short) the decile with the highest (lowest) values of COP/P. Using monthly returns and annual, 6-month lagged and groomed accounting data for non-financial U.S. common stocks during 1963 through 2018 period, he finds that: Keep Reading

Seasonal, Technical and Fundamental S&P 500 Index Timing Tests

Are there any seasonal, technical or fundamental strategies that reliably time the U.S. stock market as proxied by the S&P 500 Total Return Index? In the February 2018 version of his paper entitled “Investing In The S&P 500 Index: Can Anything Beat the Buy-And-Hold Strategy?”, Hubert Dichtl compares excess returns (relative to the U.S. Treasury bill [T-bill] yield) and Sharpe ratios for investment strategies that time the S&P 500 Index monthly based on each of:

  • 4,096 seasonality strategies.
  • 24 technical strategies (10 slow-fast moving average crossover rules; 8 intrinsic [time series or absolute] momentum rules; and, 6 on-balance volume rules).
  • 18 fundamental variable strategies based on a rolling 180-month regression, with 1950-1965 used to generate initial predictions.

In all cases, when not in stocks, the strategies hold T-bills as a proxy for cash. His main out-of-sample test period is 1966-2014, with emphasis on a “crisis” subsample of 2000-2014. He includes extended tests on seasonality and some technical strategies using 1931-2014. He assumes constant stock index-cash switching frictions of 0.25%. He addresses data snooping bias from testing multiple strategies on the same sample by applying Hansen’s test for superior predictive ability. Using monthly S&P 500 Index levels/total returns and U.S. Treasury bill yields since 1931 and values of fundamental variables since January 1950, all through December 2014, he finds that:

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