Technical Trading

Reversion-to-trend appears to hold in many financial markets. Is this concept exploitable for country stock markets? In their June 2013 paper entitled “Do ‘Dogs of the World’ Bark or Bite? Evaluating a Mean-Reversion-Based Investment Strategy”, David Smith and Vladimir Pantilei test a simple “Dogs of the World” strategy designed to exploit long-term reversion across the 45 developed and emerging country stock markets comprising the MSCI All Country World Index (ACWI). Specifically, at the end of year one of their sample period, they allocate one fifth of initial funds equally to the five country stock markets with the lowest returns that year and hold for five years. At the ends of each of years two, three, four and five, they similarly allocate one fifth of initial funds to the five worst-performing markets that year to become fully invested. At the end of each subsequent year, they replace the oldest portfolio holdings with the five equally weighted worst performers that year. The MSCI ACWI (MSCI Developed Markets Index) is the benchmark since its inception in 1988 (before 1988). All returns are in U.S. dollars. They focus on a long test with indexes but also conduct a shorter, more realistic test with exchange-traded funds (ETF) that track country stock markets (at the lowest available cost). Using monthly returns for 45 country stock market indexes as available since 1970 (most begin in the 1980s and 1990s) and for corresponding ETFs as available since 1996 (many are much younger) through 2012, they find that: Keep Reading

Do asset classes generally exhibit enough price momentum to make stop-loss and re-entry rules effective for timing them? In his June 2013 paper entitled “Assessing Stop-loss and Re-entry Strategies”, Joachim Klement analyzes four stop-loss and re-entry rule pairs for six regional stock market indexes, a U.S. real estate investment trust (REIT) index, a commodity index and spot gold. Specifically, he tests:

1. Fast out-fast in (most effective when there are multiple brief corrections): Exit (re-enter) when the cumulative loss (gain) over the past 3 (3) months exceeds some specified threshold. 
2. Fast out-slow in (most effective during a downward or sideways trend): Exit (re-enter) when the cumulative loss (gain) over the past 3 (12) months exceeds some specified threshold.
3. Slow out-fast in (most effective during an upward trend with intermittent crashes): Exit (re-enter) when the cumulative loss (gain) over the past 12 (3) months exceeds some specified threshold.
4. Slow out-slow in (most effective when momentum is weak and transaction costs are high): Exit (re-enter) when the cumulative loss (gain) over the past 12 (12) months exceeds some specified threshold.

He tests ranges of stop-loss and re-entry decision thresholds. Because asset class return volatilities differ, he scales these thresholds to the annual standard deviation of returns for each asset class. He assumes a constant exit/re-entry trading friction of 0.25% and zero return on cash. For relevant tests, he defines a secular bull (bear) market as an extended subperiod of positive returns significantly above long-term average (negative or zero real returns). Using monthly asset class index returns as available during January 1970 through April 2013 in local currencies when applicable, he finds that: Keep Reading

Is faster-than-exponential asset price growth (acceleration of price increase) inherently unsustainable and therefore predictive of an eventual crash? In his June 2013 paper entitled, “Stock Crashes Led by Accelerated Price Growth”, James Xiong applies both regressions and rankings to test whether faster-than-exponential growth over the last two or three years predicts stock price crashes. Each month, he measures past price returns in non-overlapping six-month intervals to determine whether a stock’s price is accelerating. He consider three crash risk indicators: (1) skewness, with negative skewness indicating a tendency for large negative returns; (2) excess conditional value-at-risk, a normalized version of value-at-risk that controls for volatility; and, (3) maximum drawdown, cumulative loss from the peak to the trough over a specified interval. He computes these indicators monthly based on six months of daily returns. He then relates each crash indicator to stock price acceleration over the last two six-month intervals. In a separate test, he calculates returns from equally weighted portfolios reformed monthly by sorting stocks into fifths (quintiles) based on stock price acceleration over that last two six-month intervals. Using daily returns in excess of the contemporaneous U.S. Treasury bill yield for a broad sample of U.S. common stocks (those in the top 80% of market capitalizations if priced above $2) during January 1960 through December 2011, and for the S&P 500 Index during January 1950 to December 2012, he finds that: Keep Reading

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: Keep Reading

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: Keep Reading

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: Keep Reading

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: Keep Reading

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: Keep Reading

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: Keep Reading

Do trading rules based on price relative to intermediate-term and long-term simple moving averages (SMA) outperform a buy-and-hold approach for all kinds of stocks and stock portfolios? In the January 2013 update of his paper entitled “Market Timing with Moving Averages”, Paskalis Glabadanidis examines SMA performance based on monthly returns. He uses an SMA measurement interval of 24 months for most analyses, but includes robustness tests for intervals of 6, 12, 36, 48 and 60 months. He enters (exits) a stock portfolio or individual stock whenever its monthly closing level is above (below) its SMA. 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 positions. He focuses on portfolios of U.S. stocks sorted/ranked monthly by nine stock/firm characteristics: 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. As robustness tests, he considers individual U.S. stocks and market and characteristic-sorted portfolios of stocks from seven non-U.S. developed countries (Australia, Canada, France, Germany, Italy, Japan and UK). Using monthly returns for value-weighted portfolios sorted by the above characteristics (from the Ken French Data Library) and for 18,397 individual U.S. stocks during 1960 through 2011, and monthly portfolio returns for the seven country markets mostly during 1975 through 2010, he finds that: Keep Reading