Technical Trading

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

A reader requested that we test the stock market forecasts of John Vitale as posted via daily stock market commentary at the Palisades Research web site. The Palisades Research forecasting method involves “a statistical approach to market trading. The technique is unique and proprietary. It relies on two basic elements, ‘money flow’ and ‘investor emotion.’ …The total effort of this program goes into forecasting the direction of the S&P 500 index for the single following day. …Our main program indicates that the next day’s direction can be forecast with a 60% – 70% reliability compared to 53% with buy and hold, but even this is very difficult to maintain in real life.” In this review, we use both regressions and rankings to test the accuracy of the forecasts. Using the record of daily Palisades Research stock market commentary over the period 5/16/06-5/15/08 (499 daily forecasts) and next-day daily opening levels for the S&P 500 index and the Nasdaq 100 index over the same period, we find that… Keep Reading

Is there a predictable market reaction to stocks reaching round-number n-day highs and lows? In their November 2007 paper entitled “Highs and Lows: A Behavioral and Technical Analysis”, Bruce Mizrach and Susan Weerts investigate whether there are systematic trading behaviors for stocks posting 10-day, 25-day, 50-day, 100-day, 150-day, 200-day and 52-week highs and lows. Using daily price data for 488 Nasdaq stocks and 361 NYSE stocks over the period January 1993 through October 2003, they conclude that: Keep Reading

Does technical analysis work after accounting for luck and trading frictions? More specifically, can traders reliably identify technical rules that generate future net outperformance? In the January 2008 version of their paper entitled “Technical Trading Revisited: Persistence Tests, Transaction Costs, and False Discoveries”, Pierre Bajgrowicz and Olivier Scaillet investigate the economic value of technical trading rules applied to long-run daily Dow Jones Industrial Average (DJIA) data. Their approach includes: (1) a new measure of data snooping bias to distinguish between luck and true forecasting power in backtesting; (2) out-of-sample persistence testing of recently successful trading rules; (3) determination of whether certain trading rules work consistently under specific economic conditions; and, (4) incorporation of trading costs. Using daily DJIA price and volume data for January 1897 through July 2007 to test 7,846 rules (filters, moving averages, support and resistance, channel breakouts and on-balance volume averages), they conclude that: Keep Reading