Momentum Investing

Does purifying stock return rankings of any dependence on Fama-French three-factor model risk factors enhance momentum strategy performance? In an update of their August 2009 paper entitled “Residual Momentum”, David Blitz, Joop Huij and Martin Martens suppress exposures of a conventional stock momentum strategy to market, size and book-to-market ratio risk factors by ranking stocks on residual returns instead of total returns. They calculate the residual return for each stock each month as the error term from a regression of its total returns versus the three risk factors over the past 36 months (excluding stocks without 36-month histories). For a total return momentum benchmark, they rank stocks each month based on total return over the last 12 months, excluding the most recent month. For residual return momentum, they rank stocks each month based on residual returns divided by their respective standard deviations over the past 12 months, excluding the most recent month. For both strategies, they measure the momentum effect as the average gross return on hedge portfolios that are long (short) the equally weighted tenth of stocks with the highest (lowest) past returns. They focus on a one-month holding interval, but also consider 3-month, 6-month and 12-month holding intervals (with overlapping portfolios). Using monthly returns for a broad sample of U.S. common stocks and contemporaneous three-factor returns during January 1926 through December 2009, they find that: Keep Reading

Does combining past return rankings at long (multi-year) and short (3-12 months) intervals offer a means of boosting momentum strategy returns? In their August 2013 paper entitled “Price Momentum Components: Evidence from International Market Indices”, Graham Bornholt and Mirela Malin compare strategies based on the interplay of short-term continuation and long-term reversal as applied to country stock market indexes. They define short-term as 3, 6, 9 or 12 months (focusing on 6 months). They define long-term as 36, 48 or 60 months. They consider three kinds of momentum strategies:

1. Traditional – each month, buy (sell) the fourth of country market indexes with the highest (lowest) short-term past returns.
2. Early-stage – each month, first identify the fourth of country markets that are short-term winners and the fourth that are short-term losers. Then buy (sell) the half of these winners (losers) with the lowest (highest) long-term returns, thereby focusing on indexes with recent price reversals.
3. Late-stage – each month, first identify the fourth of country markets that are short-term winners and the fourth that are short-term losers. Then buy (sell) the half of these winners (losers) with the highest (lowest) long-term returns, thereby focusing on indexes with consistent price continuation.

They weight selected indexes equally. They consider short-term holding intervals of 1, 3, 6, 9 or 12 months (with overlapping portfolios when longer than a month) and a long-term holding interval of five years. When calculating monthly returns, they insert a skip-month between the ranking and holding intervals and use a simple (equally weighted) average of returns for any active overlapping portfolios. When examining long-term performance, they do not insert a skip-month and use average returns for each month after portfolio formation. Using monthly total returns in U.S. dollars for 18 developed and 26 emerging country stock market indexes as available during January 1970 through April 2013 (220 to 520 observations per market), they find that: Keep Reading

The following table summarizes ranking of asset classes by subscribers responding during August 2013 to the following question (via the home page poll): “Which of the following asset classes do you expect to perform best in September 2013?” For comparison, the table also shows ranking of asset classes by momentum as specified in the baseline Momentum Strategy. Keep Reading

How well does stock screening research translate into performance? In the mid-1990s, James O’Shaughnessy identified “cornerstone value” and “cornerstone growth” as best-of-breed equity investment strategies. The former emphasizes dividends among large-capitalization stocks, and the latter momentum/earnings growth for a broader universe. Based on Standard and Poor’s Compustat data, he found that the value (growth) strategy returned 15% (18%) per year during 1952-1994, compared to 8.3% for the S&P 500 Index. He implemented these two strategies in late 1996 via mutual funds and publicized them in early editions of his book What Works on Wall Street: A Guide to the Best-Performing Investment Strategies of All Time. He subsequently sold the mutual funds (which apply slightly different portfolio formation rules from those specified in the original research) to Hennessy Funds in 2000, where they survive as the Hennessy Cornerstone Value Fund (HFCVX) and the Hennessy Cornerstone Growth Fund (HFCGX). Do these funds outperform simpler exchange-traded funds (ETF) that track their respective benchmarks funds: iShares Russell 1000 Value Index (IWD) for HFCVX and iShares Russell 2000 Index (IWM) for HFCGX? Using monthly total returns for HFCVX, HFCGX, IWD and IWM during May 2000 (inception of the ETFs) through July 2013, we find that: Keep Reading

The following table summarizes ranking of asset classes by subscribers responding during July 2013 to the following question (via the home page poll): “Which of the following asset classes do you expect to perform best in August 2013?” For comparison, the table also shows ranking of asset classes by momentum as specified in the baseline Momentum Strategy. Keep Reading

Can equity funds exploit widely accepted stock return anomalies? In their July 2013 paper entitled “Academic Knowledge Dissemination in the Mutual Fund Industry: Can Mutual Funds Successfully Adopt Factor Investing Strategies?”, Eduard Van Gelderen and Joop Huij investigate whether mutual funds that materially adopt investment strategies based on published asset pricing anomalies consistently outperform the stock market. They first use monthly regressions to measure degrees of use of six factor investing strategies (low-beta, small cap, value, momentum, short-term reversal and long-term reversion) across U.S. equity mutual funds. They then calculate market-adjusted returns to determine whether funds employing the strategies outperform those that do not and the market. Using monthly returns for 6,814 U.S. equity mutual funds, and contemporaneous monthly returns for the specified factors, during 1990 through 2010, they find that: Keep Reading

Are winning (losing) stocks with the strongest upward (downward) acceleration the best bets for a momentum strategy? In their July 2013 paper entitled “Investor Attention, Visual Price Pattern, and Momentum Investing”, Li-Wen Chen and Hsin-Yi Yu investigate whether visually striking patterns of past prices tend to grab investor attention, induce overreaction and amplify the momentum effect. They first rank stocks into fifths (quintiles) based on past returns to identify winners and losers (with a skip-month between the ranking interval and portfolio formation to avoid reversals). They then regress daily returns of winners and losers versus time squared over the past 12 months, with a positive (negative) coefficient indicating a convex (concave) price trajectory curvature, and further sort winner and loser quintiles into fifths based on curvature. Intuitively, winners (losers) with convex, upward accelerating (concave, downward accelerating) price trajectories most strongly attract trader attention and most reliably exhibit price momentum. They test this intuition by each month forming nine momentum hedge portfolios that are:

1. Long winners and short losers (traditional momentum approach).
2. Long winners and short convex-shaped (decelerating) losers.
3. Long winners and short concave-shaped (accelerating) losers.
4. Long concave-shaped (decelerating) winners and short losers.
5. Long convex-shaped (accelerating) winners and short losers.
6. Long convex-shaped (accelerating) winners and short concave-shaped (accelerating) losers.
7. Long concave-shaped (decelerating) winners and short convex-shaped (decelerating) losers.
8. Long convex-shaped (accelerating) winners and short convex-shaped (decelerating) losers.
9. Long concave-shaped (decelerating) winners and short (accelerating) concave-shaped losers.

Portfolios are equally weighted with baseline settings of a 12-month momentum ranking interval and a six-month holding interval (six overlapping portfolios in any month). Using monthly and daily prices and accounting data for a broad sample of U.S. common stocks, along with contemporaneous return factors and economic data, during January 1962 through December 2011, they find that: Keep Reading

Is stock return momentum persistent over a very long sample? In their July 2013 paper entitled “212 Years of Price Momentum (The World’s Longest Backtest: 1801 – 2012)”, Christopher Geczy and Mikhail Samonov extend analysis of momentum in U.S. stock prices back to 1800. They measure a stock’s momentum as its return from 11 months ago to one month ago, with the skipped month avoiding any short-term reversal. They measure the momentum effect as the return for a portfolio that is each month long (short) the equally weighted third of stocks with the highest (lowest) momentum. They define excess return as the return above the market return. Because reliable shares outstanding data are unavailable, they define the market return as the equal-weighted (rather than value-weighted) average return for all stocks in the universe. They ignore dividends (also not reliably available). They define market state in terms of sign (up or down during the same interval used for stock momentum measurement) and duration (number of consecutive months up or down). Using monthly returns for a sample of publicly traded U.S. stocks during January 1800 through December 2012, with focus on the “new” data for 1800 through 1926, they find that: Keep Reading

A subscriber inquired whether the level of momentum (past return) for each asset in the “Momentum Strategy” indicates the level of future return for that asset, and whether extreme negative momentum supports shorting an asset. In other words, do each of these asset class proxies exhibit reliably exploitable intrinsic momentum? To investigate, we regress next-month return versus past five-month return for each of the following eight asset class exchange-traded funds (ETF):

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)

Using monthly dividend-adjusted closing prices for the asset class proxies over the period July 2002 (or inception if not available then) through June 2013 (maximum 132 months), we find that: Keep Reading

Do asset classes generally exhibit enough price momentum to make stop-loss and re-entry rules effective for timing them? In his June 2013 paper entitled “Assessing Stop-loss and Re-entry Strategies”, Joachim Klement analyzes four stop-loss and re-entry rule pairs for six regional stock market indexes, a U.S. real estate investment trust (REIT) index, a commodity index and spot gold. Specifically, he tests:

1. Fast out-fast in (most effective when there are multiple brief corrections): Exit (re-enter) when the cumulative loss (gain) over the past 3 (3) months exceeds some specified threshold. 
2. Fast out-slow in (most effective during a downward or sideways trend): Exit (re-enter) when the cumulative loss (gain) over the past 3 (12) months exceeds some specified threshold.
3. Slow out-fast in (most effective during an upward trend with intermittent crashes): Exit (re-enter) when the cumulative loss (gain) over the past 12 (3) months exceeds some specified threshold.
4. Slow out-slow in (most effective when momentum is weak and transaction costs are high): Exit (re-enter) when the cumulative loss (gain) over the past 12 (12) months exceeds some specified threshold.

He tests ranges of stop-loss and re-entry decision thresholds. Because asset class return volatilities differ, he scales these thresholds to the annual standard deviation of returns for each asset class. He assumes a constant exit/re-entry trading friction of 0.25% and zero return on cash. For relevant tests, he defines a secular bull (bear) market as an extended subperiod of positive returns significantly above long-term average (negative or zero real returns). Using monthly asset class index returns as available during January 1970 through April 2013 in local currencies when applicable, he finds that: Keep Reading