Momentum Investing

A subscriber suggested applying simple momentum trading strategies to a set of leveraged equity style (size, value-growth) funds. It seems plausible that leverage may make funds react quickly and strongly to business cycle shifts that affect style performance. However, the costs of maintaining leverage are countervailing. Historical data for leveraged style funds is very limited, so we test instead a set of seven ProFunds 1.5X leveraged sector mutual funds, all of which have trading data back at least as far as December 2000:

ProFunds UltraSector Oil & Gas Inv (ENPIX)
ProFunds UltraSector Financials Inv (FNPIX)
ProFunds UltraSector Health Care Inv (HCPIX)
ProFunds Real Estate UltraSector Inv (REPIX)
ProFunds Telecom UltraSector Inv (TCPIX)
ProFunds Technology UltraSector Inv (TEPIX)
ProFunds Utilities UltraSector Inv (UTPIX)

As in “Simple Sector ETF Momentum Strategy Performance” and “Doing Momentum with Style (ETFs)”, we consider a basic momentum strategy that allocates all funds at the end of each month to the mutual fund with the highest total return over the past six months (6-1). We also consider a more cautious strategy that allocates all funds at the end of each month either to the mutual fund with the highest total return over the past six months or to cash depending on whether the S&P 500 Index is above or below its 10-month simple moving average (6-1;SMA10). Using monthly adjusted closing prices for the seven leveraged sector funds, the S&P 500 index, 3-month Treasury bills (T-bills) and S&P Depository Receipts (SPY) over the period December 2000 through November 2011 (132 months), we find that: Keep Reading

Do the poor returns and high volatility of the “buy-and-hold-is-dead” U.S. stock market since the beginning of 2000 represent a tailwind for market timers? In other words, is buy-and-hold effective as a benchmark for distinguishing between market timer luck and skill in recent years? To check, we measure the performances of various simple monthly market timing approaches (equal weighting with cash, 10-month simple moving average signals, momentum, and coin-flipping) during the 2000s. Using monthly closes for the dividend-adjusted S&P 500 Depository Receipts (SPY), the 3-month Treasury bill (T-bill) yield and the S&P 500 Index from December 1999 through October 2011 (earlier for S&P 500 Index signal calculations), we find that: Keep Reading

Research on the U.S. equity market indicates that “old” or intermediate momentum (12 months ago to 7 months ago) is much more important than “new” or recent momentum (6 months ago to two months ago, incorporating a skip-month to avoid short-term reversal) in predicting future stock returns. Do other equity markets confirm this finding? In their September 2011 preliminary paper entitled “Is Momentum an Echo?”, Amit Goyal and Sunil Wahal investigate whether other country equity markets behave similarly. Using regressions, single-sorts on past stock returns and double-sorts on intermediate and recent past stock returns, along with country-specific risk factors (market, size, book-to-market), for 36 non-U.S. country equity markets during 1991 through 2009, they find that: Keep Reading

James O’Shaughnessy (Chairman and CEO of O’Shaughnessy Asset Management) introduces his 2011 book, What Works on Wall Street (Fourth Edition): the Classic Guide to the Best-Performing Investment Strategies of All Time, by stating: “…investors seem programmed by nature to fail at investing, forever chasing the asset class that has turned in the best performance recently and heavily discounting anything that occurred more than three to five years ago. The whole purpose of What Works on Wall Street is to dissuade investors from that course of action. Only the fullness of time shows which investment strategies are the best long-term performers, and this is doubly true after the last decade’s sorry performance. …We will make the case that equities–particularly those selected using the best long-term strategies–will go on to be the best performing assets over the next 10 and 20 years. …The fourth edition of What Works on Wall Street continues to offer readers access to long-term studies of Wall Street’s most effective investment strategies.” He uses overlapping portfolios formed monthly and rebalanced annually for all tests. Using broad sets of data on U.S. firms/stocks from either 1963 or 1926 through 2009 to extend and expand his prior quantitative analyses, he concludes that: Keep Reading

Do asset classes consistently exhibit momentum over the same time frame as stocks? In his January 2006 investing policy entitled “Class OutPerformance (COP) Strategy”, Mal Williams describes a dynamic asset allocation strategy based on intermediate-term total return momentum of fund proxies (a complex calculation spanning the past 12 months, but not simply the 12-month return) for a wide range of asset classes. Implementation involves investing each month in the 10 to 15 best-performing funds out of a universe of 80 funds. In an October 2011 update of strategy tests, he selects the eight best-performing asset class proxies (heavily overweighting returns from the last three months) out of 51 possible as long as their performance is better than cash, in which case he allocates to the money market. He considers two implementation scenarios: (1) reallocate at the monthly open immediately after the fund ranking interval (for which there may be data availability issues); and, reallocate in the middle of the month after the ranking interval. Using monthly returns and semi-monthly prices for the 51 asset class proxy funds the period January 1991 through September 2011, along with contemporaneous money market yields, he finds that: Keep Reading

Is momentum on a losing streak? Or, has proliferation of momentum strategies extinguished the anomaly? In the October 2010 revision of his paper entitled “Are Momentum Strategies Still Profitable for U.S. Equity?”, Scott Wilson examines the recent performance of a momentum hedge strategy that each month buys (sells) the tenth of stocks with the highest (lowest) lagged six-month returns. He employs (overlapping) six-month holding intervals and focuses on equal weighting of stocks at formation. Using monthly data for stocks traded on the NYSE, AMEX and NASDAQ, excluding the tenth with the smallest market capitalizations and those priced below $5, during 1965 through 2009, he finds that: Keep Reading

Should investors strategically diversify across widely known equity market anomalies? In the October 2011 version of his paper entitled “Strategic Allocation to Premiums in the Equity Market”, David Blitz investigates whether investors should treat anomaly portfolios (size, value, momentum and low-volatility) as diversifying asset classes and how they can implement such a strategy.  To ensure implementation is practicable, he focuses on long-only, big-cap portfolios. To account for the trading frictions associated with anomaly portfolio maintenance and for time variation of anomaly premiums, he assumes future (expected) market and anomaly premiums lower than historical values, as follows: 3% equity market premium; 0% expected incremental size and low-volatility premiums; and, 1% expected incremental value and momentum premiums. He assumes future volatilities, correlations and market betas as observed in historical data and constrains weights of all anomaly portfolios to a maximum 40%. He considers both equal-weighted and value-weighted individual anomaly portfolios, and both mean-variance optimized and equal-weighted combinations of market and anomaly portfolios. Using portfolios constructed by Kenneth French to quantify equity market/anomaly premiums during July 1963 through December 2009 (consisting of approximately 800 of largest, most liquid U.S. stocks), he finds that: Keep Reading

Do the size effect, value premium and momentum effect derive from common firm/stock characteristics other than size, book-to-market ratio and past return? In the October 2011 version of their paper entitled “Which Firms Are Responsible for Characteristic Anomalies? A Statistical Leverage Analysis”, Kevin Aretz and Marc Aretz statistically isolate and analyze the small minority of firms that drive these three anomalies. Specifically, they exclude firms from the sample experimentally to identify those stocks that contribute the most to each anomaly (exhibit the strongest statistical leverage) and then examine in several ways the characteristics and stock price behaviors of those firms. They define size based on market capitalization, value based on book-to-market ratio and momentum based on three-month past return (which exhibits stronger momentum than 12-month past return during the sample period). They form test portfolios annually on June 30 based on current size and momentum and six-month lagged book-to-market ratio and hold from July 1 to June 30 of the next year. Using monthly stock returns, stock trading data and accounting variables for the firms then included in the S&P 1500, along with contemporaneous benchmark data, during July 1974 through December 2007, they find that: Keep Reading

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