Technical Trading

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

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: Keep Reading

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?” Keep Reading

A reader asked whether the counter trend trade for individual stocks as described on page 384 of David Aronson’s Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals applies also to the broad stock market. The trade comes from a “study that stocks that have declined sharply over the prior two weeks on declining volume display a systematic tendency to rise over the following week.” Since this trade utilizes two indicators, we check each indicator separately and then the combination of indicators. Using weekly prices and volumes for Standard & Poor’s Depository Receipts (SPY) as a tradable proxy for the broad U.S. stock market over the period February 1993 through July 2010 (913 weeks), we find that: Keep Reading

A reader inquired about the predictive powers of bullish and bearish engulfing candlesticks, defined as: Bullish – a down day followed by an up day, with the latter having a higher intraday high and lower intraday low and closing in the top quarter of the daily range; and, Bearish – an up day followed by a down day, with the latter having a higher intraday high and lower intraday low and closing in the bottom quarter of the daily range. Using daily high, low and closing levels for the S&P 500 Index from January 1962 (the earliest available with intraday data) through July 2010, we find that: Keep Reading

In his 2010 book entitled Harmonic Trading: Volume Two Advanced Strategies for Profiting from the Natural Order of the Financial Markets, author Scott Carney “offers unprecedented strategies that identify the areas where overall trend divergence and harmonic pattern completions define the most critical technical levels. In addition, the new ideas presented in this material advance the basic theory of price pattern recognition by requiring other technical conditions to exist to validate potential opportunities with improved accuracy. Specifically, the advancement of the RSI BAMM separates the minor reactive moves from the more substantial trading opportunities and provides extensive technical information regarding the future potential direction of the price action. …Essentially, the integration of other measures has resulted in even more accurate projected reversal points for trade executions and hence, more reliable technical information regarding the state of potential price action. …The advanced techniques outlined in this book incorporate only the most pertinent technical measures that substantially increase the accuracy of harmonic patterns to identify the critical turning points in the financial markets.” On a chapter-by-chapter basis, key points from the book are: Keep Reading

In his 2010 book entitled Harmonic Trading: Volume One, Profiting from the Natural Order of the Financial Markets, author Scott Carney presents “an important advancement of the gamut of technical trading strategies that seek to define opportunities in the financial markets through the identification of price patterns and the analysis of market structure. …Most important, Harmonic Trading possesses unique and effective technical measurement strategies that define critical new patterns and expound upon the existing knowledge base of general Fibonacci and price pattern theories to establish precise guidelines and extremely effective predictive tools to define and analyze market trends.” Focusing on the first three chapters, which provide background on the harmonic trading methodology, key points from the book are: Keep Reading

What factors drive profitability for trading price divergences and convergences of pairs of similar stocks based on high-frequency data? In their March 2010 paper entitled “High Frequency Equity Pairs Trading: Transaction Costs, Speed of Execution and Patterns in Returns”, David Bowen, Mark Hutchinson and Niall O’Sullivan examine the characteristics of a high-frequency pairs trading strategy that takes a long (short) position in the relatively underpriced (overpriced) stock of a normally tracking pair upon divergence in price and closes positions upon convergence. They use overlapping 264-hour formation periods to identify the top five and top 20 trading pairs, followed by 132-hour trading periods for those pairs triggered by price divergence of two or three historical standard deviations. Using 60-minute return intervals calculated from tick by tick trade data for a sample of FTSE 100 stocks over calendar year 2007, they find that: Keep Reading

Does the degree to which a stock tracks the market and its industry predict its future performance. In their July 2010 paper entitled “R2: Does It Matter for Firm Valuation?”, John Stowe and Xuejing Xing investigate how the coefficient of determination (R-squared statistic) relating individual stock returns to market/industry returns affects the stock’s market valuation and future returns. They calculate R-squared for a stock’s returns using weekly data by firm fiscal year (and apply a logarithmic transformation). Using weekly stock returns and associated firm fundamentals/characteristics for a broad sample of U.S. stocks and weekly market and industry returns (excluding financials and utilities) over the period 1970-2007, they find that: Keep Reading

What are the momentum and reversion patterns for stocks that have been consistent past winners or losers? In his June 2010 paper entitled “Does Bad Economic News Play a Greater Role in Shaping Investors’ Expectations than Good Economic News?”, Abdulaziz Alwathainani investigates the relationship between the consistency of past monthly stock performance and future returns. He defines consistent past winners (losers) as those ranking in the top (bottom) 30% of monthly returns for at least six of the last 12 months. He defines stocks ranking in the middle 40% for at least six of the last 12 months as a consistently moderate benchmark. Using monthly return and characteristics data for a broad sample of U.S. stocks spanning 1963-2007, he finds that: Keep Reading