Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for September 2021 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for September 2021 (Final)
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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.

Are Equity Multifactor ETFs Working?

Are equity multifactor strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider seven ETFs, all currently available:

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the seven equity multifactor ETFs and benchmarks as available through August 2021, we find that: Keep Reading

Do Equal Weight ETFs Beat Cap Weight Counterparts?

“Stock Size and Excess Stock Portfolio Growth” finds that an equal-weighted portfolio of the (each day) 1,000 largest U.S. stocks beats its market capitalization-weighted counterpart by about 2% per year. However, the underlying research does not account for portfolio reformation/rebalancing costs and may not be representative of other stock universes. Do exchange-traded funds (ETF) that implement equal weight for various U.S. stock indexes confirm findings? To investigate, we consider five equal-weighted ETFs, three alive and two dead:

We calculate monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly dividend-adjusted prices for the 10 ETFs as available (limited by equal-weighted funds) through June 2021, we find that: Keep Reading

Fama-French 5-factor Model and Global Stocks

Does the Fama-French  5-factor model (market, size, book-to-market, profitability, investment) of stock returns work for stocks worldwide? In their May 2021 paper entitled “Size, Value, Profitability, and Investment Effects in International Stock Returns: Are They Really There?”, Nusret Cakici and Adam Zaremba test the performance of the 5-factor model in global developed markets. They consider big and small stocks separately. They consider four regions (North America, Europe, Japan and Asia-Pacific), as well as the global market. They lag all accounting data by six months and calculate returns in U.S. dollars. Using data in U.S. dollars for 65,000 stocks from 23 countries during December 1987 through March 2019 (with tests starting July 1990), they find that:

Keep Reading

Measuring the Size Effect with Capitalization-based ETFs

Do popular capitalization-based exchange-traded funds (ETF) offer a reliable way to exploit an equity size effect? To investigate, we look at the difference in returns (small minus big) between:

  • iShares Russell 2000 Index (Smallcap) Index (IWM), and
  • SPDR S&P 500 (SPY)

Using monthly dividend-adjusted closing prices for these ETFs during May 2000 (limited by IWM) through March 2021, we find that: Keep Reading

Recent Weaknesses of Factor Investing

How have value, quality, low-volatility and momentum equity factors, and combinations of these factors, performed in recent years. In their October 2020 paper entitled “Equity Factor Investing: Historical Perspective of Recent Performance”, Benoit Bellone, Thomas Heckel, François Soupé and Raul Leote de Carvalho review and put into context recent performances of these these factors/combinations as applied to medium-capitalization and large-capitalization World, U.S. and European stock universes. They consider both long-short and long-only factor portfolios and further investigate effects of (1) neutralizing beta and sector dependencies, (2) using multiple metrics for each factor and (3) including small stocks. Using firm accounting data and stock returns to support factor portfolio construction during 1995 through early 2020, they find that:

Keep Reading

Ziemba Party Holding Presidency Strategy Update

“Exploiting the Presidential Cycle and Party in Power” summarizes strategies that hold small stocks (large stock or bonds) when Democrats (Republicans) hold the U.S. presidency. How has this strategy performed in recent years? To investigate, we consider three strategy alternatives using exchange-traded funds (ETF):

  1. D-IWM:R-SPY: hold iShares Russell 2000 (IWM) when Democrats hold the presidency and SPDR S&P 500 (SPY) when Republicans hold it.
  2. D-IWM:R-LQD: hold IWM when Democrats hold the presidency and iShares iBoxx Investment Grade Corporate Bond (LQD) when Republicans hold it.
  3. D-IWM:R-IEF: hold IWM when Democrats hold the presidency and iShares 7-10 Year Treasury Bond (IEF) when Republicans hold it.

We use calendar years to determine party holding the presidency. As benchmarks, we consider buying and holding each of SPY, IWM, LQD or IEF and annually rebalanced portfolios of 60% SPY and 40% LQD (60 SPY-40 LQD) or 60% SPY and 40% IEF (60 SPY-40 IEF). We consider as performance metrics: average annual excess return (relative to the yield on 1-year U.S. Treasury notes at the beginning of each year); standard deviation of annual excess returns; annual Sharpe ratio; compound annual growth rate (CAGR); and, maximum annual drawdown (annual MaxDD). We assume portfolio switching/rebalancing frictions are negligible. Except for CAGR, computations are for full calendar years only. Using monthly dividend-adjusted closing prices for the specified ETFs during July 2002 (limited by LQD and IEF) through December 2020, we find that:

Keep Reading

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

Is there a reliable and material market capitalization (size) effect among U.S. stocks around the turn-of-the-year (TOTY)? To check, we track cumulative returns 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. We also look at full-month December and January returns for these indexes. Using daily and monthly levels of all three indexes during December 1987 through January 2020 (33 December and 33 January observations), we find that: Keep Reading

Size as Catalyst for Value and Momentum

The conventional size (market capitalization) premium is notoriously weak since discovery almost 40 years ago. Does this poor live track record mean it is useless to investors? In their September 2020 paper entitled “Settling the Size Matter”, David Blitz and Matthias Hanauer examine whether the size premium is exploitable as a standalone anomaly or in combination with other anomalies. They consider six versions of a size factor from prior research, as follows:

  1. Adjusted for value – average of three small-cap stock portfolios minus average of three big-cap stock portfolios after sorting for book-to-market ratio.
  2. Adjusted for value, investment and profitability – average of nine small-cap stock portfolios minus average of nine big-cap stock portfolios after separately sorting on the other three factors.
  3. Adjusted for profitability – average of three small-cap stock portfolios minus average of three big-cap stock portfolios after sorting for profitability.
  4. Adjusted for quality – average of three small-cap stock portfolios minus average of three big-cap stock portfolios after sorting for quality.
  5. Adjusted for quality beta – average of three small-cap stock portfolios minus average of three big-cap stock portfolios after sorting for quality beta.
  6. Adjusted for size, investment and return on equity – average of nine small-cap stock portfolios minus average of nine big-cap stock portfolios after separately sorting on the other three factors.

All factor portfolio segments are capitalization-weighted, and all returns are in U.S. dollars. They consider regressions (implying long-short implementations) and long-only sides of these factors. They also consider size factor definitions that do not overweight size inputs, as do those above. Using data required by these definitions for U.S. stocks since July 1963 (or January 1967 for some inputs) and for international stocks since July 1990 (or July 1993 for some inputs), all through December 2019, they find that: Keep Reading

Investor Access to Factor Premiums via Funds

Are widely accepted equity factor exposures available in fact to investors via “smart beta” mutual funds and exchange-traded funds (ETF)? In their May 2020 paper entitled “Smart Beta Made Smart”, Andreas Johansson, Riccardo Sabbatucci and Andrea Tamoni test effectiveness of individual U.S. equity mutual funds and ETFs and combinations of these funds for exploiting several major equity risk factors (value, size, profitability and momentum). After assembling a sample of funds with names that indicate smart beta strategies, they iteratively (annually for size, value and profitability and daily for momentum):

  1. Apply a double-regression to each fund to identify those that are actually “closet” market index funds.
  2. Refine factor exposures of each true smart beta fund based on actual fund holdings.
  3. Construct separately for institutional and retail investors tradable long-side (mutual funds and ETFs) and short-side (ETFs only) risk factors via value-weighted combinations of the 10 funds with the strongest exposures to each factor.

Using daily, monthly, and quarterly data for U.S. equity mutual funds and ETFs with (1) names indicating smart beta strategies, (2) at least one year of returns and (3)assets over $1 billion, data for their individual component U.S. stocks and specified factor returns during January 2003 through May 2019, they find that: Keep Reading

Best Stock Portfolio Styles During and After Crashes

Are there equity styles that tend to perform relatively well during and after stock market crashes? In their April 2020 paper entitled “Equity Styles and the Spanish Flu”, Guido Baltussen and Pim van Vliet examine equity style returns around the Spanish Flu pandemic of 1918-1919 and five earlier deep U.S. stock market corrections (-20% to -25%) in 1907, 1903, 1893, 1884 and 1873. They construct three factors by:

  1. Separating stocks into halves based on market capitalization.
  2. Sorting the big half only into thirds based on dividend yield as a value proxy, 36-month past volatility or return from 12 months ago to one month ago. They focus on big stocks to avoid illiquidity concerns for the small half.
  3. Forming long-only, capitalization-weighted factor portfolios that hold the third of big stocks with the highest dividends (HighDiv), lowest past volatilities (Lowvol) or highest past returns (Mom).

They also test a multi-style strategy combining Lowvol, Mom and HighDiv criteria (Lowvol+) and a size factor calculated as capitalization-weighted returns for the small group (Small). Using data for all listed U.S. stocks during the selected crashes, they find that: Keep Reading

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