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.

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Turn of the Year and Size in U.S. Equities

The turn of the year (December-January) for the U.S. stock market includes the Santa Claus rally and the January effect. How does the stock market behave around the turn of the year for a recent sample? To check, we construct cumulative return profiles from 20 trading days before through 20 trading days after the end of the calendar year for the Russell 2000 Index, the S&P 500 Index and the Dow Jones Industrial Average (DJIA) since the inception of the Russell 2000 Index. Using daily and monthly levels of all three indexes from December 1987 through January 2015 (28 December and 28 January observations), we find that: Keep Reading

Exploiting the Trend Lag of Small Stocks?

Do small capitalization stocks exploitably lag broad market trends? In their October 2015 paper entitled “Slow Trading and Stock Return Predictability”, Matthijs Lof and Matti Suominen investigate whether overall stock market trends predict variation in the size effect and therefore the performance of small capitalization exchange-traded funds (ETF). For size effect testing, they each year at the end of June rank stocks into tenths (deciles) by market capitalization and calculate the size effect as the difference in value-weighted average returns between the smallest and largest deciles. Using daily returns, trading volumes and institutional buying and selling data for a broad sample of U.S. common stocks during 1964 through 2014 and for a selection of small capitalization ETFs as available through 2014, they find that: Keep Reading

Stock Size and Momentum Strategy Profitability Worldwide

Are there exploitable size and momentum effects among international stocks? In their August 2015 paper entitled “Size and Momentum Profitability in International Stock Markets”, Peter Schmidt, Urs Von Arx, Andreas Schrimpf, Alexander Wagner and Andreas Ziegler examine the size effect and the interplay between size and momentum strategies via long-short stock portfolios in 23 countries. They measure stock size as market capitalization and consider several ways of measuring the difference in average returns and four-factor (market, size, book-to-market, momentum) alphas between value-weighted portfolios of small stocks and big stocks. They measure stock momentum as return from 12 months ago to one month ago, with a skip-month between ranking and value-weighted portfolio formation. They assess net portfolio performance in three ways: (1) imposing estimated trading frictions (0.3%-0.4% for small stocks and 0.15% for big stocks); (2) calculating the maximum trading frictions an investor could bear; and, (3) calculating U.S. dollar trading volume for each portfolio. Using stock data for the U.S. during 1985 through 2012 and for 22 other countries mostly during 1991 through 2012, they find that: Keep Reading

Small Leveraged Value Stock Ranking System

What qualifiers can enhance the performance of a small value stock strategy? In their August 2015 paper entitled “Leveraged Small Value Equities”, Brian Chingono and Daniel Rasmussen devise and test a strategy to refine a portfolio of small capitalization value stocks of firms that with relatively high financial leverage. Specifically, their target universe at the end of each year consists of all NYSE/AMEX/NASDAQ stocks with: (1) market capitalizations between the 25th and 75th percentiles; (2) among the 25% of cheapest stocks based on EBITDA divided by enterprise value; and, (3) above median long term debt divided by enterprise value. They then rank the stocks in this universe per a group of quality and technical factors that emphasize reduction in long-term debt and improving asset turnover (revenue growth rate greater than asset growth rate). At the end of the first quarter of each following year, they reform portfolios of the top 25 and top 50 stocks in the specified universe based on this ranking. Using stock return and accounting data for a broad sample of U.S. stocks during January 1963 through December 2014, they find that: Keep Reading

Country Stock Market Factor Strategies

Do factors that predict returns in U.S. stock data also work on global stock markets at the country level? In the May 2015 version of their paper entitled “Do Quantitative Country Selection Strategies Really Work?”, Adam Zaremba and Przemysław Konieczka test 16 country stock market selection strategies based on relative market value, size, momentum, quality and volatility. For each of 16 factors across these categories, they sort country stock markets into fifths (quintiles) and measure the factor premium as return on the highest minus lowest quintiles. They consider equal, capitalization and liquidity (average turnover) weighting schemes within quintiles. They look at complementary large and small market subsamples, and complementary open (easy to invest) and closed market subsamples. Using monthly total returns adjusted for local dividend tax rates in U.S. dollars for 78 existing and discontinued country stock indexes (primarily MSCI) during 1999 through 2014, they find that: Keep Reading

Interactions among Stock Size, Stock Price and the January Effect

Is there an exploitable interaction between a stock’s market capitalization and its price? In their February 2015 paper entitled “Nominal Prices Matter”, Vijay Singal and Jitendra Tayal examine the relationship between stock prices and returns after: (1) controlling for market capitalization (size); (2) isolating the month of January; and, (3) excluding very small stocks. They each year perform double-sorts based on end-of-November data first into ranked tenths (deciles) by size and then within each size decile into price deciles. They calculate returns for January and for the calendar year with and without January. Using monthly prices and end-of-November market capitalizations for the 3,000 largest U.S. common stocks during December 1962 through December 2013, quarterly institutional ownership data for each stock during December 1980 through December 2013, and actual number of shareholders for each stock during 2004 through 2012, they find that: Keep Reading

Measuring the Size Effect with Capitalization-based ETFs

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 dividend-adjusted closing prices for these ETFs during May 2000 (the earliest month available for both) through January 2015 (177 months), we find that: Keep Reading

Interaction of Calendar Effects with Other Anomalies

Do stock return anomalies exhibit January and month-of-quarter (first, second or third, excluding January) effects? In his February 2015 paper entitled “Seasonalities in Anomalies”, Vincent Bogousslavsky investigates whether the following 11 widely cited U.S. stock return anomalies exhibit these effects:

  1. Market capitalization (size) – market capitalization last month.
  2. Book-to-market – book equity (excluding stocks with negative values) divided by market capitalization last December.
  3. Gross profitability – revenue minus cost of goods sold divided by total assets.
  4. Asset growth – Annual change in total assets.
  5. Accruals – change in working capital minus depreciation, divided by average total assets the last two years.
  6. Net stock issuance – growth rate of split-adjusted shares outstanding at fiscal year end.
  7. Change in turnover – difference between turnover last month and average turnover the prior six months.
  8. Illiquidity – average illiquidity the previous year.
  9. Idiosyncratic volatility – standard deviation of residuals from regression of daily excess returns on market, size and book-to-market factors.
  10. Momentum – past six-month return, skipping the last month.
  11. 12-month effect – average return in month t−k*12, for k = 6, 7, 8, 9, 10.

Each month, he sorts stocks into tenths (deciles) based on each anomaly variable and forms portfolios that are long (short) the decile with the highest (lowest) values of the variable. He updates all accounting inputs annually at the end of June based on data for the previous fiscal year. Using accounting data and monthly returns for a broad sample of U.S. common stocks during January 1964 to December 2013, he finds that: Keep Reading

Investor Return versus Mutual Fund Performance

Does the average mutual fund investor accrue the average fund performance, or do investor timing practices alter the equation? In their July 2014 paper entitled “Timing Poorly: A Guide to Generating Poor Returns While Investing in Successful Strategies, Jason Hsu, Brett Myers and Ryan Whitby compare the average dollar-weighted and buy-and-hold returns of different U.S. equity mutual fund styles, with focus on the value style. Dollar weighting adjusts the return stream based on the timing and magnitude of fund flows and is a more accurate measure than buy-and-hold of the returns realized by fund investors who may trade in and out of funds. Using monthly returns, monthly total assets and quarterly fund style information for a broad sample of U.S. equity mutual funds during 1991 through 2013, they find that: Keep Reading

Quality-enhanced Size Effect

Given the conflicting evidence about the import of the size effect, is there a way investors can extract a reliable premium from small stocks? In their January 2015 draft paper entitled “Size Matters, If You Control Your Junk”, Clifford Asness, Andrea Frazzini, Ronen Israel, Tobas Moskowitz and Lasse Pedersen examine whether controlling for firm quality mitigates the following seven unfavorable empirical findings that the size effect:

  1. Is weak overall in the U.S.
  2. Has not worked out-of-sample and varies significantly over time.
  3. Only works for extremely small stocks.
  4. Only works in January.
  5. Only works for market capitalization-based measures of size.
  6. Is subsumed by illiquidity.
  7. Is weak internationally.

They control for quality using a Quality-Minus-Junk (QMJ) factor based on profitability, profit growth, safety and payout. They use a portfolio test approach, ranking stocks into value-weighted tenths (deciles) each month to examine differences among stocks sorted by factor. Focusing on returns and factor metrics for a broad sample of U.S. common stocks during July 1957 (when quality metrics become available) through December 2012 and for 23 other developed country stock markets during January 1983 through December 2012, they find that: Keep Reading

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for December 2015 (Final)

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