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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.

Reversal, Momentum, Reversion and 12-month Echo Dependencies on January Returns

Are January returns important to the profitability of short-term reversal, intermediate-term momentum, long-term reversion and 12-month echo trading strategies? In her December 2010 paper entitled “Momentum, Seasonality and January”, Yaqiong Yao investigates the role of  January returns within these previously discovered anomalies. The study’s core methodology is to reform equally weighted hedge portfolios each month that are long/short stocks in extreme tenths (deciles) of  past returns over various intervals.  The one-month reversal strategy is long (short) losers (winners) based on prior month returns. Momentum strategies are long (short) winners (losers) based on past 11-month or 12-month returns, with a skip month before portfolio formation to avoid short-term reversal. The reversion strategy is long (short) losers (winners) based on past four-year returns, with a skip-year before portfolio formation to avoid intermediate-term momentum. The 12-month echo strategy is long (short) winners (losers) based on returns for the same month the prior one, two or three years. Using monthly returns for a broad sample of NYSE/AMEX stocks during 1926 through 2009, she finds that: Keep Reading

Persistently Effective Sector Selection Variables

What variables are persistently effective in picking equity sectors for tactical (monthly) trading? In their July 2010 paper entitled “Global Tactical Sector Allocation: A Quantitative Approach”, Ronald Doeswijk and Pim van Vliet investigate the effectiveness of seven variables for tactical trading of ten global equity sector indexes. They test effectiveness of these variables separately and in combination, and after their respective publication dates. The seven variables are: one-month return momentum, 12-1 return momentum (over the 11 months prior to the last month), earnings revision trend, long-term return (over the four years prior to the last year) reversion, aggregate dividend yield, Federal Reserve policy (expansive or contractive) and sell-in-May seasonal.  The ten sectors are energy, materials, industrials, consumer discretionary, consumer staples, health care, financials, information technology, telecommunication services and utilities.  Testing consists of monthly construction of equally weighted long-short portfolios based on variable conditions. For the first five variables, portfolios are long (short) the top (bottom) three sectors. The Federal Reserve policy and sell-in-May seasonal variables indicate whether to be long or short cyclical versus defensive sectors. The authors calculate net profitability based on a constant 0.60% round-trip trading friction. Using monthly sector index total returns and values for non-return variables mostly over the period 1970 through 2008, they find that: Keep Reading

Combination Momentum Strategies Not Worth the Effort?

Why does some prior research find that double sorts, first on some non-return variable and then on past returns, enhance momentum strategy performance? Are the enhancements truly distinct from momentum, or do they just pick higher momentum stocks? In their December 2010 paper entitled “One Effect or Many: Sources of Momentum Profits and Pitfalls of Double-Sorting”, Pavel Bandarchuk and Jens Hilscher investigate why sorting stocks first on some firm/stock characteristic and then on past returns elevates momentum profits. Specifically, they examine in several ways the relationship between each of size (market capitalization), return R-squared (similar to idiosyncratic volatility), turnover (12-month average), age (years listed), stock price, illiquidity (average absolute weekly return divided by weekly dollar volume) and credit rating and past returns to investigate the incremental profits of combining each with momentum. They use the logarithm of six-month past return with skip-month (effectively, a five-month return) to measure momentum. They calculate average future returns based on equal weighting and monthly portfolio reformation. Using weekly and monthly data for a broad sample of U.S. stocks spanning 1964 through 2008 (3,187 stocks per month on average), they find that: Keep Reading

OTC Stock Returns

Does the relatively illiquid, opaque, retail environment of over-the-counter (OTC) stocks make them behave differently from comparable listed stocks? In their November 2010 paper entitled “The Cross Section of Over-the-Counter Equities”, Andrew Ang, Assaf Shtauber and Paul Tetlock test the abilities of market capitalization, book-to-market ratio, liquidity, return momentum and idiosyncratic volatility to predict OTC stock returns and compare results to those for listed stocks with comparable market capitalizations. As a part of the study, they examine hedge portfolios that are long/short extreme fifths of OTC stocks ranked by these characteristics to estimate of the magnitudes of the respective  premiums. Using trading volumes, market capitalizations, book-to-market ratios (as available) and closing, bid and ask prices for a large sample of OTC-only firms with at least one Financial Industry Regulatory Authority market maker, and for comparable listed firms, during 1975 through 2008, they find that: Keep Reading

52-Week Highs for Emerging Markets Indexes

Evidence indicates that 52-week highs may be effective momentum signals for individual stocks, but probably not for major U.S. indexes. What do 52-week highs indicate for emerging markets? In their paper entitled “Predictability of Future Index Returns Based on the 52-Week High Strategy”, Mirela Malin and Graham Bornholt investigate the predictive power of 52-week highs for future returns of emerging markets indexes. To test the power of the 52-week high, they form monthly portfolios that are long (short) the fourth of emerging markets indexes that rank fractionally nearest to (farthest from) their respective 52-week highs and measure returns over the next 1, 3, 6, 9 and 12 months. They also test for comparison similar momentum portfolios with ranking intervals of 3, 6, 9 and 12 months and the same holding intervals. Both strategies insert a skip-month between ranking and portfolio formation. Using monthly dividend-adjusted levels and 52-week highs for 26 emerging markets indexes as available during January 1988 through March 2009 (171 to 255 months per index), they find that: Keep Reading

Highly Simplified Momentum Strategies

Academic tests of momentum generally involve frequent adjustments to portfolios of many stocks, such that trading frictions and shorting/capacity restrictions make implementation impractical for both large and small investors. Are there simplified approaches that successfully shed trading frictions faster than momentum returns? In the October 2010 version of their paper entitled “Feasible Momentum Strategies in the US Stock Market”,  Manuel Ammann, Marcel Moellenbeck and Markus Schmid measure the returns of simple, low-cost momentum strategies restricted to the relatively liquid U.S. stocks in the S&P 100 Index. They form portfolios monthly for nine combinations of ranking and holding periods (3, 6 and 12 months for both), including a skip-month between ranking and formation to avoid reversals. They consider the best-performing 1, 3, 5 and 10 stocks for long positions and either their worst-performing counterparts or the S&P 100 Index for short positions. They impose an overarching annual rebalancing scheme across different holding periods to suppress return volatility. Using total return data for the stocks in the S&P 100 Index as it exists at time of portfolio formation spanning 1982 through 2009, they find that: Keep Reading

Combining Momentum and Asset Growth

Both stock price momentum and asset growth rate exhibit empirical value as return predictors for individual stocks. Does combining these indicators offer enhanced value to investors? In their September 2010 paper entitled “Firm Expansion and Stock Price Momentum”, Peter Nyberg and Salla Pöyry investigate the interaction between firm-level asset growth (change in balance sheet total assets) and stock price momentum. Specifically, they measure returns for a monthly strategy that buys (sells) the prior winners (losers) within groups of stocks sorted first on on asset growth rates and then on 11-month past returns with skip-month. Using data for a broad sample of U.S. firms listed on NYSE, AMEX and NASDAQ over the period 1964-2006, they find that: Keep Reading

Extending Value and Momentum to Frontier Market Stocks

Do value and momentum strategies work in the least mature equity markets? In the September 2010 update of their paper entitled “Value and Momentum in Frontier Emerging Markets”, Wilma de Groot, Juan Pang and Laurens Swinkels examine whether the value premium based on book-to-market ratio (B/M), earnings-to-price ratio (E/P) or dividend-to-price ratio (D/P) and the momentum effect exist in frontier equity markets. Their basic methodology is to form long-short portfolios of equally weighted extreme (most and least attractive) quintiles monthly and to hold each portfolio for six months, with monthly outcomes calculated as averages for the six active portfolios (in excess of U.S. Treasury bills). Using return and accounting data for over 1,400 S&P Frontier Broad Market Index stocks  from 24 of the most liquid frontier markets over the period January 1997 through November 2008, they find that: Keep Reading

Parsing Reversal and Momentum Effects

Generalizations from the body of equity price trend research are: (1) stocks tend to exhibit short-term reversal, intermediate-term momentum and long-term reversion; and, (2) small capitalization and high-volatility stocks tend to exhibit the strongest momentum. What about the combination of size and volatility? In the September 2010 version of his paper entitled “Do Momentum and Reversals Coexist?”, Jason Wei investigates how momentum and reversal effects for individual stocks vary jointly with market capitalization and volatility. He forms portfolios monthly based on sequential size, realized volatility and past return sorts. He considers quintile ranking and holding periods of one, two, three, six and 12 months, with an intervening skip-week. Using daily price data for a broad sample of NYSE/AMEX/NASDAQ stocks spanning 1964-2009, he finds that: Keep Reading

Momentum and Moving Averages for Currencies

A reader asked: “Does a combination of rotation by relative strength (momentum) and moving averages, similar to that described in Mebane Faber’s Ivy Portfolio, work for the main currencies?” Keep Reading

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