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Technical Trading

Does technical trading work, or not? Rationalists dismiss it; behavioralists investigate it. Is there any verdict? These blog entries relate to technical trading.

Testing Japanese Candlesticks Intraday on Liquid Stocks

Do patterns formed by Japanese candlesticks, which summarize asset price behavior with a candle and two shadows indicating open-high-low-close prices over a given interval, work as intraday technical trading signals? In their August 2012 paper entitled “The Intraday Performance of Market Timing Strategies and Trading Systems Based on Japanese Candlesticks”, Matthieu Duvinage, Paolo Mazza and Mikael Petitjean investigate the power of 83 Japanese candlestick rules to predict intraday returns of the 30 components of the Dow Jones Industrial Average (DJIA) based on both stock timing metrics and optimized trading systems. They explicitly correct for data snooping bias that derives from testing a large number of rules on the same data and account for trading frictions. Using 5-minute intraday high-low-open-close prices from April 1, 2010 through April, 13 2011 for the 30 DJIA stocks (20,550 observations per stock), they find that: Keep Reading

Following S&P 500 Index Trends

How well do trend-following rules work when applied to the S&P 500 Index? In the March 2012 version of their paper entitled “Breaking into the Blackbox: Trend Following, Stop Losses, and the Frequency of Trading: The Case of the S&P 500”, Steve Thomas, James Seaton, Andrew Clare and Peter Smith evaluate a variety of simple daily moving average (SMA, 10 to 450 days), moving average crossover (25/50 to 150/350 days) and channel breakout (10-day to 450-day highs) trading rules as applied to the S&P 500 Index. They further investigate: (1) how measurement frequency affects rule performance; (3) effectiveness of combining the rules with stop-losses; and, (3) whether fundamental valuation metrics outperform the rules. They assume an index-cash switching cost of 0.2%. Using daily S&P 500 Index levels and monthly total returns from January 1952 through June 2011, daily S&P 500 Index total returns from July 1988 through June 2011 and contemporaneous Treasury bill yields as the return on cash, they find that: Keep Reading

Enhancing a Long-term Stock Market Reversion Strategy

Is it possible to determine when long-term stock market reversion is imminent? In their August 2012 paper entitled “Long-Term Return Reversal: Evidence from International Market Indices”, Mirela Malina and Graham Bornholt compare the performances of a conventional contrarian strategy that considers only long-term past returns to that of a “late-stage” contrarian strategy that buys (sells) long-term losers (winners) with relatively good (poor) recent returns, as applied to country stock market indexes. Specifically, their conventional contrarian strategy each month buys (sells) the quarter of indexes with the worst (best) returns over the past 36, 48 or 60 months and holds positions for 3, 6, 9 or 12 months (such that portfolios overlap), with a 12-month gap between ranking and holding intervals to avoid intermediate-term momentum effects. The late-stage contrarian strategy each month sorts indexes based on returns over the past 36, 48, or 60 months to identify the quarter with the worst (best) returns and then splits these winner and loser groups into halves based on returns over the past 3, 6, 9, or 12 months. The strategy then buys (sells) the long-term loser/short-term winner (long-term winner/short-term loser) indexes and holds positions for 3, 6, 9 or 12 months, with a one-month gap between ranking and holding intervals to ensure executability. Using monthly total (dividend-reinvested) returns for 18 developed and 26 emerging market indexes in U.S. dollars during January 1970 (or the earliest availability) through January 2011 (193 to 493 monthly observations across countries), they find that: Keep Reading

Testing the McClellan Oscillator and Summation Index

A reader commented and asked: “Several of my friends swear by the McClellan Summation Index for timing medium term bull/bear moves. Have you any evaluation of its usefulness?” The McClellan Summation Index derives from the McClellan Oscillator, a technical indicator developed in 1969 by Sherman and Marian McClellan, for which the daily input is the number of stocks that closed higher (advances) minus the number that closed lower (declines). The McClellan Oscillator smooths and seeks to concentrate the information in this daily breadth input stream via the difference of two exponential moving averages. The McClellan Summation Index is a running total of the daily values of the McClellan Oscillator. McClellan Financial Publications describes how to calculate the McClellan Oscillator. Advances and Declines is a public source of the historical numbers of advances and declines for U.S. exchanges. Using the daily numbers of NYSE advances and declines for March 1965 through most of June 2012 and daily dividend-adjusted closes of SPDR S&P 500 (SPY) from the end of January 1993 through most of June 2012 (about 19.5 years), we find that: Keep Reading

True Out-of-Sample Test of “Best” Technical Trading Rules

How do the technical trading rules that work best in a past study perform for new data? In the March 2012 version of their paper entitled “Predictability of the Simple Technical Trading Rules: An Out-of-Sample Test”, Jiali Fang, Ben Jacobsen and Yafeng Qin re-test performances of the 26 best technical trading rules from a 20-year old study with new data. This true out-of-sample approach avoids biases arguably endemic in retrospective testing. The selected 26 trading rules are those that perform best as applied to the Dow Jones Industrial Average (DJIA) during 1897 through 1986. These best rules are reasonably representative of rules applied in practice, comprising three groups: variable-holding interval moving average rules; fixed-holding interval moving average rules; and, trading range breakout rules. Analysis assumes long and short positions according to buy and sell signals (not long and cash). Using daily closes for DJIA during January 1987 through March 2011 and during February 1885 through December 1896, and for the S&P 500 Composite Index during January 1987 through March 2011, they find that: Keep Reading

Moving Averages and REIT Indexes

Does timing based on simple moving averages (SMA) work for U.S. Real Estate Investment Trust (REIT) indexes? If so, which moving average is best? In his March 2012 paper entitled “The Market Timing Power of Moving Averages: Evidence from US REIT Indexes”, Paskalis Glabadanidis tests the effectiveness of SMAs for timing ten value-weighted and ten similar equal-weighted U.S. REIT indexes. A monthly close above (below) its SMA signals investment in the REIT index (cash, estimated as the 30-day U.S. Treasury bill yield) the next month. He focuses on a 24-month SMA, but includes robustness tests based on 6-month, 12-month, 36-month, 48-month and 60-month SMAs. He applies baseline one-way trading frictions of 0.5% for entering and exiting a REIT index. Using monthly value-weighted and equal-weighted levels of ten U.S. REIT indexes during 1980 through 2010 (31 years), he finds that: Keep Reading

Pairs Trading and Market Turbulence

Are there market conditions most conducive to stock pairs trading? In their March 2012 paper entitled “Losing Sight of the Trees for the Forest? Pairs Trading and Attention Shifts”, Heiko Jacobs and Martin Weber assess how big-picture turbulence relates to profitability of stock pairs trading, hypothesizing that big-picture distractions draw attention away from specific opportunities. Their measure of big-picture distraction is daily regression-based aggregate unexpected returns for 49 U.S. industry portfolios, ranked (in-sample) into distraction deciles for each year. Their pairs trading approach involves each month: (1) selecting the 100 U.S. common stock pairs (out of 200 million possible) with the least divergence over the past 12 months; (2) over the next six months, entering equal long-short positions in any of these 100 pairs when normalized prices diverge by more than two historical standard deviations; and, (3) exiting pair positions when prices re-converge, or after one month if they do not re-converge. A selected pair may trade several times during its six-month active period. They consider trading with and without a one-day delay after signals. Using daily prices for reasonably large (above median market capitalization) and liquid NYSE/AMEX common stocks during 1960 through 2008, and similar data for eight other major international stock markets from the mid-1990s through 2009, they find that: Keep Reading

Frenetic Trading

How fast must traders move to operate efficiently in the high-frequency arena? In their February 2012 paper entitled “High-Frequency Technical Trading: The Importance of Speed”, Martin Scholtus and Dick van Dijk investigate execution speed sensitivity of technical trading rule performance for three highly liquid exchange-traded funds (ETF). They consider 27,424 variations of five price-based and two volume-based types of trading rules: moving average; filter; support and resistance; channel break-outs; price momentum; on-balance volume average; and, volume momentum. The baseline analysis constructs new signals every 60 seconds. They measure impact of eight execution delays (10, 20, 50, 100, 200, 500 and 1,000 milliseconds) on profitability relative to instantaneous execution. Trading frictions include bid-ask spread and impact of trading, but not transaction fees. They also measure typical levels of market activity over intervals of one day, one hour, one minute and one second. Using  complete order information for SPY, QQQQ  and IWM with millisecond timestamp accuracy during normal trading hours for January-September 2009, they find that: Keep Reading

Enhancing Dollar Cost Averaging?

Dollar cost averaging (DCA) is a very simple and intuitive way to buy more (less) of an asset when its price is low (high), thereby achieving some cost efficiency. Is there a simple and reliable way to enhance DCA? In their December 2011 paper entitled “Building a Better Mousetrap: Enhanced Dollar Cost Averaging”, Lee Dunham and Geoffrey Friesen examine allocation rules that retain attributes of traditional DCA but adjust to new information. Specifically, enhanced DCA (EDCA) rules adjust the amount invested in an asset according to its prior-month return. For example, one EDCA rule adds (subtracts) a fixed increment to (from) the planned monthly investment in an asset if its return for the prior month is negative (positive). Other alternatives adjust the incremental addition or reduction in monthly contribution depending on the value of the lagged monthly return. They employ both simulation and backtesting to measure the effects of EDCA. Using simulations of up to 30 years and monthly return data for six asset indexes and 100 mutual funds spanning 2000 through 2009, they find that: Keep Reading

Combining Realized Volatility and Simple Moving Averages

Does the effectiveness of simple moving average (SMA) crossing signals vary with stock volatility? In the August 2011 update of their paper entitled “A New Anomaly: The Cross-Sectional Profitability of Technical Analysis”, Yufeng Han, Ke Yang and Guofu Zhou investigate the application of SMAs to portfolios of stocks sorted based on realized volatility. Specifically, each year they sort stocks into deciles by volatility (standard deviation of daily returns over the past year). For each decile, they calculate a price index, an SMA for the index and daily returns based on initial equal weighting. When a decile portfolio is above (below) its SMA, they hold the portfolio (30-day Treasury bills), with a one-day delay for switches. They compare the returns for this timing strategy to buy-and-hold by decile. They focus on a 10-day SMA, but also test 20-day, 50-day, 100-day and 200-day SMAs. Using daily returns for a broad sample of U.S. stocks spanning 1963 through 2009, they find that: Keep Reading

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