Momentum Investing

We have updated the detail at “Momentum Strategy” to incorporate the historical data changes described in “Simple Asset Class ETF Momentum Strategy Data Changes”. The principal effects are to decrease the performance of the Top 1 portfolio and increase the performance of the Top 2 portfolio.

Over the coming week, we will accordingly update much of the supporting research listed at “Momentum Strategy”.

Do individuals who actively reallocate funds within their pension accounts outperform passive counterparts? In the March 2014 update of their paper entitled “Individual Investor Activity and Performance”, Magnus Dahlquist, Jose Vicente Martinez and Paul Soderlind examine the activity and performance of individual participants in Sweden’s Premium Pension System. This system allows individual participants to reallocate among available mutual funds on a daily basis with no switching fees/impediments. Information about the 1,230 funds offered during the sample period includes type (fixed income, balanced, life-cycle and equity), return and risk measured at several horizons, fee and major holdings. Most are equity funds, about half of which invest primarily in international equities. The government assigns individuals who make no choice to a default fund. Using daily net returns, fund trades and demographics for 70,755 individuals (from a random draw of individuals in the system over the entire period) and contemporaneous returns for several benchmarks during September 2000 through May 2010, they find that: Keep Reading

Does the return to momentum investing reliably reflect a reward for taking some risk? In the March 2014 version of her paper entitled “Asymmetric Risks of Momentum Strategies”, Victoria Dobrynskaya examines the performance of past winner and loser stocks, stock indexes and currency exchange rates during global equity market advances and declines. She focuses on the difference between downside and upside market betas (asymmetric beta) of momentum portfolios that are long past winners and short past losers (winner-minus-loser, or WML). She bases momentum rankings on total returns from 12 months ago to one month ago, with a month skipped before portfolio formation to avoid short-term reversal. She considers momentum portfolios formed from: a broad sample of U.S. stocks (January 1927 through July 2013): global and world regional stocks (November 1990 through August 2013); 40 country stock indexes (as they become available during January 1983 through August 2013); and, 45 currency exchange rates relative to the U.S. dollar (as they become available during October 1983 through August 2013). She also examines U.S. short-term reversal stock portfolios sorted by prior-month return (January 1927 through July 2013) and U.S. long-term reversal stock portfolios sorted by past five-year return (January 1931 through July 2013). She relies on Kenneth French’s data library for stock-level portfolio returns. Using total return data for the specified portfolios and for associated market indexes, she finds that: Keep Reading

Is relative momentum an effective way to switch between stocks and bonds? In his May 2014 paper entitled “Simple and Effective Market Timing with Tactical Asset Allocation”, Lewis Glenn compares the performances of two tactical asset allocation strategies:

1. SPY-TLT:  simple relative momentum strategy that each month holds SPDR S&P 500 (SPY) or iShares Barclays 20+ Year Treasury Bond (TLT) depending on which has the higher total return over the last three months.
2. Ivy 5: diversified trend following strategy that initially allocates equal amounts to each of five funds: exchange-traded funds (ETF) representing U.S. stocks, non-U.S. large stocks, 7-10 year U.S. Treasuries and real estate, and a mutual fund representing commodities.Thereafter, each allocation remains in the fund (goes to cash) when the fund is above (below) its 10-month simple moving average (SMA10) the prior month.

He includes “nominal” transaction fees for trades but does not account for bid-ask spread or impact of trading frictions. Using monthly dividend-adjusted closes of the specified funds during 2004 (plus prior data for momentum/trend calculations) through 2013, he finds that:

Keep Reading

Do relative momentum and trend filters operate differently on value and growth stocks? In their May 2014 paper entitled “When Growth Beats Value: Removing Tail Risk from Global Equity Momentum Strategies”, Andrew Clare, James Seaton, Peter Smith and Stephen Thomas investigate the effects of relative momentum and trend filters on portfolios of developed and emerging market broad, value and growth stock indexes. Their relative momentum filter each months picks either the top five indexes (Mom5) or top quarter of indexes (Mom25%) based on volatility-adjusted past 12-month return (return divided by standard deviation of monthly returns) at the end of the prior month. Their trend filter each month invests in an index or U.S. Treasury bills (T-bills) according to whether the index is above or below its 10-month simple moving average (SMA10) at the end of the prior month. Using monthly levels of broad, value and growth stock indexes for 23 developed country markets (since 1976) and 21 emerging country markets (since 1998) through 2012, they find that: Keep Reading

In checking data for the monthly update of “Simple Asset Class ETF Momentum Strategy” (SACEMS), we discovered changes in historical dividend/split-adjusted prices for the following strategy components:

iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)

The changes all precede June 2010, and most precede July 2008. Recent dividends do not explain the changes. While the changes are not large, they affect the Top 1 winner in one month and the Top 3 winners in eight months. Incorporating the changes therefore may affect findings in related backtests. We will update these backtests, associated research summaries and “Momentum Strategy” during June 2014 to incorporate the data changes.

These changes illustrate the risk of data instability.

The following table provides detail on percentage changes in historical adjusted prices as used in strategy backtests and months for which the changes affect the Top 3 winners: Keep Reading

What about all those criticisms of momentum investing (such as high turnover/trading frictions and crash-proneness)? In the May 2014 draft of their paper entitled “Fact, Fiction and Momentum Investing”, Clifford Asness, Andrea Frazzini, Ronen Israel and Tobias Moskowitz summarize research on the momentum anomaly and rebut ten criticisms (myths) of momentum investing. Specifically, they address claims that momentum profitability is too small, too volatile/crash-prone, works mostly on the problematic short side, works well only among small stocks and does not survive trading frictions. They focus on a “standard” definition of momentum as the past 12-month return, skipping the most recent month‘s return (to avoid microstructure and liquidity biases). Using results from widely circulated and debated academic papers and data from Kenneth French‘s website, they conclude that: Keep Reading

Are investors exhausting the potential of stocks? In his May 2014 presentation packages entitled “Has The Stock Market Been ‘Overgrazed’?” and “Momentum Has Not Been ‘Overgrazed'”, Claude Erb investigates the proposition that sanguine research and ever easier access to investments are exhausting U.S. stock market investment opportunities. In the first package, he focuses on trends in the overall equity risk premium, the size effect and the value premium. In the second, he focuses on momentum investing. Using U.S. stock market and equity factor premium returns and contemporaneous U.S. Treasury bill yields during 1926 through 2013, he concludes that: Keep Reading

How sensitive in a recent sample are outcomes from simple trend following rules to the length of the measurement interval used to detect a trend. To investigate, we consider two simple types of trend following rules as applied to the U.S. market:

1. Hold a risky asset when its price is above its x-month simple moving average (SMAx) and cash when below, with x ranging from two to 12.
2. Hold a risky asset when its x-month return, absolute or intrinsic momentum (IMx), is positive and cash when negative, with x ranging from one to 12.

Specifically, we apply these 23 rule variations to time the S&P 500 Index since the inception of SPDR S&P 500 (SPY) as an easy and flexible way to trade the index over the available sample period and two subperiods, the decade of the 2000s and the last five years. We use the yield on 3-month U.S. Treasury bills (T-bills) to approximate return on cash. We use buying and holding SPY as a benchmark for the active rules. Using monthly closing levels of the S&P 500 Index since April 1992 and dividend-adjusted prices for SPY and T-bill yields since January 1993, all through March 2014, we find that: Keep Reading

Does stock market timing based on simple moving average (SMA) and time-series (intrinsic or absolute) momentum strategies really work? In the November 2013 version of his paper entitled “The Real-Life Performance of Market Timing with Moving Average and Time-Series Momentum Rules”, Valeriy Zakamulin tests realistic long-only implementations of these strategies with estimated trading frictions. The SMA strategy enters (exits) an index when its unadjusted monthly close is above (below) the average over the last 2 to 24 months. The intrinsic momentum strategy enters (exits) an index when its unadjusted return over the last 2 to 24 months is positive (negative). Unadjusted means excluding dividends. He applies the strategies separately to four indexes: the S&P Composite Index, the Dow Jones Industrial Average, long-term U.S. government bonds and intermediate-term U.S. government bonds. When not in an index, both strategies earn the U.S. Treasury bill (T-bill) yield. He considers two test methodologies: (1) straightforward inception-to-date in-sample rule optimization followed by out-of-sample performance measurement, with various break points between in-sample and out-of-sample subperiods; and, (2) average performance across two sets of bootstrap simulations that preserve relevant statistical features of historical data (including serial return correlation for one set). He focuses on Sharpe ratio (including dividends) as the critical performance metric, but also considers terminal value of an initial investment. He assumes the investor is an institutional paying negligible broker fees and trading in small orders that do not move prices, such that one-way trading friction is the average bid-ask half-spread. He ignores tax impacts of trading. With these assumptions, he estimates a constant one-way trading friction of 0.5% (0.1%) for stock (bond) indexes. Using monthly closes and dividends/coupons for the four specified indexes and contemporaneous T-bill yields during January 1926 through December 2012 (87 years), he finds that: Keep Reading