Momentum Investing

We developed the Simple Asset Class ETF Momentum Strategy (SACEMS) about six years ago and the Simple Asset Class ETF Value Strategy (SACEVS) about two years ago independently, focusing on the separate logic of asset choices for each. As tested in “SACEMS-SACEVS Mutual Diversification”, these two strategies are mutually diversifying, so combining them works better in some ways than using one or the other. Beginning May 2017, we are making four changes to these strategies for ease of implementation and combination, with modest compromises in logic. Specifically, we are: Keep Reading

Is there an easy way to turn conventional stock momentum crashes into gains? In the March 2017 version of her paper entitled “Dynamic Momentum and Contrarian Trading”, Victoria Dobrynskaya examines the timing of momentum crashes and tests a simple dynamic strategy designed to turn the crashes into gains. This strategy follows a conventional stock momentum strategy most of the time, but flips to a contrarian strategy for three months after each market plunge with a lag of one month. The conventional momentum hedge portfolio is each month long the tenth (decile) or third (tercile), depending on sample breadth, of stocks with the highest cumulative returns from 12 months ago to one month ago and short the tenth or third with the lowest cumulative returns. The contrarian hedge portfolio flips the long and short positions. For her baseline case, she defines a market plunge as a monthly return more than 1.5 standard deviations of monthly returns below the average monthly market return (measured in-sample). For most analyses, she employs the Fama-French U.S. equal-weighted and value-weighted extreme decile momentum hedge portfolios during January 1927 through July 2015. For global developed market analyses, she employs extreme tercile momentum hedge portfolios from various sources during November 1990 through March 2016. She also considers long-only momentum portfolios for emerging markets: one broad during June 1991 through March 2016) and one narrow (Latin American only) during June 1995 through March 2016. Using this data, she finds that: Keep Reading

What are the most common strategies for trading commodity futures? In their brief January 2017 article entitled “Commodity Futures Trading Strategies: Trend-Following and Calendar Spreads”, Hilary Till and Joseph Eagleeye describe the two most common strategies among commodity futures traders: (1) trend-following, wherein non-discretionary traders automatically screen markets based on technical factors to detect beginnings and ends of trends across different timeframes; and, (2) calendar-spread trading, wherein traders exploit commercial/institutional supply and demand mismatches that affect price spreads between commodity futures contract delivery months. Examples of the latter are seasonal inventory build and draw cycles (as for natural gas) and precise roll cycles for expiring contracts included in commodity futures indexes. Based on the body of research and examples, they conclude that: Keep Reading

Do momentum and reversal stock anomalies stripped of market, size and book-to-market risks (residual anomalies) outperform their conventional forms? In their March 2017 paper entitled “Residual Momentum and Reversal Strategies Revisited”, Joop Huij and Simon Lansdorp compare performances of residual and conventional momentum (using returns from 12 months ago to one month ago) and reversal (using last-month returns) strategies for U.S., European, Japanese, Asia-Pacific and emerging market stocks. They calculate anomaly performance from portfolios that are each month long (short) the equally weighted fifth, or quintile, of stocks with the highest (lowest) expected momentum and reversal returns. To check robustness, they focus on tests segmented into a residual anomaly discovery subperiod (January 1986 through December 2008) and a recent subperiod (January 2009 through December 2015). Using monthly returns as available (only since January 1993 for emerging markets) for the specified stocks, they find that: Keep Reading

Are widely used stock factor premiums amenable to timing based on the ratio of aggregate valuation of stocks in the long side to aggregate valuation of stocks in the short side of the factor portfolio (the value spread)? In their March 2017 paper entitled “Contrarian Factor Timing is Deceptively Difficult”, Clifford Asness, Swati Chandra, Antti Ilmanen and Ronen Israel test a strategy that times factor portfolios based on the value spread, in single-factor or multi-factor portfolios. They consider three annually rebalanced factor hedge portfolios: (1) value (High Minus Low book-to-market ratio, or HML); (2) momentum (Up Minus Down, or UMD); and, (3) low beta (Betting Against Beta, or BAB). Their main measure for calculating the value spread is book-to-market ratio, so that a high (low) value spread implies a cheap (expensive) factor. To standardize the value spread, they use z-scores (number of standard deviations above or below the historical average, with positive values indicating undervalued). They use the first 120 months of data to calculate the first z-score. They compare performances of factor portfolios without timing to performances of the same portfolios with a timing overlay that varies capital weights for a factor between 50% and 150% of its passive weight according to the factor’s value spread (scaled to total portfolio weight 100%). They consider variants that are and are not industry neutral. Using factor and return data for large-capitalization U.S. stocks during 1968 through 2016, they find that: Keep Reading

How diversifying are different equity factors within and across country stock markets? In his January 2016 paper entitled “The Power of Equity Factor Diversification”, Ulrich Carl analyzes diversification properties of six equity factors (market excess return, size, value, momentum, low-beta and quality) across 20 developed stock markets. He defines each factor conventionally as returns to a portfolio that is each month long (short) stocks with the highest (lowest) expected returns based on that factor. He considers: (1) cross-country correlations for each factor; (2) cross-factor correlations for each country; (3) cross-country, cross-factor correlations; (4) dynamics of cross-country correlations for each factor based on rolling 36-month windows of returns; and, (5) cross-country correlations for each factor for the 30% lowest and 30% highest market excess returns (tail events). He also applies principal component analysis as another way to evaluate how diverse the 120 country-factor return streams are. Finally, he constructs cross-factor and cross-country portfolios to assess economic value of diversification properties. Using monthly returns in U.S. dollars for the six factors in each of the 20 countries during January 1991 through April 2015, he finds that: Keep Reading

Are strategies that exploit return autocorrelation good places to look for complementary (diversifying) return streams? In the March 2017 version of their paper entitled “Momentum and Covered Calls almost Everywhere”, Stephen Choi, Gil-Lyeol Jeong and Hogun Park examine trend following and covered call strategies at the asset class level both separately and in combination. Their asset class universe consists of three equity indexes, three bond indexes, three commodity indexes and one real estate investment trust (REIT) index. Their trend following (or time series momentum) strategy, which exploits positive autocorrelation of monthly index returns, is long (short) an index when its end-of-month level is above (below) its 12-month simple moving average. Their covered call strategy, which exploits negative autocorrelation (reversion) of index returns, is continuous, such as specified for the CBOE S&P 500 BuyWrite Index. They compare trend following and covered call strategies, separately and in combination, with buy-and-hold for single-class indexes and for multi-class portfolios of indexes. They consider three ways to construct multi-class portfolios (see “Tests of Strategic Allocations Based on Risk Metrics”): (1) maximum diversification (MDR), which maximizes the ratio of the sum of volatilities for individual assets divided by overall portfolio volatility; (2) equal risk contribution (ERC), a form of risk parity with adjustments for correlation; and, (3) equal weight (EW). They rebalance these portfolios quarterly, with volatility/correlation inputs for MDR and ERC based on a 3-year rolling window of historical data. They focus portfolio testing for only 10 years (2007-2016) based on availability of data for covered call indexes. Using the specified data as available from the end of 1971 through 2016, they find that: Keep Reading

Are the returns of factors widely used to predict the cross-section of stock returns themselves predictable? In the January 2016 draft of his paper entitled “Equity Factor Predictability”, Ulrich Carl analyzes predictability of market, size (market capitalization), value (book-to-market ratio), momentum (returns from 12 months ago to one month ago), low beta (betting against beta) and quality factor returns. All factor returns derive from hedge portfolios that are long (short) stocks with high (low) expected returns based on their factor values. He employs a broad range of economic and financial variables in four sets and multiple ways of testing predictability to ensure robustness of findings and limit model/data snooping bias. Predictability tests he applies include: combinations of simple forecasts (mean or median of single-variable regression forecasts); principal component analysis to distill forecasting variables into a few independent predictive factors; and, methods that adjust variable emphasis according to their respective past performances. He considers several predictability evaluation metrics, including: mean squared error compared to that of the historical average return; utility gain of timing based on predictability; and, information ratio (difference in return divided by difference in risk) relative to the historical average return. He mostly examines next-month forecasts with a one-month gap between predictive variable measurement and forecasted return over two test periods: 1975-2013 and 1950-2013. Using monthly returns for the six factors (start dates ranging from 1928 to 1958), a large set of financial variables since 1928 and a large set of economic variables since 1962, all through November 2013, he finds that: Keep Reading

How attractive are purified factor portfolios, constructed to focus on one factor by avoiding exposures to other factors? In their January 2017 paper entitled “Pure Factor Portfolios and Multivariate Regression Analysis”, Roger Clarke, Harindra de Silva and Steven Thorley explore a multivariate regression approach to generating pure factor portfolios. They consider five widely studied factors: value (earnings yield); momentum (cumulative return from 12 months ago to one month ago); size (market capitalization); equity market beta; and, profitability (gross profit margin). They also consider bond beta (regression of stock returns on 10-year U.S. Treasury note returns) to examine interest rate risk. They each month reform two types of factor portfolios:

1. Primary – a factor portfolio with weights that deviate simply from market weights based on analysis of just one factor, with differences from market portfolio weights scaled by market capitalization.
2. Pure – a factor portfolio derived from a multiple regression that isolates each factor, ensuring that it has zero exposures to all other factors.

They measure factor portfolio performance based on: average difference in monthly returns between each factor portfolio and the market portfolio; annualized standard deviation of the underlying monthly return differences; 1-factor (market) alpha; and, information ratio (alpha divided by incremental risk to the market portfolio). Using return and factor data for the 1,000 largest U.S. stocks during 1967 through 2016, they find that: Keep Reading

Are there plenty of exchange-traded funds (ETF) offering positive or negative exposures to widely accepted factor premiums? In his February 2017 paper entitled “Are Exchange-Traded Funds Harvesting Factor Premiums?”, David Blitz analyzes the exposures of U.S. equity ETFs to market, size, value, momentum and volatility factors. Specifically, he calculates factor betas (exposures) from a multi-factor regression of monthly excess (relative to the risk-free rate) total returns for each ETF versus market, small-minus-big size (SMB), high-minus-low value (HML), winners-minus-losers momentum (WML) and low-minus-high volatility (LV-HV) factor returns during 2011 through 2015. His overall sample consists of 415 U.S. equity ETFs with least 36 months of return history as of the end of 2015. He also considers subsamples consisting of: (1) 103 smart beta ETFs that explicitly target factor premiums, including fundamentally weighted and high-dividend funds; and, (2) the remaining 312 conventional ETFs, including sector funds and funds with conflicting factor exposures. He includes lists of the 10 ETFs with the most positive and the 10 ETFs with the most negative exposures to each factor from among the 100 largest ETFs. Using monthly Assets under Management (AuM) and total returns for the specified 415 ETFs, along with the monthly risk-free rate and the selected factor premiums during January 2011 through December 2015, he finds that: Keep Reading