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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.

The Industry 52-week High Effect

Are 52-week highs and lows useful equity price momentum indicators at the industry level? In their March 2011 paper entitled “Industry Information and the 52-Week High Effect”, Xin Hong, Bradford Jordan and Mark Liu compare the 52-week high effect for industries to that for individual stocks. This effect consists of the future outperformance (underperformance) of stocks currently near their respective 52-week highs (lows). Using monthly closes and rolling 52-week (intraday) highs for all stocks listed on NYSE, AMEX and NASDAQ and 20 value-weighted industry indexes constructed from SIC codes for these firms over the period July 1963 through 2009, they find that: Keep Reading

Interaction of Investor Sentiment and Stock Return Anomalies

Does aggregate investor sentiment affect the strength of well-known U.S. stock return anomalies? In their January 2011 paper entitled “The Short of It: Investor Sentiment and Anomalies”, Robert Stambaugh, Jianfeng Yu and Yu Yuan explore the interaction of aggregate investor sentiment with 11 cross-sectional stock return anomalies. Their approach reflects expectations that: (1) overpricing of stocks is more common than underpricing due to short-sale constraints; and, (2) a high sentiment level amplifies overpricing. Specifically, they consider the effect of investor sentiment on hedge portfolios that are long (short) the highest(lowest)-performing) value-weighted deciles of stocks sorted on: financial distress (two measures), net stock issuance, composite equity issuance, total accruals, net operating assets, momentum, gross profit-to-assets, asset growth, return-on-assets and investment-to-assets. They use a long-run sentiment index derived from principal component analysis of six sentiment measures: trading volume as measured by NYSE turnover; the dividend premium; the closed-end fund discount; the number of and first-day returns on Initial Public Offerings; and, the equity share in new issues. They measure anomaly alphas relative to the three-factor model (adjusting for market, size, book-to-market). Using monthly sentiment and stock return anomaly data as available over the period July 1965 through January 2008, they find that: Keep Reading

Interactions of Momentum, Valuation and Idiosyncratic Volatility

For what kind of stocks does momentum work best? In his March 2011 paper entitled “Growth Options, Idiosyncratic Volatility and Momentum”, Umut Celiker investigates the interactions among valuation (market to-book ratio, arguably a proxy for firm growth opportunities), valuation uncertainty (idiosyncratic volatility) and stock price momentum. For calendar-time analysis, he ranks stocks each month into quintiles by past six-month return, with a skip-month, and holds an equal-weighted hedge portfolio that is long the top (winner) quintile and short the bottom (loser) quintile for the next six months. For event analysis, he extends the holding interval to 60 months to explore momentum persistence/reversal. He computes stock idiosyncratic volatility relative to the S&P 500 Index over the prior 36 months. He defines the up (down) market state as the top 80% (bottom 20%) of months based on 60-month past value-weighted market returns averaged for each of the lagged six months. Most analysis focuses on the up market state. Using monthly firm accounting and stock price data for a broad sample of U.S. stocks over the period 1965 to 2008, he finds that: Keep Reading

Robustness Tests for Ten Popular Stock Return Anomalies

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

Bottom-up Anomalies vs. Top-down Portfolio Efficiency

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

Exclude Japan from Momentum Portfolios?

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

Concentrating the Value Premium and Momentum with FSCORE

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

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

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