Technical Trading

Does pairs trading work for exchange-traded funds (ETF)? In the November 2010 version of their paper entitled “ETF Arbitrage”, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti examine arbitrage opportunities among three dollar-traded ETFs designed to track the S&P 500 Index: SPDR S&P 500 (SPY); iShares S&P 500 Index (IVV); and, the Swiss Exchange iShares S&P 500 (IUSA). The baseline strategy requires a minimum 0.2% ETF price divergence as a conservative threshold for long-short trade initiations to ensure that a large number of very small divergences, arguably subsumed by transaction fees, do not dominate results. This strategy terminates trades upon ETF price convergence. The authors conservatively assume a 15-second execution delay for opening and closing (fill or kill) orders. Using “cleaned” high-frequency, post-decimalization price data from normal trading hours minus the first and last 5 minutes for SPY and IVV (IUSA) from February 2001 (June 2004) through August 2010, they find that: More…

Is pairs trading (buying the loser and selling the winner of close-substitute stocks that have diverged unusually in price) profitable after accounting for reasonable trading friction? In the November 2010 version of their paper entitled “Are Pairs Trading Profits Robust to Trading Costs?”, Binh Do and Robert Faff examine the impact of trading friction (commissions, market impact and short selling fees) on pairs trading profitability. Their baseline pairs trading strategy consists of: (1) finding a partner for each stock that minimizes normalized price spread during a 12-month formation period; (2) screening the best pairs based on lowest tracking error; (3) within six months after pairs identification, opening long/short positions in the underpriced/overpriced members of the best pairs with normalized price divergences of at least two standard deviations; and, (4) closing the trade at the first price convergence or, otherwise, at the end of the six-month trading interval. They also consider 29 strategy refinements that address industry affinity, frequency of past price divergence-convergence and/or magnitude of past price divergence. Using prices and industry designations for relatively liquid U.S. stocks over the period July 1962 through December 2009, they find that: More…

Do stock indexes react systematically to extreme price levels, such as 52-week highs and 52-week lows? To investigate, we consider the behaviors of the Dow Jones Industrial Average (DJIA), the S&P 500 Index and the NASDAQ Composite Index over the 13 weeks after 52-week highs and lows during their available histories. Using weekly levels of these indexes from October 1928, January 1950 and February 1971, respectively, through October 2010, we find that: More…

The conclusion of “10-Month SMA Timing Signals Over the Long Run” is that 10-month simple moving average (SMA) timing signals (with current price above/below its 10-month SMA viewed as bullish/bearish) are mostly beneficial over the long run. However, this study involves complex modeling assumptions that limit confidence in its conclusion. How well do 10-month SMA crossing signals work for an investable proxy of the U.S. stock market over a recent sample period? To check, we test several variations of a 10-month SMA timing strategy on S&P Depository Receipts (SPY) since the introduction of this exchange-traded fund. Using daily and monthly closes for SPY, both unadjusted and adjusted for dividends, from inception in January 1993 through September 2010 and contemporaneous 3-month Treasury bill (T-bill) yields, we find that: More…

Current price versus 10-month simple moving average (SMA) is a widely used indicator of asset and asset class trend, with current price above/below its 10-month SMA viewed as bullish/bearish. How has this indicator performed for U.S. equities over the long run? To investigate, we employ the long-run data set of Robert Shiller to construct a very long backtest of 10-month SMA crossing signals. This data set includes monthly levels of the S&P Composite Index, calculated as average of daily closes during the month. This method of calculation deviates from that most often used for SMA signals, but arguably suppresses the effects of the turn of the month and other aspects of intra-month volatility on the SMA signals. Using S&P Composite Index levels, associated dividend yields and contemporaneous long-term interest rates (comparable to yields on 10-year Treasury notes) from the Shiller data set spanning January 1871 through September 2010 (1,677 months or about 140 years), we find that: More…

Does high-frequency trading based on intensive data mining earn huge profits? In their September 2009 paper entitled “Empirical Limitations on High Frequency Trading Profitability”,  Michael Kearns, Alex Kulesza and Yuriy Nevmyvaka estimate the maximum possible profit from aggressive high-frequency trading (entry/exit via market orders with holding periods no longer than 10 seconds). They determine potential profit metrics based on high-resolution data for the most liquid NASDAQ stocks and then extrapolate to a large universe of U.S. stocks via regressions on less detailed data. They consistently err on the side of overestimation to define upper bounds on total available profit by: assuming perfect trader hindsight for trade selection; computing optimally profitable trade sizes;  excluding exchange fees and broker commissions; and, modeling opportunities in a way that overstates the number of profitable trades. Using a complete set of order placements, cancellations, modifications and trade executions for a set of 19 highly liquid NASDAQ stocks during 2008 and a less granular set of contemporaneous data for other U.S. stocks, they find that: More…

A reader commented and asked:

“Some have suggested that the end-of-the-month effect benefits monthly simple moving average strategies that trade on the last day of the month. Is there an optimal day of the month for long-term SMA calculation and does the end-of-the-month effect explain the optimal day?”

To investigate, we compare 21 variations of a 10-month SMA timing strategy based on shifting monthly return calculation cycles relative to the end of the month (EOM), as applied to S&P Depository Receipts (SPY) as a tradable proxy for the stock market that incorporates portfolio formation costs. Using daily dividend-adjusted closing levels of SPY from inception (1/29/93) through 9/17/10 and contemporaneous three-month Treasury bill (T-bill) yields, we find that: More…

Reader Mike Korell of ChartsEdge suggested an evaluation of his own S&P 500 Index forecasts for inclusion in Gurus. These “stock market forecasts are based on cycle data which has been analyzed by a Pattern Recognition Program. This use of artificial intelligence reduces the effect of personal bias and allows the simultaneous cycle analysis of many input variables.” To construct a statistical evaluation, we focus on the open and close levels of the ChartsEdge weekly forecasts, apparently issued on Sundays. Using estimates of the forecasted S&P 500 Index open and close levels from inspection of the ChartsEdge weekly charts and actual contemporaneous S&P 500 Index weekly open and close data for weeks beginning 9/8/08 through 9/7/10 (104 weekly returns), we find that: More…

A reader asked: “Would you be willing to test or comment on the 8/14 Wall Street Journal article ‘Hindenberg Omen Flashes’?” The Hindenburg Omen is a complex technical signal that, including confirmation via clusters of signals, consists of simultaneous satisfaction of five rules for NYSE stocks. Different informal sources indicate some variation in the rules among practitioners. For the sake of consistency in rule application, we consider the “confirmed” Hindenburg Omens cited by Robert McHugh in his 8/21/10 article entitled “We Get An Official Confirmed Hindenburg Omen On August 20th, 2010″. This article states that, after Hindenburg Omens, “plunges can occur as soon as the next day, or as far into the future as four months.” Using the dates of the Hindenburg Omens reported in these articles and weekly closing levels of the S&P 500 Index during 1/3/86 through 8/13/10, we find that: More…

A reader asked: “Does a combination of rotation by relative strength (momentum) and moving averages, similar to that described in Mebane Faber’s Ivy Portfolio, work for the main currencies?” More…