Technical Trading

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: More…

Several readers have inquired about the performance of Sy Harding’s Street Smart Report Online, which includes the Seasonal Timing Strategy. This strategy combines “the market’s best average calendar entry [October 16] and exit [April 20] days with a technical indicator, the Moving Average Convergence Divergence (MACD).” According to Street Smart Report Online, applying this strategy to a Dow Jones Industrial Average (DJIA) index fund generated a cumulative return of 190.6% during 1999 through 2011, compared to 64.4% for the DJIA itself. As a robustness test, we apply this strategy to the SPDR S&P 500 (SPY) exchange-traded fund since its inception. Using daily dividend-adjusted closing prices for SPY and the daily 13-week Treasury bill (T-bill) yield from 1/29/93 (the earliest available for SPY) through 5/7/12, we find that: More…

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: More…

Are there predictable long-term cycles in U.S. stock market returns? In the March 2012 update of his paper entitled “Low-Frequency Waves and the Medium to Long-Term US Stock Market Outlook”, Valeriy Zakamuline investigates mean reversion of the S&P Composite Index over intervals ranging from two to 40 years. Using real (Consumer Price Index-adjusted) S&P Composite Index annual returns over the period 1871 through 2010 (140 years), he finds that: More…

Are simple moving average (SMA) rules pervasively effective in boosting risk-adjusted stock portfolio performance? In his March 2012 paper entitled “Market Timing with Moving Averages”, Paskalis Glabadanidis tests SMA rules for entering and exiting value-weighted portfolios formed by sorts of a broad universe of U.S. stocks based on a variety of firm/stock characteristics. He uses a baseline SMA length of 24 months for most analyses, but includes robustness tests for lengths of 6, 12, 36, 48 and 60 months. He tests portfolios sorted (mostly into deciles) by: market value (size); book-to-market ratio; cash flow-to-price ratio, earnings-to-price ratio, dividend-price ratio, short-term reversal, medium-term momentum, long-term price reversal and industry classification. His moving average rule is to enter (exit) a stock portfolio whenever its monthly closing level  is above (below) the moving average. When not in stocks, funds earn the contemporaneous 30-day U.S. Treasury bill yield. He applies a trading friction of 0.5% of portfolio value when entering and exiting stocks. Using monthly value-weighted returns for sets of ten portfolios sorted by the above characteristics (from the Ken French Data Library) during 1960 through 2011, he finds that: More…

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: More…

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: More…

Referring to “10-month Versus 40-week Versus 200-day SMA”, a reader inquired whether using measurement intervals of longer than a month to calculate simple moving averages (SMA) would generate fewer trades than monthly intervals and therefore lower trading frictions and better performance. To check we compare the performance of moving averages based on 12 months (12M), six bi-months (6Bi-M) and four quarters (4Q). Using monthly dividend-adjusted closes for SPDR S&P 500 (SPY) from inception in January 1993 through January 2012, along with the contemporaneous monthly 3-month Treasury bill (T-bill) yield (19 years), we find that: More…

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: More…

A subscriber suggested comparing intrinsic momentum to simple moving average (SMA) as alternative signals for equity market entry and exit. To investigate, we compare the long run performances of entry and exit signals from intrinsic momentum over commonly used past intervals of 3, 6, 9 and 12 months and from the 10-month SMA (based on conclusions in “Is There a Best SMA Calculation Interval for Long-term Crossing Signals?”). We consider two cases for intrinsic momentum signals: in stocks (cash) when past return is positive (negative); and, (2) in stocks (cash) when average monthly past return is above (below) the average monthly risk-free rate over the same measurement interval. Using monthly data for the 13-week Treasury bill (T-bill) yield as the risk-free rate and the Dow Jones Industrial Average (DJIA) as a proxy for the U.S. stock market during January 1934 through November 2011 (about 78 years), we find that: More…