Technical Trading

Does the head-and-shoulders stock price pattern embody investor attitudes that traders can exploit to earn abnormal returns? Or, does it represent an opportunity for the statistics-challenged to be fooled by randomness? In their October 2006 paper entitled “The Predictive Power of ‘Head-and-Shoulders’ Price Patterns in the U.S. Stock Market”, Gene Savin, Paul Weller and Janis Zvingelis use a pattern recognition algorithm, as filtered based on the experience of a technical analyst, to determine whether head-and-shoulders price patterns formed across intervals of 63 trading days have predictive power for future stock returns over the next few months. Using daily price data during 1990-1999 for all stocks in the S&P 500 and Russell 2000 indexes as of June 1990, they conclude that: Keep Reading

Does candlestick technical analysis (examining relationships among opening, high, low and closing prices over the past 1-3 days to identify continuation and reversal signals) generate abnormal returns? In their recent paper entitled “Market Timing with Candlestick Technical Analysis”, Ben Marshall, Martin Young and Lawrence Rose test the profitability of trading stocks included in the Dow Jones Industrial Average based on 28 different candlestick signals. They assume a ten-day holding period after trading at the close on the day after a signal appears. Using stock price data for 1/1/92-12/31/02, they conclude that: Keep Reading

In his 2007 book Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals, David Aronson opens with two contentions: (1) “much of the wisdom comprising the popular version of TA does not qualify as legitimate knowledge;” and, (2) “TA must evolve into a rigorous observational science if it is to deliver on its claims and remain relevant.” Taken in parts, this book offers sound methods for analysis. Taken as an integrating whole, it offers insightful context for evaluating a broad range of financial analyses/claims presented by others. Here is a chapter-by-chapter review of some of the insights in this book: Keep Reading

Are trades based on complex technical patterns, such as head-and-shoulders, rational speculations or noise? In other words, do such patterns reliably indicate opportunities to capture excess returns? In her July 1998 paper entitled “Identifying Noise Traders: The Head-And-Shoulders Pattern in U.S. Equities”, Carol Osler investigates whether head-and-shoulders trading is significant and whether it is profitable. In their August 2000 paper entitled “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation”, Andrew Lo, Harry Mamaysky and Jiang Wang apply advanced empirical methods (compare with fingerprint identification or face recognition) to evaluate technical analysis patterns such as head-and-shoulders and double-bottoms. These papers conclude that: Keep Reading

Is there a way to end the endless debate on the merits of technical analysis? In his September 2006 paper entitled “On the Analogy Between Scientific Study of Technical Analysis and Ethnopharmacology”, Waldemar Stronka proposes bringing technical analysis into the financial economics fold in a manner analogous to the successful incorporation of folk medicine by pharmacology. Specifically, he notes that: Keep Reading

Can technical traders make money if they focus on stocks that are small, illiquid or in specific industries? In their September 2006 paper entitled “Is Technical Analysis Profitable on U.S. Stocks with Certain Size, Liquidity or Industry Characteristics?”, Ben Marshall, Sun Qian and Martin Young test three widely used technical trading rules: (1) the variable length moving average rule: (2) the fixed length moving average rule; and, (3) the trading range break-out rule. Using daily close data for 1,065 NYSE and NASDAQ stocks trading over the entire period 1990-2004, they find that: Keep Reading

Barchart.com offers free short-term, intermediate-term and long-term technical assessments of stocks and exchange traded funds (ETF). Barchart.com, Inc. claims that their “market information is being used by millions of investors every month.” An obstacle to assessing the usefulness of their technical indicators is unavailability of historical data. To overcome this obstacle, we have recorded their average indicators for S&P 500 Depository Receipts (SPY) daily to assemble a statistically meaningful history for that ETF, which tracks the S&P 500 index. Whenever an indicator average is “Hold,” we assign a value of 0%. From the seven months of data collected, encompassing both market advances and declines, we conclude that: Keep Reading

What can small-trade volume tell us about the behavior and success of retail investors? Two December 2005 papers tackle this question. In a paper entitled “Small Trades and the Cross-section of Stock Returns”, Soeren Hvidkjaer investigates the effect of retail investor trading behavior on stock returns by studying intermediate-term and long-term returns for stocks with small-trade buying or selling pressures. In a paper entitled “Do Noise Traders Move Markets?”, Brad Barber, Terrance Odean and Ning Zhu offer a similar study, adding an analysis of the short-term returns for stocks with small-trade buying or selling pressures. Their joint findings are: Keep Reading

Researchers have recently focused on divergence of investor opinion as an indicator of future stock returns, but measuring this divergence using publicly available data has been problematic. In his April 2005 paper entitled “Measuring Investors’ Opinion Divergence”, Jon Garfinkel uses a non-public indicator of the stock valuations of investors to validate four public indicators: bid-ask spread, unexplained volume, forecast variability among analysts and stock return volatility. His non-public indicator is the standard deviation of the differences between all limit order prices and the most recent trade price, capturing actual investor price targets. Using data from 1995-1996 for the one non-public and four public indicators and focusing on activities before and after 150 selected NYSE trading halts, he concludes that: Keep Reading

We have selected for retrospective review a few all-time “best selling” research papers of the past few years from the General Financial Markets category of the Social Science Research Network (SSRN). Here we summarize the March 1998 paper entitled “The Dow Theory: William Peter Hamilton’s Track Record Re-Considered” (download count nearly 5,900) by Stephen Brown, William Goetzmann and Alok Kumar. This research applies risk adjustment and out-of-sample testing to re-examine Alfred Cowles’ 1934 debunking of the Dow Theory (as defined by the 255 editorials of William Peter Hamilton in The Wall Street Journal during 1902-1929). They conclude that: Keep Reading