Size Effect

Does adding profitability (see “Gross Profitability as a Stock Return Predictor”) to the Fama-French three-factor model of future stock returns result in a better model? In the June 2013 draft of their paper entitled “A Four-Factor Model for the Size, Value, and Profitability Patterns in Stock Returns”, Eugene Fama and Kenneth French examine whether profitability usefully augments their three-factor model. They consider evidence from monthly double sorts into: (1) size and book-to-market capitalization ratio (B/M) quintiles (25 portfolios); and, (2) size and pre-tax profitability (PTP) quintiles (25 portfolios). They also consider monthly triple sorts by size, B/M and PTP. Using price and firm accounting data for a broad sample of U.S. common stocks during July 1963 through December 2012, 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

“Market Capitalization Concentration as Stock Market Predictor” summarizes research finding that the change in the level of concentration of total market capitalization in the largest firms may be a useful predictor of stock market returns. Does a simple trading strategy based on this finding beat the market? To investigate, we examine the ratio of the S&P 500 Index (representing large stocks) and the Russell 2000 Index (representing small stocks). When the concentration of total market capitalization in large firms is high (low), this ratio is high (low). In concert with the referenced research, we derive trading signals from the lagged annual change in this index ratio. Using monthly levels of the S&P 500 Index, the Russell 2000 Index and the monthly yield on 13-week Treasury bills (T-bills) from inception of the Russell 2000 Index in September 1987 through September 2012, we find that: Keep Reading

Do changes in total market capitalization shares of large-capitalization and small-capitalization stocks predict future equity returns? In their September 2012 paper entitled “Davids, Goliaths, and Business Cycles”, Jefferson Duarte and Nishad Kapadia investigate whether a predictor based on concentration of market valuation predicts market returns. Specifically, they test the power of annual change in the logarithm of the fraction of total stock market capitalization captured by the largest 250 firms to predict future stock market returns. They call this indicator Goliaths Versus Davids (GVD). They compare the predictive power of GVD to those of eight other variables: (1) the default spread (difference between BAA and AAA corporate bond yields); (2) the term spread (difference between 10-year Treasury note and the 1-month Treasury bill yields); (3) the stock market dividend-price ratio; (4) the cyclically adjusted price-earnings ratio; (5) the consumption, wealth, income ratio; (6) the investment-to-capital ratio; (7) the book-to-market ratio of the Dow Jones Industrial Average; and, (8) the net payment yield of all stocks. Using quarterly data for a broad sample of U.S. common stocks and U.S. stock market returns during April 1926 through April 2011, 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

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

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

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

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