Momentum Investing

In their March 2011 paper entitled “The Shrinking Space for Anomalies”, George Jiang and Andrew Zhang investigate the robustness of ten well-known anomalies by iteratively “shrinking the stock space” in two ways to determine whether and how the anomalies really work. The ten anomaly variables are: size, book-to-market ratio, momentum, two liquidity measures, idiosyncratic volatility, accrual, capital expenditure, sales growth and net share issuance. The first way of “shrinking the stock space” involves: (1) ranking the universe of stocks by each of the ten anomaly variables into deciles; (2) iteratively trimming deciles from side of a variable distribution that a hedge portfolio would sell and the side that a hedge portfolio would buy; and, (3) retesting the strength of the anomaly associated with the variable after each iterative trimming. The second way of “shrinking the stock space” involves: (1) trimming from the sample stocks with the smallest market capitalizations and the most extreme book-to-market ratios until size, book-to-market and momentum no longer have significant four-factor alphas for value-weighting and equal equal-weighting (thereby “perfecting” the sample for the four-factor model); and, (2) retesting the strength of the anomalies associated with the other seven variables using the perfected sample. This approach obviates weaknesses in alpha measurement via the commonly applied but imperfect three-factor (market, size, book-to-market) and four-factor (plus momentum) risk models. Using firm characteristics and trading data for all non-financial NYSE, AMEX, and NASDAQ common stocks over the period July 1962 through December 2007, they find that: Keep Reading

How do widely recognized stock return anomalies (return variations unexplained by asset pricing models) mesh with efficient portfolio selection theory? In their paper entitled “Investing in Stock Market Anomalies”, Turan Bali, Stephen Brown and Ozgur Demirtas examine five prominent stock market anomalies whose existence is robust through time and across markets (size, book-to-market, short-term reversal, intermediate-term momentum and long-term reversion) in contexts of efficient portfolio selection via mean-variance and stochastic dominance methods. In other words, they test whether portfolios that apply these anomalies exhibit exceptionally good combinations of return and volatility, or obviously outperform on a purely statistical basis. Both these portfolio selection methods have shortcomings related to their inclusion of extreme, impractical choices. The authors consider relaxed (“Almost”) versions of these methods that prohibit such choices as “pathological.” The authors form value-weighted size and book-to-market portfolios annually and value-weighted reversal, momentum and reversion portfolios monthly. Using monthly data for July 1926 through December 2008 (990 months) for a broad sample of U.S. stocks to construct diversified anomaly portfolios, they find that: Keep Reading

Does momentum not work for Japanese equities? In his March 2011 paper entitled “Momentum in Japan: The Exception that Proves the Rule”, Clifford Asness examines whether the failure of stock price momentum in Japan materially undermines belief in momentum investing. He argues that any such examination should adopt the context of value and momentum as an integrated system. His methodology is to rank stocks representing the top 90% of capitalization within each of the U.S., UK, Europe (excluding UK) and Japan into three equal groups by value (book-to-market ratio, with book value lagged six months) or momentum (12-month past return, skipping the most recent month). The spreads in value-weighted returns between the top and bottom thirds define the value and momentum premiums within each geographic market. Using monthly returns for the selected stocks over the period July 1981 through December 2010 (29.5 years), he finds that: Keep Reading

Can financial statement analysis expose stocks that investors incorrectly view as value or growth (glamor)? In their February 2011 paper entitled “Identifying Expectation Errors in Value/Glamour Strategies: A Fundamental Analysis Approach”, Joseph Piotroski and Eric So investigate stock misvaluation by contrasting firm performance expectations implied by value/growth classification with a simple financial statement metric that differentiates improving versus deteriorating financial performance. This metric (FSCORE, scale 0 to 9), based on nine binary financial statement parameters, measures both the overall financial condition of a firm and the degree to which the firm has improved this condition over the prior year. The authors examine how FSCORE interacts with five widely used relative valuation metrics (book-to-market ratio, cash flow-to-price ratio, earnings-to-price ratio, sales growth and equity share turnover) and with momentum. Using annual financial data and stock returns for a broad sample of firms over the period 1972 through 2008 (117,412 firm-year observations), they find that: Keep Reading

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

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

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

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

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

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