Momentum Investing

Stock price momentum strategies sometime crash, greatly detracting from long-term performance. Is there a reliable way to avoid the crashes? In the April 2012 version of their paper entitled “Managing the Risk of Momentum”, Pedro Barroso and Pedro Santa-Clara investigate usefulness of momentum portfolio volatility as a crash protection signal. They construct a momentum portfolio return series based on equal allocations to the risk-free asset (one-month Treasury bill), a value-weighted long side of momentum winners and a value-weighted short side of momentum losers, reformed and rebalanced monthly. They measure momentum for all NYSE/AMEX/NASDAQ stocks based on 11-month lagged returns plus a skip-month, and define winners and losers based on the top and bottom decile cutoffs for NYSE stock momentum. Using daily and monthly momentum portfolio returns and monthly U.S. equity risk factors (market, size, book-to-market) based on stock prices for July 1926 through December 2011, they find that: Keep Reading

Do “new” momentum stocks outperform “old” ones? In the March 2012 version of their paper entitled “Limited Attention, Salience, and Stock Returns” [apparently removed from SSRN, casting doubt on findings], Avanidhar Subrahmanyam, Jason Wei and Hsin-Yi Yu analyze whether stocks newly entering and exiting extreme momentum deciles exhibit unusual future returns because of heightened investor attention. Their benchmark (6-6) strategy consists of conventional overlapping winner-minus-loser momentum portfolios that are long/short those stocks with the highest/lowest returns over the past six months, formed monthly (after a skip-month) and held for six months. They classify a stock as a 6-6 arriver if it is not in any of the five preceding overlapping winner-minus-loser portfolios and a 6-6 dropper if it was in at least one winner-minus-loser portfolio active during the previous five months but is not in any of the active portfolios this month. They also consider arriving and dropping stocks defined relative to ranking intervals of 3, 9 and 12 months and holding intervals of 1, 2, 3, 9 and 12 months. Within portfolios, they weight the winner and loser sides equally and each stock within the winner or loser side equally. Using daily and monthly prices and volumes for NYSE, AMEX and NASDAQ common stocks priced above $5, along with contemporaneous risk factor and robustness test data as available, during 1962 through 2010, they find that: Keep Reading

It is arguable that many exchange-traded fund (ETF) momentum strategy tests derive more from logical/programming simplicity than common portfolio management practices. Does momentum work for portfolios of ETFs when melded with the latter? In his March 2012 paper entitled “Tactical Asset Allocation Using Relative Strength”, John Lewis tests ETF momentum in the context of real-world portfolio practices. He employs a universe of nearly 100 ETFs encompassing U.S. equity sectors and styles, international/global equities, bonds, commodities, real estate and currencies, including some inverse funds. After initial selection of top ETFs, he replaces weakening funds with strong ones as needed based on daily (or weekly) prices rather than at a fixed interval, depending on four parameters: (1) momentum ranking interval; (2) number of ETFs in the portfolio; (3) buy rank threshold; and, (4) sell rank threshold. To test robustness, he conducts multiple trials based on random selection of ETFs above the buy rank threshold. Specifically, he presents seven examples of 100 iterations of 10-ETF portfolios randomly selected from the top 25% of the ETF universe based on momentum ranking intervals of one month to two years, replacing ETFs when they fall out of the top 25%. Portfolios are apparently equally weighted at initial formation. Examples ignore dividends, management fees and trading frictions. Using daily returns for the ETFs from the end of 1999 through the end of 2011 (12 years), he finds that: Keep Reading

Do market capitalization (size) and book-to-market ratio systematically affect intermediate-term momentum and long-term reversal for individual stocks? In their February 2012 paper entitled “Momentum and Reversal: Does What Goes Up Always Come Down?”, Jennifer Conrad and Deniz Yavuz examine whether size and book-to-market ratio interact with momentum portfolio performance over intervals of 0-6, 6-12, 12-24 and 24-36 months after formation. They designate a stock as a winner (loser) if its 6-month lagged return is higher (lower) than the average for all stocks, with a skip-month before portfolio formation. They weight stocks within momentum portfolios by the absolute difference between its lagged 6- month return and that of all stocks, normalizing so that winner and loser sides contribute equally. They define three hedge portfolio types to measure risk factor-momentum interaction:

1. MAX portfolios are long (short) past winners that are small and/or high book-to-market (losers that are large and/or low book-to-market).
2. MIN portfolios are long (short) past winners that are large and/or low book-to-market (losers that are small and/or high book-to-market).
3. ZERO portfolios are long (short) past winners (losers) with similar size and book-to-market characteristics.

They sort stocks by size and book-to-market into thirds. When combining factors, they define stocks as high (low) risk group if they are in the high-risk (low-risk) third for one factor and in or above (below) the middle-risk third for the other. Using returns and factor characteristics for a broad sample of U.S. stocks during 1965 through December 2010, they find that: Keep Reading

Do widely acknowledged U.S. equity market anomalies exist in other stock markets? If so, why? In his November 2011 paper entitled “Equity Anomalies Around the World”, Steve Fan investigates whether a number of equity market anomalies found among U.S. stocks (asset growth, book-to-market ratio, investment-to-assets ratio, six-month momentum with skip-month, net stock issuance, size and total accruals) also occur in other equity markets and the degree to which such anomalies relate to stock-unique (idiosyncratic) risk. He measures raw anomaly strength based on gross returns from hedge (“zero-cost”) portfolios that are long and short equally weighted extreme quintiles of stocks ranked annually for each accounting variable and every six months for momentum (with overlapping momentum portfolios). To estimate alphas, he adjusts raw returns for the three Fama-French risk factors (market, book-to-market, size) or three alternative investment-based risk factors (market, investment, return on assets). Using monthly common stock return data and associated firm characteristics/accounting data for 43 country stock markets during 1989 through 2009, he finds that: Keep Reading

It is arguable that many stock momentum strategy tests derive more from logical/programming simplicity than common portfolio management practices. Does momentum work for portfolios of U.S. stocks when melded with the latter? In the January 2012 update of his paper entitled “Relative Strength and Portfolio Management”, John Lewis tests individual stock momentum in the context of real-world stock portfolio practices. After initial selection of top stocks, he replaces weakening stocks with strong ones as needed rather than at a fixed interval, depending on four parameters: (1) momentum ranking interval; (2) number of stocks in the portfolio; (3) buy rank threshold; and, (4) sell rank threshold. To test robustness, he conducts multiple trials based on random selection of stocks above the buy rank threshold. Specifically, he presents nine examples of 100 iterations of 50-stock portfolios randomly selected from the top 10% of the S&P 900 (S&P 500 large-cap plus S&P 400 mid-cap) based on momentum ranking intervals of one month to five years, replacing stocks when they fall out of the top 25%. Portfolios are apparently equally weighted at initial formation. Examples ignore dividends, management fees and trading frictions. Using daily returns (excluding dividends) for the S&P 900 stocks over the period 1996 through 2011 (16 years), he finds that: Keep Reading

Does momentum investing work for currencies as it does for equities? In the December 2011 version of their paper entitled “Currency Momentum Strategies”, Lukas Menkhoff, Lucio Sarno, Maik Schmeling and Andreas Schrimpf investigate momentum strategies in foreign exchange (FX) markets. FX markets are generally more liquid than equity markets, with huge transaction volumes, low trading frictions and no short-selling constraints. The study’s principal analytic approach is to rank 48 currencies monthly based on returns over the past one, three, six, nine and 12 months and use the rankings to form six eight-currency portfolios for holding intervals ranging from one to 60 months. The monthly winners (losers) are the portfolios with the highest (lowest) past returns. Using monthly FX spot and one-month forward price and bid-ask data for 48 currencies relative to the U.S. dollar as available over the period January 1976 through January 2010, they find that: Keep Reading

In the brief August 2011 paper entitled “Paired-switching for Tactical Portfolio Allocation”, flagged by a subscriber, Akhilesh Maewal and Joel Bock investigate the efficiency of a simple momentum strategy applied to pairs of exchange-traded funds (ETF) with negative return correlations. Every 13 weeks (four times per year), they rank the performances of the two funds over the prior thirteen weeks and buy the fund that has the higher return. They ignore trading frictions. Using weekly adjusted closing levels of SPDR S&P 500 (SPY), iShares Barclays 20+ Year Treas Bond (TLT), iShares MSCI EAFE Index (EFA) and Vanguard Total Stock Market ETF (VTI) over the period from about October 2002 through about June 2011, they find that: Keep Reading

Over the past few days, in the background, we have updated and rationalized “Simple Sector ETF Momentum Strategy” (update pending) and “Doing Momentum with Style (ETFs)” (update just published). Updated means adding data for December 2011. Rationalized means making the two analyses more similar in data processing and presentation approaches.

We have also put together, and will soon publish, a new “Simple Asset Class ETF Momentum Strategy” that extends the general methodology to a set of exchange-traded funds (ETF) plus cash that proxy for nine asset classes.

During this process, because of growing complexity, we introduced logical programming that automates identification of monthly ETF winners and loading of subsequent monthly returns. In validating this programming, we found errors in old winners data for “Doing Momentum with Style (ETFs)” that were material to findings because they occurred during high market volatility. Specifically, the errors led to incorrect conclusions that a simple momentum strategy applied to style ETFs likely outperforms both an equally weighted portfolio of style ETFs and a simple momentum strategy applied to sector ETFs. After correction of the errors in today’s update, findings are that a simple momentum strategy applied to style ETFs performs about the same as an equally weighted portfolio of style ETFs and a simple momentum strategy applied to sector ETFs. We apologize for the errors.

We found no such errors in “Simple Sector ETF Momentum Strategy”.

Can investors exploit idiosyncratic volatility exhibited by commodity futures? In their December 2011 paper entitled “Idiosyncratic Volatility Strategies in Commodity Futures Markets”, Adrian Fernancez-Perez, Ana-Maria Fuertes and Joelle Miffre investigate the usefulness of idiosyncratic volatility as a predictor of commodity futures returns. They define idiosyncratic volatility of commodity futures as return volatility not explained by contemporaneous variation in hedging pressure. They calculate hedging pressure from CFTC Commitments of Traders reports by relating long positions to total positions across trader categories. Return calculations assume: (1) holding the first nearby contract up to one month before maturity and then rolling to the next-nearest contract; (2) trading on a fully collateralized basis, meaning that half of trading capital earns the risk-free rate (three-month Treasury bill yield); and, (3) reporting only returns in excess of the risk-free rate, which averages about 3.3% annually over the sample period. They test all combinations of commodity ranking (whether for idiosyncratic volatility, return momentum or roll return) and portfolio holding intervals of 4, 13, 26 and 52 weeks. They calculate alpha by regressing long-short commodity futures portfolio returns against the same-interval hedging pressure risk premium. Using Friday settlement prices of nearest and second-nearest contracts for 27 commodity futures and weekly hedging pressure data during September 30, 1992 through March 25, 2011, they find that: Keep Reading