Value Premium

Is there a most practical way to make value and momentum work together across stocks? In the April 2016 version of their paper entitled “Combining Value and Momentum”, Gregg Fisher,  Ronnie Shah and Sheridan Titman examine long-only stock portfolios that seek exposure to both value and momentum while suppressing trading frictions. They define value as high book-to-market ratio based on book value lagged at least four months. They define momentum as return from 12 months ago to one month ago. They consider two strategies for integrating value and momentum:

1. Each month, choose stocks with the highest simple average value and momentum percentile ranks. They suppress turnover with buy-sell ranges, either 90-70 or 95-65. For example, the 90-70 range adds stocks with ranks higher than 90 not already in the portfolio and sells stocks in the portfolio with ranks less than 70. 
2. After initially forming a value portfolio, each month buy stocks only when both value and momentum are favorable, and sell stocks only when both are unfavorable. This strategy weights value more than momentum, because momentum signals change more quickly than value signals. For this strategy, they each month calculate value and momentum scores for each stock as percentages of aggregate market capitalizations of other stocks with lower or equal value and momentum. They suppress turnover with a 90-70 or 95-65 buy-sell range, but the range applies only to the value score. There is a separate 50 threshold for momentum score, meaning that stocks bought (sold) must have momentum score above (below) 50.

They consider large-capitalization stocks (top 1000) and small-capitalization stocks (the rest) separately, with all portfolios value-weighted. They calculate turnover as the total amount bought or sold each month relative to portfolio size. They consider two levels of round-trip trading frictions based on historical bid-ask spreads and broker fees: high levels (based on 1993-1999 data) are 2.94% for small stocks and 1.06% for large stocks; low levels (based on 2000-2013 data) are 0.82% for small stocks and 0.41% large stocks. They focus on net Sharpe ratio as a performance metric. Using monthly data for a broad sample of U.S. common stocks during January 1974 through December 2013, they find that: Keep Reading

Can a typical investor exploit the high returns reported for Piotroski’s FSCORE strategy as applied to U.S. stocks? In their October 2015 paper entitled “The Piotroski F-Score: A Fundamental Value Strategy Revisited from an Investor’s Perspective”, Christopher Krauss, Tom Kruger and Daniel Beerstecher examine whether individual investors can exploit the American Association of Individual Investors’ (AAII) interpretation of this strategy (24% gross annual return over the last decade). They consider equal-weighted and value-weighted long-only (FSCORE 8 and 9) and long-short (short the S&P 500 Index) versions of the strategy, with monthly or weekly rebalancing. They first calculate gross performance and then progressively add realistic obstacles to/costs of trading. They assume average round-trip trading frictions of 0.2% for broker commissions plus 0.5% for bid-ask spreads (but no costs for shorting the S&P 500 Index). Using AAII’s FSCORE screen to generate monthly and weekly portfolios of U.S. stocks via AAII’s Stock Investor Pro platform matched to total stock returns from Datastream during January 2005 through April 2015, they find that: Keep Reading

What kinds of smart beta work best? In their January 2016 paper entitled “A Taxonomy of Beta Based on Investment Outcomes”, Sanne De Boer, Michael LaBella and Sarah Reifsteck compare and contrast smart beta (simple, transparent, rules-based) strategies via backtesting of 12 long-only smart beta stock portfolios. They assign these portfolios to a framework that translates diversification, fundamental weighting and factor investing into core equity exposure and style investing (see the figure below). They constrain backtests to long-only positions, relatively investable/liquid stocks and quarterly rebalancing, treating developed and emerging markets separately. Backtest outputs address gross performance, benchmark tracking accuracy and portfolio turnover. Using beta-related data for developed market stocks during 1979 through 2014 and emerging market stocks during 2001 through 2014, they find that: Keep Reading

Is outperformance of low-volatility stocks just a manifestation of the value premium (outperformance of stocks with high book-to-market ratios compared to stocks with low book-to-market ratios)? In his February 2016 paper entitled “The Value of Low Volatility”, David Blitz examines the interaction of the value premium with returns of long-only portfolios of low-volatility U.S. stocks over various sample periods. His low-volatility portfolios consist of the 30% of stocks with the lowest standard deviations of monthly total returns during the preceding 36 months, reformed monthly. He considers large and small stocks separately, delineated by median NYSE market capitalization, either value-weighted or equal-weighted. Using monthly data for a broad sample of U.S. stocks and the value premium during 1926 through 2014, he finds that: Keep Reading

Do simple ratios such as book-to-market value and earnings-to-market price really identify value stocks? In their January 2016 paper entitled “Facts About Fictional Value Investing”, U-Wen Kok, Jason Ribando and Richard Sloan examine the effectiveness of “value” investing as implemented via sorts on simple fundamental ratios. They investigate interactions of these ratios with firm capitalization and test whether it is the value numerator or the price denominator that drives mean reversion of extreme value ratios. Using data for a broad sample of U.S. stocks with focus on recent decades, they find that: Keep Reading

Is it possible to test factor models of stock returns directly on individual stocks rather than on portfolios of stocks sorted per preconceived notions of factor importance. In their November 2015 paper entitled “Tests of Alternative Asset Pricing Models Using Individual Security Returns and a New Multivariate F-Test”, Shafiqur Rahman, Matthew Schneider and Gary Antonacci apply a statistical method that allows testing of equity factor models directly on individual stocks. Results are therefore free from the information loss and data snooping bias associated with sorting stocks based on some factor into portfolios. They test several recently proposed multi-factor models based on five or six of market, size, value (different definitions), momentum, liquidity (based on turnover), profitability and investment factors. They compare alternative models via 100,000 Monte Carlo simulations each in terms of ability to eliminate average alpha and appraisal ratio (absolute alpha divided by residual variance) across individual stocks. Using monthly returns and stock/firm characteristics for the 407 Russell 3000 Index stocks with no missing monthly returns during January 1990 through December 2014 (300 months), they find that: Keep Reading

How do the behaviors of time-series (absolute) and cross-sectional (relative) carry, momentum and value strategies differ? In the November 2015 version of their paper entitled “Dissecting Investment Strategies in the Cross Section and Time Series”, Jamil Baz, Nicolas Granger, Campbell Harvey, Nicolas Le Roux and Sandy Rattray explore time-series and cross-sectional carry, momentum and value strategies as applied to multiple asset classes. They adapt to each asset class the following general definitions:

• Carry – buy (sell) futures on assets for which the forward price is lower (higher) than the spot price.
• Momentum – buy (sell) assets that have outperformed (underperformed) over the past 6-12 months.
• Value – buy (sell) assets for which market price is lower (higher) than estimated fundamental price.

For cross-sectional portfolios, they rank assets within each class-strategy and form portfolios that are long (short) the equally weighted six assets with the highest (lowest) expected returns, rebalanced daily except for currency carry and value trades. For time-series portfolios, they take an equal long (short) position in each asset within a class-strategy according to whether its expected return is positive (negative). When combining strategies within an asset class, they use equal weighting. When combining across asset classes, they scale each class-strategy portfolio to a 15% annualized volatility target. Using daily contract closing bid-ask midpoints for 26 equity futures, 14 interest rate swaps, 31 currency exchange rates and 16 commodity futures during January 1990 through April 2015, they find that: Keep Reading

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

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

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