Momentum Investing

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

A subscriber suggested applying a simple momentum trading strategy to a set of leveraged equity style (size, value-growth) exchanged-traded funds (ETF), including leveraged long and leveraged short counterparts to exploit both positive and negative markets. It seems plausible that leverage may make funds react quickly and strongly to business cycle shifts that affect style performance. However, the costs of maintaining leverage are countervailing. We test a set of 12 ProShares 2X and -2x leveraged sector ETFs, all of which have trading data back at least as far as April 2007:

ProShares Ultra Russell1000 Value (UVG)
ProShares Ultra Russell1000 Growth (UKF)
ProShares Ultra Russell MidCap Value (UVU)
ProShares Ultra Russell MidCap Growth (UKW)
ProShares Ultra Russell2000 Value (UVT)
ProShares Ultra Russell2000 Growth (UKK)

ProShares UltraShort Russell1000 Value (SJF)
ProShares UltraShort Russell1000 Growth (SFK)
ProShares UltraShort Russell MidCap Val (SJL)
ProShares UltraShort Russell MCap Growth (SDK)
ProShares UltraShort Russell2000 Value (SJH)
ProShares UltraShort Russell2000 Growth (SKK)

As in “Simple Sector ETF Momentum Strategy Performance” and “Doing Momentum with Style (ETFs)”, we consider a basic momentum strategy that allocates all funds at the end of each month to the ETF with the highest total return over the past six months (6-1). Using monthly adjusted closing prices for the 12 leveraged style ETFs and S&P Depository Receipts (SPY) over the period April 2007 through November 2011 (only 56 months), we find that: Keep Reading

A subscriber suggested applying simple momentum trading strategies to a set of leveraged equity style (size, value-growth) funds. It seems plausible that leverage may make funds react quickly and strongly to business cycle shifts that affect style performance. However, the costs of maintaining leverage are countervailing. Historical data for leveraged style funds is very limited, so we test instead a set of seven ProFunds 1.5X leveraged sector mutual funds, all of which have trading data back at least as far as December 2000:

ProFunds UltraSector Oil & Gas Inv (ENPIX)
ProFunds UltraSector Financials Inv (FNPIX)
ProFunds UltraSector Health Care Inv (HCPIX)
ProFunds Real Estate UltraSector Inv (REPIX)
ProFunds Telecom UltraSector Inv (TCPIX)
ProFunds Technology UltraSector Inv (TEPIX)
ProFunds Utilities UltraSector Inv (UTPIX)

As in “Simple Sector ETF Momentum Strategy Performance” and “Doing Momentum with Style (ETFs)”, we consider a basic momentum strategy that allocates all funds at the end of each month to the mutual fund with the highest total return over the past six months (6-1). We also consider a more cautious strategy that allocates all funds at the end of each month either to the mutual fund with the highest total return over the past six months or to cash depending on whether the S&P 500 Index is above or below its 10-month simple moving average (6-1;SMA10). Using monthly adjusted closing prices for the seven leveraged sector funds, the S&P 500 index, 3-month Treasury bills (T-bills) and S&P Depository Receipts (SPY) over the period December 2000 through November 2011 (132 months), we find that: Keep Reading

Do the poor returns and high volatility of the “buy-and-hold-is-dead” U.S. stock market since the beginning of 2000 represent a tailwind for market timers? In other words, is buy-and-hold effective as a benchmark for distinguishing between market timer luck and skill in recent years? To check, we measure the performances of various simple monthly market timing approaches (equal weighting with cash, 10-month simple moving average signals, momentum, and coin-flipping) during the 2000s. Using monthly closes for the dividend-adjusted S&P 500 Depository Receipts (SPY), the 3-month Treasury bill (T-bill) yield and the S&P 500 Index from December 1999 through October 2011 (earlier for S&P 500 Index signal calculations), we find that: Keep Reading