Technical Trading

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

In their March 2005 paper entitled “Re-Examining the Profitability of Technical Analysis with White’s Reality Check and Hansen’s SPA Test”, Po-Hsuan Hsu and Chung-Ming Kuan examine the profitability of a very large universe of technical trading rules and strategies against the Dow Jones Industrial Average (DJIA), S&P 500, NASDAQ Composite and Russell 2000 stock indexes. Their approach and findings, using data from 1989-2002, are as follows: Keep Reading

In a March 2005 update of his paper entitled “Simple Technical Trading Strategies: Returns, Risk and Size”, Satyajit Chandrashekar investigates the effectiveness of simple moving average technical trading strategies across ten market capitalization size deciles for the period 1963-2002. He finds that: Keep Reading

Anyone attempting or contemplating day-trading should take a look at “Information-based Trading, Price Impact of Trades, and Trade Autocorrelation” of May 2004 by Kee Chung, Mingsheng Li and Thomas McInish. The authors examine how the price impact of trades and the persistence of trading direction relate to the probability of information-based trading, as opposed to liquidity trading (for example, by market makers) or noise trading (by the uninformed). Here’s what they find: Keep Reading