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Size Effect

Do the stocks of small firms consistently outperform those of larger companies? If so, why, and can investors/traders exploit this tendency? These blog entries relate to the size effect.

Buyback Size Effect?

Do companies reliably repurchase their stocks at bargain prices, thus providing signals for investors to tag along? In the January 2012 update of their paper entitled “Do Firms Buy Their Stock at Bargain Prices? Evidence from Actual Stock Repurchase Disclosures”, Azi Ben-Rephael, Jacob Oded and Avi Wohl use detailed repurchase data from SEC filings since the beginning of 2004 (effective date for amendments requiring detailed reporting) to examine the timeliness of open market repurchases. Unlike much prior research, they focus on repurchase executions and not announcements. Using information from 10-Q and 10-K filings about actual monthly stock repurchases by S&P 500 firms (as of January 2004) and contemporaneous share price data for 2004 through 2006 (14,669 monthly observations for 416 firms with at least one repurchase), they find that: Keep Reading

When and Why of the Size Effect

Does the size effect vary in an usefully predictable way? In the October 2011 revision of his paper entitled “Predicting the Small Stock Premium Over Different Horizons: What Do We Learn About Its Source?”, Valeriy Zakamulin examines whether eight U.S. market/economic variables exploitably predict the small stock premium at monthly, quarterly, semiannual and annual horizons. The eight variables are: (1) stock market return; (2) stock market dividend yield; (3) equity value premium; (4) stock return momentum; (5) default spread (Moody’s BAA-AAA corporate bond yield spread); (6)one-month Treasury bill yield; (7) U.S. Treasuries term premium (30-year bond yield minus one-month bill yield); and, (8) inflation rate. Using monthly data for the potentially predictive variables and for a broad sample of U.S. stocks/firms during January 1927 through December 2010 (1008 months, 252 quarters and 84 years), he finds that: Keep Reading

Harvesting Equity Market Premiums

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

Statistically Recasting the Big Three Anomalies

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

Size Effect and the Economy

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

Best Style by Investment Horizon

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

Creative Destruction Risk Premium

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

Value Premium as Risk Compensation

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

Predicting Variation in the Size Effect

Does the size effect vary in a predictable way? In the May 2011 version of his paper entitled “Explaining the Dynamics of the Size Premium”, Valeriy Zakamulin investigates relationships between eight market/economic variables and the size effect in U.S. stocks to identify the best model of size effect variation. The eight variables are: (1) stock market return; (2) stock market dividend yield; (3) equity value premium; (4) stock return momentum; (5) default spread  (Moody’s BAA-AAA corporate bond yield spread); (6) Treasury bill yield; (7) U.S. Treasuries term premium  (30-year bond yield minus one-month bill yield); and, (8) inflation rate. He then tests the exploitability of the best model via a strategy that switches between small-capitalization and large-capitalization stocks out of sample based on inception-to-date historical data. Using annual data for the eight potentially predictive variables and annual and monthly data for the magnitude of the size effect among NYSE, AMEX and NASDAQ stocks as available over the period 1927 through 2009 (83 years), he finds that: Keep Reading

Individual Stocks Versus Portfolios

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

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