Value Premium

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

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

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

Are valuation metrics for Real Estate Investment Trusts (REIT) useful indicators of future returns? In his June 2012 paper entitled “Modern Portfolio Theory as Applied to REITs”, Jeffrey Kerrigan evaluates the value premium among REITs. At the end of each month, he reforms equally weighted portfolios of the fifths (quintiles) of REIT stocks with the highest and lowest book-to-market price ratios and calculates the returns of these portfolios over the next month. He estimates the REIT value premium based on the difference in performance between these two portfolios. Using monthly returns for 93 REITs as available during December 1995 through March 2012, he finds that: Keep Reading

What works for value stock investors? In his April 2012 paper entitled “Value Investing: Investing for Grown Ups?”, Aswath Damodaran explores success factors for three distinct types of value investing: (1) mechanical screening for stocks with value characteristics such as low earnings multiple, high book-to-market ratio and high return on investment; (2) taking contrarian positions in fallen, unpopular stocks; and, (3) buying large positions in poorly managed, low-valued companies and actively driving turnarounds. Using quantitative examples for U.S. stocks for all three types of value investing, he concludes that: Keep Reading

Do market capitalization (size) and book-to-market ratio systematically affect intermediate-term momentum and long-term reversal for individual stocks? In their February 2012 paper entitled “Momentum and Reversal: Does What Goes Up Always Come Down?”, Jennifer Conrad and Deniz Yavuz examine whether size and book-to-market ratio interact with momentum portfolio performance over intervals of 0-6, 6-12, 12-24 and 24-36 months after formation. They designate a stock as a winner (loser) if its 6-month lagged return is higher (lower) than the average for all stocks, with a skip-month before portfolio formation. They weight stocks within momentum portfolios by the absolute difference between its lagged 6- month return and that of all stocks, normalizing so that winner and loser sides contribute equally. They define three hedge portfolio types to measure risk factor-momentum interaction:

1. MAX portfolios are long (short) past winners that are small and/or high book-to-market (losers that are large and/or low book-to-market).
2. MIN portfolios are long (short) past winners that are large and/or low book-to-market (losers that are small and/or high book-to-market).
3. ZERO portfolios are long (short) past winners (losers) with similar size and book-to-market characteristics.

They sort stocks by size and book-to-market into thirds. When combining factors, they define stocks as high (low) risk group if they are in the high-risk (low-risk) third for one factor and in or above (below) the middle-risk third for the other. Using returns and factor characteristics for a broad sample of U.S. stocks during 1965 through December 2010, they find that: Keep Reading

Do widely acknowledged U.S. equity market anomalies exist in other stock markets? If so, why? In his November 2011 paper entitled “Equity Anomalies Around the World”, Steve Fan investigates whether a number of equity market anomalies found among U.S. stocks (asset growth, book-to-market ratio, investment-to-assets ratio, six-month momentum with skip-month, net stock issuance, size and total accruals) also occur in other equity markets and the degree to which such anomalies relate to stock-unique (idiosyncratic) risk. He measures raw anomaly strength based on gross returns from hedge (“zero-cost”) portfolios that are long and short equally weighted extreme quintiles of stocks ranked annually for each accounting variable and every six months for momentum (with overlapping momentum portfolios). To estimate alphas, he adjusts raw returns for the three Fama-French risk factors (market, book-to-market, size) or three alternative investment-based risk factors (market, investment, return on assets). Using monthly common stock return data and associated firm characteristics/accounting data for 43 country stock markets during 1989 through 2009, he finds that: Keep Reading

Is the value premium seasonal? In their 2012 paper entitled “Is the Value Effect Seasonal? Evidence from Global Equity Markets”, Praveen Kumar Das and Uma Rao investigate the intersection of the January effect and the value premium in stock market indexes around the world. They consider market capitalization-weighted value and growth stock portfolios for the following indexes: Asia Pacific; Europe, Australasia and Far East (EAFE); Europe, with and without UK; Scandinavian countries; UK; U.S.; and, Japan. They define value (growth) stocks as the 30% with the highest (lowest) book-to-market ratios within their respective market indexes. Using monthly stock prices and lagged annual book-to-market ratios for stocks in these markets during 1975 (or inception if unavailable that early) through 2007, they find that: Keep Reading

James O’Shaughnessy (Chairman and CEO of O’Shaughnessy Asset Management) introduces his 2011 book, What Works on Wall Street (Fourth Edition): the Classic Guide to the Best-Performing Investment Strategies of All Time, by stating: “…investors seem programmed by nature to fail at investing, forever chasing the asset class that has turned in the best performance recently and heavily discounting anything that occurred more than three to five years ago. The whole purpose of What Works on Wall Street is to dissuade investors from that course of action. Only the fullness of time shows which investment strategies are the best long-term performers, and this is doubly true after the last decade’s sorry performance. …We will make the case that equities–particularly those selected using the best long-term strategies–will go on to be the best performing assets over the next 10 and 20 years. …The fourth edition of What Works on Wall Street continues to offer readers access to long-term studies of Wall Street’s most effective investment strategies.” He uses overlapping portfolios formed monthly and rebalanced annually for all tests. Using broad sets of data on U.S. firms/stocks from either 1963 or 1926 through 2009 to extend and expand his prior quantitative analyses, he concludes that: Keep Reading

Should investors strategically diversify across widely known equity market anomalies? In the October 2011 version of his paper entitled “Strategic Allocation to Premiums in the Equity Market”, David Blitz investigates whether investors should treat anomaly portfolios (size, value, momentum and low-volatility) as diversifying asset classes and how they can implement such a strategy.  To ensure implementation is practicable, he focuses on long-only, big-cap portfolios. To account for the trading frictions associated with anomaly portfolio maintenance and for time variation of anomaly premiums, he assumes future (expected) market and anomaly premiums lower than historical values, as follows: 3% equity market premium; 0% expected incremental size and low-volatility premiums; and, 1% expected incremental value and momentum premiums. He assumes future volatilities, correlations and market betas as observed in historical data and constrains weights of all anomaly portfolios to a maximum 40%. He considers both equal-weighted and value-weighted individual anomaly portfolios, and both mean-variance optimized and equal-weighted combinations of market and anomaly portfolios. Using portfolios constructed by Kenneth French to quantify equity market/anomaly premiums during July 1963 through December 2009 (consisting of approximately 800 of largest, most liquid U.S. stocks), he finds that: Keep Reading