Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for September 2020 (Final)
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Momentum Investing Strategy (Strategy Overview)

Allocations for September 2020 (Final)
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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.

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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Online, Real-time Test of AI Stock Picking

Will equity funds “managed” by artificial intelligence (AI) outperform human investors? To investigate, we consider the performance of AI Powered Equity ETF (AIEQ), which “seeks long-term capital appreciation within risk constraints commensurate with broad market US equity indices.” Per the offeror, the EquBot model supporting AIEQ: “…leverages IBM’s Watson AI to conduct an objective, fundamental analysis of U.S.-listed common stocks and real estate investment trusts…based on up to ten years of historical data and apply that analysis to recent economic and news data. Each day, the EquBot Model…identifies approximately 30 to 125 companies with the greatest potential over the next twelve months for appreciation and their corresponding weights… The EquBot model limits the weight of any individual company to 10%. At times, a significant portion of the Fund’s assets may consist of cash and cash equivalents.” We use SPDR S&P 500 (SPY) as a simple benchmark for AIEQ performance. Using daily and monthly dividend-adjusted closes of AIEQ and SPY from AIEQ inception (October 18, 2017) through June 2020, we find that: Keep Reading

Are Stock Quality ETFs Working?

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.
  • 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.
  • 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.
  • 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.
  • 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.

Because some available sample periods are very short, we use daily return statistics, including compound annual growth rate (CAGR) and maximum drawdown (MaxDD). We use four benchmarks according to fund descriptions: SPDR S&P 500 (SPY), iShares MSCI ACWI ex U.S. ETF (ACWX), Vanguard Russell 1000 Index Fund ETF (VONE) and iShares Russell 3000 ETF (IWV). Using daily returns for the stock quality ETFs and benchmarks as available through June 2020, we find that:

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Stock Market Valuation Ratio Trend Evolution

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, we find that: Keep Reading

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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Do High-dividend Stock ETFs Beat the Market?

A subscriber asked about current evidence that high-dividend stocks outperform the market. To investigate, from a practical perspective, we compare performances of five high-dividend stock exchange-traded funds (ETFs) with relatively long histories to that of SPDR S&P 500 (SPY) as a proxy for the U.S. stock market. The five high-dividend stock ETFs are:

  • iShares Select Dividend (DVY), with inception November 2003.
  • PowerShares Dividend Achievers ETF (PFM), with inception September 2005.
  • SPDR S&P Dividend ETF (SDY), with inception November 2005.
  • WisdomTree Dividend ex-Financials ETF (DTN), with inception June 2006.
  • Vanguard High Dividend Yield ETF (VYM), with inception November 2006.

For each of these ETFs, we compare average monthly total (dividend-reinvested) return, standard deviation of total monthly returns, monthly reward-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. We also look at alphas and betas for the five ETFs based on simple regressions of monthly returns versus SPY returns. Using monthly total returns for the five high-dividend stock ETFs and SPY over available sample periods through February 2020, we 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

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