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.
Does a Weak Dollar Favor Large Capitalization Stocks? June 10, 2011
When the dollar weakens, large capitalization U.S. firms may benefit from their international footprints, generating substantial revenues around the globe in local currencies and converting those revenues into an increased number of dollars on their income statements. Should investors therefore shift toward (away from) large capitalization stocks when the dollar weakens (strengthens)? To check, we compare the performance of the Dow Jones Industrial Average (DJIA) (representing internationally positioned, large capitalization stocks) and the Russell 2000 Index (representing small capitalization stocks) during and after the dollar trends against the euro. We focus on non-overlapping three-month measurement intervals to match corporate earnings release frequency. Using data for the stock indexes and the dollar-euro exchange rate over the period January 2000 through May 2011 (about 45 quarters), we find that: More…
Creative Destruction Risk Premium May 23, 2011
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: More…
Value Premium as Risk Compensation May 20, 2011
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: More…
Predicting Variation in the Size Effect May 18, 2011
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: More…
Equity Investing Based on Liquidity April 29, 2011
Is the variation of individual stock returns with liquidity a sound investment foundation? In the April 2011 version of their paper entitled “Liquidity as an Investment Style”, Roger Ibbotson, Zhiwu Chen and Wendy Hu examine the viability and distinctiveness of a liquidity investment style and investigate the portfolio-level performance of liquidity in combination with size, value and momentum investment styles. They define liquidity as annual turnover, number of shares traded divided by number of shares outstanding, a metric fairly independent of market capitalization. They hypothesize that stocks with relatively low (high) turnover tend to be near the bottom (top) of their ranges of expectation. Their liquidity style thus overweights (underweights) stocks with lower (higher) annual turnover. Using monthly data for the 3,500 U.S. stocks with the largest market capitalizations (with some screening for price, market capitalization, stock type and data availability) over the period 1972-2010, they find that: More…
Individual Stocks Versus Portfolios April 20, 2011
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: More…
Measuring the Size Effect with Capitalization-based ETFs April 12, 2011
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:
Using monthly adjusted closing prices (incorporating dividends) for these ETFs during May 2000 (the earliest month available for both) through March 2011 (131 months), we find that: More…
Exploiting the Presidential Cycle and Party in Power April 6, 2011
Are there reliable ways to exploit differences in asset class returns under Democratic and Republican U.S. presidents? In his April 2011 paper entitled “Is the 60-40 Stock-Bond Pension Fund Rule Wise?”, William Ziemba examines relationships between the U.S. presidential election cycle and long-run returns for several asset classes. Specifically, he investigates the differential performance of large capitalization stocks, small capitalization stocks and bonds when Democrats and Republicans hold the presidency. Using annual asset class return data for 1998 through 2010 to extend prior calculations for 1937-1997 and 1942-1997, he finds that:
Robustness Tests for Ten Popular Stock Return Anomalies March 28, 2011
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: More…
Bottom-up Anomalies vs. Top-down Portfolio Efficiency March 21, 2011
How do widely recognized stock return anomalies (return variations unexplained by asset pricing models) mesh with efficient portfolio selection theory? In their paper entitled “Investing in Stock Market Anomalies”, Turan Bali, Stephen Brown and Ozgur Demirtas examine five prominent stock market anomalies whose existence is robust through time and across markets (size, book-to-market, short-term reversal, intermediate-term momentum and long-term reversion) in contexts of efficient portfolio selection via mean-variance and stochastic dominance methods. In other words, they test whether portfolios that apply these anomalies exhibit exceptionally good combinations of return and volatility, or obviously outperform on a purely statistical basis. Both these portfolio selection methods have shortcomings related to their inclusion of extreme, impractical choices. The authors consider relaxed (“Almost”) versions of these methods that prohibit such choices as “pathological.” The authors form value-weighted size and book-to-market portfolios annually and value-weighted reversal, momentum and reversion portfolios monthly. Using monthly data for July 1926 through December 2008 (990 months) for a broad sample of U.S. stocks to construct diversified anomaly portfolios, they find that: More…
