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Investing Research Articles

Equity Factor Census

Should investors trust academic equity factor research? In their February 2019 paper entitled “A Census of the Factor Zoo”, Campbell Harvey and Yan Liu announce a comprehensive database of hundreds of equity factors from top academic journals and working papers through January 2019, including a link to citation and download information. They distinguish among six types of common factors and five types of firm characteristic-based factors. They also explore incentives for factor discovery and reasons why many factors are lucky findings that exaggerate expectations and disappoint in live trading. Finally, they announce a project that allows researchers to add published and working papers to the database. Based on their census of published factors and analysis of implications, they conclude that: Keep Reading

Asset Class Momentum Faster During Bear Markets?

A subscriber asked whether the optimal momentum ranking (lookback) interval for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) shrinks during bear markets for U.S. stocks. This strategy each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

To investigate, we compare SACEMS monthly performance statistics when the S&P 500 Index at the previous monthly close is above (bull market) or below (bear market) its 10-month simple moving average. We consider Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners for the baseline SACEMS lookback interval. In a robustness test for the EW Top 3 portfolio, we consider lookback intervals ranging from one to 12 months. Using monthly total (dividend-adjusted) returns for the specified assets since February 2006 (limited by DBC) and the monthly level of the S&P 500 Index since September 2005, all through February 2019, we find that:

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Weekly Summary of Research Findings: 4/1/19 – 4/5/19

Below is a weekly summary of our research findings for 4/1/19 through 4/5/19. 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

Asset Class Short-term Momentum Over the Long Run

Do assets other than individual stocks exhibit a short-term (1-month) reversal effect? In their February 2019 paper entitled “Short-Term Momentum (Almost) Everywhere”, Adam Zaremba, Andreas Karathanasopoulos and Huaigang Long investigate short-term return predictability within long run global samples spanning five asset classes: equity indexes, government bonds, treasury bills, commodity futures and currencies. Each month they sort assets by class or overall into fifths (quintiles) on prior-month return. For classes with at least 10 assets available, they then construct long-short hedge portfolios that are long (short) the equal-weighted quintile of assets with the highest (lowest) prior-month returns. Using monthly returns for 45 equity indexes, 54 government bonds, 52 government bills, 48 commodity futures and 62 currency exchange rates in U.S. dollars as available during 1800 through 2018, they find that: Keep Reading

Mutual Fund Investors Irrationally Naive?

Do retail investors rationally account for risks as modeled in academic research when choosing actively managed equity mutual funds? In their March 2019 paper entitled “What Do Mutual Fund Investors Really Care About?”, Itzhak Ben-David, Jiacui Li, Andrea Rossi and Yang Song investigate whether simple, well-known signals explain active mutual fund investor behavior better than academic asset pricing models. Specifically, they compare abilities of Morningstar’s star ratings and recent returns versus formal pricing models to predict net fund flows. They consider the Capital Asset Pricing Model (CAPM) and alphas calculated with 1-factor (or market-adjusted), 3-factor (plus size and book-to-market) and 4-factor (plus momentum) models of stock returns. They consider degree of agreement between signals for a fund (such as number of Morningstar stars and sign of a factor model alpha) and the sign of net capital flow for that fund. They also analyze spreads between net flows to top and bottom funds ranked according to Morningstar stars and fund alphas, taking the number of 5-star and 1-star funds to determine the number of top-ranked and bottom-ranked funds, respectively. Using monthly returns and Morningstar ratings for 3,432 actively managed U.S. equity mutual funds and contemporaneous market, size, book-to-market and momentum factor returns during January 1991 through December 2011 (to match prior research), they find that:

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Alternative Beta Live

Have long-short alternative beta (style premium) strategies worked well in practice? In their February 2019 paper entitled “A Decade of Alternative Beta”, Antti Suhonen and Matthias Lennkh use actual performance data to assess alternative beta strategies across asset classes from the end of 2007 through the end of 2017, including quantification of fees and potential survivorship bias in public data. Specifically, they form three equal volatility weighted (risk parity) composite portfolios of strategies at the ends of each year during 2007-2016, 2007-2011 and 2012-2016. Each portfolio includes all the strategies launched during the first year and then adds strategies launched each following year at the end of that year. When a strategy dies (is discontinued by the offeror), they reallocate its weight to surviving strategies within the portfolio. They also create two additional portfolios for each period/subperiod that segregate equities and non-equities. They further evaluate alternative beta strategy diversification benefits by comparing them to conventional asset class portfolios. Using weekly post-launch excess returns in U.S. dollars for 349 reasonably unique live and dead alternative beta strategies offered by 17 global investment banks, spanning 14 styles and having at least one year of history during 2008 through 2017, they find that:

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Academia Creating Anomalies?

Does widespread investor acceptance of the capital asset pricing model (CAPM) of stock returns drive undervaluation of stocks with low past alphas? In his February 2019 paper entitled “The Unintended Impact of Academic Research on Asset Returns: The CAPM Alpha”, Alex Horenstein examines whether such acceptance distorts the U.S. stock market. Specifically, he each year at the beginning of January reforms a betting against alpha (BAA) hedge portfolio that is long (short) stocks with alphas lower (higher) than the median based on monthly returns over the past five years. He then weights stocks according to their respective alpha ranks, rescales the long and short sides separately to have market beta 1.0 and holds for one year. He analyzes performance of this portfolio and eight widely accepted equity factors (size, value, momentum, profitability, investment, short-term reversal, long-term reversion and betting against beta) during three subperiods: (1) pre-CAPM era (1932-1964); (2) CAPM era (1965-1992); and, (3) smart beta era (1993-2015). Using total returns for a broad sample of U.S. common stocks and returns for eight accepted equity factors during January 1927 through December 2015, he finds that:

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Stocks Plus Trend Following Managed Futures?

A subscriber asked about an annually rebalanced portfolio of 50% stocks and 50% trend following managed futures as recommended in a 2014 Greyserman and Kaminski book [Trend Following with Managed Futures: The Search for Crisis Alpha], suggesting Equinox Campbell Strategy I (EBSIX) as an accessible managed futures fund. To investigate, we consider not only EBSIX (inception March 2013) but also a longer trend following hedge fund index with monthly returns back to December 1999. This alternative “is an equally weighted index of 37 constituent funds…designed to provide a broad measure of the performance of underlying hedge fund managers who invest with a trend following strategy.” The correlation of monthly returns between this index and EBSIX during April 2013 through February 2019 is 0.84, indicating strong similarity. We use SPDR S&P 500 (SPY) as a proxy for stocks. Using annual returns for EBSIX during 2014-2018 and for the trend following hedge fund index and SPY during 2000-2018, we find that: Keep Reading

Weekly Summary of Research Findings: 3/25/19 – 3/29/19

Below is a weekly summary of our research findings for 3/25/19 through 3/29/19. 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 Momentum Strategy Applied to TSP Funds

A subscriber asked about applying the “Simple Asset Class ETF Momentum Strategy” to the funds available to U.S. federal government employees via the Thrift Savings Plan (TSP). To investigate, we test the strategy on the following five funds:

G Fund: Government Securities Investment Fund (G)
F Fund: Fixed Income Index Investment Fund (F)
C Fund: Common Stock Index Investment Fund (C)
S Fund: Small Cap Stock Index Investment Fund (S)
I Fund: International Stock Index Investment Fund (I)

We each month rank these funds based on returns over past (lookback) intervals of one to 12 months. We test Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly fund winners. We employ as a benchmark a naively diversified EW portfolio of all five funds, rebalanced monthly (EW All). Using monthly returns for the five funds from initial availability of all five (January 2001) through February 2019, we find that:

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