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 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)
SPDR Dow Jones REIT (RWR)
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

Momentum in a Mean-variance Optimization Framework

Is intermediate-term asset class momentum a useful way to generate inputs (return, volatility and correlation forecasts) for a multi-class mean-variance optimization strategy? In their May 2015 paper entitled “Momentum and Markowitz: a Golden Combination”, Wouter Keller, Adam Butler and Ilya Kipnis test the effectiveness of using intermediate-term lookback intervals (1 to 12 months) to generate monthly long-only mean-variance optimized portfolios. They argue that such lookback intervals are more likely than conventional long (multi-year) intervals to provide forecasts that persist during one-month portfolio holding intervals. They name their approach Classical Asset Allocation (CAA). To test CAA, in addition to adopting the practical long-only constraint, they further:

  1. Select from the efficient frontier a target annualized portfolio volatility of either 10% (aggressive) or 5% (conservative).
  2. Forecast asset returns by averaging results from lookback intervals of 1, 3, 6 and 12 months.
  3. Forecast covariances (volatility-correlation relationships) from a 12-month lookback interval.
  4. Cap portfolio weights for risky assets at 25%, but do not cap weights for 3-month U.S. Treasury bills (T-bills) and 10-year U.S. Treasury notes (T-notes).
  5. Consider three universes of 8, 16 and 39 asset class proxies.
  6. Use equal weighting (EW) of all assets in a universe as a benchmark.

They introduce an optimizer program to streamline calculation of optimal portfolio weights. Using monthly total returns for 39 indexes spanning multiple asset classes as available during January 1914 through December 2014, they find that: Keep Reading

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 April 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

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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)

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
13.0% 10.0% 8.0%
Strategy Overview
Recent Research
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