Momentum Investing

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

Can individual investors reliably extract excess returns from long-only stock momentum? In their April 2014 paper entitled “Profitable Momentum Trading Strategies for Individual Investors”, Bryan Foltice and Thomas Langer examine whether a long-only stock momentum portfolio holding the top one to 50 stocks outperforms the stock market on a net basis. Their approach avoids issues of shorting costs/feasibility and explores trade-offs between gross performance and trading frictions. Their baseline analysis: (1) ranks stocks based on six-month total return; (2) forms an equally weighted portfolio of the top 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40 and 50 stocks at the next close; (3) holds the portfolio for 12 months; and, (4) estimates net returns nine initial investment amounts ranging from $5,000 to $1,000,000. They approximate one-way trading friction a $10 commission plus a half bid-ask spread specified by range of market capitalization (0.21% to 0.75%) plus a relatively small SEC fee. For stocks retained from the prior portfolio, the bid-ask spread applies only to the change in position size from rebalancing to equal weight. Finally, they investigate whether employing overlapping portfolios of small numbers of stocks (forming portfolios more frequently than annually but still holding each portfolio for a year) suppresses volatility and improves risk-adjusted performance. They use buy-and-hold the S&P 500 Total Return Index as a benchmark. Using data for live and dead NYSE stocks with contemporaneous market capitalizations over $20 million during 1991 through 2010 (an average of 2,201 stocks per month), they find that: Keep Reading

As a follow-up to “Mutual Fund Hot Hand Performance Robustness Test”, a subscriber suggested testing a portfolio that each year holds the top two Fidelity diversified equity funds plus the top two Vanguard diversified equity funds from “Mutual Fund Hot Hand Performance” (four funds total). Such a portfolio should suppress volatility, particularly the effects of any outlier returns, while maintaining tax-friendly capital gains treatment. We extend that suggestion to consider the top three funds from each of the Fidelity and Vanguard sets. We use June-to-June annual returns starting 1993 with availability of SPDR S&P 500 (SPY) as a widely used and easily investable benchmark. The number of Fidelity (Vanguard) funds available for the initial ranking in 1993 is 23 (10), growing to 61 (49) by 2013. Using monthly total returns for SPY and the Fidelity and Vanguard diversified equity mutual funds as available from Yahoo!Finance during June 1993 through June 2014 (21 years), we find that: Keep Reading