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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.

Equity Investing Based on Liquidity

Does the variation of individual stock returns with liquidity support an investment style? In the January 2014 update of their paper entitled “Liquidity as an Investment Style”, Roger Ibbotson and Daniel Kim examine the viability and distinctiveness of a liquidity investment style and investigate the portfolio-level performance of liquidity in combination with size, value and momentum styles. They define liquidity as annual turnover, number of shares traded divided by number of shares outstanding. They hypothesize that stocks with relatively low (high) turnover tend to be near the bottom (top) of their ranges of expectation. Their liquidity style thus overweights (underweights) stocks with low (high) annual turnover. They define size, value and momentum based on market capitalization, earnings-to-price ratio (E/P) and past 12-month return, respectively. They reform test portfolios via annual sorts into four ranks (quartiles), with initial equal weights and one-year holding intervals. Using monthly data for the 3,500 U.S. stocks with the largest market capitalizations (re-selected each year) over the period 1971 through 2013, they find that: Keep Reading

Stock Markets Have Value and Size, Too?

Do country stock markets exhibit useful aggregate value and size metrics? In his December 2013 paper entitled “Macro Model for Macro Funds”, Adam Zaremba investigates whether macro size and value factors for country stock markets predict country stock index returns. Specifically, he calculates size and value factors at the country level in each of 66 countries. The size factor is the market capitalization of all listed firms in a country index. The value factor is the book-to-market value ratio (B/M) of all firms in a country index aggregated according to the index weighting methodology. He uses both MSCI country indexes and extant local country indexes to measure country market returns. He tests relationships between country-level size and value factors and future returns by each month separately constructing portfolios of the equally weighted top 30%, middle 40% and bottom 30% of country markets based on aggregate size and value factors. He also measures the performance of fully collateralized portfolios that are each month long (short) the equally weighted top (bottom) 30% of country markets based on aggregate size and value factors separately. To test sensitivity to the currency used, he performs all calculations separately in U.S. dollars, euros and yen. Using monthly accounting and return data as specified during June 2000 through November 2013, he finds that: Keep Reading

Value and Momentum Behaviors in Developed Markets

How do value and momentum interact with each other and with size, economic and liquidity factors worldwide? In the November 2013 version of their paper entitled “Size, Value, and Momentum in Developed Country Equity Returns: Macroeconomic and Liquidity Exposures”, Nusret Cakici and Sinan Tan address this question for developed markets. They use long-short, factor-sorted portfolios to measure size, value and momentum premiums. They consider future Gross Domestic Product (GDP) growth and future consumption growth as economic factors. They consider both funding liquidity (a potential indicator of investor margin cost, focusing on the difference between interbank lending rate and short-term deposit yield) and stock market liquidity (the estimated cost of trading stocks). Using monthly stock returns, firm accounting data and economic data for 23 developed countries during January 1990 through March 2012, they find that: Keep Reading

Reward for the Risk of Value Worldwide?

Do book value-to-price ratio (B/P) and earnings-to-price ratio (E/P) indicate reward-for-risk opportunities at the country level worldwide? In their September 2013 paper entitled “Risky Value”, Atif Ellahie, Michael Katz and Scott Richardson investigate relationships among these valuation ratios, earnings growth and future returns at the country level for 30 countries over the past two decades. They construct monthly country-level valuation and earnings growth outlooks from capitalization-weighted firm fundamentals and earnings forecasts. They then relate these measures to country capitalization-weighted stock market future excess returns (relative to local risk-free rates), with the return measurement interval commencing four month after fundamentals are available. They replace negative country-level E/P values with zero. Using monthly firm-level fundamentals and stock data, as well as macroeconomic forecasts, for 30 countries during March 1993 through June 2011 (6,600 country-month observations), they find that: Keep Reading

Mutual Funds Successfully Exploiting Academic Research?

Can equity funds exploit widely accepted stock return anomalies? In their July 2013 paper entitled “Academic Knowledge Dissemination in the Mutual Fund Industry: Can Mutual Funds Successfully Adopt Factor Investing Strategies?”, Eduard Van Gelderen and Joop Huij investigate whether mutual funds that materially adopt investment strategies based on published asset pricing anomalies consistently outperform the stock market. They first use monthly regressions to measure degrees of use of six factor investing strategies (low-beta, small cap, value, momentum, short-term reversal and long-term reversion) across U.S. equity mutual funds. They then calculate market-adjusted returns to determine whether funds employing the strategies outperform those that do not and the market. Using monthly returns for 6,814 U.S. equity mutual funds, and contemporaneous monthly returns for the specified factors, during 1990 through 2010, they find that: Keep Reading

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

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