Momentum Investing

How do specialized stock indexes relate to commonly used equity risk factors? In his February 2012 paper entitled “Evaluating Alternative Beta Strategies”, Xiaowei Kang examines risk exposures (betas), construction methodologies and historical performances of alternative stock indexes such as those based on value, low-volatility and diversification strategies. He considers five risk factors: (1) market, representing excess return of the market capitalization-weighted U.S. stock market; (2) size, representing return from a portfolio that is long small-cap stocks and short large-cap stocks; (3) value, representing return from a portfolio that is long high book-to-market stocks and short low book-to-market stocks; (4) momentum, representing return from a portfolio that is long past winning stocks and short past losing stocks; and, (5) volatility, representing return from a portfolio that is long high-volatility stocks and short low-volatility stocks. Using monthly returns for several specialized indexes and the specified risk factors as available through 2011, he finds that: Keep Reading

The model “Simple Asset Class ETF Momentum Strategy” (SACEMS) explores combinations of diversification and momentum as applied to exchange-traded fund (ETF) proxies for asset classes. As introduced, this strategy employed a baseline momentum ranking interval (six-month lagged ETF total return) to the following asset class ETFs, plus cash:

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)

However, “Simple Asset Class ETF Momentum Strategy Robustness/Sensitivity Tests” shows that the six-month momentum ranking interval is not optimal in terms of average monthly return or cumulative return over the available sample period. With angst over data snooping bias, we are revising the model strategy by substituting an historically optimal momentum ranking interval. Using the SACEMS dataset from (data through August 2012) to compare performances of the baseline and optimal calculation intervals, we find that: Keep Reading

What does price momentum of stocks, whether total or risk-adjusted, imply about future returns of associated corporate bonds? In their August 2012 paper entitled “Residual Equity Momentum for Corporate Bonds”, Daniel Haesen, Patrick Houweling and Jeroen Van Zundert compare the predictive powers of total stock price momentum and risk-adjusted (residual) stock price momentum to predict returns of same-firm bonds. To focus on firm effects, they remove the influence of interest rates by measuring bond returns in excess of duration-matched U.S. Treasury instruments. They form (overlapping) bond portfolios monthly by: (1) ranking firms into fifths (quintiles) based on cumulative stock returns in excess of the risk-free rate over a past interval (base case, six months); (2) skipping a month; and, (3) forming a hedge portfolio that is long (short) for the next 1, 3, 6 or 12 months the equally weighted bonds of firms in the quintile with the highest (lowest) past stock returns. They calculate residual stock returns via 36-month lagged rolling regressions of excess stock returns versus the Fama-French model risk factors (market, size, book-to-market). Using monthly returns for U.S. investment grade and high-yield corporate bonds and associated stocks (2,442 firms), and for duration-matched U.S. Treasury instruments and the three equity risk factors, during January 1994 through September 2011, they find that: Keep Reading

Do equity styles and sectors exhibit exploitable momentum? In their August 2012 paper entitled “Do Style and Sector Indexes Carry Momentum?”, Linda Chen, George Jiang and Kevin Zhu investigate whether nine style indexes and 12 sector indexes exhibit price momentum. Each month, they form an equally weighted momentum portfolio that is long (short) the third of indexes with the highest (lowest) cumulative returns over the past 3, 6 or 12 months and hold for the next 1, 3, 6 or 12 months. They also test a dynamic style momentum portfolio that each month overweights (underweights) by 10% the third of past winner (loser) style indexes, with 0.2% monthly rebalancing friction. Using monthly levels for the selected indexes during July 1997 through October 2007, they find that: Keep Reading

Based on results from “Simple Sector ETF Momentum Strategy Performance”, “Does the Turn-of-the-Month Effect Work for Sectors?” and “Long-term SMA and TOTM Combination Strategy”, a subscriber proposed: “Have you ever thought of combining the three? When SPY is above a long term average, buy the best performing sector ETF using the TOTM strategy.” To investigate, we consider the 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)

We determine sector momentum based on total return over the past six months (6-1). We define bull-bear stock market state according to whether SPDR S&P 500 (SPY) is above-below its 200-day simple moving average (SMA). We define the turn-of-the-month (TOTM) as the eight-trading day interval from the close five trading days before the first trading day of a month to the close on the fourth trading day of the month. Using daily dividend-adjusted closes for the sector ETFs and SPY from 12/22/98 through 8/10/12 (164 months), we find that: Keep Reading

Is it possible to determine when long-term stock market reversion is imminent? In their August 2012 paper entitled “Long-Term Return Reversal: Evidence from International Market Indices”, Mirela Malina and Graham Bornholt compare the performances of a conventional contrarian strategy that considers only long-term past returns to that of a “late-stage” contrarian strategy that buys (sells) long-term losers (winners) with relatively good (poor) recent returns, as applied to country stock market indexes. Specifically, their conventional contrarian strategy each month buys (sells) the quarter of indexes with the worst (best) returns over the past 36, 48 or 60 months and holds positions for 3, 6, 9 or 12 months (such that portfolios overlap), with a 12-month gap between ranking and holding intervals to avoid intermediate-term momentum effects. The late-stage contrarian strategy each month sorts indexes based on returns over the past 36, 48, or 60 months to identify the quarter with the worst (best) returns and then splits these winner and loser groups into halves based on returns over the past 3, 6, 9, or 12 months. The strategy then buys (sells) the long-term loser/short-term winner (long-term winner/short-term loser) indexes and holds positions for 3, 6, 9 or 12 months, with a one-month gap between ranking and holding intervals to ensure executability. Using monthly total (dividend-reinvested) returns for 18 developed and 26 emerging market indexes in U.S. dollars during January 1970 (or the earliest availability) through January 2011 (193 to 493 monthly observations across countries), they find that: Keep Reading

Is there a reliable signal for exiting a stock momentum strategy before months during which the strategy crashes? In the June 2012 version of their paper entitled “Tail Risk in Momentum Strategy Returns”, Kent Daniel, Ravi Jagannathan and Soohun Kim investigate conditions under which a basic U.S. stock momentum strategy performs very poorly and develop a model to anticipate these conditions. Their momentum strategy is each month long (short) the equally weighted tenth of NYSE, AMEX, and NASDAQ stocks with the highest (lowest) lagged 11-month returns, with a skip month between ranking interval and portfolio formation. Their method of anticipating conditions associated with poor momentum returns is a fairly complex two-regime (calm or turbulent) market state model derived from momentum portfolio returns relative to market returns during the momentum ranking interval and other lagged market return statistics. Using the monthly momentum decile portfolio returns from Kenneth French’s data library for July 1929 through December 2010 (978 months), they find that: Keep Reading

What drives the momentum effect among individual U.S. stocks? In their June 2012 paper entitled “Momentum, Risk, and Underreaction”, Mark Rachwalski and Quan Wen investigate the sources of profits for momentum strategies applied to individual stocks. They measure momentum profitability as average monthly returns to three series of equal-weighted hedge portfolios that each month are long (short) the tenth of stocks with the highest (lowest) returns over the previous three (3-1-1), six (6-1-1), and 12 (12-1-1) months, with a skip-month between ranking intervals and return measurement months to avoid short-term reversal. They test dependence of momentum profitability on five factors: (1) long-term idiosyncratic volatility (IV), the standard deviation of individual stock returns unexplained by the Fama-French model based on daily data from five years ago to six months ago; (2) short-term IV, based on daily data from six months to one week ago; (3) long-term distress risk (corporate default probability) based on daily data from five years ago to six months ago; (4) short-term distress risk based on daily data from six months to one week ago; and, (5) corporate bond beta relative to the BAA yield based on the last two years of daily data. Using daily return data for a broad sample of U.S. stocks, firm accounting information related to default probabilities and corporate bond yield data supporting analysis for 1988 through 2010, they find that: Keep Reading

How do general economic conditions and economy-wide default risk shocks affect corporate bond returns, especially past winners and losers? In the May 2012 draft of their paper entitled “Sources of Momentum in Bonds”, Hwagyun Kim, Arvind Mahajan and Alex Petkevich investigate the relationship between U.S. corporate bond momentum portfolio returns and U.S. aggregate default risk. They measure the momentum effect as average monthly gross returns of overlapping hedge portfolios formed each month by buying (selling) the equally weighted tenth of bonds with the highest (lowest) total cumulative returns over the past six months and holding for six months, with a skip-month between ranking and holding intervals. They measure aggregate default risk as the prior-month yield spread between the Moody’s CCC corporate bond index and the 10-year U.S. Treasury note. They define default risk shocks as deviations from the linear relationships between default risk this month and its values the prior two months. They define high (low) default risk shock conditions as those above (below) the inception-to-date median value of the series. Using price and yield data for all listed U.S. corporate bonds (excluding convertible bonds, asset-backed securities and bonds with very low capitalization) during January 1995 (101 bonds) through December 2010 (2,513 bonds), they find that: Keep Reading

Are there economic conditions that favor stock price momentum investing? In the May 2012 draft of their paper entitled “Momentum and Aggregate Default Risk”, Arvind Mahajan, Alex Petkevich and Ralitsa Petkova investigate the relationship between stock momentum portfolio returns and U.S. aggregate default risk. They measure the momentum effect as average monthly gross returns of overlapping hedge portfolios formed each month by buying (selling) the equally weighted tenth of stocks with the highest (lowest) cumulative returns over the past six months and holding for six months, with a skip-month between ranking and holding intervals. They measure aggregate default risk as the prior-month yield spread between the Moody’s CCC corporate bond index and the 10-year U.S. Treasury note. They define default risk shocks as deviations from the linear relationships between default risk this month and its values the prior two months. They define high (low) default risk shock conditions as those above (below) the inception-to-date median value of the series. Using monthly returns for a very broad sample of AMEX/NYSE/NASDAQ stocks during 1960 through 2009 and monthly default risk spreads since 1954, they find that: Keep Reading