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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Simple Asset Class ETF Momentum Strategy Robustness/Sensitivity Tests

How sensitive is the performance of the “Simple Asset Class ETF Momentum Strategy” to selecting ranks other than winners and to choosing a momentum ranking interval other than five months? This strategy each month ranks the following eight asset class exchange-traded funds (ETF), plus cash, on past return and rotates to the strongest class:

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)
3-month Treasury bills (Cash)

Available data are so limited that sensitivity test results may mislead. With that reservation, we perform two robustness/sensitivity tests: (1) comparison of returns for all nine ranks of winner through loser based on a ranking interval of five months and a holding interval of one month (5-1); and, (2) comparison of winner returns for ranking intervals ranging from one to 12 months (1-1 through 12-1) and for a six-month lagged six-month ranking interval (12:7-1) per “Isolating the Decisive Momentum (Echo?)”, all with one-month holding intervals. Using monthly adjusted closing prices for the asset class proxies and the yield for Cash over the period July 2002 (or inception if not available then) through April 2014 (154 months), we find that: Keep Reading

Momentum Strategy Winners Adjustment

The order of the first and second place winners is now reversed from that shown at the close yesterday because of a price change on Yahoo!Finance after 4:00PM. Keep Reading

Momentum Strategy, Value Strategy and Trading Calendar Updates

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

We have updated the Trading Calendar to incorporate data for April 2015.

 

Preliminary Momentum Strategy Strategy Updates

The home page and “Momentum Strategy” now show preliminary asset class momentum strategy positions for May 2015. Differences in past returns between the fourth place and the top three places are large enough that the top three probably will not change by the close, but their order may change.

Effects of Execution Delay on Simple Asset Class ETF Momentum Strategy

“Optimal Monthly Cycle for Simple Asset Class ETF Momentum Strategy?” investigates whether using a monthly cycle other than end-of-month (EOM) to determine the winning asset improves 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:

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)
3-month Treasury bills (Cash)

In response, a subscriber asked whether sticking with an EOM cycle for determining the winner, but delaying signal execution, affects strategy performance. To investigate, we compare 23 variations of the strategy that all use EOM to determine the winning asset but shift execution from the contemporaneous EOM to the next open or to closes over the next 21 trading days (about one month). For example, an EOM+5 Close variation uses an EOM cycle to determine winners but delays execution until the close five trading days after EOM. Using daily dividend-adjusted opens and closes for the asset class proxies and the yield for Cash from the end of July 2002 (or inception if not available then) through the end of March 2015 (153 months), we find that: Keep Reading

Optimal Monthly Cycle for Simple Asset Class ETF Momentum Strategy?

As explored for a 10-month simple moving average (SMA) in “Optimal Cycle for Monthly SMA Signals?”, subscribers have inquired whether there is a best time of the month for measuring momentum in 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:

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)
3-month Treasury bills (Cash)

To investigate, we compare 21 variations of the strategy based on shifting the monthly return calculation cycle relative to trading days from the end of the month (EOM). For example, an EOM+5 cycle ranks assets based on closing prices five trading days after EOM each month. Using daily dividend-adjusted closes for the asset class proxies and the yield for Cash from late July 2002 (or inception if not available then) through early April 2014 (about 153 months), we find that: Keep Reading

Market Timing with Moving Averages Over the Very Long Run

Which moving average rules and measurement (lookback) intervals work best? In the March 2015 version of his paper entitled “Market Timing with Moving Averages: Anatomy and Performance of Trading Rules” Valeriy Zakamulin compares market timing rules based on different kinds of moving averages, including simple momentum. He first compares the mathematics of these rules to identify similarities and differences. He then conducts very long run out-of-sample tests of a few trading rules with distinct weighting schemes to measure their market timing effectiveness. He tries both an expanding window (inception-to-date) and rolling windows to discover optimal lookback intervals. He uses Sharpe ratio as his principal performance metric. He estimates one-way trading friction as a constant 0.25%. Using monthly returns for the S&P Composite Index and for the risk-free asset during January 1860 through December 2009, he finds that: Keep Reading

Momentum Risk Management Strategies

Which stock momentum return predictor works best? In his March 2015 paper entitled “Momentum Crash Management”, Mahdi Heidari compares the crash protection effectiveness of seven stock momentum return predictors, categorized into two groups: 

  1. Overall stock market statistics: prior-month market return; change in monthly market return; volatility of market returns (standard deviation of weekly returns for the past 52 weeks); cross-sectional dispersion of daily stock returns for the past month; and, market illiquidity (value-weighted average of the monthly averages of daily price impacts of trading for all stocks).
  2. Momentum return series statistics: volatility of momentum returns (standard deviation of monthly returns over the past six months); and monthly change in volatility of momentum returns.

He measures momentum conventionally by first ranking all stocks by their returns from 12 months ago to one month ago and then after the skip-month forming a hedge portfolio that is long (short) the value-weighted tenth of stocks with the highest (lowest) past returns. He next tests the power of the above seven variables to predict the resulting monthly momentum return series. Finally, he tests dynamic momentum risk management strategies that execute the conventional momentum strategy (go to cash) when each of the seven predictors is below (above) the 90 percentile of its values over the last five years. Using daily and monthly returns, daily trading volumes and shares outstanding for a broad sample of U.S. common stocks during January 1926 through December 2013, he finds that: Keep Reading

Carry and Trend Implications for Future Returns Across Asset Classes

Are positive carry and positive trend conditions consistently favorable across asset classes? In their March 2015 paper entitled “Carry and Trend in Lots of Places”, Vineer Bhansali, Josh Davis, Matt Dorsten and Graham Rennison employ futures prices to investigate whether the adages “don’t pay too much to hold an investment” and “don’t fight the trend” actually work across four major asset classes: equities, bonds, commodities and currencies. For testing, they select five liquid markets with relatively long futures histories within each asset class. They define carry as annualized excess return assuming that spot prices do not change. They define trend as positive (negative) if the futures price today is above (below) its one-year trailing moving average. They specify four states for each market:

  1. Positive carry and positive trend (Carry + / Trend +).
  2. Positive carry and negative trend (Carry + / Trend -).
  3. Negative carry and positive trend (Carry – / Trend +).
  4. Negative carry and negative trend (Carry – / Trend -).

They then calculate average subsequent daily excess returns for each market by state and annualize results. Using daily futures data as available and some simulated futures data (from spot prices) for 20 major markets across four asset classes during 1960 through 2014, they find that: Keep Reading

SACEMS-SACEVS Mutual Diversification

Are the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) mutually diversifying. To check, we relate quarterly returns for the SACEVS Best Value and the SACEMS Top 1 exchange-traded fund (ETF) selections and look at the performance of an equally weighted portfolio of these two strategies (50-50). Using quarterly gross returns for SACEVS Best Value and SACEMS Top 1 during January 2003 through December 2014, we find that: Keep Reading

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Current Momentum Winners

ETF Momentum Signal
for May 2015 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
14.5% 15.0%
Top 3 ETFs SPY
14.6% 7.8%
Strategy Overview
Current Value Allocations

ETF Value Signal
for 2nd Quarter 2015 (Final)

Cash

IEF

LQD

SPY

The asset with the highest allocation is the holding of the Best Value strategy.
Gross Compound Annual Growth Rates
(Since September 2002)
Best Value Weighted 60-40
13.7% 9.6% 8.6%
Strategy Overview
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