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Value Investing Strategy (Strategy Overview)

Allocations for July 2024 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for July 2024 (Final)
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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.

Asset Class Momentum Strategy

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

Momentum Not Working?

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

Harvesting Equity Market Premiums

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

Statistically Recasting the Big Three Anomalies

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

Intrinsic 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

When Momentum Does and Doesn’t Work

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

Disappearance of the Momentum Effect

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

Momentum Overview from the Discoverers

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

Combining Return Reversal and Industry Momentum

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

Predicting Variation in the Size Effect

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

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