Momentum Investing

What is the most effective way to hedge against equity market crashes? In their June 2017 paper entitled “The Best Strategies for the Worst Crises”, Michael Cook, Edward Hoyle, Matthew Sargaison, Dan Taylor and Otto Van Hemert examine active and passive strategies with potential to generate positive returns during the worst crises. They test these strategies across the seven S&P 500 Index drawdowns of more than 15% during 1985 through 2016. They focus on two active strategies:

1. Time-series (intrinsic or absolute) momentum long-short portfolio comprised of 50 liquid futures and forwards series spanning currencies, equity indexes, bonds, agricultural products, energy and metals. They consider return lookback intervals of 1, 3 and 12 months. They apply risk adjustments, risk allocations by class and finally a scale factor targeting 10% annualized portfolio volatility. They consider three extensions of the strategy that preclude or restrict positive exposure to equity market beta.
2. Quality factor long-short portfolios comprised of intermediate and large capitalization U.S. stocks. These portfolios ares long (short) the highest-ranked (lowest-ranked) stocks, as selected based on one of 18 metrics representing profitability, growth in profitability, safety and payout. Rankings are risk-adjusted and portfolios are equity market beta-neutral. They again apply a scale factor targeting 10% annualized portfolio volatility. They also consider several composite factor portfolios by averaging individual factor rankings and weighting for dollar neutrality, beta neutrality, sector neutrality and/or volatility balancing.

Using daily data for all indicated assets during 1985 through 2016, they find that: Keep Reading

Does a dual momentum selection/weighting approach applied to the U.S. Treasuries term structure identify a safe haven superior to any one duration? In his February 2015 paper entitled “The Search for Crisis Alpha: Weathering the Storm Using Relative Momentum”, Nathan Faber tests a dual momentum safe haven based on U.S. Treasuries of different durations as proxied by either constant maturity indexes or exchange-traded funds (ETFs). He constructs constant maturity indexes from 1-year, 3-year, 5-year, 7-year, 10-year and 20-year constant maturity U.S. Treasuries yields by each month accruing a coupon and repricing at the new yield. For ETFs, he uses total returns for five iShares U.S. Treasuries ETFs: SHY (1-3 years), IEI (3-5 years), IEF (7-10 years), TLH (10-20 years) and TLT (20+ years). The dual momentum approach consists of the following steps:

1. Calculate the return from 10 months ago to one month ago for each duration.
2. Subtract from the return of each duration that of 1-year U.S. Treasuries (SHY) if using constant maturity indexes (ETFs) to calculate an excess return as a measure of intrinsic (absolute or time series) momentum.
3. Discard any durations with negative excess returns.
4. Rank remaining durations based on risk-adjusted excess returns, with variances used to indicate risk, as a measure of relative momentum and assign weights to these durations based on their ranks. If no durations have positive excess returns, assign 100% weight to 1-year U.S. Treasuries (or SHY if using ETFs).

He then investigates the performance of this dual momentum strategy as a safe haven during S&P 500 crises defined in two ways: (1) drawdowns of at least 20% peak to trough; or, (2) monthly declines of at least 5%. He further tests a specific strategy that is long the S&P 500 Index (or SPY if using ETFs) when above its 10-month SMA (SMA10) and in either the dual momentum safe haven portfolio or in a fixed duration (1-year or 20+ years) when below its SMA10. Using data for the yields/indexes/funds specified above since 1962 for constant maturity index tests and since 2003 for ETF tests, all through 2014, he finds that: Keep Reading

Does average sign of recent returns work as well as recent cumulative return as a momentum metric? In their May 2017 paper entitled “Returns Signal Momentum”, Fotis Papailias, Jiadong Liu and Dimitrios Thomakos introduce and test a momentum strategy (RSM) based on the equally weighted average signs (1 for positive and 0 for negative) of past returns over a given lookback interval. This metric employs each of the past returns during the lookback interval, not a single cumulative return as in times series (intrinsic or absolute) momentum. It considers only signs of past returns, not their magnitudes as in conventional relative momentum. They focus on monthly returns over a lookback interval of 12 months. They test RSM on a universe of 55 of the most liquid futures/forwards: 24 commodities; 9 currency exchange rates versus the U.S. dollar; 9 developed country equity indexes; and, 13 government bonds of various maturities from six developed countries. Their strategy is each month long (short) a contract series when average sign of its last 12 monthly returns is above (below) a threshold. They consider two types of thresholds: (1) fixed over the test period, with the featured optimal value selected by experimentation; and, (2) time-varying, each month choosing the best-performing value (from among 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 and 0.8) over the prior 24 months. Using returns for the 55 futures/forwards series as available to support a strategy test period of January 1985 through March 2015, they find that:
Keep Reading

How well do time series (intrinsic) and cross-sectional (relative) momentum work for different types of currency exchange rates? In their April 2017 paper entitled “Momentum in Traditional and Cryptocurrencies Made Simple”, Janick Rohrbach, Silvan Suremann and Joerg Osterrieder compare the effectiveness of time series and cross-sectional momentum as applied to three groups of currency exchange rates: G10 currencies; non-G10 conventional currencies; and, cryptocurrencies. To measure momentum they employ three pairs (one fast and one slow) of exponential moving averages (EMA) spanning short, intermediate and long horizons. When the fast EMA of a pair is above (below) the slow EMA, the trend is positive (negative). They extract a momentum signal for each exchange rate from these three EMA pairs by:

1. For each EMA pair, taking the difference between the fast and slow EMA.
2. For each EMA pair, dividing the output of step 1 by the standard deviation of the exchange rate over the last three months to scale currency fluctuations to the same magnitude.
3. For each EMA pair, dividing the output of step 2 by its own standard deviation over the last year to suppress series volatility.
4. For each EMA pair, mapping all outputs of step 3 to signals between -1 and 1.
5. Averaging the signals across the three EMA pairs to produce an overall momentum signal.

The time series portfolio holds all currencies weighted each day according to their respective prior-day overall momentum signals.  The cross-sectional portfolio is each day long (short) the three currencies with the highest (lowest) overall momentum signals. Key performance metrics are annualized average gross return, annualized standard deviation of returns, annualized gross Sharpe ratio (assuming risk-free rate 0%) and maximum drawdown. Using daily foreign currency exchange rates for 23 conventional currencies and seven cryptocurrencies versus the U.S. dollar as available through late March 2017, they find that: Keep Reading

Does stock momentum purified of market, size and book-to-market factor risks (idiosyncratic or residual or pure momentum) distinctly outperform conventional momentum? In their April 2017 paper entitled “The Idiosyncratic Momentum Anomaly”, David Blitz, Matthias Hanauer and Milan Vidojevic revisit idiosyncratic past stock return as a return predictor. They specify conventional momentum as total return from 12 months ago to one month ago. To calculate idiosyncratic momentum, for each stock each month they: (1) estimate idiosyncratic return as the part of total return not explained by Fama-French 3-factor (market, size and book-to-market) model betas determined from the prior 36 months; and, (2) calculate idiosyncratic momentum as the volatility-adjusted sum of monthly idiosyncratic returns from 12 months ago to one month ago. They then calculate idiosyncratic momentum factor returns from a monthly reformed hedge (Winners-Minus-Losers, or WML) portfolio that is long big and small stocks with the highest idiosyncratic momentum and short big and small stocks with the lowest. Using monthly stocks/firms data for a broad sample of U.S. common stocks since December 1925, for stocks/firms and currencies in Europe, Asia-Pacific and Japan since January 1989 and for stocks/firms and currencies in emerging markets since January 1992, all through December 2015, they find that: Keep Reading

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