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.

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Valuation/Trend Hedging of a Value and Momentum Stock Portfolio

Is there a way to suppress the volatility and drawdowns of a mixed value and momentum stock strategy while retaining most of its benefit? In his September 2015 paper entitled “Learning to Play Offense and Defense: Combining Value and Momentum from the Bottom up, and the Top Down”, Mebane Faber examines the feasibility of a strategy that combines market valuation and market trend timing (defense) with a mixed value and momentum stock selection strategy (offense). Specifically:

For offense, he each month: (1) ranks stocks by each of price-to-earnings, price-to-book and earnings before interest and taxes-to-total enterprise value ratios and then re-ranks them by the average of the three separate value rankings; (2) ranks stocks by each of 3-month, 6-month and 12-month past returns and then re-ranks them by the average of the three separate momentum rankings; and, (3) forms an equally weighted portfolio of the top 100 value and top 100 momentum stocks and holds for three months (three overlapping portfolios).

For defense, he each month: (1) hedges half of the portfolio by shorting the S&P 500 Index if the long-term real earnings yield for the S&P 500 (inverse of the Cyclically Adjusted Price-Earnings ratio, CAPE or P/E10 as calculated by Robert Shiller, minus the most recently available actual 12-month U.S. inflation rate) is in the 20% of its lowest inception-to-date monthly values; and, (2) hedges half of the portfolio by shorting the S&P 500 Index if the index is below its 12-month simple moving average. 

The overall portfolio can therefore be 100% long “offense” stocks, 50% hedged or market neutral. He does not account for costs of portfolio reformations or hedging. Using monthly total returns for all NYSE stocks in the top 60% of market capitalizations, monthly levels of the S&P 500 Total Return Index and monthly values of CAPE during 1964 through 2014, he finds that: Keep Reading

Small Leveraged Value Stock Ranking System

What qualifiers can enhance the performance of a small value stock strategy? In their August 2015 paper entitled “Leveraged Small Value Equities”, Brian Chingono and Daniel Rasmussen devise and test a strategy to refine a portfolio of small capitalization value stocks of firms that with relatively high financial leverage. Specifically, their target universe at the end of each year consists of all NYSE/AMEX/NASDAQ stocks with: (1) market capitalizations between the 25th and 75th percentiles; (2) among the 25% of cheapest stocks based on EBITDA divided by enterprise value; and, (3) above median long term debt divided by enterprise value. They then rank the stocks in this universe per a group of quality and technical factors that emphasize reduction in long-term debt and improving asset turnover (revenue growth rate greater than asset growth rate). At the end of the first quarter of each following year, they reform portfolios of the top 25 and top 50 stocks in the specified universe based on this ranking. Using stock return and accounting data for a broad sample of U.S. stocks during January 1963 through December 2014, they find that: Keep Reading

Country Stock Market Dual-factor Strategies

Do dual-sorts of country stock market predictive factors add value to single-sorts? In the July 2015 version of his paper entitled “Combining Equity Country Selection Strategies” Adam Zaremba first re-examines earnings-price ratio (E/P), momentum (return from 12 months ago to one month ago), skewness (based on the last 24 monthly returns) and turnover ratio (average monthly turnover for the past 12 months) as country stock market predictive factors. He then investigates whether combined sorts on two factors outperform single-factor sorts. For each individual factor, he sorts country stock markets into fifths (quintiles) and measures the factor premium as the difference in returns between the highest and lowest quintiles. He focuses on market capitalization weighting within quintiles but considers equal and liquidity (average turnover) weighting schemes as robustness checks. For dual sorts, he computes combined ranking as the average of component factor rankings and then forms quintile portfolios. Using monthly total returns adjusted for local dividend tax rates in U.S. dollars for 78 existing and discontinued country stock indexes (primarily MSCI) during 1999 through March 2015, he finds that: Keep Reading

SACEVS Input Risk Premiums and FFR

The “Simple Asset Class ETF Value Strategy” seeks diversification across a small set of asset class exchanged-traded funds (ETF), plus a monthly tactical edge from potential undervaluation of three risk premiums:

  1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
  2. Credit – monthly difference between the Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
  3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

Premium valuations are relative to historical averages. How might this strategy react to increases in the Federal Funds Rate (FFR)? Using end-of-month values of the three risk premiums, FFR, total 12-month U.S. inflation and core 12-month U.S. inflation during January 1990 (limited by availability of specific FFR targets) through June 2015 (306 months), we find that: Keep Reading

Equity Factor Investing Update

Has (hypothetical) equity factor investing worked as well in recent years as indicated in past studies? In his July 2015 paper entitled “Factor Investing Revisited”, David Blitz updates his prior study quantifying the performance of allocations to U.S. stocks based on three factor premiums: (1) value (high book-to-market ratio); (2) momentum (high return from 12 months ago to one month ago); and, (3) low-volatility (low standard deviation of total returns over the last 36 months). He considers two additional factor allocations: (4) operating profitability (high return on equity); and, (5) investment (low asset growth). He specifies each factor portfolio as the 30% of U.S. stocks with market capitalizations above the NYSE median that have the highest expected returns, reformed monthly for momentum and low-volatility and annually for the other factors. He considers both equal-weighted and value-weighted portfolios for each factor. He also summarizes recent research on the role of small-capitalization stocks, factor timing, long-only versus long-short portfolios, applicability to international stocks and applicability to other asset classes. Using value, momentum, profitability and investment factor portfolio returns from Kenneth French’s library and low-volatility portfolio returns as constructed from a broad sample of U.S. stocks during July 1963 through December 2014, he finds that: Keep Reading

Country Stock Market Factor Strategies

Do factors that predict returns in U.S. stock data also work on global stock markets at the country level? In the May 2015 version of their paper entitled “Do Quantitative Country Selection Strategies Really Work?”, Adam Zaremba and Przemysław Konieczka test 16 country stock market selection strategies based on relative market value, size, momentum, quality and volatility. For each of 16 factors across these categories, they sort country stock markets into fifths (quintiles) and measure the factor premium as return on the highest minus lowest quintiles. They consider equal, capitalization and liquidity (average turnover) weighting schemes within quintiles. They look at complementary large and small market subsamples, and complementary open (easy to invest) and closed market subsamples. Using monthly total returns adjusted for local dividend tax rates in U.S. dollars for 78 existing and discontinued country stock indexes (primarily MSCI) during 1999 through 2014, they find that: Keep Reading

Enhanced Value Strategies for U.S. Stocks

What is the best way to implement a value strategy for U.S. stocks? In their May 2015 paper entitled “Optimizing Value”, Ran Leshem, Lisa Goldberg and Alan Cummings investigate how the choice of value metric and implementation approach affect value strategy performance. They first compare book value-to-price ratio (B/P) and earnings-to-price ratio (E/P) based on returns for portfolios of the top 30% of stocks based on each metric, reformed frictionlessly each month since 1951. They then compare practical implementations that reform portfolios of S&P 500 stocks quarterly since 1973 (with round-trip trading friction 0.12%) by: (1) selecting the top 30% stocks based on the value metrics; or, (2) tilting S&P 500 Index weights based on the metrics. Finally, they add constraints to avoid value portfolio sector concentrations. Using Ken French’s value factor data since 1951 and data for S&P 500 stocks since 1973, both through December 2013, they find that: Keep Reading

Summarizing Value (and Momentum) Investing

When does value investing work and how does it work best? In the April 2015 initial draft of their paper entitled “Fact, Fiction, and Value Investing”, Clifford Asness, Andrea Frazzini, Ronen Israel and Tobias Moskowitz address areas of confusion about value investing. They describe value as the tendency of cheap securities to outperform expensive ones based on some valuation method. They broadly specify the value premium as the return achieved by holding or overweighting cheap securities and shorting or underweighting expensive ones. They focus on systematic (mechanical), diversified value strategies based on quantified metrics such as book-to-market ratio or earnings-price ratio. Their context is firm belief that such strategies are great investments. Based on academic studies and simple tests with recent data, largely from Kenneth French’s data library, they conclude that: Keep Reading

Carry and Trend Implications for Future Returns Across Asset Classes

Are positive carry and positive trend conditions consistently favorable across asset classes? In their March 2015 paper entitled “Carry and Trend in Lots of Places”, Vineer Bhansali, Josh Davis, Matt Dorsten and Graham Rennison employ futures prices to investigate whether the adages “don’t pay too much to hold an investment” and “don’t fight the trend” actually work across four major asset classes: equities, bonds, commodities and currencies. For testing, they select five liquid markets with relatively long futures histories within each asset class. They define carry as annualized excess return assuming that spot prices do not change. They define trend as positive (negative) if the futures price today is above (below) its one-year trailing moving average. They specify four states for each market:

  1. Positive carry and positive trend (Carry + / Trend +).
  2. Positive carry and negative trend (Carry + / Trend -).
  3. Negative carry and positive trend (Carry – / Trend +).
  4. Negative carry and negative trend (Carry – / Trend -).

They then calculate average subsequent daily excess returns for each market by state and annualize results. Using daily futures data as available and some simulated futures data (from spot prices) for 20 major markets across four asset classes during 1960 through 2014, they find that: Keep Reading

Measuring the Value Premium with Style ETFs

Do popular style-based exchange-traded funds (ETF) confirm the existence of a reliably exploitable value premium? To investigate, we compare the difference in returns (value minus growth) 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)

Using monthly dividend-adjusted closing prices for these ETFs during August 2001 (the earliest month available for IWP-IWS) through January 2015 (162 months), we find that: Keep Reading

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Current Momentum Winners

ETF Momentum Signal
for December 2015 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
12.0% 12.3%
Top 3 ETFs SPY
12.6% 7.4%
Strategy Overview
Current Value Allocations

ETF Value Signal
for November 2015 (Final)





The asset with the highest allocation is the holding of the Best Value strategy.
Gross Compound Annual Growth Rates
(Since September 2002)
Best Value Weighted 60-40
12.8% 9.9% 8.0%
Strategy Overview
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