Momentum Investing

Do predictive powers of the size, value and momentum factors observed for individual stocks translate to the country level? In the November 2014 version of his paper entitled “Country Selection Strategies Based on Value, Size and Momentum”, Adam Zaremba investigates country-level value, size and momentum premiums, and tests whether the value and momentum premiums are equally strong across markets of different sizes and evaluates a country-level multi-factor asset pricing model. He measures factors at the country level as:

• Value: aggregate book-to-market ratio, with aggregate 12-month earnings-to-price-ratio, cash flow-to-price ratio and dividend yield as alternatives where available.
• Size: total market capitalization of country stocks.
• Momentum: cumulative return over preceding 12, 9, 6 or 3 months excluding the last month to avoid short-term reversal.

He relies on capitalization-weighted, U.S. dollar-denominated gross total return MSCI equity indexes as available, with Dow Jones and STOXX indexes as fallbacks (an average 56 indexes per month over time). He includes discontinued country indexes. He uses one-month LIBOR as the risk-free rate. Each month, he ranks countries by value, size and momentum into value-weighted or equal-weighted fifths (quintiles). He also performs double-sorts first on size and then on value or momentum. Using monthly firm/stock data for listed stockswithin 78 country indexes as available during February 1999 through September 2014 (147 months), he finds that: Keep Reading

Is the Commodity Trading Advisor (CTA) segment so crowded that flows of funds into or out of them around the turn of the month materially affect prices? In the October 2014 version of his paper entitled “The MOM-TOM Effect: Detecting the Market Impact of CTA Trading”, Otto Van Hemert explores whether the trend-following or time series momentum (MOM) style employed by many CTAs is so crowded that inflows around the turn of the month (TOM) affect momentum strategy returns. He notes that most CTA-managed funds offer monthly liquidity, thereby concentrating flows at month ends. He defines TOM as the last two days of a month plus the first day of the next month. He tests whether there is an above average return for MOM strategies during TOM (MOM-TOM effect). He uses the Newedge CTA Index (an equal-weighted aggregate of the largest CTAs open to new investments) and the Newedge Trend Index (an equal-weighted aggregate of the MOM style CTAs that are open to new investments) as proxies for the overall market and the MOM style, respectively. Using daily returns for these two indexes during January 2000 through March 2014, he finds that: Keep Reading

Is there a way to predict when stock price momentum strategies will thrive or crash? In the October 2014 update of their draft paper entitled “Time-Varying Momentum Payoffs and Illiquidity”, Doron Avramov, Si Cheng and Allaudeen Hameed investigate the relationship between future momentum strategy profitability and market illiquidity. They measure momentum conventionally as the average gross monthly return of a portfolio that is each month long the value-weighted tenth (decile) of common stocks with the highest and short the value-weighted decile of common stocks with the lowest returns from 12 months ago to one month ago (with a skip-month to avoid short-term reversal). Their stock illiquidity metric is the Amihud measure (average daily price impact per monetary volume traded over the past month), and they measure market illiquidity as the value-weighted average stock illiquidity. Using daily and monthly prices and market capitalizations for a broad sample of U.S. common stocks, monthly equity risk factors, investor sentiment and firm earnings data as available during January 1926 through December 2011, they find that: Keep Reading

What conditions foretell stock momentum strategy crashes? In their October 2014 paper entitled “Momentum Trading, Return Chasing, and Predictable Crashes”, Benjamin Chabot, Eric Ghysels and Ravi Jagannathan examine stock momentum strategy performance for both widely used historical U.S. data (starting in 1926 through 2012) and for a hand-collected sample of stocks listed on the London Stock Exchange during 1866 to 1907. They consider two methods of measuring momentum strategy returns. One is the gross return to the Fama-French momentum factor portfolio. The other is the gross return to a portfolio that is each month long (short) the value-weighted 30% of stocks with the highest (lowest) returns per the Fama-French momentum decile portfolios. Both methods define momentum conventionally as the return from 12 months ago to one month ago, with a skip-month before portfolio formation to avoid short-term reversal. They focus on conditions that precede momentum strategy crashes based on a model that considers three factors: (1) the risk-free rate; (2) past stock market return; and, (3) past momentum strategy return. Using the specified stock return data sets, they find that: Keep Reading

Are gains from tax-loss harvesting, the systematic taking of capital losses to offset capital gains, additive to or subtractive from premiums from portfolio tilts toward common factors such as value, size, momentum and volatility (smart beta)? In their October 2014 paper entitled “Factor Tilts after Tax”, Lisa Goldberg and Ran Leshem look at the effects on portfolio performance of combining factor tilts and tax-loss harvesting. They call the incremental return from tax-loss harvesting tax alpha, which (while investor-specific) is typically in the range 1%-2% per year for wealthy investors holding broad capitalization-weighted portfolios. They test six long-only factor tilts based on Barra equity factor models: (1) value (high earnings yield and book-to-market ratio); (2) momentum (high recent past return); (3) value/momentum; (4) small/value; (5) quality (value stocks with low earnings variability, leverage and volatility); and, (6) minimum volatility/value (low volatility with diversification constraint and value tilt). Their overall benchmark is the MSCI All Country World Index (ACWI). Their tax alpha benchmark derives from a strategy that harvests losses in a capitalization-weighted portfolio (no factor tilts) without deviating far from the overall benchmark. The rebalancing interval is monthly for all portfolios. Using monthly returns for stocks in the benchmark index during January 1999 through December 2013, they find that: Keep Reading

In the preface to his 2015 book entitled Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk, author Gary Antonacci states: “We need a way to earn long-term above-market returns while limiting our downside exposure. This book shows how momentum investing can make that desirable outcome a reality. …the academic community now accepts momentum as the ‘premier anomaly’ for achieving consistently high risk-adjusted returns. Yet momentum is still largely undiscovered by most mainstream investors. I wrote this book to help bridge the gap between the academic research on momentum, which is extensive, and its real-world application… I finally show how dual momentum—a combination of relative strength and trend-following…is the ideal way to invest.” Based on a survey of related research and his own analyses, he concludes that: Keep Reading

A subscriber proposed a simple test of the concept underlying the First Trust Dorsey Wright Focus 5 ETF (FV). This exchange-traded fund (ETF) intends to track the Dorsey Wright Focus Five Index, an equally weighted and weekly reformed portfolio of the five First Trust sector and industry ETFs with the highest price momentum according to the Dorsey, Wright & Associates relative strength ranking system. In the absence of a detailed specification for this ranking system, the subscriber proposed a conceptual test applying the rules for the “Simple Asset Class ETF Momentum Strategy” to the FV universe, which consists of the following 23 ETFs:

First Trust NASDAQ-100-Technology Sector Index Fund (QTEC))
First Trust NYSE Arca Biotechnology Index Fund (FBT)
First Trust Dow Jones Internet Index Fund (FDN)
First Trust ISE-Revere Natural Gas Index Fund (FCG)
First Trust ISE Water Index Fund (FIW)
First Trust S&P REIT Index Fund (FRI)
First Trust Consumer Discretionary AlphaDEX Fund (FXD)
First Trust Consumer Staples AlphaDEX Fund (FXG)
First Trust Health Care AlphaDEX Fund (FXH)
First Trust Technology AlphaDEX Fund (FXL)
First Trust Energy AlphaDEX Fund (FXN)
First Trust Financials AlphaDEX Fund (FXO)
First Trust Industrials/Producer Durables AlphaDEX Fund (FXR)
First Trust Utilities AlphaDEX Fund (FXU)
First Trust Materials AlphaDEX Fund (FXZ)
First Trust FTSE EPRA/NAREIT Developed Markets Real Estate Index Fund (FFR)
First Trust NASDAQ ABA Community Bank Index Fund (QABA)
First Trust NASDAQ Clean Edge Smart Grid Infrastructure Index Fund (GRID)
First Trust ISE Global Copper Index Fund (CU)
First Trust ISE Global Platinum Index Fund (PLTM)
First Trust NASDAQ CEA Smartphone Index Fund (FONE)
First Trust ISE Cloud Computing Index Fund (SKYY)
First Trust NASDAQ Technology Dividend Index Fund (TDIV)

At the end of each month, we allocate all funds to the equally weighted set of the five of these 23 ETFs with the highest total return over the past five months. Using monthly dividend-adjusted closing prices for these ETFs during May 2007 (when 15 of the ETFs are available) through August 2014 (88 months), we find that: Keep Reading

Does playing trends both ways via periodic rebalancing (betting on reversion) and momentum (betting on continuation) reliably produce attractive outcomes? In the August 2014 version of their paper entitled “Rebalancing Risk”, Nick Granger, Doug Greenig, Campbell Harvey, Sandy Rattray and David Zou investigate the effects of adding a momentum overlay to a conventionally rebalanced stocks-bonds portfolio. They note that periodic rebalancing to fixed asset class weights tends to perform well in trendless markets exhibiting mean reversion but suffers during extended trends. They consider simple examples using a 60% target allocation to the S&P 500 Index and a 40% allocation to 10-year U.S. Treasury notes (T-note), rebalanced monthly or quarterly. Their momentum strategy employs a complex daily moving average cross-over model with target volatility 10% that has an average annual turnover of 400%. Using both theoretical arguments and empirical analysis of daily and monthly asset class proxy returns during January 1990 through February 2014, they find that: Keep Reading

Does the stock momentum anomaly interact with the quarterly financial cycle? In his August 2014 paper entitled “Seasonal Patterns in Momentum and Reversal in the U.S. Stock Market: The Consequences of Tax-Loss Sales and Window Dressing”, David Brown examines whether tax-loss selling and window dressing at the ends of calendar quarters affect U.S. stock momentum strategy returns. Each month, he ranks stocks by returns over the last 12 months, skipping the last month to avoid reversal, and then forms a momentum hedge portfolio that is long (short) the capitalization-weighted tenth of stocks with the highest (lowest) past returns, making the long and short sides of the portfolio equal in magnitude. He then measures how this portfolio performs by calendar month to check for end-of-quarter effects. He also investigates whether the level of capital losses among stocks in the portfolio affects performance. Using monthly returns for NYSE, AMEX and NASDAQ common stocks, along with contemporaneous risk-free rates and Fama-French model risk factor returns, during January 1927 through December 2013, he finds that: Keep Reading

Does focus on nearest-expiration contracts in commodity futures momentum strategies leave money on the table? In their May 2014 paper entitled “Exploiting Commodity Momentum Along the Futures Curves”, Wilma De Groot, Dennis Karstanje and Weili Zhou investigate commodities futures momentum strategies that consider all available contract expirations. They hypothesize that a broadened contract universe could increase roll yield, reduce volatility and lower portfolio turnover. Their generic benchmark strategy each month buys (sells) the equally weighted half of commodities with the highest (lowest) 12-month returns using nearest-expiration contracts. They consider three alternatives to the generic strategy:

1. Optimal-roll momentum: each month ranks commodities in the same way as the generic strategy, but buys the most backwardated contract for each winner commodity and sells the most contangoed contract for each loser commodity from among contracts with expirations up to 12 months.
2. All-contracts momentum: each month first select for each commodity the contract expiration with the strongest and weakest momentum. Then rank the commodities based on these contracts and buy (sell) the equally weighted half with the highest (lowest) momentum.
3. Low-turnover roll momentum: modify the optimal-roll momentum strategy by holding each position until it is about to expire or until it switches sides (long-to-short or short-to-long), whichever occurs first.

They assume fully collateralized portfolios, such that total monthly return for each position is change in month-end settlement price plus the risk-free interest rate (U.S. Treasury bill yield) earned by the collateral. They focus on changes in settlement prices (excess returns). They consider several ways of estimating trading frictions. Using daily and monthly prices of S&P GSCI components during January 1990 through September 2011 (initially 18 commodity series growing to all 24 by July 1997), they find that: Keep Reading