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 Universe Enhancers?

Would adding a systematically chosen exchange-traded fund (ETF) or note (ETN) asset class proxy to the base set used in the “Simple Asset Class ETF Momentum Strategy” improve performance? To investigate, we consider adding each of the following 22 ETFs/ETNs (suggested over time by subscribers) one at a time to the strategy:

iPath S&P 500 VIX Short-Term Futures (VXX)
iPath S&P 500 VIX Medium-Term Futures (VXZ)
VelocityShares Daily Inverse VIX Short-Term (XIV)
ProShares UltraShort S&P 500 (SDS)
Guggenheim Frontier Markets (FRN)
iPath DJ-UBS Copper Total Return Sub-Index (JJC)
United States Oil (USO)
JPMorgan Alerian MLP Index (AMJ)
iShares 7-10 Year Treasury Bond (IEF)
iShares TIPS Bond (TIP)
Vanguard Total Bond Market (BND)
iShares iBoxx High-Yield Corporate Bond (HYG)
iShares Core US Credit Bond (CRED)
SPDR Barclays International Treasury Bond (BWX)
PowerShares DB G10 Currency Harvest (DBV)
SPDR Dow Jones International Real Estate (RWX)
UBS ETRACS Wells Fargo Business Development Companies (BDCS)
PowerShares Closed-End Fund Income Composite  (PCEF)
AlphaClone Alternative Alpha (ALFA)
IQ Hedge Multi-Strategy Tracker (QAI)
PowerShares Global Listed Private Equity  (PSP)
First Trust US IPO Index (FPX)

The base set consists of:

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)

We evaluate adding an asset to the base set via its effect on monthly net return-risk ratio (average monthly net return divided by standard deviation of monthly returns, a rough Sharpe ratio). Since the added assets have different sample periods, we rationalize by focusing on the difference in return-risk ratio (the ratio of the base set with the asset minus the ratio of the base set only) over the period the added asset is available. We then relate the resulting 22 differences in return-risk ratio to four characteristics of the respective added assets: (1) average monthly return; (2) standard deviation of monthly returns; (3) average (pairwise) cross-correlation of monthly returns with the base set assets; and, (4) serial correlation of monthly returns. The objective is to determine whether any of these four characteristics explain asset contribution to the momentum strategy. Using dividend/split-adjusted monthly prices for the above 31 asset class proxies as available during July 2002 through November 2014 (a maximum of 149 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 November 2014.
  • Updated Market Models backtest charts and the market valuation metrics map based on data available through November 2014.

We have updated the Trading Calendar to incorporate data for November 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 December 2014. Differences in past returns among assets are large enough that there is very little chance that the top three will change by the (early) close. There is a slim possibility that the top two could switch places.

At this point, four of nine asset classes have negative cumulative returns over the past five months.

Factor Model of Country Stock Market Returns?

Do predictive powers of the size, value and momentum factors observed for individual stocks translate to the country level? In the November 2014 version of his paper entitled “Country Selection Strategies Based on Value, Size and Momentum”, Adam Zaremba investigates country-level value, size and momentum premiums, and tests whether the value and momentum premiums are equally strong across markets of different sizes and evaluates a country-level multi-factor asset pricing model. He measures factors at the country level as:

  • Value: aggregate book-to-market ratio, with aggregate 12-month earnings-to-price-ratio, cash flow-to-price ratio and dividend yield as alternatives where available.
  • Size: total market capitalization of country stocks.
  • Momentum: cumulative return over preceding 12, 9, 6 or 3 months excluding the last month to avoid short-term reversal.

He relies on capitalization-weighted, U.S. dollar-denominated gross total return MSCI equity indexes as available, with Dow Jones and STOXX indexes as fallbacks (an average 56 indexes per month over time). He includes discontinued country indexes. He uses one-month LIBOR as the risk-free rate. Each month, he ranks countries by value, size and momentum into value-weighted or equal-weighted fifths (quintiles). He also performs double-sorts first on size and then on value or momentum. Using monthly firm/stock data for listed stockswithin 78 country indexes as available during February 1999 through September 2014 (147 months), he finds that: Keep Reading

Momentum-driven Turn-of-the-month Effect in Commodity Futures

Is the Commodity Trading Advisor (CTA) segment so crowded that flows of funds into or out of them around the turn of the month materially affect prices? In the October 2014 version of his paper entitled “The MOM-TOM Effect: Detecting the Market Impact of CTA Trading”, Otto Van Hemert explores whether the trend-following or time series momentum (MOM) style employed by many CTAs is so crowded that inflows around the turn of the month (TOM) affect momentum strategy returns. He notes that most CTA-managed funds offer monthly liquidity, thereby concentrating flows at month ends. He defines TOM as the last two days of a month plus the first day of the next month. He tests whether there is an above average return for MOM strategies during TOM (MOM-TOM effect). He uses the Newedge CTA Index (an equal-weighted aggregate of the largest CTAs open to new investments) and the Newedge Trend Index (an equal-weighted aggregate of the MOM style CTAs that are open to new investments) as proxies for the overall market and the MOM style, respectively. Using daily returns for these two indexes during January 2000 through March 2014, he finds that: Keep Reading

Market Liquidity Necessary for Momentum Strategy Profitability?

Is there a way to predict when stock price momentum strategies will thrive or crash? In the October 2014 update of their draft paper entitled “Time-Varying Momentum Payoffs and Illiquidity”, Doron Avramov, Si Cheng and Allaudeen Hameed investigate the relationship between future momentum strategy profitability and market illiquidity. They measure momentum conventionally as the average gross monthly return of a portfolio that is each month long the value-weighted tenth (decile) of common stocks with the highest and short the value-weighted decile of common stocks with the lowest returns from 12 months ago to one month ago (with a skip-month to avoid short-term reversal). Their stock illiquidity metric is the Amihud measure (average daily price impact per monetary volume traded over the past month), and they measure market illiquidity as the value-weighted average stock illiquidity. Using daily and monthly prices and market capitalizations for a broad sample of U.S. common stocks, monthly equity risk factors, investor sentiment and firm earnings data as available during January 1926 through December 2011, they find that: Keep Reading

140-year Stock Momentum Strategy Crash Test

What conditions foretell stock momentum strategy crashes? In their October 2014 paper entitled “Momentum Trading, Return Chasing, and Predictable Crashes”, Benjamin Chabot, Eric Ghysels and Ravi Jagannathan examine stock momentum strategy performance for both widely used historical U.S. data (starting in 1926 through 2012) and for a hand-collected sample of stocks listed on the London Stock Exchange during 1866 to 1907. They consider two methods of measuring momentum strategy returns. One is the gross return to the Fama-French momentum factor portfolio. The other is the gross return to a portfolio that is each month long (short) the value-weighted 30% of stocks with the highest (lowest) returns per the Fama-French momentum decile portfolios. Both methods define momentum conventionally as the return from 12 months ago to one month ago, with a skip-month before portfolio formation to avoid short-term reversal. They focus on conditions that precede momentum strategy crashes based on a model that considers three factors: (1) the risk-free rate; (2) past stock market return; and, (3) past momentum strategy return. Using the specified stock return data sets, they find that: Keep Reading

Intrinsic Momentum or SMA for Avoiding Crashes?

A subscriber suggested comparing intrinsic momentum (also called absolute momentum and time series momentum) to simple moving average (SMA) as alternative signals for equity market entry and exit. To investigate across a wide variety of economic and market conditions, we measure the long run performances of entry and exit signals from intrinsic momentum over past intervals of one to 12 months (designated MR1 through MR12).  Based on conclusions in “Is There a Best SMA Calculation Interval for Long-term Crossing Signals?”, we compare these performances with that the 10-month SMA (designated SMA10). We consider two cases for intrinsic momentum signals: (1) in stocks (cash) when past return is positive (negative); and, (2) in stocks (cash) when average monthly past return is above (below) the average monthly risk-free rate over the same measurement interval. Using monthly data for the 13-week Treasury bill (T-bill) yield as the risk-free rate and the Dow Jones Industrial Average (DJIA) as a proxy for the U.S. stock market during January 1934 through September 2014 (over 80 years), we find that: Keep Reading

Smart Beta Interactions with Tax-loss Harvesting

Are gains from tax-loss harvesting, the systematic taking of capital losses to offset capital gains, additive to or subtractive from premiums from portfolio tilts toward common factors such as value, size, momentum and volatility (smart beta)? In their October 2014 paper entitled “Factor Tilts after Tax”, Lisa Goldberg and Ran Leshem look at the effects on portfolio performance of combining factor tilts and tax-loss harvesting. They call the incremental return from tax-loss harvesting tax alpha, which (while investor-specific) is typically in the range 1%-2% per year for wealthy investors holding broad capitalization-weighted portfolios. They test six long-only factor tilts based on Barra equity factor models: (1) value (high earnings yield and book-to-market ratio); (2) momentum (high recent past return); (3) value/momentum; (4) small/value; (5) quality (value stocks with low earnings variability, leverage and volatility); and, (6) minimum volatility/value (low volatility with diversification constraint and value tilt). Their overall benchmark is the MSCI All Country World Index (ACWI). Their tax alpha benchmark derives from a strategy that harvests losses in a capitalization-weighted portfolio (no factor tilts) without deviating far from the overall benchmark. The rebalancing interval is monthly for all portfolios. Using monthly returns for stocks in the benchmark index during January 1999 through December 2013, they find that: Keep Reading

Simple Asset Class Momentum Strategy Applied to Mutual Funds

A subscriber inquired whether a longer test of the “Simple Asset Class ETF Momentum Strategy” is feasible using mutual funds rather than exchange-traded funds (ETF) as asset class proxies. To investigate, we consider the following set of mutual funds (partly adapted from the paper summarized in “Asset Allocation Combining Momentum, Volatility, Correlation and Crash Protection”):

Oppenheimer Commodity Strategy Total Return A (QRAAX)
Vanguard Emerging Markets Stock Index Investor Shares (VEIEX)
Fidelity Diversified International (FDIVX)
First Eagle Gold A (SGGDX)
Vanguard Total Stock Market Index Investor Shares (VTSMX)
Vanguard Small Capitalization Index Investor Shares  (NAESX)
Vanguard REIT Index Investor Shares (VGSIX)
Vanguard Long-Term Treasury Investor Shares (VUSTX)
3-month Treasury bills (Cash)

The investigation includes basic tests performed in “Simple Asset Class ETF Momentum Strategy”, robustness tests performed in “Simple Asset Class ETF Momentum Strategy Robustness/Sensitivity Tests” and some of the extensions explored in “Alternative Asset Class ETF Momentum Allocations”. The selected mutual funds all have monthly prices available as of the end of March 1997. Monthly strategy returns, as limited by the kinds of tests performed, commence in April 1998. Using monthly dividend-adjusted closing prices for the above mutual funds and the yield for Cash during March 1997 through September 2014 (212 months), we find that: Keep Reading

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