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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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 May 2015.

 

Preliminary Momentum Strategy Strategy Updates

The home page and “Momentum Strategy” now show preliminary asset class momentum strategy positions for June 2015. The difference in past returns between the third and fourth places is very small, and they may change order by the close.

Simple Asset Class Leveraged ETF Momentum Strategy

Subscribers have asked whether substituting leveraged exchange-traded funds (ETF) in the “Simple Asset Class ETF Momentum Strategy” might enhance performance. To investigate, we execute the strategy with the following eight 2X leveraged ETFs, plus cash:

ProShares Ultra DJ-UBS Commodity (UCD)
ProShares Ultra MSCI Emerging Markets (EET)
ProShares Ultra MSCI EAFE (EFO)
ProShares Ultra Gold (UGL)
ProShares Ultra S&P500 (SSO)
ProShares Ultra Russell 2000 (UWM)
ProShares Ultra Real Estate (URE)
ProShares Ultra 20+ Year Treasury (UBT)
3-month Treasury bills (Cash)

We allocate all funds at the end of each month to the asset class leveraged ETF or cash with the highest total return over the past five months (5-1). Using monthly adjusted closing prices for the specified ETFs and the yield for Cash over the period January 2010 (the earliest month prices for all eight ETFs are available) through Apriol 2014 (only 64 months), we find that: Keep Reading

Skewness-enhanced Stock Momentum

Can investors amplify stock return momentum by screening past winners and losers based on return skewness? In their April 2015 paper entitled “Expected Skewness and Momentum”, Heiko Jacobs, Tobias Regele and Martin Webee explore the interaction of expected stock return skewness and momentum. They measure expected skewness as maximum daily return over the preceding month, which predicts future skewness more accurately than does past skewness. Their benchmark is a conventional momentum portfolio that is each month long (short) the fifth, or quintile, of stocks with the highest (lowest) returns from 12 months ago to one month ago. To test the interaction of expected skewness and momentum, they first sort stocks into quintiles based on expected skewness and then sort each expected skewness quintile into quintiles based on momentum (25 total portfolios). Their skewness-weakened momentum portfolio is long (short) winners (losers) with relatively high/positive (low/negative) expected skewness. Their skewness-enhanced momentum portfolio is long (short) winners (losers) with relatively low/negative (high/positive) expected skewness. Using daily and monthly returns for a broad sample of U.S. common stocks (excluding very small and illiquid stocks) during January 1926 through December 2011, they find that: Keep Reading

Simple Asset Class Value and Momentum Diversification with Mutual Funds

“SACEMS-SACEVS Mutual Diversification” finds that the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) are mutually diversifying. Do the longer samples available for the “Simple Asset Class Value Strategy Applied to Mutual Funds” and the “Simple Asset Class Momentum Strategy Applied to Mutual Funds” confirm this finding? To check, we relate quarterly returns for the Best Value selections from the former and momentum winner (Top 1) mutual fund selections from the latter and look at the performance of an equally weighted portfolio of these two strategies (50-50). Using quarterly gross returns for the two strategies from the second quarter of 1998 through the first quarter of 2015, we find that: Keep Reading

Summarizing Value (and Momentum) Investing

When does value investing work and how does it work best? In the April 2015 initial draft of their paper entitled “Fact, Fiction, and Value Investing”, Clifford Asness, Andrea Frazzini, Ronen Israel and Tobias Moskowitz address areas of confusion about value investing. They describe value as the tendency of cheap securities to outperform expensive ones based on some valuation method. They broadly specify the value premium as the return achieved by holding or overweighting cheap securities and shorting or underweighting expensive ones. They focus on systematic (mechanical), diversified value strategies based on quantified metrics such as book-to-market ratio or earnings-price ratio. Their context is firm belief that such strategies are great investments. Based on academic studies and simple tests with recent data, largely from Kenneth French’s data library, they conclude that: Keep Reading

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

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

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Current Momentum Winners

ETF Momentum Signal
for June 2015 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
14.3% 14.6%
Top 3 ETFs SPY
14.4% 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
Recent Research
Popular Posts
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