Momentum Investing

Most momentum investing strategies employ cross-sectional or relative strength by taking long (short) positions in assets exhibiting medium-term price strength (weakness). Is momentum also exploitable intrinsically, wherein an investor estimates momentum of an asset relative to its own medium-term history (time series)? In their August 2010 paper entitled “Time Series Momentum”, flagged by a reader, Tobias Moskowitz, Yao Hua Ooi and Lasse Pedersen investigate time series momentum in liquid futures contracts (typically nearest or next nearest) spanning nine equity indexes, 12 currency pairs, 24 commodities and 13 government bonds. They focus on a (12-1) test strategy that each month takes a one-month long (short) position in each contract series with a higher (lower) return than Treasury bills over past 12 months. When combining different contract series into a portfolio, they weight each position to make an equal expected contribution to portfolio volatility (divide by lagged standard deviation of returns). Using daily prices for these 58 futures, Treasury bills and relevant benchmark indexes from 1985 through 2009, along with contemporaneous weekly Commitments of Traders (COT) reports as available from CFTC, they find that: Keep Reading

Does the effectiveness of momentum investing vary with market state? In the October 2011 version of their paper entitled “Market Cycles and the Performance of Relative-Strength Strategies”, Chris Stivers and Licheng Sun investigate how market cycles (bull versus bear) affect the profitability of medium-term and long-term relative strength investing strategies. They consider both firm-level and industry-level value-weighted relative strength strategies with equal ranking and holding intervals of 6, 12, 18, 24 and 36 months (ten total strategies), with an intervening skip-month. For the firm level, strategies are long (short) the top (bottom) tenth of ranking interval winners (losers). For the industry level, strategies are long (short) the top (bottom) five ranking interval winners (losers). Bull (bear) market states are those following 15% cumulative advances (declines) from previous troughs (peaks). Using monthly return data for individual NYSE/AMEX stocks and for 30 value-weighted industries during 1962 through 2010, they conclude that: Keep Reading

Has the stock market adapted to widespread investor efforts to exploit intermediate-term return momentum? In their paper entitled “Momentum Loses Its Momentum: The Implication on Market Efficiency”, Debarati Bhattacharya, Raman Kumar and Gokhan Sonaer evaluate the robustness of momentum returns in the U.S. stock market over time via consideration of three subperiods: 1965-1989 (SP1), 1990-1998 (SP2), and 1999-2010 (SP3). They focus on SP3 to measure post-discovery persistence of the momentum effect. They form overlapping portfolios monthly by ranking stocks into deciles (tenths) based on six-month cumulative past returns and holding for six months or 12 months (and 24 months for one test). Using monthly returns and firm characteristics for a broad sample of U.S. stocks over the period 1965 through 2010, they find that: Keep Reading

What is the state of momentum investing? In their January 2011 paper entitled “Momentum”, Narasimhan Jegadeesh and Sheridan Titman summarize equity price momentum research and discuss explanations for the momentum anomaly. Specifically, they address equity momentum investing performance during 1990 through 2009, and the firm characteristics and market conditions that affect momentum returns. Based on a review of momentum research since 1990, their key points are: Keep Reading

Does a strategy of combining monthly individual stock return reversal with monthly industry momentum enhance results compared to the separate strategies. In their August 2011 paper entitled “One-month Individual Stock Return Reversals and Industry Return Momentum”, Marc Simpson, Emiliano Giudici and John Emery examine the relationship between individual stock return reversals and industry momentum by considering three strategies: (1) a conventional reversal strategy that each month buys (shorts) individual stock losers (winners); (2) a simple industry momentum strategy that each month buys (shorts) the previous month’s winning (losing) industry portfolio; and, (3) a combined reversal-industry momentum strategy that buys (shorts) the losing (winning) stocks within the previous month’s winning (losing) industry portfolio. Using monthly returns, SIC codes and the Fama-French definitions for ten industries over the period January 1931 through December 2010 (960 months) , they find that: Keep Reading

Does the size effect vary in a predictable way? In the May 2011 version of his paper entitled “Explaining the Dynamics of the Size Premium”, Valeriy Zakamulin investigates relationships between eight market/economic variables and the size effect in U.S. stocks to identify the best model of size effect variation. The eight variables are: (1) stock market return; (2) stock market dividend yield; (3) equity value premium; (4) stock return momentum; (5) default spread  (Moody’s BAA-AAA corporate bond yield spread); (6) Treasury bill yield; (7) U.S. Treasuries term premium  (30-year bond yield minus one-month bill yield); and, (8) inflation rate. He then tests the exploitability of the best model via a strategy that switches between small-capitalization and large-capitalization stocks out of sample based on inception-to-date historical data. Using annual data for the eight potentially predictive variables and annual and monthly data for the magnitude of the size effect among NYSE, AMEX and NASDAQ stocks as available over the period 1927 through 2009 (83 years), he finds that: Keep Reading

When implemented via exchange-traded funds (ETF), does an equity sector momentum strategy beat an equity style momentum strategy? How do these approaches compare to a geographic equity momentum strategy? In his paper entitled “Optimal Momentum”, runner-up for the 2011 Wagner Award presented by the National Association of Active Investment Managers, Gary Antonacci uses ETFs to compare style, sector and geographic momentum strategies. He uses a six-month ranking period to select the top two of six iShares value-growth-size ETFs, the top three of nine SPDR sector ETFs and the top two of four iShares region/country ETFs each month, with a 0.2% per fund switching friction. In addition, he experiments with adding short-term and intermediate-term Treasury ETFs and then gold to the geographic momentum ranking process. His benchmarks are the Russell 1000 ETF (IWB), the AQR Momentum Index (adjusted by debiting an estimated annual trading friction of 0.7%) and equally weighted portfolios of the ETF groups (rebalanced monthly). Using eight years of monthly ETF prices (2003 through 2010) and 34 years of related monthly index levels, he concludes that: Keep Reading

“The Industry 52-week High Effect” summarizes findings that the 52-week high effect, the future outperformance (underperformance) of stocks currently near their respective 52-week highs (lows), is stronger and more consistent for 20 industries than for individual stocks. Do findings apply to equity sectors that are somewhat broader than the 20 industries? Specifically, might such a strategy outperform past six-month return when applied to the following nine sector exchange-traded funds (ETF) defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR), all of which have trading data back to December 1998:

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)

To check, we consider three strategies based on closeness of each sector ETF to its 12-month high, defined as ratio of monthly close to highest monthly close over the prior 12 months. The three strategies are to: (1) allocate all funds each month to the sector ETF closest to its 12-month high at the end of the preceding month (12MH-1); (2) allocate all funds each month to the sector ETF closest to its 12-month high at the end of the month before the preceding month (12MH-1;1); and, (3) allocate all funds each quarter to the sector ETF closest to its 12-month high at the end of the month before the end of the quarter (12MH-3;1). Strategy (2) addresses the concern that a sector ETF surging toward a 12-month might experience some reversion the next month, and strategy (3) addresses the concern (based on the methodology in “The Industry 52-week High Effect”) that the effect materializes over several months. For comparison, we include the strategy of monthly allocation to the sector ETF with the highest total return over the past six months (6-1). Using monthly dividend-adjusted closing prices for the nine sector ETFs and S&P Depository Receipts (SPY) over the period December 1998 through March 2011 (148 months), we find that: Keep Reading

Are 52-week highs and lows useful equity price momentum indicators at the industry level? In their March 2011 paper entitled “Industry Information and the 52-Week High Effect”, Xin Hong, Bradford Jordan and Mark Liu compare the 52-week high effect for industries to that for individual stocks. This effect consists of the future outperformance (underperformance) of stocks currently near their respective 52-week highs (lows). Using monthly closes and rolling 52-week (intraday) highs for all stocks listed on NYSE, AMEX and NASDAQ and 20 value-weighted industry indexes constructed from SIC codes for these firms over the period July 1963 through 2009, they find that: Keep Reading

Does aggregate investor sentiment affect the strength of well-known U.S. stock return anomalies? In their January 2011 paper entitled “The Short of It: Investor Sentiment and Anomalies”, Robert Stambaugh, Jianfeng Yu and Yu Yuan explore the interaction of aggregate investor sentiment with 11 cross-sectional stock return anomalies. Their approach reflects expectations that: (1) overpricing of stocks is more common than underpricing due to short-sale constraints; and, (2) a high sentiment level amplifies overpricing. Specifically, they consider the effect of investor sentiment on hedge portfolios that are long (short) the highest(lowest)-performing) value-weighted deciles of stocks sorted on: financial distress (two measures), net stock issuance, composite equity issuance, total accruals, net operating assets, momentum, gross profit-to-assets, asset growth, return-on-assets and investment-to-assets. They use a long-run sentiment index derived from principal component analysis of six sentiment measures: trading volume as measured by NYSE turnover; the dividend premium; the closed-end fund discount; the number of and first-day returns on Initial Public Offerings; and, the equity share in new issues. They measure anomaly alphas relative to the three-factor model (adjusting for market, size, book-to-market). Using monthly sentiment and stock return anomaly data as available over the period July 1965 through January 2008, they find that: Keep Reading