Size Effect

The Trading Calendar presents full-year and monthly cumulative performance profiles for the overall stock market (S&P 500 Index) based on its average daily behavior since 1950. How much do the corresponding monthly behaviors of the various size and value/growth styles deviate from an overall equity market profile? To investigate, we consider the the following six exchange-traded funds (ETF) that cut across capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

Using monthly dividend-adjusted closing prices for the style ETFs and S&P Depository Receipts (SPY) over the period August 2001 through January 2013 (139 months, limited by data for IWS/IWP), we find that: More…

How sensitive is the performance of “Doing Momentum with Style (ETFs)” to selecting ranks other than winners and to choosing a momentum ranking interval other than six months? This strategy each month ranks the following six style exchange-traded funds (ETF) on past return and rotates to the strongest style:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

Available data are so limited that sensitivity test results may mislead. With that reservation, we perform two robustness/sensitivity tests: (1) comparison of returns for all six ranks of winner through loser based on a ranking interval of six months and a holding interval of one month (6-1); and, (2) comparison of winner returns for ranking intervals ranging from one to 12 months (1-1 through 12-1) and for a six-month lagged six-month ranking interval (12:7-1) per “Isolating the Decisive Momentum (Echo?)”, all with one-month holding intervals. Using monthly adjusted closing prices for the style ETFs and SPDR S&P 500 (SPY) over the period August 2001 through December 2012 (138 months), we find that: More…

“Beat the Market with Hot-Anomaly Switching?” concludes that “a trader who periodically switches to the hottest known anomaly based on a rolling window of past performance may be able to beat the market. Anomalies appear to have their own kind of momentum.” Does momentum therefore work for style-based exchange-traded funds (ETF)? To investigate, we apply a simple momentum strategy to the following six ETFs that cut across market capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

The simple (6-1) strategy allocates all funds each month to the one style ETF with the highest total return over the past six months. A six-month ranking period is intuitively large enough to gauge style momentum but small enough to react to changes in business conditions that might favor one style over others. An alternative, more cautious strategy allocates at the end of each month all funds either to the style ETF with the highest total return over the past six months or to cash depending on whether the S&P 500 Index is above or below its 10-month simple moving average (6-1;SMA10). Using monthly adjusted closing prices for the style ETFs, the S&P 500 index, 3-month Treasury bills (T-bills) and S&P Depository Receipts (SPY) over the period August 2001 through December 2012 (138 months, limited by data for IWS and IWP), we find that: More…

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: More…

“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: More…

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: More…

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: More…

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: More…

Do popular capitalization-based exchange-traded funds (ETF) confirm the existence of a reliably exploitable size effect? To investigate, we compare the difference in equally weighted returns (small minus large) for the following matched pair of small-large ETFs:

• iShares Russell 2000 Index (Smallcap) Index (IWM)
• iShares Russell 1000 (Largecap) Index (IWB)

Using monthly adjusted closing prices (incorporating dividends) for these ETFs during May 2000 (the earliest month available for both) through June 2012 (146 months), we find that: More…

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: More…