Technical Trading

How do the returns from pairs trading (bets on the re-convergence of prices for similar stocks that have historically tracked but recently diverged) play out? Are there systematic ways to enhance pairs trading profitability? In the November 2008 version of their paper entitled “An Anatomy of Pairs Trading: the Role of Idiosyncratic News, Common Information and Liquidity”, Joseph Engelberg, Pengjie Gao and Ravi Jagannathan explore how news events and liquidity shocks relate to pairs trading profitability. Using daily prices for a broad set of stocks spanning January 1992 through June 2006 and related news items and trading data for January 1993 through December 2005, they conclude that: Keep Reading

In his 2008 book, Short Term Trading Strategies That Work, Larry Connors, CEO and Founder of The Connors Group, shares “more than two decades of research and trading knowledge.” He states in the introductory chapter:

“Philosophically, I live in the world of reversion to the mean when it comes to trading. What that simply means is that something stretched too far will snap back. I didn’t come up with that idea. It’s been around for decades. What I have done though is an attempt to quantify it.“

Most of the quantifying tests use daily data spanning 1995-2007 for a large sample of stocks and the S&P 500 index. Based on past reviews of hundreds of anomaly studies, here are a few observations on the analyses presented in this book: Keep Reading

Do 52-week highs and lows trigger unusual trading and returns for individual stocks? In their recent paper entitled “Volume and Price Patterns Around a Stock’s 52-Week Highs and Lows: Theory and Evidence”, Steven Huddart, Mark Lang and Michelle Yetman examine the evidence that past price extremes influence trading decisions, with focus on 52-week highs and lows. Using weekly volume and closing prices for a random sample of 2,000 stocks listed for at least a year during November 1982 through December 2006 (24 years), they conclude that: Keep Reading

Do short-term relative mispricings of equity sectors offer a means to capture abnormal returns? To investigate, we measure the returns from trading potential “errors” in the relative price movements of a pair of sector exchange-traded funds (ETF) selected from the following:

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

Using daily adjusted closing prices (incorporating dividends) for these ETFs during 12/22/98-8/15/08, we find that: Keep Reading

We occasionally select for retrospective review an all-time “best selling” research paper of the past few years from the General Financial Markets category of the Social Science Research Network (SSRN). Here we summarize the February 2006 version of the paper entitled “Pairs Trading: Performance of a Relative Value Arbitrage Rule” (download count over 14,500) by Evan Gatev, William Goetzmann and Geert Rouwenhorst. The study tests the trading of mismatches in the dividend-adjusted prices of pairs of stocks that historically move together. Specifically, when the price spread between such a pair widens, short the winner and buy the loser and hold until prices converge. Using daily stock prices for a broad sample of reasonably liquid stocks over the period 1962-2002, the authors conclude that: Keep Reading

Do stop-loss orders (automated position exits based on a cumulative loss threshold) enhance returns and reduce risk? In their 2008 paper entitled “The Value of Stop Loss Strategies” Adam Lei and Huihua Li investigate whether traders using stop-loss strategies to exit losing positions in individual stocks outperform a comparable buy-and-hold strategy. They test the following strategy alternatives: holding periods of three months, six months or one year; stop-loss thresholds of 5, 10 or 20 daily return standard deviations; reinvestment of stopped out positions in either the S&P 500 index or the one-month Treasury bill; and, a fixed stop price or a trailing stop price that follows stock price upward (but not downward). Using historical and simulated daily return data for a broad sample of NYSE/AMEX-listed stocks and random buy dates over the period 1970-2005, they conclude that: Keep Reading

Does systematic use of stop-loss orders (automated position exits based on a cumulative loss threshold) improve net returns? Both the April 2008 paper entitled “Re-examining the Hidden Costs of the Stop-Loss” by Kira Detko, Wilson Ma and Guy Morita and the May 2008 draft paper entitled “When Do Stop-Loss Rules Stop Losses?” by Kathryn Kaminski and Andrew Lo address this question with theory and empirical tests. They conclude that: Keep Reading

Does technical analysis work in equity markets around the globe? In the July 2008 version of their paper entitled “Technical Analysis Around the World: Does it Ever Add Value?”, Ben Marshall, Rochester Cahan and Jared Cahan apply bootstrapping techniques to investigate the profitability of 5,806 technical trading rules (filters, moving averages, support and resistance analyses, and channel break-outs) in the 23 developed and 26 emerging equity markets that comprise the Morgan Stanley Capital Index (MSCI). Using daily data for all 49 markets over the period 2001-2007, they conclude that: Keep Reading

A reader suggested that reversion in the relationship between PowerShares QQQ (QQQQ) and iShares Russell 2000 Index (IWM) may support short-term trading. To check, we consider: (1) the QQQQ-IWM ratio over the long term; (2) this ratio relative to its six-month moving average; and, (3) unusual daily divergences between these two exchange-traded funds. Using daily adjusted closing prices for QQQQ and IWM over the period 5/26/00 (the earliest available for IWM) through 7/29/08, we find that: Keep Reading

A reader inquired about the “Short Term Stock Selector” designed by Robert Hesler, which “has provided neural network generated swing trading predictions [approximately daily] since 1996. …All buy and sell recommendations are based on 19 technical indicators. Some indicators pertain to the market in general while others pertain to individual stock attributes.” We focus for this review on the most profitable type of trades (Type A), for which the site claims a 66.3% win rate and a 40% annual return on investment over the period 4/11/96 through 6/17/08. Using the detailed listing of 12,340 closed Type A trades over this period, we find that: Keep Reading