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.

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Net Performance of SMA and Intrinsic Momentum Timing Strategies

Does stock market timing based on simple moving average (SMA) and time-series (intrinsic or absolute) momentum strategies really work? In the November 2013 version of his paper entitled “The Real-Life Performance of Market Timing with Moving Average and Time-Series Momentum Rules”, Valeriy Zakamulin tests realistic long-only implementations of these strategies with estimated trading frictions. The SMA strategy enters (exits) an index when its unadjusted monthly close is above (below) the average over the last 2 to 24 months. The intrinsic momentum strategy enters (exits) an index when its unadjusted return over the last 2 to 24 months is positive (negative). Unadjusted means excluding dividends. He applies the strategies separately to four indexes: the S&P Composite Index, the Dow Jones Industrial Average, long-term U.S. government bonds and intermediate-term U.S. government bonds. When not in an index, both strategies earn the U.S. Treasury bill (T-bill) yield. He considers two test methodologies: (1) straightforward inception-to-date in-sample rule optimization followed by out-of-sample performance measurement, with various break points between in-sample and out-of-sample subperiods; and, (2) average performance across two sets of bootstrap simulations that preserve relevant statistical features of historical data (including serial return correlation for one set)He focuses on Sharpe ratio (including dividends) as the critical performance metric, but also considers terminal value of an initial investment. He assumes the investor is an institutional paying negligible broker fees and trading in small orders that do not move prices, such that one-way trading friction is the average bid-ask half-spread. He ignore tax impacts of trading. With these assumptions, he estimates a constant one-way trading friction of 0.5% (0.1%) for stock (bond) indexes. Using monthly closes and dividends/coupons for the four specified indexes and contemporaneous T-bill yields during January 1926 through December 2012 (87 years), he finds that: Keep Reading

Utilities Sector as Stock Market Tell

Does the utilities sector exhibit a useful lead-lag relationship with the broad stock market? In their January 2014 paper entitled “An Intermarket Approach to Beta Rotation: The Strategy, Signal and Power of Utilities”, Charles Bilello and Michael Gayed test a simple strategy that holds either the U.S. utilities sector or the broad U.S. stock market based on their past relative strength. Specifically, when utilities are relatively stronger (weaker) than the market based on total return over the last four weeks, hold utilities (the market) the following week. They call this strategy the Beta Rotation Strategy (BRS) because it seeks to rotate into utilities (the market) when the investing environment favors low-beta (high-beta) stocks. They perform both an ideal (frictionless) long-term test and a short-term net performance test using exchange-traded funds (ETF). Using weekly total returns for the Fama-French utilities sector and broad market since July 1926 and for the Utilities Select Sector SPDR (XLU) and Vanguard Total Stock Market (VTI) since July 2001, all through July 2013, they find that: Keep Reading

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

Add Stop-loss to Asset Class Momentum Strategy?

In response to “Stop-losses to Avoid Stock Momentum Crashes?”, a subscriber inquired whether a stop-loss rule would improve the performance of the “Simple Asset Class ETF Momentum Strategy”. This strategy each month allocates all funds to the one of the following eight asset class exchange-traded funds (ETF), or cash, with the highest total return over the past five months (designated the 5-1 strategy):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

To investigate, we add to this strategy a stop-loss rule that: (1) exits the current winner ETF if its intra-month return falls below a specified threshold; and, (2) re-enters the basic strategy by buying the next winner ETF at the end of the month. Using monthly dividend-adjusted/split-adjusted monthly lows and closes for the asset class proxies and the yield for Cash during July 2002 (or inception if not available then) through March 2014 (141 months), we find that: Keep Reading

Stop-losses to Avoid Stock Momentum Crashes?

Can stop-loss rules solve the stock momentum crash problem? In the March 2014 version of their paper entitled “Taming Momentum Crashes: A Simple Stop-loss Strategy”, Yufeng Han and Guofu Zhou test the effectiveness of a simple stop-loss rule in limiting the downside risk of a stock momentum strategy. Each month, they rank stocks into tenths (deciles) based on cumulative returns over the past six months, with the top (bottom) decile designated as winners (losers). After a skip-month, they form an equally weighted portfolio that is long (short) the winners (losers) and hold for one month, except: during the holding month, they sell (buy back) any winner (loser) stocks that fall below (rise above) the portfolio formation price by at least 10% based on either daily opens or closes. If an opening price breaches the 10% stop-loss level, they assume liquidation at the open. If an opening price does not breach the threshold but the same-day closing price does, they assume liquidation at the stop-loss level. They assume funds from liquidation earn the U.S. Treasury bill (T-bill) yield for the balance of the month. For robustness, they consider 5% and 15% stop-losses, capitalization-weighted portfolios and momentum based on past 12-month return. Using daily opening/closing prices as available and monthly market capitalizations for a broad sample of U.S. common stocks, daily T-bill yield and monthly U.S. equity risk factors (market, size, book-to-market) during January 1926 through December 2011, 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

“Best” Indicator Consistency Across Samples

A subscriber inquired whether “The Only Indicator You Will Ever Need” really works. This technical indicator, applied to the Dow Jones Industrial Average by Jay Kaeppel, is a multi-parameter composite based on monthly closes as follows:

  1. Calculate the asset’s return over the past 11 months.
  2. Calculate the asset’s return over the past 14 months.
  3. Average these two past returns.
  4. Each month, calculate the 10-month front-weighted moving average of this average (multiply the most recent value by 10, the next most recent by 9, the value for the month before that by 8, etc. Then sum the products and divide by 55.)
  5. Hold the asset (cash) if this weighted moving average is above (below) its value three months ago.

We designate this indicator 11-14WMA3. To test 11-14WMA3 in realistic scenarios, we apply it to the entire available histories for three exchange-traded funds (ETF): SPDR S&P 500 (SPY)SPDR Dow Jones Industrial Average (DIA) and iShares Russell 2000 (IWM). We consider both buy-and-hold and a conventional 10-month simple moving average timing strategy (SMA10) as benchmarks. SMA10 holds the ETF (cash) when the ETF’s most recent monthly close is above (below) its 10-month SMA. Using monthly dividend-adjusted closes for the ETFs from their respective inceptions through January 2014 and the contemporaneous yield on 13-week U.S. Treasury bills (T-bills), we find that: Keep Reading

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