Momentum Investing

Is there a most practical way to make value and momentum work together across stocks? In the April 2016 version of their paper entitled “Combining Value and Momentum”, Gregg Fisher,  Ronnie Shah and Sheridan Titman examine long-only stock portfolios that seek exposure to both value and momentum while suppressing trading frictions. They define value as high book-to-market ratio based on book value lagged at least four months. They define momentum as return from 12 months ago to one month ago. They consider two strategies for integrating value and momentum:

1. Each month, choose stocks with the highest simple average value and momentum percentile ranks. They suppress turnover with buy-sell ranges, either 90-70 or 95-65. For example, the 90-70 range adds stocks with ranks higher than 90 not already in the portfolio and sells stocks in the portfolio with ranks less than 70. 
2. After initially forming a value portfolio, each month buy stocks only when both value and momentum are favorable, and sell stocks only when both are unfavorable. This strategy weights value more than momentum, because momentum signals change more quickly than value signals. For this strategy, they each month calculate value and momentum scores for each stock as percentages of aggregate market capitalizations of other stocks with lower or equal value and momentum. They suppress turnover with a 90-70 or 95-65 buy-sell range, but the range applies only to the value score. There is a separate 50 threshold for momentum score, meaning that stocks bought (sold) must have momentum score above (below) 50.

They consider large-capitalization stocks (top 1000) and small-capitalization stocks (the rest) separately, with all portfolios value-weighted. They calculate turnover as the total amount bought or sold each month relative to portfolio size. They consider two levels of round-trip trading frictions based on historical bid-ask spreads and broker fees: high levels (based on 1993-1999 data) are 2.94% for small stocks and 1.06% for large stocks; low levels (based on 2000-2013 data) are 0.82% for small stocks and 0.41% large stocks. They focus on net Sharpe ratio as a performance metric. Using monthly data for a broad sample of U.S. common stocks during January 1974 through December 2013, they find that: Keep Reading

Does adding crash protection based on global market breadth enhance the reliability of dual momentum? In their April 2016 paper entitled “Protective Asset Allocation (PAA): A Simple Momentum-Based Alternative for Term Deposits”, Wouter Keller and Jan Willem Keuning examine a multi-class, dual-momentum portfolio allocation strategy with crash protection based on multi-market breadth. Their principal goal is consistently positive returns, at least 95% (99%) of 1-year rolling returns not below 0% (-5%). Their investment universe is 13 exchange-traded funds (ETF), 12 risky (SPY, QQQ, IWM, VGK, EWJ, EEM, IYR, GSG, GLD, HYG, LQD, TLT) and one safe (IEF). Each month, they:

1. Measure the momentum of each risky ETF as ratio of current price to simple moving average (SMA) of monthly prices over the past 3, 6, 9 or 12 months, minus one.
2. Specify the safe ETF allocation as number of risky assets with non-positive momentum divided by 12 (low crash protection), 9 (medium crash protection) or 6 (high crash protection). For example, if 3 of 12 risky assets have zero or negative momentum, the IEF allocation for high crash protection is 3/6 = 50%.
3. Allocate the balance of the portfolio to the equally weighted 1, 2, 3, 4, 5 or 6 risky assets with the highest positive momentum (reducing the number of risky assets held if not enough have positive momentum).

The interactions of four SMA measurement intervals, three crash protection levels and six risky asset groupings yield 72 combinations. They first identify the optimal combination in-sample during 1971 through 1992 and then test this combination out-of-sample since 1992. Prior to ETF inception dates, they simulate ETF prices based on underlying indexes. They assume constant one-way trading frictions 0.1%, acknowledging that this level may be too low for early years and too high for recent years. They focus on a monthly rebalanced 60% allocation to SPY and 40% allocation to IEF (60/40) as a benchmark. Using monthly simulated/actual ETF total return series during December 1969 through December 2015, they find that: Keep Reading

Does intricate optimization of the relationship between past month-by-month returns and future month-by-month returns substantially outperform a simple stock return momentum strategy based on some fixed past return interval? In their March 2016 paper entitled “Tree-Based Conditional Portfolio Sorts: The Relation between Past and Future Stock Returns”, Benjamin Moritz and Tom Zimmermann apply the machine learning concept of tree-based conditional portfolio sorts to determine which past monthly stock returns provide independent information about future monthly returns. This methodology handles a large number of independent variables, exposes non-linear relationships and emphasizes systematic out-of-sample testing. Their solution (“intricate” momentum) is an average model that smooths potentially anomalous predictions of many specific models, each employing different subsets of predictive variables on different subsamples (to mitigate overfitting). They make intricate momentum adaptive by annually updating the average model based on the last five years of data to determine how each of the monthly returns during the last 24 months predict each of the monthly returns over the next 12 months, generating a total of 45 annual predictions commencing five years after the start of the sample. Their test portfolio takes equally weighted long (short) positions in the tenth of stocks with the highest (lowest) predicted returns during each of these 12 months. Using monthly returns and stock/firm characteristics for a broad sample of U.S. stocks during 1963 through 2013, they find that: Keep Reading

Subscribers have requested extension of the momentum ranking interval robustness test in “Simple Asset Class ETF Momentum Strategy Robustness/Sensitivity Tests” to portfolios other than the momentum winner (Top 1), which each month ranks the following eight asset class exchange-traded funds (ETF), plus cash, on past return and rotates to the strongest class:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

We consider the following additional five portfolios: equally weighted top two (EW Top 2); equally weighted top three (EW Top 3); loser (Bottom 1); equally weighted bottom two (EW Bottom 2); and, equally weighted bottom three (EW Bottom 3). We consider momentum ranking intervals ranging from one month (1-1) to 12 months (12-1), all with one-month holding intervals (monthly portfolio reformation). The sample starts with the first month for which all ETFs are available (February 2006) and portfolio formation starts with the first month allowed by the longest momentum ranking interval (March 2007). We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key portfolio performance statistics, ignoring monthly reformation costs. Using monthly total returns for the specified assets during February 2006 through February 2016, we find that: Keep Reading

What kinds of smart beta work best? In their January 2016 paper entitled “A Taxonomy of Beta Based on Investment Outcomes”, Sanne De Boer, Michael LaBella and Sarah Reifsteck compare and contrast smart beta (simple, transparent, rules-based) strategies via backtesting of 12 long-only smart beta stock portfolios. They assign these portfolios to a framework that translates diversification, fundamental weighting and factor investing into core equity exposure and style investing (see the figure below). They constrain backtests to long-only positions, relatively investable/liquid stocks and quarterly rebalancing, treating developed and emerging markets separately. Backtest outputs address gross performance, benchmark tracking accuracy and portfolio turnover. Using beta-related data for developed market stocks during 1979 through 2014 and emerging market stocks during 2001 through 2014, they find that: Keep Reading

Do reversal, momentum and reversion effects hold among German stocks? In his January 2016 paper entitled “Trading Strategies Based on Past Returns – Evidence from Germany”, Martin Schmidt examines the performance of short-term reversal, intermediate-term momentum, long-term reversion and seasonality strategies in the German stock market. The seasonal strategy considers one-month returns from multiples of 12 months ago. His general approach is to each month (1) rank stocks into tenths (deciles) of a specified segment or pattern of past returns and (2) measure the performance next month of a value-weighted or equal-weighted portfolio that is long the top decile and short the bottom decile. For value weighting, he caps weight at 50%. Using monthly prices for a broad sample of German stocks during January 1955 through June 2014, he finds that: Keep Reading

Does a time series (absolute or intrinsic) momentum strategy work at the stock level? In their January 2016 paper entitled “The Enduring Effect of Time-Series Momentum on Stock Returns Over Nearly 100-Years” Ian D’Souza, Voraphat Srichanachaitrchok, George Wang  and Yaqiong Yao test the significance of time series momentum among individual stocks. Their baseline time series momentum strategy consists of each month calculating cumulative returns for each stock from 12 months ago to one month ago and taking a long (short) position for one month in stocks with positive (negative) past returns. For comparison, they also test a cross-sectional, or relative, momentum strategy that is each month long (short) the tenth, or decile, of stocks with the highest (lowest) cumulative returns over the same measurement interval. They skip the month between past return measurement and portfolio formation to avoid a reversal effect. They consider both value and equal weighting. They then test a dual momentum strategy that each month: (1) identifies time series momentum winners and losers; (2) ranks these two groups separately into fifths (quintiles); and, (3) buys the top quintile of time series winners and sells the bottom quintile of time series losers. Using monthly data for a broad U.S. stock sample during 1926 through 2014 and for stock samples from 13 other developed markets during mostly 1975 through 2014, they find that: Keep Reading

How can trend following (intrinsic or absolute or time series momentum) beat the market, while ostensibly similar return chasing transfers wealth from naive to smart investors? In their January 2016 paper entitled “Return Chasing and Trend Following: Superficial Similarities Mask Fundamental Differences”, Victor Haghani and Samantha McBride offer a plausible and testable definition of return chasing and explore its differences from trend following. They characterize trend followers as mechanical and decisive and return chasers as discretionary and slow moving. For quantitative comparison, they consider three long-only, no-leverage strategies:

1. 50-50 (benchmark): 50% equities and 50% U.S. Treasury bills (T-bills), rebalanced monthly.
2. Trend following: 100% stocks (T-bills) when real stock market return over the past year is greater than (less than) 2.5%.
3. Return chasing: increase (decrease) exposure to stocks each month by 20% of however much real stock market return exceeds (falls short of) 2.5% over the past year, holding the balance in T-bills.

They test these strategies with Robert Shiller’s long-run U.S. stock market data spanning 1871 through 2015 and with separately specified Monte Carlo simulation (5,000 runs of 20 years based on weekly simulated prices). Using these two approaches, they find that: Keep Reading

Do U.S. stock return anomalies exhibit exploitable momentum? In their December 2016 paper entitled “Scaling Up Market Anomalies”, Doron Avramov, Si Cheng, Amnon Schreiber and Koby Shemer test momentum across stock return anomalies. Their investment universe consists of the long and short sides of 15 stock portfolios, each long (short) the top (bottom) tenth of stocks based on sorting by one of the following 15 variables: failure probability, O-Score, net stock issuance, composite equity issuance, total accruals, net operating assets, momentum, gross profitability, asset growth, return on assets, abnormal capital investment, standardized unexpected earnings, analyst dispersion, idiosyncratic volatility and book-to-market ratio. They each month rank the 15 anomaly portfolios by prior-month return and test an anomaly momentum strategy that is long (short) the long (short) sides of the top five winner (bottom five loser) portfolios. They also consider top three-bottom three and top four-bottom four long-short strategies. Their benchmark is the equally weighted combination of all 15 anomaly portfolios. Using daily and monthly data for a broad sample of U.S. common stocks during 1976 through 2013, they find that: Keep Reading

How sensitive is the performance of the “Simple Sector ETF Momentum Strategy” to selecting ranks other than winners and to choosing a momentum ranking interval other than six months? This strategy each month ranks the following nine sector exchange-traded funds (ETF) on past return and rotates to the strongest sector:

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

Available data are so limited that sensitivity test results may mislead. With that reservation, we perform two robustness/sensitivity tests: (1) comparison of returns for all nine ranks of winner through loser based on a ranking interval of six months and a holding interval of one month (6-1); and, (2) comparison of winner returns for ranking intervals ranging from one to 12 months (1-1 through 12-1) and for a six-month lagged six-month ranking interval (12:7-1) per “Isolating the Decisive Momentum (Echo?)”, all with one-month holding intervals. Using monthly adjusted closing prices for the sector ETFs and SPDR S&P 500 (SPY) over the period December 1998 through December 2015 (205 months), we find that: Keep Reading