Technical Trading

Mark Hulbert’s 9/5/07 column addresses the 9-to-1 up day event, a bullish technical signal publicized by Martin Zweig in a 1986 book. It occurs when at least 90% of daily NYSE volume belongs to advancing issues. When the signal occurs in multiples over short periods, as it has recently, prospects for equities are “quite bullish” according to Mark Hulbert. A reader comments and inquires:

“A statistician [David Aronson, author of Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals] confirms the significance of Zweig’s original observation. I don’t know whether he considered all possible confounding factors, such as low volume days, effect of externalities on the market, and others I can’t think of. This analysis sounds like so much epidemiological research, finding associations but never proving causality. For example, in the decade of the 1980s, alternate papers found that coffee consumption (greater than three cups per day) is and is not associated with increased risk of cancer of the pancreas. How much credence do you place in Hulbert’s article?”

Using S&P 500 index data for 1942-2006 (67 years), David Aronson finds an average return of about 5.2% in the 60 trading days after double 9-to-1 up days, significantly greater than the average return of about 1.1% during intervals of 60 trading days when there has not been such a signal. To follow up, we pose some questions to David Aronson and then consider strategies an investor might employ to exploit double 9-to-1 up day signals, as follows: Keep Reading

Is the Bullish Percent Index a useful indicator of overall stock market or sector direction by reliably identifying overbought/oversold conditions from which stock prices are likely to revert? In a study published in the 2005 Journal of Technical Analysis, Andrew Hyer relates the simple average Bullish Percent across 40 stock market sectors (BPAVG) to future broad stock market returns. Using weekly levels of BPAVG as calculated by Dorsey, Wright & Associates and overall stock market returns over the next 100 calendar days based on the Value Line Geometric Index for a total sample period of 1/6/98-1/24/05 (about 368 weeks or 26 intervals of 100 calendar days), he concludes that: Keep Reading

Do relatively low transaction costs and ease of short selling enable profitable technical trading in commodity futures markets? In their recent paper entitled “Can Commodity Futures be Profitably Traded with Quantitative Market Timing Strategies?”, Ben Marshall, Rochester Cahan and Jared Cahan investigate the effectiveness of 7,846 quantitative trading rules from five rule families (Filter, Moving Average, Support and Resistance, Channel Breakouts, and On-Balance Volume) for 15 kinds of commodity futures contracts. They test these rules for cocoa, coffee, cotton, crude oil, feeder cattle, gold, heating oil, live cattle, oats, platinum, silver, soy beans, soy oil, sugar and wheat futures. Their testing includes two bootstrapping methodologies, adjustment for data snooping bias and evaluations over different time periods. Using daily price and volume data for 1984-2005, they conclude that: Keep Reading

A reader requested a test of the TradingMarkets 5% VIX rule, which states:

“Do not buy stocks (or the market) anytime the VIX is 5% below its moving average. Why? Because since 1989, the S&P 500 cash market has “lost” money on a net basis 5 days following the times the VIX has been 5% below its 10 day ma.”

“Since 1989, whenever the VIX has been 5% or more above its 10 day ma, the S&P 500 has achieved returns which are better than 2 1/2 to 1 compared to the average weekly returns of all weeks.”

The reader also asked whether one can improve the signal by using a 4% or 6% threshold rather than 5%, or by using a holding interval longer or shorter than five days. We first reproduce the results claimed by TradingMarkets, then investigate whether the signals are of economic value to traders, and finally test sensitivity of results to parameter changes. Using daily CBOE Volatility Index (VIX) and S&P 500 index data for 1/2/90-7/11/07 (4415 trading days), we find that: Keep Reading

Is a bad week or month in the stock market an indicator of further immediate deterioration? Using weekly and monthly S&P 500 index closing levels since 1950 (2,998 weeks and 689 months), we find that: Keep Reading

A reader notes and asks: “It is frequently said that stocks at 52-week highs are the most likely to outperform in the future. Is there any academic evidence to support this assertion?” In their October 2004 Journal of Finance article entitled “The 52-Week High and Momentum Investing”, Thomas George and Chuan-Yang Hwang examine the explanatory power of the 52-week high in the context of momentum investing. They compare the 52-week high as a momentum indicator to benchmark momentum strategies that employ six months of past returns to forecast six months of future returns. Using price data for a broad range of stocks over the period 1963-2001, they find that: Keep Reading

Technical analysis seeks to exploit stock mispricings derived from postulated investor/trader psychological biases. Does short-term technical analysis actually produce abnormal returns? Or, do its adherents persist based on a misperception that they are to some degree in control of random rewards. In their February 2006 paper entitled “Does Intraday Technical Analysis in the U.S. Equity Market Have Value?”, Ben Marshall, Rochester Cahan and Jared Cahan investigate whether intraday technical analysis is profitable in the overall U.S. equity market. Specifically, they apply a combination of statistically rigorous bootstrapping tests to 7,846 trading rules from five rule families (Filter, Moving Average, Support and Resistance, Channel Breakouts, and On-Balance Volume). Using 5-minute data for Standard and Poor’s Depository Receipts (SPDR) over the period 1/1/02-12/31/03 (encompassing both bear and bull trends), they conclude that: Keep Reading

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