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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.

Growth Investing Success Factors

What is growth investing, and how well does it work? How can investors enhance this investment style? In his July 2012 paper entitled “Growth Investing: Betting on the Future?”, Aswath Damodaran examines different approaches to growth investing: focusing on companies with small market capitalization; playing initial public offerings (IPO); seeking growth at a reasonable price (GARP); and, activist venture capital-like investing. He defines growth investing as pursuit of market undervaluation of future growth, looking for bargains based on overlooked growth potential. Based on the body of growth investing research, he finds that: Keep Reading

Deconstructing the Size Effect

What calendar and technical factors drive the size effect? In the June 2012 version of his paper entitled “Predictable Dynamics in the Small Stock Premium”, Valeriy Zakamulin explores the interaction of the size effect with the January effect and both prior-month and prior-year stock market returns. He defines the size effect based on the Small-Minus-Big (SMB) factor of the Fama-French three-factor model of stock returns. A positive (negative) value for the effect means that small (big) stocks outperform big (small) stocks. Using market factor and SMB factor returns from the library of Kenneth French and National Bureau of Economic Research (NBER) business cycle dates during 1927 through 2011 (85 years), he finds that: Keep Reading

Interaction of Momentum/Reversal with Size and Value

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

Testing U.S. Equity Anomalies Worldwide

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

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

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