Technical Trading

A subscriber inquired about the TransDow trading strategy, which seeks to exploit a relationship between the Dow Jones Transportation Average (DJTA) and the Dow Jones Industrial Average (DJIA). Specifically, this strategy:

• Computes the 10-week simple moving average (SMA) of the ratio of the weekly closes of DJTA to DJIA.
• Enters (exits) a long position in a risky asset, such as iShares Dow Jones Transportation Average (IYT), whenever the ratio crosses above (below) the SMA, 
• Includes a stop-loss of -4% for any week, requiring a fresh signal for re-entry (the ratio crossing below the SMA and then crossing above it again).

The strategy creator finds that this strategy works well over a very long sample period based on indexes. However: (1) development of the strategy may impound data snooping bias; (2) the sample appears to have a lucky start for the strategy (just before the 1929 crash); and, (3) the testing methodology appears to ignore trading frictions (both from weekly signals and from transforming an index into a tradable asset), dividends and return on cash. Using weekly closes for DJTA and DJIA since November 2003 and for IYT (dividend-adjusted) since inception in January 2004, all through April 2013 (485-492 weeks), we find that: More…

Should traders discard boring, rather than exciting (outlier), data? In his February 2013 paper entitled “Filtered Market Statistics and Technical Trading Rules” (the National Association of Active Investment Managers’ 2013 Wagner Award third place winner), George Yang proposes to filter out as noise the cluster of daily stock market returns near zero for technical analysis purposes. Specifically, he suggests excluding daily returns less than about 0.2 standard deviation in magnitude. He tests this proposition on three groups of widely used technical trading rules as applied to daily returns of the S&P 500 Index over the past 23 years, comprised of low (1990-1999)and high (2000-2012) volatility regimes. The groups of rules are:

1. Two-day runs or streaks: positioning for mean reversion by going long (short) after two-day down (up) streaks.
2. Dual moving average crossings: going long (100% short, 50% short or to cash) when a short-term moving average crosses above (below) a long-term moving average, focusing on a 200-day long-term average.
3. Channel breakouts: when currently long (short), switch to short (long) when the daily close is below (above) the minimum (maximum) close over the past 150-250 trading days.

For all rules, he retains the number of sampled days in any filtered look-back interval by going further back in time. Using daily closes of the S&P 500 Index during 1990 through 2012 (5,797 trading days), he finds that: More…

What works better for currency trading, technical or fundamental analysis? In their April 2013 working paper entitled “Exchange Rate Expectations of Chartists and Fundamentalists”, Christian Dick and Lukas Menkhoff compare the behavior and performance of technical analysts (chartists) and fundamental analysts (fundamentalists) based on monthly surveys of several hundred German professional dollar-euro exchange rate forecasters, in combination with respondent self-assessments regarding emphasis on technical and fundamental analysis. Forecasts are directional only (whether the dollar will depreciate, stay the same or appreciate versus the euro) at a six-month horizon. The authors examine three self-assessments (from 2004, 2007 and 2011) to classify forecasters as chartists (at least 40% weight to technical analysis), fundamentalists (at least 80% weight to fundamental analysis) or intermediates. Using responses from 396 survey respondents encompassing 33,861 monthly time-stamped forecasts and contemporaneous dollar-euro exchange rate data during January 1999 through September 2011 (153 months), they find that: More…

Analyses in “Shorting VXX with Crash Protection” suggest that one-month momentum may be a useful signal for trading in and out of a short position in iPath S&P 500 VIX Short-Term Futures ETN (VXX). A subscriber inquired whether a short-term version of this signal is effective. Specifically, how useful is a strategy that goes short VXX (to cash) at the close when the same-day VXX return is negative (positive)? To test this daily momentum signal, we consider basic daily return statistics and two VXX shorting scenarios: (1) shorting an initial amount of VXX and letting this position ride indefinitely (Let It Ride); and, (2) shorting a fixed amount of VXX and resetting this fixed position daily (Fixed Reset). For tractability, we ignore shorting costs/fees, but we do consider the trading frictions associated with entering and exiting a short position in VXX based on the daily momentum signal. Using daily reverse split-adjusted closing prices for VXX from the end of January 2009 through mid-April 2013, we find that: More…

Is stock market timing based on a 10-month simple moving average (SMA) just lucky, or does this rule realistically reflect some underlying cyclic market behavior? In the April 2013 version of his paper entitled “Fooled by Data-Mining: The Real-Life Performance of Market Timing with Moving Averages”, Valeriy Zakamulin examines the real-life (out-of-sample) performance of an SMA-based market timing strategy to assess the data mining bias in the popular 10-month SMA. By real-life, he means a strategy that is in a stock index (U.S. Treasury bills) when the index is above (below) its historically optimal SMA. The optimal SMA is the moving average over the past 2 to 24 months that generates the best performance within available historical data. He considers a wide range of optimization metrics, including: Sharpe ratio, Omega ratio, Sortino ratio, Calmar ratio, value-at-risk (VAR)-related ratios and others. Because results are similar for all metrics, he focuses on Sharpe ratio. He assumes that trading friction for switching between stocks and U.S. Treasury bills is 0.5% of the balance. He considers total returns of both the S&P Composite Index and the Dow Jones Industrial Average (DJIA), in both cases using the first four years of data to determine the first optimal SMA and then each month determining a new out-of-sample optimal SMA based on inception-to-date data. Using monthly closing prices and dividends of the S&P Composite Index and DJIA, and contemporaneous Treasury bill yields, during January 1926 through December 2012 (87 years, or 1,044 months), he finds that: More…

A subscriber requested a comparison of the performance of a 10-month simple moving average (SMA) rule applied to the U.S. stock market and the tracked “Simple Asset Class Momentum Strategy” variations. For the former, we calculate monthly returns for a strategy that invests in SPDR S&P 500 (SPY) when the month-end S&P 500 Index crosses above its 10-month SMA and 13-week U.S. Treasury bills (T-bills) when it crosses below. For this test, we assume that switches take place at the same close as the signals (requiring slight anticipation of signals) and ignore trading frictions. Using monthly closes of the S&P 500 Index during November 2005 through February 2013, monthly closes for SPY and the T-bill yield during August 2006 (the earliest all assets in the momentum strategy are available) through February 2013, and contemporaneous monthly gross returns for the momentum strategy, we find that: More…

Current price versus 10-month simple moving average (SMA) is a widely used indicator of asset and asset class trend, with current price above/below its 10-month SMA viewed as bullish/bearish. How has this indicator performed for U.S. equities in aggregate over the long run? To investigate, we employ the long-run data set of Robert Shiller to construct a very long backtest of 10-month SMA crossing signals. This data set includes monthly levels of the S&P Composite Index, calculated as average of daily closes during the month. This method of calculation deviates from that most often used for SMA signals, but arguably suppresses the effects of the turn of the month and any other monthly patterns on SMA signals. Using S&P Composite Index levels, associated dividend yields and contemporaneous long-term interest rates (comparable to yields on 10-year Treasury notes) from the Shiller data set spanning January 1871 through December 2012 (1,704 months or about 142 years), we find that: More…

A reader proposed: “I recently found something interesting while analyzing the ratio of the equal-weighted S&P 500 Index to its market capitalization-weighted counterpart. Whenever this ratio declines (out of an uptrend), the market crashes (July 2007, September-October 2008, July 2011). Also, when this ratio starts rising, the recovery commences (April 2009). The indicator seems to warn of problematic times ahead. …Perhaps this ratio provides insight into whether money is moving into the market (ratio rising) or out of the market (ratio falling). Could you take a look at this to see whether this ratio is a great indicator?” To investigate, we employ S&P 500 SPDR (SPY) and Rydex S&P 500 Equal Weight (RSP) as tradable proxies for the capitalization-weighted and equal-weighted S&P 500 Index, respectively. Using weekly and monthly dividend-adjusted values of SPY and RSP from the end of April 2003 (limited by data for RSP) through February 2013 (514 weeks), we find that: More…

Do time-series (intrinsic) momentum rules for timing stocks beat comparable simple moving average (SMA) rules? In the February 2013 version of their paper entitled “Time-Series Momentum Versus Moving Average Trading Rules”, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti compare and contrast the stock portfolio timing results of intrinsic momentum and SMA rules. They compare intrinsic momentum timing rules that buy (sell) when price moves above (below) its value 10, 50, 100 or 200 trading days ago to SMA timing rules that buy (sell) when price moves above (below) its SMA over the same look-back intervals. They focus on a long-only strategy applied to five value-weighted size (quintile) portfolios of U.S. stocks, switching to U.S. Treasury bills (T-bill) when on sell signals. As an alternative, they consider shorting stocks when on sell signals. They also test some timing rules on ten international stock markets (Australia, Canada, France, Germany, Italy, Japan, the Netherlands, Sweden, Switzerland and the UK). Using data for U.S. size portfolios from Ken French’s website during 1963 through 2011 and for international stock market indexes during 1973 through 2011, along with contemporaneous T-bill yields, they find that: More…

“Pervasiveness and Robustness of SMA Effectiveness for Stocks” summarizes research finding that long-term simple moving averages (SMA) pervasively outperform a buy-and-hold approach for U.S. stocks and stock portfolios during 1960-2011 and for seven developed stock markets during 1975-2010. Does this research, which focuses on a 24-month SMA, discover some essential cyclical nature of equity markets? To check, we test the effectiveness of a 24-month SMA timing strategy versus a buy-and-hold approach for three U.S. stock market series: (1) SPDR S&P 500 (SPY) since January 1995; (2) the underlying S&P 500 Index since January 1952; and, (3) the Dow Jones Industrial Average (DJIA) since January 1934. These start dates allow calculation of initial 24-month SMAs, with the DJIA test limited by data for the U.S. Treasury bills (T-bill) yield. The 24-month SMA timing strategy shifts to stocks (T-bills) when the monthly close crosses above (below) the 24-month SMA. We also test a comparable 23-month intrinsic momentum strategy, which is in stocks (T-bills) when the lagged 23-month return is positive (negative). For both timing strategies, we assume that the investor can slightly anticipate signals and execute trades at the same close. Using monthly returns for SPY since January 1993 (dividend-adjusted), the S&P 500 Index since January 1950 and DJIA since January 1932, along with contemporaneous monthly 3-month T-bill yields, all through January 2013, we find that: More…