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Value Premium

Is there a reliable benefit from conventional value investing (based on the book-to-market value ratio)? these blog entries relate to the value premium.

Value Investing Not Dead?

Based on the conventional definition of the value premium, value underperforms growth over last 13.5 years with maximum drawdown of a long value-short growth portfolio -55%. Is value investing dead? In the August 2020 update of their paper entitled “Reports of Value’s Death May Be Greatly Exaggerated”, Robert Arnott, Campbell Harvey, Vitali Kalesnik and Juhani Linnainmaa examine arguments that value investing is dead. They first employ bootstrapping to estimate the likelihood of the recent deep value premium drawdown by resampling 6-month value factor returns 1,000,000 times using the historical sample up to December 2006. They then examine the historical context of recent behaviors of each of three components of the value premium:

  1. Migration rates of value (growth) stocks toward growth (value) due to mean reversion of the underpinning valuation ratio.
  2. Relative profitability of value stocks versus growth stocks.
  3. Relative valuation (average price-to-book value ratio) of value stocks versus growth stocks.

They assess statistically whether these recent behaviors signal temporary deviations or permanent changes in components of the value premium. Using value premium data for July 1963 through June 2020, they find that:

Keep Reading

Size as Catalyst for Value and Momentum

The conventional size (market capitalization) premium is notoriously weak since discovery almost 40 years ago. Does this poor live track record mean it is useless to investors? In their September 2020 paper entitled “Settling the Size Matter”, David Blitz and Matthias Hanauer examine whether the size premium is exploitable as a standalone anomaly or in combination with other anomalies. They consider six versions of a size factor from prior research, as follows:

  1. Adjusted for value – average of three small-cap stock portfolios minus average of three big-cap stock portfolios after sorting for book-to-market ratio.
  2. Adjusted for value, investment and profitability – average of nine small-cap stock portfolios minus average of nine big-cap stock portfolios after separately sorting on the other three factors.
  3. Adjusted for profitability – average of three small-cap stock portfolios minus average of three big-cap stock portfolios after sorting for profitability.
  4. Adjusted for quality – average of three small-cap stock portfolios minus average of three big-cap stock portfolios after sorting for quality.
  5. Adjusted for quality beta – average of three small-cap stock portfolios minus average of three big-cap stock portfolios after sorting for quality beta.
  6. Adjusted for size, investment and return on equity – average of nine small-cap stock portfolios minus average of nine big-cap stock portfolios after separately sorting on the other three factors.

All factor portfolio segments are capitalization-weighted, and all returns are in U.S. dollars. They consider regressions (implying long-short implementations) and long-only sides of these factors. They also consider size factor definitions that do not overweight size inputs, as do those above. Using data required by these definitions for U.S. stocks since July 1963 (or January 1967 for some inputs) and for international stocks since July 1990 (or July 1993 for some inputs), all through December 2019, they find that: Keep Reading

Are Equity Multifactor ETFs Working?

Are equity multifactor strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider seven ETFs, all currently available:

  • iShares Edge MSCI Multifactor USA (LRGF) – holds large and mid-cap U.S. stocks with focus on quality, value, size and momentum, while maintaining a level of risk similar to that of the market.
  • iShares Edge MSCI Multifactor International (INTF) – holds global developed market ex U.S. large and mid-cap stocks based on quality, value, size and momentum, while maintaining a level of risk similar to that of the market.
  • Goldman Sachs ActiveBeta U.S. Large Cap Equity (GSLC) – holds large U.S. stocks based on good value, strong momentum, high quality and low volatility.
  • John Hancock Multifactor Large Cap (JHML) – holds large U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns.
  • John Hancock Multifactor Mid Cap (JHMM) – holds mid-cap U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns.
  • JPMorgan Diversified Return U.S. Equity (JPUS) – holds U.S. stocks based on value, quality and momentum via a risk-weighting process that lowers exposure to historically volatile sectors and stocks.
  • Xtrackers Russell 1000 Comprehensive Factor (DEUS) – seeks to track, before fees and expenses, the Russell 1000 Comprehensive Factor Index, which seeks exposure to quality, value, momentum, low volatility and size factors.

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We use four benchmarks according to fund descriptions: SPDR S&P 500 (SPY), iShares MSCI ACWI ex US (ACWX), SPDR S&P MidCap 400 (MDY) and iShares Russell 1000 (IWB). Using monthly returns for the seven equity multifactor ETFs and benchmarks as available through August 2020, we find that: Keep Reading

Interest Rates and the Equity 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

Investor Access to Factor Premiums via Funds

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

Best Stock Portfolio Styles During and After Crashes

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

Divergence of Book Value from Actual Value?

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:

Keep Reading

Value Investing Still on Death Row?

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

Exploiting Stock Anomaly Value and Momentum

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

Measuring the Value Premium with Value and Growth ETFs

Do popular style-based exchange-traded funds (ETF) offer a reliable way to exploit the value premium? To investigate, we compare differences in returns (value-minus-growth, or V – G) for each of the following three matched pairs of value-growth ETFs:

  • iShares Russell 2000 (Smallcap) Growth Index (IWO)
  • iShares Russell 2000 (Smallcap) Value Index (IWN)
  • iShares Russell Midcap Growth Index (IWP)
  • iShares Russell Midcap Value Index (IWS)
  • iShares Russell 1000 (Largecap) Growth Index (IWF)
  • iShares Russell 1000 (Largecap) Value Index (IWD)

To aggregate, we define monthly value return as the equally weighted average monthly return of IWN, IWS and IWD and monthly growth return as the equally weighted average monthly return of IWO, IWP and IWF. Using monthly dividend-adjusted closing prices for these ETFs during August 2001 (limited by IWP and IWS) through February 2020, we find that: Keep Reading

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