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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Equity Factor Investing Update

Has (hypothetical) equity factor investing worked as well in recent years as indicated in past studies? In his July 2015 paper entitled “Factor Investing Revisited”, David Blitz updates his prior study quantifying the performance of allocations to U.S. stocks based on three factor premiums: (1) value (high book-to-market ratio); (2) momentum (high return from 12 months ago to one month ago); and, (3) low-volatility (low standard deviation of total returns over the last 36 months). He considers two additional factor allocations: (4) operating profitability (high return on equity); and, (5) investment (low asset growth). He specifies each factor portfolio as the 30% of U.S. stocks with market capitalizations above the NYSE median that have the highest expected returns, reformed monthly for momentum and low-volatility and annually for the other factors. He considers both equal-weighted and value-weighted portfolios for each factor. He also summarizes recent research on the role of small-capitalization stocks, factor timing, long-only versus long-short portfolios, applicability to international stocks and applicability to other asset classes. Using value, momentum, profitability and investment factor portfolio returns from Kenneth French’s library and low-volatility portfolio returns as constructed from a broad sample of U.S. stocks during July 1963 through December 2014, he finds that: Keep Reading

Intrinsic Momentum in International Equity and Commodity Indexes

Is time series (intrinsic or absolute) momentum evident in international stock indexes and commodity indexes? In the June 2015 version of their paper entitled “The Trend is Your Friend: Time-Series Momentum Strategies Across Equity and Commodity Markets”, Athina Georgopoulou and George Wang test intrinsic momentum trading strategies that are each month long (short) equally weighted indexes with a positive (negative) cumulative return. They consider a range of look-back intervals for measuring cumulative index returns. They insert a skip-month between the look-back interval and index portfolio reformation to avoid short-term reversal. They consider a range of subsequent holding intervals. Using monthly closes in U.S. dollars for 45 equity indexes (25 developed and 22 emerging markets) and monthly excess returns for 22 commodity indexes during December 1969 through December 2013, 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 monthly 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, rebalanced monthly (50-50). Using monthly gross returns for SACEVS Best Value and SACEMS Top 1 during January 2003 through June 2015, we find that: Keep Reading

Momentum Strategy, Value Strategy and Trading Calendar Updates

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

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

Preliminary Momentum and Value Strategy Updates

The home page and “Momentum Strategy” now show preliminary asset class ETF momentum strategy positions for July 2015. The differences in past returns among the top four places are fairly large, and the past returns for the top three positions are sufficiently above the Cash return, that selections are unlikely to change by the close. However, markets are volatile.

The home page and “Value Strategy” now show preliminary ETF allocations related to term, credit and equity premiums for the third quarter of 2015. These allocations could shift slightly by the close.

More International Equity Market Granularity for SACEMS?

A subscriber asked whether more granularity in international equity choices for the “Simple Asset Class ETF Momentum Strategy” (SACEMS), as considered by the Decision Moose, would improve performance. To investigate, we replace the iShares MSCI Emerging Markets Index (EEM) and the iShares MSCI EAFE Index (EFA) with four regional international equity exchange-traded funds (ETF). The universe of assets then becomes:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Pacific ex Japan (EPP)
iShares MSCI Japan (EWJ)
SPDR Gold Shares (GLD)
iShares Europe (IEV)
iShares Latin America 40 (ILF)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

We compare original (SACEMS) and modified (SACEMS Granular) winner portfolios, allocating all funds at the end of each month to the asset class ETF or cash with the highest total return over the past five months. Using monthly dividend-adjusted closing prices for the asset class proxies and the yield for Cash over the period July 2002 through May 2015 (156 months), we find that: Keep Reading

The Decision Moose Asset Allocation Framework

A reader suggested a review of the Decision Moose asset allocation framework of William Dirlam. “Decision Moose is an automated framework for making intermediate-term investment decisions.” Decision Moose focuses on asset class momentum, as augmented by monetary policy, exchange rate and interest rate indicators. Its signals tell followers when to switch from one index fund to another among nine encompassing a broad range of asset classes, including equity indexes for several regions of the globe. The trading system is a long-only approach that allocates 100% of funds to the index “having the highest probability of price appreciation.” The site includes a history of switch recommendations since the end of August 1996, with gross performance. To evaluate Decision Moose, we assume that switches and associated trading returns are as described (out of sample, not backtested) and compare the returns to those for the dividend-adjusted S&P 500 Depository Receipts (SPY) over the same intervals. Using Decision Moose signals and performance data during 8/30/96 through 6/5/15 (nearly 19 years), we find that: Keep Reading

Relative vs. Intrinsic Past Return Reversal, Momentum and Reversion

Which works best, strategies comparing past returns among assets (relative or cross-sectional) or strategies requiring positive past raw/excess returns (intrinsic or absolute or time series)? In their May 2015 paper entitled “Cross-Sectional and Time-Series Tests of Return Predictability: What is the Difference?”, Amit Goyal and Narasimhan Jegadeesh investigate differences between relative and intrinsic past return strategies, focusing on individual U.S. common stocks. For relative return strategies, they construct portfolios that are long (short) stocks with above-average (below-average) past returns, with the long and short sides weighted equally. For intrinsic return strategies, they construct portfolios that are long (short) stocks with past returns greater (less) than the risk-free rate, with each stock weighted equally. They consider past return measurement (ranking) intervals and holding intervals each ranging from one month to 60 months. They also compare relative and intrinsic past return strategies across and within global asset classes (17 equity indexes, 21 bond indexes, 24 commodity spot series and 8 currencies). Finally, they apply the relative and intrinsic concepts to individual U.S. stock financial ratios (book/market, gross profit/assets, asset growth and accruals/assets), with relative comparing once a year to the average for all stocks and intrinsic comparing once a year stock-by-stock to respective median values over the past five years. Using data for a broad sample of U.S. common stocks above the 20th percentile of NYSE market capitalization during 1946 through 2013, and for the asset class series during 1985 through 2013, they find that: Keep Reading

Asset Class Price Momentum Over the Very Long Run

Is there strong evidence for price momentum within and across all major asset classes over the long run? In the May 2015 version of their paper entitled “215 Years of Global Multi-Asset Momentum: 1800-2014 (Equities, Sectors, Currencies, Bonds, Commodities and Stocks)”, Christopher Geczy and Mikhail Samonov examine the momentum effect for very long price histories within and across major asset class universes consisting of: 47 country equity markets; 301 global equity sectors; 43 government bond series; 76 commodities; 48 currencies; and, 34,795 U.S. stocks. Their baseline momentum metric is return from 12 months ago to two months ago, with two skipped months to avoid any short-term reversal, followed by a one-month holding interval. Their baseline momentum portfolio is long (short) the third of assets with highest (lowest) momentum, equally weighted and reformed monthly. Their benchmarks are the equally weighted returns of all assets in the universes. Using monthly price data for the specified assets as available during 1800 through 2014 (215 years), they find that: Keep Reading

Country Stock Market Factor Strategies

Do factors that predict returns in U.S. stock data also work on global stock markets at the country level? In the May 2015 version of their paper entitled “Do Quantitative Country Selection Strategies Really Work?”, Adam Zaremba and Przemysław Konieczka test 16 country stock market selection strategies based on relative market value, size, momentum, quality and volatility. For each of 16 factors across these categories, they sort country stock markets into fifths (quintiles) and measure the factor premium as return on the highest minus lowest quintiles. They consider equal, capitalization and liquidity (average turnover) weighting schemes within quintiles. They look at complementary large and small market subsamples, and complementary open (easy to invest) and closed market subsamples. Using monthly total returns adjusted for local dividend tax rates in U.S. dollars for 78 existing and discontinued country stock indexes (primarily MSCI) during 1999 through 2014, they find that: Keep Reading

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Current Momentum Winners

ETF Momentum Signal
for July 2015 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
13.8% 14.1%
Top 3 ETFs SPY
14.0% 7.5%
Strategy Overview
Current Value Allocations

ETF Value Signal
for July 2015 (Final)





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
13.0% 10.0% 8.0%
Strategy Overview
Recent Research
Popular Posts
Popular Subscriber-Only Posts