Value Premium

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

Do the size effect, value premium and momentum effect derive from common firm/stock characteristics other than size, book-to-market ratio and past return? In the October 2011 version of their paper entitled “Which Firms Are Responsible for Characteristic Anomalies? A Statistical Leverage Analysis”, Kevin Aretz and Marc Aretz statistically isolate and analyze the small minority of firms that drive these three anomalies. Specifically, they exclude firms from the sample experimentally to identify those stocks that contribute the most to each anomaly (exhibit the strongest statistical leverage) and then examine in several ways the characteristics and stock price behaviors of those firms. They define size based on market capitalization, value based on book-to-market ratio and momentum based on three-month past return (which exhibits stronger momentum than 12-month past return during the sample period). They form test portfolios annually on June 30 based on current size and momentum and six-month lagged book-to-market ratio and hold from July 1 to June 30 of the next year. Using monthly stock returns, stock trading data and accounting variables for the firms then included in the S&P 1500, along with contemporaneous benchmark data, during July 1974 through December 2007, they find that: Keep Reading

Does the size effect vary with the state of the economy? In his October 2010 paper entitled “The Behaviour of Small Cap vs. Large Cap Stocks in Recessions and Recoveries: Empirical Evidence for the United States and Canada”, Lorne Switzer examines the relative performance of small versus large capitalization stocks around economic peaks and troughs (per NBER business cycle data). Using monthly returns for U.S. (Canadian) stocks starting with January 1926 (1987), associated firm characteristics and contemporaneous economic and equity market benchmark data through August 2010, he finds that: Keep Reading

Should investors with different horizons prefer different styles (large versus small capitalization and value versus growth)? In their 2010 paper entitled “Time, Risk and Investment Styles”, Zugang Liu and Jia Wang investigate how equity investment style risks vary with investment horizon. They focus on the downside of asset returns rather than overall volatility to measure risk, arguing that investor risk aversion consistently relates to potential loss but not to return standard deviation. Specifically, lower partial standard deviation (LPSD) is appropriate for risk-averse investors because it assigns higher weights to greater losses, and shortfall risk is appropriate for aggressive investors because it considers only probability of loss (not size of loss). The authors use both rolling window and bootstrap methodologies to compare equity style expected shortfall and LPSD over horizons of one, five, ten, 15, 20, 30 and 40 years. Using returns for six style indexes for a broad sample of U.S. stocks (intersections of first, third and fifth size quintiles with highest and lowest book-to-market ratio quintiles) and Treasury bill yields over the period July 1926 through December 2008 (82.5 years), they find that: Keep Reading

Are some firms more at risk of creative destruction by new technologies? If so, does the market offer a premium to investors in such firms? In his March 2011 paper entitled “Creative Destruction and Asset Prices”, Joachim Grammig explores the concept of creative destruction as an explanation for the size effect and the value premium under the proposition that associated firms have a higher probability of being destroyed by technological change. He defines the pace of technological change as the annual percentage change in U.S. patents issued (patent activity growth). Using annual counts of newly issued patent from the U.S. Patent and Trademark Office and annual data on 25 portfolios of U.S. stocks formed by double-sorts on size and book-to-market ratio over the period 1927 through 2008, he finds that: Keep Reading