Value Premium

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

Are value stocks priced low because the companies are in financial distress? In their May 2011 paper entitled “Is the Value Premium Really a Compensation for Distress Risk?”, Wilma de Groot and Joop Huij investigate the relationships between the value premium and alternative measures of firm distress risk. Their core methodology employs monthly double-sorts on firm book-to-market ratio and each of four measures of firm financial risk: (1) financial leverage (debt-to-assets ratio); (2) a structural model of distance-to-default; (3) credit spread (between firm bonds and maturity-matched Treasuries); and, (4) credit rating. Using data to calculate these measures for the 1,500 largest U.S. firms, along with associated monthly stock prices, over the period September 1991 (limited by availability of credit spread data) through December 2009, they find that: Keep Reading

Can portfolios exhibit properties not evident from, or even contrary to, average properties of their component assets? In the April 2011 draft of their paper entitled “The Sources of Portfolio Returns: Underlying Stock Returns and the Excess Growth Rate”, Jason Greene and David Rakowski provide a framework for distinguishing two sources of portfolio return: (1) weighted average growth rates of component assets; and, (2) portfolio “excess growth rate” derived from diversification (component return volatilities and correlations). They apply this framework to investigate equity portfolio equal-weighting versus value-weighting, and to isolate the sources of the size effect and the value premium. They establish consistency in return measurements by matching rebalancing frequency and return measurement interval. Using monthly returns and firm characteristics for a broad sample of U.S. stocks over the period 1960 through 2009, they find that: Keep Reading

For what kind of stocks does momentum work best? In his March 2011 paper entitled “Growth Options, Idiosyncratic Volatility and Momentum”, Umut Celiker investigates the interactions among valuation (market to-book ratio, arguably a proxy for firm growth opportunities), valuation uncertainty (idiosyncratic volatility) and stock price momentum. For calendar-time analysis, he ranks stocks each month into quintiles by past six-month return, with a skip-month, and holds an equal-weighted hedge portfolio that is long the top (winner) quintile and short the bottom (loser) quintile for the next six months. For event analysis, he extends the holding interval to 60 months to explore momentum persistence/reversal. He computes stock idiosyncratic volatility relative to the S&P 500 Index over the prior 36 months. He defines the up (down) market state as the top 80% (bottom 20%) of months based on 60-month past value-weighted market returns averaged for each of the lagged six months. Most analysis focuses on the up market state. Using monthly firm accounting and stock price data for a broad sample of U.S. stocks over the period 1965 to 2008, he finds that: Keep Reading

In their March 2011 paper entitled “The Shrinking Space for Anomalies”, George Jiang and Andrew Zhang investigate the robustness of ten well-known anomalies by iteratively “shrinking the stock space” in two ways to determine whether and how the anomalies really work. The ten anomaly variables are: size, book-to-market ratio, momentum, two liquidity measures, idiosyncratic volatility, accrual, capital expenditure, sales growth and net share issuance. The first way of “shrinking the stock space” involves: (1) ranking the universe of stocks by each of the ten anomaly variables into deciles; (2) iteratively trimming deciles from side of a variable distribution that a hedge portfolio would sell and the side that a hedge portfolio would buy; and, (3) retesting the strength of the anomaly associated with the variable after each iterative trimming. The second way of “shrinking the stock space” involves: (1) trimming from the sample stocks with the smallest market capitalizations and the most extreme book-to-market ratios until size, book-to-market and momentum no longer have significant four-factor alphas for value-weighting and equal equal-weighting (thereby “perfecting” the sample for the four-factor model); and, (2) retesting the strength of the anomalies associated with the other seven variables using the perfected sample. This approach obviates weaknesses in alpha measurement via the commonly applied but imperfect three-factor (market, size, book-to-market) and four-factor (plus momentum) risk models. Using firm characteristics and trading data for all non-financial NYSE, AMEX, and NASDAQ common stocks over the period July 1962 through December 2007, they find that: Keep Reading