Momentum Investing

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

Research on the U.S. equity market indicates that “old” or intermediate momentum (12 months ago to 7 months ago) is much more important than “new” or recent momentum (6 months ago to two months ago, incorporating a skip-month to avoid short-term reversal) in predicting future stock returns. Do other equity markets confirm this finding? In their September 2011 preliminary paper entitled “Is Momentum an Echo?”, Amit Goyal and Sunil Wahal investigate whether other country equity markets behave similarly. Using regressions, single-sorts on past stock returns and double-sorts on intermediate and recent past stock returns, along with country-specific risk factors (market, size, book-to-market), for 36 non-U.S. country equity markets during 1991 through 2009, they find that: Keep Reading

James O’Shaughnessy (Chairman and CEO of O’Shaughnessy Asset Management) introduces his 2011 book, What Works on Wall Street (Fourth Edition): the Classic Guide to the Best-Performing Investment Strategies of All Time, by stating: “…investors seem programmed by nature to fail at investing, forever chasing the asset class that has turned in the best performance recently and heavily discounting anything that occurred more than three to five years ago. The whole purpose of What Works on Wall Street is to dissuade investors from that course of action. Only the fullness of time shows which investment strategies are the best long-term performers, and this is doubly true after the last decade’s sorry performance. …We will make the case that equities–particularly those selected using the best long-term strategies–will go on to be the best performing assets over the next 10 and 20 years. …The fourth edition of What Works on Wall Street continues to offer readers access to long-term studies of Wall Street’s most effective investment strategies.” He uses overlapping portfolios formed monthly and rebalanced annually for all tests. Using broad sets of data on U.S. firms/stocks from either 1963 or 1926 through 2009 to extend and expand his prior quantitative analyses, he concludes that: Keep Reading