Momentum Investing

Does momentum investing work when implemented via mutual fund alpha? In his February 2012 paper entitled “Short Term Alpha as a Predictor of Future Mutual Fund Performance” (the National Association of Active Investment Managers’ 2012 Wagner Award runner-up), Michael Hartmann examines a momentum-based approach for selecting outperforming equity mutual funds by investment style. He considers nine equity investment styles: Large Capitalization Growth, Large Capitalization Blend, Large Capitalization Value, Mid Capitalization Growth, Mid Capitalization Blend, Mid Capitalization Value, Small Capitalization Growth, Small Capitalization Blend and Small Capitalization Value. He measures momentum based on fund alpha calculated by linear regression of returns versus those of the S&P 500 Index over the past 20, 40, 60, 80 and 100 calendar days. He then forms non-overlapping portfolios of the three highest-alpha funds (weighted equally) for each style every 45, 70, 95, 120, 135 and 170 calendar days over the entire sample period and compares compound annual return rates for these portfolio series to those for corresponding Russell total return style indexes. Using daily total returns for open-ended mutual funds currently available via the no-transaction mutual fund platform at Charles Schwab & Co. and daily returns for the S&P 500 Index from the end of June 1999 through December 2011, along with sample period compound rates of return for Russell benchmark indexes, he finds that:

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A subscriber asked: “Have you looked at combining sector and asset class momentum models? This strategy would add alternative asset classes plus cash to the nine sectors.” A combined strategy encompasses nine sector exchange-traded funds (ETF) defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR) per “Simple Sector ETF Momentum Strategy” plus the eight ETFs and cash that cut across asset classes per “Simple Asset Class ETF Momentum Strategy” (SACEMS), as follows:

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

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 consider a simple (6-1) strategy that allocates all funds each month to the one sector or asset class ETF/cash with the highest total return over the past six months (effectively pitting the sector winner against the asset class winner). Using monthly dividend-adjusted closing prices for the ETFs over the period July 2002 (limited by data availability for enough asset class ETFs) through April 2012 (118 months), we find that: Keep Reading

A subscriber commented and asked: “You compare style ETF momentum to sector ETF momentum in ‘Doing Momentum with Style (ETFs)’. Can you mix style and sector ETFs to form a combined momentum strategy and compare it with the individual style and sector momentum strategies?” A combined strategy encompasses the nine sector exchange-traded funds (ETF) defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR) plus the six ETFs that cut across market capitalization (large, medium and small) and value versus growth:

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

We consider a simple (6-1) strategy that allocates all funds each month to the one sector or style ETF with the highest total return over the past six months (effectively pitting the sector winner against the style winner). Using monthly dividend-adjusted closing prices for these 15 ETFs over the period August 2001 (limited by data availability for IWS/IWP) through April 2012 (129 months), we find that: Keep Reading

What is the safest way to exploit asset price momentum? In his April 2012 paper entitled “Risk Premia Harvesting Through Momentum”  (the National Association of Active Investment Managers’ 2012 Wagner Award winner with different title), Gary Antonacci investigates systematic capture of upside volatility at the asset class level via a momentum/diversification/absolute return strategy that:

• Exploits momentum via long positions in winners, based on 12-month lagged total returns with no skip month, re-evaluated monthly.
• Maintains diversification by:
  • Using indexes rather than individual securities; and,
  • Holds the equally weighted winners from each the following pairs of competing indexes: gold versus long-term Treasury bonds; U.S. equities versus foreign equities; high yield bonds versus intermediate credit bonds; and equity real estate investment trusts (REIT) versus mortgage REITs.
• Mitigates risk by substituting Treasury bills (T-bills) for each pairwise winner that has not outperformed T-bills during the 12-month ranking interval.

Using monthly total returns for indexes constructed from targeted classes of equities, bonds, REITs and spot gold, along with contemporaneous 90-day Treasury bill yields, during January 1974 (or the earliest available) through December 2011, he finds that: Keep Reading

Stock price momentum strategies sometime crash, greatly detracting from long-term performance. Is there a reliable way to avoid the crashes? In the April 2012 version of their paper entitled “Managing the Risk of Momentum”, Pedro Barroso and Pedro Santa-Clara investigate usefulness of momentum portfolio volatility as a crash protection signal. They construct a momentum portfolio return series based on equal allocations to the risk-free asset (one-month Treasury bill), a value-weighted long side of momentum winners and a value-weighted short side of momentum losers, reformed and rebalanced monthly. They measure momentum for all NYSE/AMEX/NASDAQ stocks based on 11-month lagged returns plus a skip-month, and define winners and losers based on the top and bottom decile cutoffs for NYSE stock momentum. Using daily and monthly momentum portfolio returns and monthly U.S. equity risk factors (market, size, book-to-market) based on stock prices for July 1926 through December 2011, they find that: Keep Reading

Do “new” momentum stocks outperform “old” ones? In the March 2012 version of their paper entitled “Limited Attention, Salience, and Stock Returns” [apparently removed from SSRN, casting doubt on findings], Avanidhar Subrahmanyam, Jason Wei and Hsin-Yi Yu analyze whether stocks newly entering and exiting extreme momentum deciles exhibit unusual future returns because of heightened investor attention. Their benchmark (6-6) strategy consists of conventional overlapping winner-minus-loser momentum portfolios that are long/short those stocks with the highest/lowest returns over the past six months, formed monthly (after a skip-month) and held for six months. They classify a stock as a 6-6 arriver if it is not in any of the five preceding overlapping winner-minus-loser portfolios and a 6-6 dropper if it was in at least one winner-minus-loser portfolio active during the previous five months but is not in any of the active portfolios this month. They also consider arriving and dropping stocks defined relative to ranking intervals of 3, 9 and 12 months and holding intervals of 1, 2, 3, 9 and 12 months. Within portfolios, they weight the winner and loser sides equally and each stock within the winner or loser side equally. Using daily and monthly prices and volumes for NYSE, AMEX and NASDAQ common stocks priced above $5, along with contemporaneous risk factor and robustness test data as available, during 1962 through 2010, they find that: Keep Reading

It is arguable that many exchange-traded fund (ETF) momentum strategy tests derive more from logical/programming simplicity than common portfolio management practices. Does momentum work for portfolios of ETFs when melded with the latter? In his March 2012 paper entitled “Tactical Asset Allocation Using Relative Strength”, John Lewis tests ETF momentum in the context of real-world portfolio practices. He employs a universe of nearly 100 ETFs encompassing U.S. equity sectors and styles, international/global equities, bonds, commodities, real estate and currencies, including some inverse funds. After initial selection of top ETFs, he replaces weakening funds with strong ones as needed based on daily (or weekly) prices rather than at a fixed interval, depending on four parameters: (1) momentum ranking interval; (2) number of ETFs in the portfolio; (3) buy rank threshold; and, (4) sell rank threshold. To test robustness, he conducts multiple trials based on random selection of ETFs above the buy rank threshold. Specifically, he presents seven examples of 100 iterations of 10-ETF portfolios randomly selected from the top 25% of the ETF universe based on momentum ranking intervals of one month to two years, replacing ETFs when they fall out of the top 25%. Portfolios are apparently equally weighted at initial formation. Examples ignore dividends, management fees and trading frictions. Using daily returns for the ETFs from the end of 1999 through the end of 2011 (12 years), he finds that: Keep Reading

Do market capitalization (size) and book-to-market ratio systematically affect intermediate-term momentum and long-term reversal for individual stocks? In their February 2012 paper entitled “Momentum and Reversal: Does What Goes Up Always Come Down?”, Jennifer Conrad and Deniz Yavuz examine whether size and book-to-market ratio interact with momentum portfolio performance over intervals of 0-6, 6-12, 12-24 and 24-36 months after formation. They designate a stock as a winner (loser) if its 6-month lagged return is higher (lower) than the average for all stocks, with a skip-month before portfolio formation. They weight stocks within momentum portfolios by the absolute difference between its lagged 6- month return and that of all stocks, normalizing so that winner and loser sides contribute equally. They define three hedge portfolio types to measure risk factor-momentum interaction:

1. MAX portfolios are long (short) past winners that are small and/or high book-to-market (losers that are large and/or low book-to-market).
2. MIN portfolios are long (short) past winners that are large and/or low book-to-market (losers that are small and/or high book-to-market).
3. ZERO portfolios are long (short) past winners (losers) with similar size and book-to-market characteristics.

They sort stocks by size and book-to-market into thirds. When combining factors, they define stocks as high (low) risk group if they are in the high-risk (low-risk) third for one factor and in or above (below) the middle-risk third for the other. Using returns and factor characteristics for a broad sample of U.S. stocks during 1965 through December 2010, they find that: Keep Reading

Do widely acknowledged U.S. equity market anomalies exist in other stock markets? If so, why? In his November 2011 paper entitled “Equity Anomalies Around the World”, Steve Fan investigates whether a number of equity market anomalies found among U.S. stocks (asset growth, book-to-market ratio, investment-to-assets ratio, six-month momentum with skip-month, net stock issuance, size and total accruals) also occur in other equity markets and the degree to which such anomalies relate to stock-unique (idiosyncratic) risk. He measures raw anomaly strength based on gross returns from hedge (“zero-cost”) portfolios that are long and short equally weighted extreme quintiles of stocks ranked annually for each accounting variable and every six months for momentum (with overlapping momentum portfolios). To estimate alphas, he adjusts raw returns for the three Fama-French risk factors (market, book-to-market, size) or three alternative investment-based risk factors (market, investment, return on assets). Using monthly common stock return data and associated firm characteristics/accounting data for 43 country stock markets during 1989 through 2009, he finds that: Keep Reading

It is arguable that many stock momentum strategy tests derive more from logical/programming simplicity than common portfolio management practices. Does momentum work for portfolios of U.S. stocks when melded with the latter? In the January 2012 update of his paper entitled “Relative Strength and Portfolio Management”, John Lewis tests individual stock momentum in the context of real-world stock portfolio practices. After initial selection of top stocks, he replaces weakening stocks with strong ones as needed rather than at a fixed interval, depending on four parameters: (1) momentum ranking interval; (2) number of stocks in the portfolio; (3) buy rank threshold; and, (4) sell rank threshold. To test robustness, he conducts multiple trials based on random selection of stocks above the buy rank threshold. Specifically, he presents nine examples of 100 iterations of 50-stock portfolios randomly selected from the top 10% of the S&P 900 (S&P 500 large-cap plus S&P 400 mid-cap) based on momentum ranking intervals of one month to five years, replacing stocks when they fall out of the top 25%. Portfolios are apparently equally weighted at initial formation. Examples ignore dividends, management fees and trading frictions. Using daily returns (excluding dividends) for the S&P 900 stocks over the period 1996 through 2011 (16 years), he finds that: Keep Reading