Value Premium

Do interest rate effects explain/predict the poor performance of value stocks over the past decade, and especially during 2017 through early 2020? In their May 2020 paper entitled “Value and Interest Rates: Are Rates to Blame for Value’s Torments?”, Thomas Maloney and Tobias Moskowitz investigate interactions between equity value factors and the interest rate environment. They first examine theoretical relationships and then explore relationships between several ways to measure the U.S. equity value premium and interest rates empirically, including interest rate level, change in short-term rates, change in long-term rates and slope of the yield curve. They look at subperiods and some international evidence. Finally, they assess ability of interest rate variables to predict the value premium and thereby inform factor timing strategies. Using U.S. interest rate and firm/stock data inputs for several ways of estimating the value premium as available since January 1954, and similar data for Japan, Germany and the UK since 1988, all through December 2019, they find that: Keep Reading

Are widely accepted equity factor exposures available in fact to investors via “smart beta” mutual funds and exchange-traded funds (ETF)? In their May 2020 paper entitled “Smart Beta Made Smart”, Andreas Johansson, Riccardo Sabbatucci and Andrea Tamoni test effectiveness of individual U.S. equity mutual funds and ETFs and combinations of these funds for exploiting several major equity risk factors (value, size, profitability and momentum). After assembling a sample of funds with names that indicate smart beta strategies, they iteratively (annually for size, value and profitability and daily for momentum):

1. Apply a double-regression to each fund to identify those that are actually “closet” market index funds.
2. Refine factor exposures of each true smart beta fund based on actual fund holdings.
3. Construct separately for institutional and retail investors tradable long-side (mutual funds and ETFs) and short-side (ETFs only) risk factors via value-weighted combinations of the 10 funds with the strongest exposures to each factor.

Using daily, monthly, and quarterly data for U.S. equity mutual funds and ETFs with (1) names indicating smart beta strategies, (2) at least one year of returns and (3)assets over $1 billion, data for their individual component U.S. stocks and specified factor returns during January 2003 through May 2019, they find that: Keep Reading

Are there equity styles that tend to perform relatively well during and after stock market crashes? In their April 2020 paper entitled “Equity Styles and the Spanish Flu”, Guido Baltussen and Pim van Vliet examine equity style returns around the Spanish Flu pandemic of 1918-1919 and five earlier deep U.S. stock market corrections (-20% to -25%) in 1907, 1903, 1893, 1884 and 1873. They construct three factors by:

1. Separating stocks into halves based on market capitalization.
2. Sorting the big half only into thirds based on dividend yield as a value proxy, 36-month past volatility or return from 12 months ago to one month ago. They focus on big stocks to avoid illiquidity concerns for the small half.
3. Forming long-only, capitalization-weighted factor portfolios that hold the third of big stocks with the highest dividends (HighDiv), lowest past volatilities (Lowvol) or highest past returns (Mom).

They also test a multi-style strategy combining Lowvol, Mom and HighDiv criteria (Lowvol+) and a size factor calculated as capitalization-weighted returns for the small group (Small). Using data for all listed U.S. stocks during the selected crashes, they find that: Keep Reading

Why do recent studies find that the value premium declines over time? In their April 2020 paper entitled “The Fundamental-to-Market Ratio and the Value Premium Decline”, Andrei Gonçalves and Gregory Leonard investigate whether book value (book equity, BE) is a good proxy for actual fundamental value (fundamental equity, FE). They measure FE for each firm at the end of June from accounting data as of the end of the prior calendar year as autoregression-estimated cash flow (payouts, including share buybacks) with a uniform discount rate across firms. They then sort stocks into tenths (deciles) based on BE-to-Market Equity ratio (BE/ME) or FE-to-Market Equity ratio (FE/ME) based on end-of-June stock prices. Finally, they annually reform capitalization-weighted portfolios that are long (short) the deciles with the highest (lowest) ratios to compare BE-based and FE-based value premiums. Using BE and FE inputs, market capitalizations and prices for all U.S.-listed common stocks except utilities and financials during July 1973 through June 2019, they find that:

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Does a decade of underperformance by some widely followed stock value strategies mean it is time to throw in the towel? In their March 2020 paper entitled “Is (Systematic) Value Investing Dead?”, Ronen Israel, Kristoffer Laursen and Scott Richardson assess the value of (near-term, say two years) fundamental information as a driver of stock returns from both theoretical and empirical perspectives. They consider five widely used measures of value: (1) book value-to-price ratio (B/P); (2) earnings-to-price ratio (E/P); (3) forward earnings-to-price ratio (FEP); (4) sales-to-enterprise value ratio (S/EV); and, (5) cash flow-to-enterprise value ratio (CF/EV). They also investigate several recent arguments against value investing. Using data for large (small) capitalization stocks spanning 21 (25) countries with sample periods starting between 1984 and 2002 and all extending through 2019, they find that: Keep Reading

Do stock anomaly (factor premium) portfolios exhibit exploitable value and momentum? In their February 2020 paper entitled “Value and Momentum in Anomalies”, Deniz Anginer, Sugata Ray, Nejat Seyhun and Luqi Xu investigate exploitability of time variation in the predictive ability of 13 published U.S. stock accounting and price-based anomalies based on: (1) anomaly momentum (1-month premiums); and/or (2) anomaly value (adjusted average book-to-market ratios). Specifically, they each month:

• For each anomaly, form a value-weighted portfolio that is long (short) the tenth, or decile, of stocks with the highest (lowest) expected returns.
• For each long-short anomaly portfolio:
  • Measure its value as last-year average book-to-market ratio minus its average of average book-to-market ratios over the previous five years.
  • Measure its momentum as last-month return.
• Form a value portfolio of anomaly portfolios that holds the equal-weighted top seven based on value, rebalanced annually.
• Form a momentum portfolio of anomaly portfolios that holds the equal-weighted top seven based on momentum, rebalanced monthly.
• Form a combined value-momentum portfolio of anomaly portfolios that holds those in the top seven of both value and momentum, equal-weighted and rebalanced monthly.

Their benchmark is the equal-weighted, monthly rebalanced portfolio of all anomaly portfolios (1/N). Using data required to construct anomaly portfolios and monthly delisting-adjusted returns for U.S. common stocks excluding financial stocks and stocks priced under $1 during January 1975 through December 2014, they find that: Keep Reading

“Verification Tests of the Smart Money Indicator” reports performance results for a specific version of the Smart Money Indicator (SMI) stocks-bonds timing strategy, which exploits differences in futures and options positions in the S&P 500 Index, U.S. Treasury bonds and 10-year U.S. Treasury notes between institutional investors (smart money) and retail investors (dumb money). Do these sentiment-based results diversify those for the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS)? To investigate, we look at correlations of annual returns between variations of SMI (no lag between signal and execution, 1-week lag and 2-week lag) and each of SACEMS equal-weighted (EW) Top 3 and SACEVS Best Value. We then look at average gross annual returns, standard deviations of annual returns and gross annual Sharpe ratios for the individual strategies and for equal-weighted, monthly rebalanced portfolios of the three strategies. Using gross annual returns for the strategies during 2008 through 2019, we find that: Keep Reading

Does the value premium for U.S. stocks, as measured by book-to-market ratio, persist after its initial discovery/publication in 1992? In their January 2020 paper entitled “The Value Premium”, Eugene Fama and Kenneth French assess whether the value premium in the U.S. declines or disappears in a post-publication sample that is as long as the discovery sample. Unlike many researchers, they focus on difference in returns between high book-to-market (value) stocks and the value-weighted market, not the return spread between value and low book-to-market (growth) stocks. To control for firm market capitalization effects, they consider separately stocks with capitalizations above (big) and below (small) the NYSE median. They specify value (growth) stocks as those at or above the 70th (below the 30th) percentile of book-to-market values of NYSE stocks. They re-sort stocks at the end of each June, with book-to-market ratio measured at the end of the fiscal year during the prior calendar year. The overall value premium is the capitalization-weighted combination of big value and small value. Using annual book-to-market ratios and market capitalizations, and monthly returns, for all NYSE, AMEX and NASDAQ stocks during July 1963 through June 2019, they find that:

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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

Do both the long and short sides of portfolios used to quantify widely accepted equity factors benefit investors? In their November 2019 paper entitled “When Equity Factors Drop Their Shorts”, David Blitz, Guido Baltussen and Pim van Vliet decompose and analyze gross performances of long and short sides of U.S. value, momentum, profitability, investment and low-volatility equity factor portfolios. The employ 2×3 portfolios, segmenting first by market capitalization into halves and then by selected factor variables into thirds. The extreme third with the higher (lower) expected return constitutes the long (short) side of a factor portfolio. When looking at just the long (short) side of factor portfolios, they hedge market beta via a short (long) position in liquid derivatives on a broad market index. Using monthly returns for the specified 2×3 portfolios during July 1963 through December 2018, they find that:

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