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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.

Pure Versus Buffered SMA Crossing Signals

A reader observed: “One of the problems with simple moving average (SMA) crossing rules is the churning from random price movements across the average. Lars Kestner proposes improvements to SMA crossing rules that signal:

  • BUY when: (1) the close crosses over an SMA of the highs (rather than the closes); and, (2) the SMA of the closes is greater today than yesterday.
  • SELL when the close crosses below an SMA of the lows (rather than the closes).

These rules create a self-adaptive band around the SMA to identify true trends rather then noise, while retaining most of the responsiveness of daily measurements.” Do these buffered SMA crossing rules outperform pure rules that simply buy (sell) on crossovers (crossunders) based on daily closes? To check, we compare the terminal values from pure and buffered rules for a 200-day SMA (SMA200) applied to both the Dow Jones Industrial Average (DJIA) and its exchange traded fund (ETF) proxy, SPDR Dow Jones Industrial Average (DIA). Using daily highs, lows and closes for DJIA since October 1928 and DIA since January 1998, both through early February 2014, and the contemporaneous 3-month Treasury bill yield as the return on cash, we find that: Keep Reading

Best Pairs Trading Method?

Pairs traders often use a normalized price gap threshold of two standard deviations to generate signals for opening trades. Is there a better metric for generating these signals? In the January 2014 version of their paper entitled “Pairs Trading with Copulas”, Wenjun Xie, Qi Rong Liew, Yuan Wu and Xi Zou compare the performances of pairs trading signals based on copulas and normalized price gaps. A copula allows for non-linearity, asymmetry and price level-sensitivity in the relationship between prices of the two members of a pair, while a normalized price gap does not. Since stock price distributions generally exhibit these non-normalities, a copula approach could improve pairs trading efficiency. For testing, the authors assume a common pairs identification and parameter/distribution estimation interval of the past 252 trading days, during which they identify the pairs from 89 U.S. utility stocks with the lowest sum of daily squared normalized price deviations. They then trade each of these best pairs during the next 126 trading days based on either copula or normalized price gap rules. They buy the underperformer and sell the outperformer at the end of the day that prices diverge through a specified threshold and close both positions at the end of the day that prices converge (or at the end of the trading interval if they do not converge). Alternatively, they impose a one-day delay in signal execution to allow time for data collection/processing. They calculate performance based on actual deployed capital, thereby accounting for idle capital (in other words, at the portfolio level). Using daily prices for the 89 U.S. utility stocks during January 2003 through December 2012, they find that:

Keep Reading

A Few Notes on Investing with the Trend

In the preface to his 2014 book entitled Investing with the Trend: A Rules-Based Approach to Money Management, author Greg Morris, Chairman of the Investment Committee and Chief Technical Analyst for Stadion Money Management LLC, states: “This book is a collection of almost 40 years of being involved in the markets, sharing some things I have learned and truly believe… You will discover early that sometimes I might seem overly passionate about what I’m saying, but hopefully you will realize that is because I have well-formed opinions and just want to ensure that the message is straightforward and easily understood. It is not only a book on trend following but a source of technical analysis information… If I had to nail down a single goal for the book, it would be to provide substantial evidence that there are ways to be successful at investing that are outside the mainstream of Wall Street. Although it will appear my concern is about modern finance, it is actually directed toward the investment management world and its misuse of the tools of modern finance.” Based on his 40 years of experience and supporting analyses, he concludes that: Keep Reading

Momentum and Trend-following for European Equity Sectors/Countries

Are momentum and trend-following strategies effective in tactical asset allocation to European equity sectors and countries? In the July 2013 version of their paper entitled “European Equity Investing Through the Financial Crisis: Can Risk Parity, Momentum or Trend Following Help to Reduce Tail Risk?”, Andrew Clare, James Seaton, Peter Smith and Steve Thomas apply momentum and trend-following strategies to portfolios of European sector and country indexes. Specifically, they consider three long-only sets of portfolios, as follows:

  1. Simple momentum: the equal-weighted top 8 or top 4 sectors or countries ranked by simple total return over the previous 1, 3, 6 or 12 months, or over the interval from 2 to 6 months ago, or the interval from 7 through 12 months ago.
  2. Risk-adjusted momentum: The inverse volatility-weighted top 8 or top 4 sectors and/or countries ranked over the same intervals by risk-adjusted returns (with both weighting and risk-adjusted returns based on daily returns over the past 120 days).
  3. Risk-adjusted momentum with SMA10: move positions in the risk-adjusted momentum portfolios to 3-month U.S. Treasury bills whenever the current value of the STOXX 600 Index is below its 10-month simple moving average (SMA10). 

They ignore trading frictions involved in strategy implementations. Using monthly total returns in U.S. dollars for 19 European equity sector and 15 European country indexes during 1988 through 2011, they find that: Keep Reading

UK Pairs Trading Net Performance

Does stock pairs trading work reliably in the mature UK market? In their November 2013 paper entitled “Pairs Trading in the UK Equity Market: Risk and Return”, David Bowen and Mark Hutchinson examine the profitability of pairs trading in this market via overlapping portfolios. Each month, they normalize prices for all stocks in the universe and select the five and 20 pairs of stocks with the lowest sums of squared daily normalized price differences over the past 12 months. They then trade these pairs over the next six months by buying (selling) the relatively undervalued (overvalued) stock in a pair when their normalized prices diverge by more than two standard deviations of daily price differences (from the 12-month pair selection interval). They close positions when normalized prices converge, or on the last day of the six-month trading interval. They include trading frictions derived from an estimate of the bid-ask spread, with a baseline friction of about 0.7% per round trip (open and close). Using daily prices for a broad sample of UK common stocks during January 1979 through December 2012, they find that: Keep Reading

Higher Measurement Frequency and Stop-losses for Trend Followers?

Motivation to avoid being “burned by the turn” tempts trend followers to increase measurement frequency and/or use stop-losses. Do these approaches help momentum players jump the turn? In their October 2013 paper entitled “The Significance of Trading Frequency and Stop Loss in Trend Following Strategies”, Farzine Hachemian, Sebastien Tavernier and Anne-Sophie Van Royen assess whether increasing measurement frequency from weekly to daily and imposing stop-loss rules enhance the performance of trend-following strategies based on simple moving averages (SMA). They consider a set of 117 timing strategies that go long (short) when a fast SMA is higher (lower) than a slow SMA, with SMAs measured either weekly or daily. For the weekly (daily) signals, the fast SMA measurement interval ranges from 4 to 52 weeks (20 to 260 days) in increments of 4 weeks (20 days). Slow SMA measurement intervals range from 8 to 64 weeks (40 to 320 days) with the same increments. To avoid whipsaws, they insert a buffer equal to the 13-week (65-day) standard deviation of the fast SMA. They apply these strategies to 39 rolling series of the most liquid futures covering all asset classes and most geographies. They apply a round-trip trading friction of $30 and assume zero return on any cash above the required margin. They then add two kinds of stop-losses to the strategies, reset every six months: (1) a loss of five times the standard deviation of weekly or daily returns; or, (2) a loss of 1% of portfolio value. After a stop loss, they re-enter a similar position when the trading strategy generates a new signal or price recovers its previous high watermark. Using futures return data as specified during January 2000 through December 2012, they find that: Keep Reading

Asset Allocation Based on Trends Defined by Moving Averages

Does trading based on simple moving average crossings reliably improve the performance of a portfolio diversified across asset classes? In the February 2013 update of his paper entitled “A Quantitative Approach to Tactical Asset Allocation”, Mebane Faber examines the effects of applying a 10-month simple moving average (SMA10) timing rule separately to each of the following five total return indexes a part of an equally weighted, monthly rebalanced portfolio: (1) S&P 500 Index; (2) 10-Year Treasury note constant duration index; (3) MSCI EAFE international developed markets index; (4) Goldman Sachs Commodity Index (GSCI); and, (5) National Association of Real Estate Investment Trusts index. Specifically, at the end of each month, he enters from cash (exits to cash) any index crossing above (below) its SMA10. Entry and exit dates are the same a signal dates (requiring some anticipation of signals before the close). The return on cash is the 90-day Treasury bill (T-bill) yield. Calculations ignore trading frictions and tax implications. Using monthly total return series for selected indexes mostly during 1972 through 2012, he finds that: Keep Reading

Stock Market Dogs of the World?

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

Intrinsic Momentum Framed as Stop-loss/Re-entry Rules

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

Extreme Appreciation as a Stock Crash Indicator

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

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