Technical Trading

Evidence indicates that 52-week highs may be effective momentum signals for individual stocks, but probably not for major U.S. indexes. What do 52-week highs indicate for emerging markets? In their paper entitled “Predictability of Future Index Returns Based on the 52-Week High Strategy”, Mirela Malin and Graham Bornholt investigate the predictive power of 52-week highs for future returns of emerging markets indexes. To test the power of the 52-week high, they form monthly portfolios that are long (short) the fourth of emerging markets indexes that rank fractionally nearest to (farthest from) their respective 52-week highs and measure returns over the next 1, 3, 6, 9 and 12 months. They also test for comparison similar momentum portfolios with ranking intervals of 3, 6, 9 and 12 months and the same holding intervals. Both strategies insert a skip-month between ranking and portfolio formation. Using monthly dividend-adjusted levels and 52-week highs for 26 emerging markets indexes as available during January 1988 through March 2009 (171 to 255 months per index), they find that: Keep Reading

Is the aggregate effect of technical trading visible and exploitable at the equity index level? In his March 2007 paper entitled “The Interaction between the Aggregate Behavior of Technical Trading Systems and Stock Price Dynamics”, Stephan Schulmeister investigates how S&P 500 Index futures prices relate to the aggregate trading signals of 2,580 widely used trend-following and contrarian technical trading rules (moving average, momentum and relative strength) implemented with 30-minute data. Using 30-minute data for S&P 500 Index futures over the period 1983-2000, he finds that: Keep Reading

Is the aggregate effect of technical trading visible and exploitable in currency exchange rate trading? In his 2008 paper entitled “Aggregate Trading Behaviour of Technical Models and the Yen/Dollar Exchange Rate 1976-2007”, Stephan Schulmeister investigates the interaction between the aggregate signaling of 1,024 moving average and momentum rules and the behavior of the yen/dollar exchange rate. Using daily yen/dollar exchange rate data over the period 1976-2007, he finds that: Keep Reading

Is technology driving the profitability of technical rules in financial markets from low-frequency trading to high-frequency trading? In his March 2007 paper entitled “The Profitability of Technical Stock Trading Has Moved from Daily to Intraday Data”, Stephan Schulmeister investigates how well 2,580 widely used trend-following and contrarian technical trading rules (moving average, momentum and relative strength) exploit trend and reversal behavior of the S&P 500 Index futures markets. In estimating trading returns, he assumes: (1) that futures positions roll on the tenth day of the month from the expiring contract to the contact expiring three months later, and (2) that trading friction is 0.01% per trade. Using daily open (30-minute) data for the S&P 500 Index spot (futures) prices during 1960-2000 (1983-2006), he finds that: Keep Reading

In his 2010 book Trade the Trader: Know Your Competition and Find Your Edge for Profitable Trading, author Quint Tatro observes that “…what most investors don’t understand as they start to learn their basic technical patterns…is they are the ones actually in play. Seasoned traders are no longer just cuing off of charts or indicators, they are also analyzing those same charts to determine what the amateurs are doing, and are seeking to profit from the ignorance of the newcomers. It’s a chess game where the successful traders are thinking two and three moves ahead, playing off the basic strategy of the newcomers. Those simply pursuing a basic path of understanding technical analysis will find it is a road that ultimately leads to frustration, whereas those looking to trade the traders will be met with an endless world of opportunity. …If you don’t know on which side you fall, odds are you are someone’s next meal.” Some notable points from the book are: Keep Reading

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: Keep Reading

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 frictions? 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: Keep Reading

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: Keep Reading

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: Keep Reading

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: Keep Reading