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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.

Best Way to Trade Trends?

What is the best way to generate price trend signals for trading futures/forward contracts? In their December 2013 paper entitled “CTAs – Which Trend is Your Friend?”, Fabian Dori, Manuel Krieger, Urs Schubiger and Daniel Torgler compare risk-adjusted performances of three ways of translating trends into trading signals:

  1. Binary signals (up or down) trigger 100% long or 100% short trades. When trends are strong (ambiguous), this approach generates little trading (whipsaws/over-commitment to weak trends). The price impact of trading via this approach may be substantial for large traders.
  2. Continuously scaled signals trigger long or short trades with position size scaled according to the strength of up or down trend; the stronger the trend, the larger the position. Changes in trend strength generate incremental position adjustments.
  3. Empirical distribution signals trigger long or short trades with position size scaled according to the historical relationship between trend strength and future return. The strongest trend may not indicate the strongest future return, and may actually indicate return (and therefore position) reversal. Changes in trend strength generate position adjustments.

They test these three approaches for comparable trends exhibited by 96 futures/forward contract series, including: 30 currency pairs, 19 equity indexes, 11 government bond indexes, 8 short-term interest rates (STIR) and 28 commodities. They consider two risk-adjusted return metrics: annualized return divided by annualized volatility, and annualized return divided by maximum drawdown. They ignore trading frictions. Using prices for these 96 series from 1993 to 2013, they find that: Keep Reading

Google Trends Data vs. Past Returns

Are Google Trends data an independently useful tool in predicting stock returns? In their March 2014 paper entitled “Do Google Trend Data Contain More Predictability than Price Returns?”, Damien Challet and Ahmed Bel Hadj Ayed apply non-linear machine learning methods to measure whether Google Trends data outperform past returns in predicting future stock returns. They focus on avoiding bias derived from choice of keywords (choosing words with obvious retrospective, but dubious prospective, import) and test strategy parameter optimization. Since Google Trends data granularity is weekly, they employ a six-month calibration interval to predict weekly stock returns. They apply a 0.2% trading friction for all backtested trades. Using weekly returns and Google Trends data for stock tickers and firm names plus other simple, non-overfitted words for the S&P 100 stocks as available through late April 2013, they find that: Keep Reading

Equity Investing Based on Liquidity

Does the variation of individual stock returns with liquidity support an investment style? In the January 2014 update of their paper entitled “Liquidity as an Investment Style”, Roger Ibbotson and Daniel Kim examine the viability and distinctiveness of a liquidity investment style and investigate the portfolio-level performance of liquidity in combination with size, value and momentum styles. They define liquidity as annual turnover, number of shares traded divided by number of shares outstanding. They hypothesize that stocks with relatively low (high) turnover tend to be near the bottom (top) of their ranges of expectation. Their liquidity style thus overweights (underweights) stocks with low (high) annual turnover. They define size, value and momentum based on market capitalization, earnings-to-price ratio (E/P) and past 12-month return, respectively. They reform test portfolios via annual sorts into four ranks (quartiles), with initial equal weights and one-year holding intervals. Using monthly data for the 3,500 U.S. stocks with the largest market capitalizations (re-selected each year) over the period 1971 through 2013, they find that: Keep Reading

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

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