Momentum Investing

Do financial market prices reliably exhibit momentum? If so, why, and how can traders best exploit it? These blog entries relate to momentum investing/trading.

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Add Stop-loss to Asset Class Momentum Strategy?

In response to “Stop-losses to Avoid Stock Momentum Crashes?”, a subscriber inquired whether a stop-loss rule would improve the performance of the “Simple Asset Class ETF Momentum Strategy”. This strategy each month allocates all funds to the one of the following eight asset class exchange-traded funds (ETF), or cash, with the highest total return over the past five months (designated the 5-1 strategy):

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)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

To investigate, we add to this strategy a stop-loss rule that: (1) exits the current winner ETF if its intra-month return falls below a specified threshold; and, (2) re-enters the basic strategy by buying the next winner ETF at the end of the month. Using monthly dividend-adjusted/split-adjusted monthly lows and closes for the asset class proxies and the yield for Cash during July 2002 (or inception if not available then) through March 2014 (141 months), we find that: Keep Reading

Models, Trading Calendar and Momentum Strategy Updates

We have updated the S&P 500 Market Models summary as follows:

  • Extended Market Models regressions/rolled projections by one month based on data available through March 2014.
  • Updated Market Models backtest charts and the market valuation metrics map based on data available through March 2014.

We have updated the Trading Calendar to incorporate data for March 2014.

We have updated the the monthly asset class momentum winners and associated performance data at Momentum Strategy.

Preliminary Momentum Strategy Update

The home page and “Momentum Strategy” now show preliminary asset class momentum strategy positions for April 2014. Differences in past returns across the first, second and third places are relatively large and unlikely to change by the close. The difference between the third and fourth places is small enough that they might switch order by the close.

Stop-losses to Avoid Stock Momentum Crashes?

Can stop-loss rules solve the stock momentum crash problem? In the March 2014 version of their paper entitled “Taming Momentum Crashes: A Simple Stop-loss Strategy”, Yufeng Han and Guofu Zhou test the effectiveness of a simple stop-loss rule in limiting the downside risk of a stock momentum strategy. Each month, they rank stocks into tenths (deciles) based on cumulative returns over the past six months, with the top (bottom) decile designated as winners (losers). After a skip-month, they form an equally weighted portfolio that is long (short) the winners (losers) and hold for one month, except: during the holding month, they sell (buy back) any winner (loser) stocks that fall below (rise above) the portfolio formation price by at least 10% based on either daily opens or closes. If an opening price breaches the 10% stop-loss level, they assume liquidation at the open. If an opening price does not breach the threshold but the same-day closing price does, they assume liquidation at the stop-loss level. They assume funds from liquidation earn the U.S. Treasury bill (T-bill) yield for the balance of the month. For robustness, they consider 5% and 15% stop-losses, capitalization-weighted portfolios and momentum based on past 12-month return. Using daily opening/closing prices as available and monthly market capitalizations for a broad sample of U.S. common stocks, daily T-bill yield and monthly U.S. equity risk factors (market, size, book-to-market) during January 1926 through December 2011, they find that: Keep Reading

Avoiding the Momentum Crash Crowd

Is there a way to avoid the stock momentum crashes that occur when the positive feedback loop between past and future returns breaks down? In his November 2013 paper entitled “Crowded Trades, Short Covering, and Momentum Crashes, Philip Yan investigates the power of the interaction between short interest and institutional trading activity to explain stock momentum crashes and thereby offer a way to avoid these crashes. Each month he sorts stocks into ranked tenths (deciles) based on returns from 12 months ago to one month ago (skipping the most recent month to avoid reversals). He reforms each month baseline winner and loser portfolios from the value-weighted deciles of extreme high and low returns, respectively. He then segments the loser portfolio into crowded losers (stocks that are most shorted and have the highest institutional exit rate) and non-crowded losers (stocks that are most shorted but do not have the highest institutional exit rate). The most shorted losers are those within the fifth of stocks with the highest short interest ratios (short interest divided by shares outstanding). The losers with the highest institutional exit rates are those within the fifth of stocks with the most shares completely liquidated by institutional investors divided by shares outstanding. He defines three value-weighted long-short portfolios: (1) the baseline portfolio buys the baseline winners and shorts the baseline losers; (2) the crowded portfolio buys the baseline winners and shorts the crowded losers; and, (3) the “non-crowded portfolio buys the baseline winners and shorts the non-crowded losers”. Using daily and monthly stock return, monthly short interest and quarterly institutional ownership data during January 1980 through September 2012, high-frequency short sales data during 2005 through 2012, and monthly price data for 63 futures contract series as available during January 1980 through June 2013, he finds that: Keep Reading

Simple Debt Class Mutual Fund Momentum Strategy

A subscriber requested confirmation of the performance of a simple momentum strategy that each month selects the best performing debt mutual fund based on total return over the past three months. To investigate, we test a simple strategy on the following eight mutual funds (those with the longest histories from a proposed list of 14 funds):

T. Rowe Price Tax-Free High Yield Bonds (PRFHX)
T. Rowe Price New Income (PRCIX)
Vanguard GNMA Securities (VFIIX)
T. Rowe Price International Bonds (RPIBX)
Vanguard Long-Term Treasury Bonds (VUSTX)
Fidelity Convertible Securities (FCVSX)
T. Rowe Price High-Yield Bonds (PRHYX)
Fidelity New Markets Income (FNMIX)

As proposed, we allocate all funds at the end of each month to the fund with the highest total return over the past three months (3-1). We determine the first winner in January 1995 to accommodate momentum measurement interval sensitivity testing. Using monthly dividend-adjusted closing prices for the eight funds during January 1994 (as limited by FNMIX) through February 2014 (242 months), we find that: Keep Reading

Estimating Snooping Bias for a Multi-parameter Strategy

A subscriber flagged an apparently very attractive exchange-traded fund (ETF) momentum-volatility-correlation strategy that, as presented, generates a optimal compound annual growth rate of 45.7% with modest maximum drawdown. The strategy chooses from among the following seven ETFs:

ProShares Ultra S&P500 (SSO)
iShares MSCI Emerging Markets (EEM)
iShares Latin America 40 (ILF)
iShares MSCI Pacific ex-Japan (EPP)
Vanguard Extended Duration Treasuries Index ETF (EDV)
iShares 1-3 Year Treasury Bond (SHY)

The steps in the strategy are, at the end of each month:

  1. For the first six of the above ETFs, compute log returns over the last three months and standard deviation (volatility) of log returns over the past six months.
  2. Standardize these the monthly sets of past log returns and volatilities based on their respective means and standard deviations.
  3. Rank the six ETFs according to the sum of 0.75 times standardized past log return plus 0.25 times past standardized volatility.
  4. Buy the top-ranked ETF for the next month.
  5. However, if at the end of any month, the correlation of SSO and EDV monthly log returns over the past four months is greater than 0.75, instead buy SHY for the next month.

The developer describes this strategy as an adaptation of someone else’s strategy and notes that he has tested a number of systems. How material might the implied secondary and primary data snooping bias be in the performance of this system? To investigate, we examine the fragility of performance statistics to variation of each strategy parameter separately. As presented, the author substitutes other ETFs for the two with the shortest histories to extend the test period backward in time. We use only price histories as available for the specified ETFs, limited by EDV with inception January 2008. Using monthly adjusted closing prices for the above seven ETFs and for SPDR S&P 500 (SPY) during January 2008 through February 2014 (74 months), we find that: Keep Reading

Big Three Factors across Countries

Are there parallels at the country stock market level of the size, value and momentum effects observed for individual stocks? In their January 2014 paper entitled “Value, Size and Momentum across Countries”, Adam Zaremba and Przemysław Konieczka investigate country-level value, size and momentum premiums. They measure these factors at the country level as:

  • Value (V): book-to-market ratio of country stocks aggregated via the weighting scheme used to construct the country stock index at the time of portfolio formation.
  • Size (S): total market capitalization of country stocks at the time of portfolio formation.
  • Long-Term Momentum (LTM): country index return during the 12 months before portfolio formation.
  • Short-Term Momentum (STM): country index return during the month before portfolio formation.

They calculate these factors using either MSCI equity indexes (47 indexes available at the beginning of the sample period) or local stock indexes (only 24 indexes available at the beginning of the sample period). They measure the country-level premium for each factor as the return on an equally weighted portfolio that is each month long (short) the 30% of countries with the highest (lowest) expected returns for that factor. They fully collateralize short sides with reserves in the risk-free rate. They also calculate a total market return as the capitalization-weighted average return across all country markets. They perform calculations separately in U.S. dollars, euros and yen. Using monthly firm/stock data for listed stocks as available within 66 countries from the end of May 2000 through November 2013, and contemporaneous Fama-French model U.S. factors, they find that: Keep Reading

Equity Investing Based on Liquidity

Does the variation of individual stock returns with liquidity support an investment style? In the January 2014 update of their paper entitled “Liquidity as an Investment Style”, Roger Ibbotson and Daniel Kim examine the viability and distinctiveness of a liquidity investment style and investigate the portfolio-level performance of liquidity in combination with size, value and momentum styles. They define liquidity as annual turnover, number of shares traded divided by number of shares outstanding. They hypothesize that stocks with relatively low (high) turnover tend to be near the bottom (top) of their ranges of expectation. Their liquidity style thus overweights (underweights) stocks with low (high) annual turnover. They define size, value and momentum based on market capitalization, earnings-to-price ratio (E/P) and past 12-month return, respectively. They reform test portfolios via annual sorts into four ranks (quartiles), with initial equal weights and one-year holding intervals. Using monthly data for the 3,500 U.S. stocks with the largest market capitalizations (re-selected each year) over the period 1971 through 2013, they find that: Keep Reading

Enhancing Momentum with Relative Trend Strength

Does a stronger stock price trend, up or down, indicate a bigger momentum effect? In their February 2014 paper entitled “Trend Salience, Investor Behaviors and Momentum Profitability”, Paul Docherty and Gareth Hurst test variations of a conventional stock momentum strategy that consider both past returns and rate of change of past returns relative to other stocks. Specifically, each year they reform a universe of the 500 stocks listed on the Australian Stock Exchange with the largest market capitalizations. Then each month, they rank stocks in the current universe based on past cumulative returns, designating the top fifth (quintile) as winners and bottom quintile as losers. They then further categorize each winner (loser) stock as salient if the ratio of its geometric mean return over the past 3, 6 or 9 months to its geometric mean return over the past 12 months is greater (less) than the quintile median of this ratio. Finally, they each month form equally weighted momentum and salience portfolios (with a skip-month between ranking and portfolio formation) and hold for overlapping intervals of 3, 6, 9 or 12 months. These portfolios include:

  1. Conventional momentum: long (short) the winners (losers).
  2. Salient momentum: long (short) salient winners (salient losers).
  3. Non-salient momentum: long (short) non-salient winners (non-salient losers).

Using monthly return data for the specified Australian stocks during January 1992 through December 2011, they find that: Keep Reading

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Current Momentum Winners

ETF Momentum Signal
for April 2014 (Final)

Momentum ETF Winner

Second Place ETF

Third Place ETF

Gross Momentum Portfolio Gains
(Since August 2006)
Top 1 ETF Top 2 ETFs
217% 197%
Top 3 ETFs SPY
197% 68%
Strategy Overview
Recent Research
Popular Posts
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