Investing Research Articles

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Better Four-factor Model of Stock Returns?

Are the widely used Fama-French three-factor model (market, size, book-to-market ratio) and the Carhart four-factor model (adding momentum) the best factor models of stock returns? In their September 2014 paper entitled “Digesting Anomalies: An Investment Approach”, Kewei Hou, Chen Xue and Lu Zhang construct the q-factor model comprised of market , size, investment and profitability factors and test its ability to predict stock returns. They also test its ability to account for 80 stock return anomalies (16 momentum-related, 12 value-related, 14 investment-related, 14 profitability-related, 11 related to intangibles and 13 related to trading frictions). Specifically, the q-factor model describes the excess return (relative to the risk-free rate) of a stock via its dependence on:

  1. The market excess return.
  2. The difference in returns between small and big stocks.
  3. The difference in returns between stocks with low and high investment-to-assets ratios (change in total assets divided by lagged total assets).
  4. The difference in returns between high-return on equity (ROE) stocks and low-ROE stocks.

They estimate the q-factors from a triple 2-by-3-by-3 sort on size, investment-to-assets and ROE. They compare the predictive power of this model with the those of the Fama-French and Carhart models. Using returns, market capitalizations and firm accounting data for a broad sample of U.S. stocks during January 1972 through December 2012, they find that: Keep Reading

Forget CAPM Beta?

Does the Capital Asset Pricing Model (CAPM) make predictions useful to investors? In his October 2014 paper entitled “CAPM: an Absurd Model”, Pablo Fernandez argues that the assumptions and predictions of CAPM have no basis in the real world. A key implication of CAPM for investors is that an asset’s expected return relates positively to its expected beta (regression coefficient relative to the expected market risk premium). Based on a survey of related research, he concludes that: Keep Reading

Earnings per Share Growth in the Long Run

Can the U.S. stock market continue to deliver its historical return? In the preliminary draft of his paper entitled “A Pragmatist’s Guide to Long-run Equity Returns, Market Valuation, and the CAPE”, John Golob poses two questions:

  1. What long-run real return should investors expect from U.S. equities?
  2. Do popular metrics reliably indicate when the U.S. equity market is overvalued?

He notes that the body of relevant research presents no consensus on the answers to these questions, which both relate to long-term growth in corporate earnings per share. Recent forecasts for real stock market returns range from as low as 2% to about 6% (close to the 6.5% average since 1871), reflecting disagreements about how slow GDP growth, low dividends, share buybacks and the profitability of retained earnings affect earnings per share growth. The author introduces Federal Reserve Flow of Funds (U.S. Financial Accounts) and S&P 500 aggregate book value to gauge effects of stock buybacks. He also assesses the logic of using Shiller’s cyclically adjusted price-earnings ratio (CAPE or P/E10) as a stock market valuation metric. Using S&P 500 Index price and dividend data, related earnings data and U.S. financial and economic data as available during 1871 through 2013, he concludes that: Keep Reading

2015 Wagner Award Call for Papers

The deadline for submission of papers for the 2015 Wagner Award, presented by the National Association for Active Investment Management (NAAIM), is March 2, 2015. Per the “Call for Papers”:

“The competition is open to all investment practitioners, academic faculty and doctoral candidates in the field. …All submitted papers should be recent, unpublished and of a quality appropriate for publication in a peer-reviewed academic journal. …Papers must be of practical significance to practitioners of active investing. The prize will be awarded to a paper resulting from research into active investment management, which NAAIM broadly defines as investment strategies and techniques that improve upon the risk-adjusted return obtainable from a passive, buy-and-hold, investment strategy.  …Three prizes will be awarded. The best paper will receive the Wagner Award valued at $10,000; second place will receive $3,000 and third will receive $1,000. …the grand prizewinner will be invited to present his / her paper at the NAAIM annual conference: ‘Uncommon Knowledge 2015,’ May 3-6 at the Newport Beach Marriott Resort and Spa in Newport Beach, California. Free conference attendance, U.S. air travel and lodging will be provided.”

See “Generating Parameter Sensitivity Distributions to Mitigate Snooping Bias”, “Exploitation of Stock Deviations from Statistical Equilibrium” and “Relative Strength of 10-year and 30-year Treasuries as Regime Indicator” for summaries of the 2014 Wagner Award first, second and third place papers, respectively.

See “Equity Sector Selection Based on Credit Risk”, “Volatility Trading Strategies” and “Taking the Noise Out of Technical Trading” for summaries of the 2013 Wagner Award first, second and third place papers, respectively.

See “Melding Momentum, Diversification and Absolute Return”“Mutual Fund Alpha Momentum” and “Active Asset Allocation via Drawdown Control” for summaries of the 2012 Wagner Award first, second and third place papers, respectively.

See “Capital Management with Clustered Signals”“Which Kind of (ETF) Momentum Is Best?” and “Enhancing/Streamlining Asset Rotation” for summaries of the 2011 Wagner Award first, second and third place papers, respectively.

See “Exploiting the Predictability of Volatility” and “Selling Calls or Puts According to Trend” for summaries of the 2010 Wagner Award first and second place papers, respectively.

The editor of will be a judge for the 2015 Wagner Award. has no other affiliation with NAAIM.

Snooping for Fun and No Profit

How much distortion can data snooping inject into expected investment strategy performance? In their October 2014 paper entitled “Statistical Overfitting and Backtest Performance”, David Bailey, Stephanie Ger, Marcos Lopez de Prado, Alexander Sim and Kesheng Wu note that powerful computers let researchers test an extremely large number of model variations on a given set of data, thereby inducing extreme overfitting. In finance, this snooping often takes the form of refining a trading strategy to optimize its performance within a set of historical market data. The authors introduce a way to explore snooping effects via an online simulator that finds the optimal (maximum Sharpe ratio) variant of a simple trading strategy by testing all possible integer values for strategy parameters as applied to a set of randomly generated daily “returns.” The simple trading strategy each month trades a single asset by (1) choosing a day of the month to enter either a long or a short position and (2) exiting after a specified number of days or a stop-loss condition. The randomly generated “returns” come from a source Gaussian (normal) distribution with zero mean. The simulator allows a user to specify a maximum holding period, a maximum percentage stop loss, sample length (number of days), sample volatility (number of standard deviations) and sample starting point (random number generator seed). After identifying optimal parameter values on “backtest” data, the simulator runs the optimal strategy variant on a second set of randomly generated returns to show the effect of backtest overfitting. Using this simulator, they conclude that: Keep Reading

Weekly Summary of Research Findings: 10/20/14 – 10/24/14

Below is a weekly summary of our research findings for 10/20/14 through 10/24/14. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Simple Asset Class Momentum Strategy Applied to Mutual Funds

A subscriber inquired whether a longer test of the “Simple Asset Class ETF Momentum Strategy” is feasible using mutual funds rather than exchange-traded funds (ETF) as asset class proxies. To investigate, we consider the following set of mutual funds (partly adapted from the paper summarized in “Asset Allocation Combining Momentum, Volatility, Correlation and Crash Protection”):

Oppenheimer Commodity Strategy Total Return A (QRAAX)
Vanguard Emerging Markets Stock Index Investor Shares (VEIEX)
Fidelity Diversified International (FDIVX)
First Eagle Gold A (SGGDX)
Vanguard Total Stock Market Index Investor Shares (VTSMX)
Vanguard Small Capitalization Index Investor Shares  (NAESX)
Vanguard REIT Index Investor Shares (VGSIX)
Vanguard Long-Term Treasury Investor Shares (VUSTX)
3-month Treasury bills (Cash)

The investigation includes basic tests performed in “Simple Asset Class ETF Momentum Strategy”, robustness tests performed in “Simple Asset Class ETF Momentum Strategy Robustness/Sensitivity Tests” and some of the extensions explored in “Alternative Asset Class ETF Momentum Allocations”. The selected mutual funds all have monthly prices available as of the end of March 1997. Monthly strategy returns, as limited by the kinds of tests performed, commence in April 1998. Using monthly dividend-adjusted closing prices for the above mutual funds and the yield for Cash during March 1997 through September 2014 (212 months), we find that: Keep Reading

Survey of Recent Research on Constructing and Monitoring Portfolios

What’s the latest research on portfolio construction and risk management? In the the introduction to the July 2014 version of his (book-length) paper entitled “Many Risks, One (Optimal) Portfolio”, Cristian Homescu states: “The main focus of this paper is to analyze how to obtain a portfolio which provides above average returns while remaining robust to most risk exposures. We place emphasis on risk management for both stages of asset allocation: a) portfolio construction and b) monitoring, given our belief that obtaining above average portfolio performance strongly depends on having an effective risk management process.” Based on a comprehensive review of recent research on portfolio construction and risk management, he reports on:

Keep Reading

Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for September 2014. The actual total (core) inflation rate for September is lower than (about the same as) forecasted.

The new actual and forecasted inflation rates will flow into Real Earnings Yield Model projections at the end of the month.

End-of-Quarter Effect

Does the U.S. stock market offer a predictable pattern of returns around the ends of calendar quarters? Do funds deploy cash to bid stocks up at quarter ends to boost portfolio values at the end of reporting periods (with subsequent reversals)? Or, do they sell stocks to raise cash for fund redemptions? Is the end-of-quarter effect the same as the Turn-of-the-Month (TOTM) effect? To investigate, we examine average daily stock market returns from 10 trading days before to 10 trading days after the ends of calendar quarters. We compare these returns to those for turns of calendar months. Using daily closes for the S&P 500 Index for January 1950 through September 2014 (259 quarters), we find that: Keep Reading

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Current Momentum Winners

ETF Momentum Signal
for October 2014 (Final)

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Gross Momentum Portfolio Gains
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