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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.

Profitability as a Fourth Stock Return Forecast Factor

Does adding profitability (see “Gross Profitability as a Stock Return Predictor”) to the Fama-French three-factor model of future stock returns result in a better model? In the June 2013 draft of their paper entitled “A Four-Factor Model for the Size, Value, and Profitability Patterns in Stock Returns”, Eugene Fama and Kenneth French examine whether profitability usefully augments their three-factor model. They consider evidence from monthly double sorts into: (1) size and book-to-market capitalization ratio (B/M) quintiles (25 portfolios); and, (2) size and pre-tax profitability (PTP) quintiles (25 portfolios). They also consider monthly triple sorts by size, B/M and PTP. Using price and firm accounting data for a broad sample of U.S. common stocks during July 1963 through December 2012, they find that: Keep Reading

Worldwide Variation in the Value Premium

Is the value premium consistent across equity markets worldwide? In their May 2013 paper entitled “Value around the World”, Nilufer Caliskan and Thorsten Hensyz measure returns for stock portfolios sorted on value in 41 countries and investigate how cultural differences affect the magnitude of the value premium. Each month in each country, they sort stocks based on prior-year price-to-book value ratio into equally weighted fifths (quintiles), designating the bottom quintile as the value portfolio and the top quintile as the growth portfolio. The value premium is the difference in average monthly gross returns between the value and growth portfolios. They use survey responses from economics students (for the 2006-2010 International Test on Risk Attitudes) to derive two measures of cultural differences, patience and risk aversion. Patience is the percentage of respondents willing to wait for a higher return, and risk aversion is the average reward-to-potential loss ratio required by respondents. Using monthly stock prices and annual book values for firms in 41 country markets as available during December 1979 (various series begin in the 1980s, 1990s and 2000s) through December 2011, along with the specified survey responses, they find that: Keep Reading

Extracting Strategic Benefits from a Commodities Allocation

Can commodities still be useful for portfolio diversification, despite their recent poor aggregate return, high volatility and elevated return correlations with other asset classes? In the May 2013 version of their paper entitled “Strategic Allocation to Commodity Factor Premiums”, David Blitz and Wilma de Groot examine the performance and diversification power of the commodity market portfolio and of alternative commodity momentum, carry and low-risk (low-volatility) portfolios. They define the commodity market portfolio as the S&P GSCI (production-weighted aggregation of six energy, seven metal and 11 agricultural commodities). The commodity long-only (long-short) momentum portfolio is each month long the equally weighted 30% of commodities with the highest returns over the past 12 months (and short the 30% of commodities with the lowest returns). The commodity long-only (long-short) carry portfolio is each month long the equally weighted 30% of commodities with the highest annualized ratios of nearest to next-nearest futures contract price (and short the 30% of commodities with the lowest ratios). The commodity long-only (long-short) low-risk portfolio is each month long the equally weighted 30% of commodities with the lowest daily volatilities over the past three years (and short the 30% of commodities with the highest volatilities). They also consider a combination that equally weights the commodity momentum, carry and low-risk portfolios. For comparison to U.S. stocks, they use returns of long-only, equally weighted “big-momentum” and “big-value” (comparable to commodity carry) stock portfolios from Kenneth French, and a similarly constructed “big-low-risk” stock portfolio. For comparison with bonds, they use the total return of the JP Morgan U.S. government bond index. For all return series and allocation strategies, they ignore trading frictions. Using daily and monthly futures index levels and contract prices for the 24 commodities in the S&P GSCI as available during January 1979 through June 2012, along with contemporaneous returns for a broad sample of U.S. stocks, they find that: Keep Reading

Optimal Quality and Value Combination?

Does adding fundamental firm quality metrics to refine stock sorts based on traditional value ratios, book-to-market ratio (B/M) and earnings-to-price ratio (E/P), improve portfolio performance? In his 2013 paper entitled “The Quality Dimension of Value Investing”, Robert Novy-Marx tests combination strategies to determine which commonly used quality measures most enhance the performance of value ratios. He considers such quality metrics as Piotroski’s FSCORE, earnings accrualsgross profitability (GP) and return on invested capital (ROIC). His general test approach is to reform capitalization-weighted portfolios annually from stocks sorted at the end of each June according to value ratios and quality metrics for the previous calendar year. He uses the 1000 largest (2000 next largest) stocks by market capitalization to represent large (small) stocks. He considers both long-only (long the top 30%) and long-short (long the top 30% and short the bottom 30%) portfolios. He also considers the incremental benefit of incorporating stock price momentum based on return over the previous 11 months with a skip-month (11-1) into stock selection. He estimates trading frictions based on calculated turnover and effective bid-ask spreads. Using stock prices and associated firm fundamentals during July 1963 through December 2011, he finds that: Keep Reading

Intrinsic Value and Momentum Across (Futures) Asset Classes

Do time series carry (intrinsic value) and time series momentum (intrinsic momentum) strategies work across asset classes? What drives their returns, and how do they interact? In the January 2013 very preliminary version of their paper entitled “The Returns to Carry and Momentum Strategies: Business Cycles, Hedge Fund Capital and Limits to Arbitrage”, Jan Danilo Ahmerkamp and James Grant examine intrinsic value strategy and intrinsic momentum strategy returns for 55 worldwide futures contract series spanning equities, bonds, currencies, commodities and metals, including the effects of business cycle/economic conditions and institutional ownership. They study futures (rather than spot/cash) markets to minimize trading frictions and avoid shorting constraints. They calculate futures contract returns relative to the nearest-to-maturity futures contract (not spot/cash market) price. The momentum signal is lagged 12-month cumulative raw return. The carry (value) signal is the lagged 12-month average normalized price difference between second nearest-to-maturity and nearest contracts. They test strategies that are each month long (short) contracts with positive (negative) value or momentum signals. They also test a combination strategy that is long (short) contracts with both value and momentum signals positive (negative). For comparability of assets, they weight contract series within multi-asset portfolios by inverse volatility, estimated as the average absolute value of daily returns over the past three months. Their benchmark is a long-only portfolio of all contracts weighted by inverse volatility. Using daily settlement prices for the nearest and second nearest futures contracts of the 55 series (10 equities, 12 bonds, 17 commodities, nine currencies and seven metals) as available during 1980 through 2012, they find that: Keep Reading

A Few Notes on Quantitative Value

Wesley Gray (founder and executive managing member of Empiritrage LLC and Turnkey Analyst LLC) and Tobias Carlisle (founder and managing member of Eyquem Investment Management LLC) describe their 2013 book, Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors, as “first and foremost about value investment–treating stock as part ownership of a business valued through analysis of fundamental financial statement data. …There are several quantitative measures that lead to better performance…: enhancing the margin of safety, identifying the highest quality franchises, and finding the cheapest stocks. We canvass the research in each, test it in our own system, and then combine the best ideas in each category into a comprehensive quantitative value strategy.” Using price and fundamental data for a broad sample of U.S. stocks (over about 40 years ending with 2011) to confirm and refine key findings of value investing research streams, they argue and find that: Keep Reading

Beta, Value and Momentum for Industries

Do industries exhibit the market beta, value and momentum anomalies overall and in recent data? In his August 2012 paper entitled “The Failure of the Capital Asset Pricing Model (CAPM): An Update and Discussion”, Graham Bornholt examines the beta, value and momentum anomalies using returns for 48 U.S. industries. Each month, he forms three groups of eight equally weighted portfolios of industries ranked separately by: (1) beta based on rolling regressions of industry returns versus value-weighted market returns over the past 60 months; (2) value based on the latest available industry book-to-market ratios (value-weighted composites of component firm book-to-market ratios, updated annually); and, momentum based on lagged six-month industry returns. There are therefore six industries in each portfolio. Using monthly industry returns from Kenneth French’s website, monthly returns for the value-weighted U.S. stock market in excess of the one-month U.S. Treasury bill yield, and industry component book-to-market ratios during July 1963 through December 2009 he finds that: Keep Reading

When Stock Picking Works

When should an investor favor picking individual stocks over holding a stock index fund? In their November 2012 paper entitled “On Diversification”, Ben Jacobsen and Frans de Roon derive from Modern Portfolio Theory simple rules to compare concentrated investment in a portfolio of one or a few stocks to a broad, diversified (value-weighted) benchmark portfolio. The essential rule is that a concentrated portfolio is preferable to the benchmark portfolio if the product of its expected Sharpe ratio and the expected correlation of its returns with the benchmark’s returns exceeds the expected Sharpe ratio of the benchmark. They apply derivative thumb rules to real stocks to determine conditions under which stock picking is preferable to buying and holding a diversified benchmark portfolio. Using theoretical derivations and monthly returns and fundamentals for the 500 largest non-financial companies as of the end of the sample period with a history of at least five years during 1926 through 2011, they find that: Keep Reading

Accidental Alpha

How can equity weighting strategies and their opposites both outperform the stock market? In the October 2012 version of their paper entitled “The Surprising ‘Alpha’ from Malkiel’s Monkey and Upside-down Strategies”, Rob Arnott, Jason Hsu, Vitali Kalesnik and Phil Tindall challenge beliefs underlying a variety of stock investment strategies that beat a capitalization-weighted benchmark by examining the performance of portfolios based on opposite beliefs. If the original beliefs determine success, then their opposites should underperform. They limit their investigation to long-only stock weightings based on original beliefs and opposites based on inverse weights or complement weights. To ensure portfolio feasibility, they restrict U.S. and global universes to large-capitalization stocks. They reform portfolios at the end of each year. When needed in portfolio construction, they estimate historical parameters (such as volatility) using five years of lagged monthly data. They consider capitalization-weighted, equal-weighted and diversity-weighted benchmarks and use a conventional four-factor (market, size, book-to-market and momentum) model to calculate strategy alphas. They ignore trading frictions. Using monthly returns for the top 1,000 U.S. stocks by market capitalization during 1964 through 2010 and for large-capitalization global stocks during 1991 through 2010, they find that: Keep Reading

Common Factor Exposures of Specialized Stock Indexes

How do specialized stock indexes relate to commonly used equity risk factors? In his February 2012 paper entitled “Evaluating Alternative Beta Strategies”, Xiaowei Kang examines risk exposures (betas), construction methodologies and historical performances of alternative stock indexes such as those based on value, low-volatility and diversification strategies. He considers five risk factors: (1) market, representing excess return of the market capitalization-weighted U.S. stock market; (2) size, representing return from a portfolio that is long small-cap stocks and short large-cap stocks; (3) value, representing return from a portfolio that is long high book-to-market stocks and short low book-to-market stocks; (4) momentum, representing return from a portfolio that is long past winning stocks and short past losing stocks; and, (5) volatility, representing return from a portfolio that is long high-volatility stocks and short low-volatility stocks. Using monthly returns for several specialized indexes and the specified risk factors as available through 2011, he finds that: Keep Reading

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