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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Value-Momentum Switching Based on Value Premium Persistence

Can investors exploit monthly persistence in the value premium for U.S. stocks? In his February 2014 paper entitled “Exploiting Factor Autocorrelation to Improve Risk Adjusted Returns”, Kevin Oversby investigates whether investors can exploit the fact that the Fama-French model high-minus-low (HML) value factor exhibits positive monthly autocorrelation (persistence). The HML factor derives from the difference in performance between portfolios of stocks with high and low book-to-market ratios. Prior published research indicates that the value premium concentrates in small firms, so he focuses on stocks with market capitalizations below the NYSE median. His test strategies each month invest in capitalization-weighted small value (small growth or small momentum) Fama-French portfolios when the prior-month sign of the HML factor is positive (negative). The strategies additionally retreat to a risk-free asset (such as U.S. Treasury bills) if the prior-month return for the test strategy is negative. Using HML factor values and monthly portfolio returns for small value, small growth and small momentum Fama-French portfolios, he finds that: Keep Reading

Exploitation of Technical Analysis by Hedge Funds?

Do hedge fund managers who use technical analysis beat those who do not? In their May 2014 paper entitled “Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry”, David Smith, Na Wang, Ying Wang and Edward Zychowicz examine the relative performance of users and non-users of technical analysis among hedge fund managers in different sentiment environments. They hypothesize that short-selling constraints prevent market correction of mispricings when sentiment is high (overly optimistic), but not when sentiment is low (overly pessimistic). Discovery of mispricings via technical analysis may therefore be more effective when sentiment is high. To test their hypothesis, they compare the performance of hedge funds that report using technical analysis to that of hedge funds that do not, with focus on the state of market sentiment. They define the market sentiment state as high or low depending on whether the monthly Baker-Wurgler market sentiment measure is above or below its full-sample median. Using end-of-period status on use/non-use of technical analysis and monthly returns for 3,290 live and 1,845 dead funds from the Lipper TASS hedge fund database and monthly market sentiment data during January 1994 through December 2010, they find that: Keep Reading

Testing 93 Technical Market Indicators

Does technical market analysis work? In their June 2014 paper entitled “Technical Market Indicators: An Overview”, Jiali Fang, Yafeng Qin and Ben Jacobsen examine the profitability of 93 market indicators as applied to the S&P 500. Of the 93, 50 are market sentiment indicators that attempt to predict market behavior based on the supposition that stock prices tend to rise (fall) when bullish (bearish) sentiment dominates. The remaining 43 are market strength indicators that attempt to predict market trend continuation based on breadth of movements as indicated by volume, number of advancing/declining issues and number of periodic highs/lows. 65 of the 93 indicators are raw (such as numbers of advancing and declining stocks per day), and 28 involve measures constructed to suppress noise (such as number of advancing issues minus number of declining stocks). The authors use the S&P 500 as a test market because of its long history. They consider entire sample periods, equal subperiods, different economic regimes (expansion or contraction) and different sentiment regimes (bullish or bearish as indication of degree of investor irrationality). They employ a generous 10% significance level for statistical tests, with and without estimated trading frictions of 0.10% for switching between the market and a risk-free asset. Using the longest samples available for each indicator through the end of 2010 or 2011 (averaging 54 years and as long as 200 years), they find that: Keep Reading

SMAs for Measurement Intervals of Longer Than a Month

In reaction to “10-month Versus 40-week Versus 200-day SMA”, a reader inquired whether using measurement intervals of longer than a month to calculate simple moving averages (SMA) would suppress trading compared to monthly intervals and thereby lower trading frictions and improve performance. To check, we compare the performance of simple moving averages based on 12 months (SMA12M), six bi-months (SMA6B) and four quarters (SMA4Q). SMA6B samples six data points bimonthly, with each measurement spanning 11 months. SMA4Q samples four data points quarterly, with each measurement spanning 10 months. Using monthly dividend-adjusted closes for SPDR S&P 500 (SPY) from inception in January 1993 through April 2014 (about 21 years), along with the contemporaneous monthly 3-month Treasury bill (T-bill) yieldwe find that: Keep Reading

Optimal Cycle for Monthly SMA Signals?

A reader commented and asked:

“Some have suggested that the end-of-the-month effect benefits monthly simple moving average strategies that trade on the last day of the month. Is there an optimal day of the month for long-term SMA calculation and does the end-of-the-month effect explain the optimal day?”

To investigate, we compare 21 variations of a 10-month simple moving average (SMA10) timing strategy based on shifting the monthly return calculation cycle relative to trading days from the end of the month (EOM) and applied to SPDR S&P 500 (SPY) as a tradable proxy for the U.S. stock market. Using daily dividend-adjusted and unadjusted closes for SPY from inception (end of January 1993) through mid-May 2014 and contemporaneous three-month Treasury bill (T-bill) yields, we find that: Keep Reading

Value vs. Growth with Trend/Momentum Filters

Do relative momentum and trend filters operate differently on value and growth stocks? In their May 2014 paper entitled “When Growth Beats Value: Removing Tail Risk from Global Equity Momentum Strategies”, Andrew Clare, James Seaton, Peter Smith and Stephen Thomas investigate the effects of relative momentum and trend filters on portfolios of developed and emerging market broad, value and growth stock indexes. Their relative momentum filter each months picks either the top five indexes (Mom5) or top quarter of indexes (Mom25%) based on volatility-adjusted past 12-month return (return divided by standard deviation of monthly returns) at the end of the prior month. Their trend filter each month invests in an index or U.S. Treasury bills (T-bills) according to whether the index is above or below its 10-month simple moving average (SMA10) at the end of the prior month. Using monthly levels of broad, value and growth stock indexes for 23 developed country markets (since 1976) and 21 emerging country markets (since 1998) through 2012, they find that: Keep Reading

Technical Analysis a Drag?

Does technical analysis boost or depress performance for individual investors? In their February 2014 paper entitled “Technical Analysis and Individual Investors”, Arvid Hoffmann and Hersh Shefrin combine actual trading histories and results of a survey to investigate the use of technical analysis by individual investors. The 2006 survey solicits objectives, strategies and traits from a large group of individual clients of an online Dutch discount broker. The survey explicitly asks about use technical analysis and/or fundamental analysis. The authors use actual trading records to measure individual investment performance. Using 5500 survey responses matched to detailed trading histories spanning January 2000 through March 2006, they find that: Keep Reading

Exploitation of Stock Deviations from Statistical Equilibrium

Is is feasible to exploit stock price deviation from a purely statistical estimate of equilibrium? In his February 2014 paper entitled “Back to Black” (the National Association of Active Investment Managers’ 2014 Wagner Award second place winner), Arthur Grabovsky investigates exploitation of a model based on assumptions that: (1) unpredictable investor behavior sometimes makes stock price deviate from equilibrium; and, (2) price then tends to revert back to equilibrium. He defines equilibrium based on the conventional Capital Asset Pricing Model (CAPM), which holds that an asset’s returns depend on its alpha, market beta and an unexplained (random) noise factor. He employs daily double regressions over rolling windows of 60 trading days to measure how far and in what direction noise makes price trend away from its equilibrium alpha-beta relationship. He normalizes this drift as a number of standard deviations of the average noise factor. He then tests the tendency of stocks that drift too high (low) to revert to alpha-beta equilibrium and devises a long-only strategy to exploit prices that drift too low. He performs sensitivity tests on: (1) the threshold for exiting stocks that are reverting from “too low”; (2) the number of stocks an investor must hold for reliable portfolio performance; and, (3) different levels of trading frictions.  Finally, he considers how different market conditions affect strategy performance. He selects the total return Russell 3000 Index as a market proxy and benchmark. Using daily prices for the market and a broad sample of U.S. stocks with market capitalizations over $100 million during January 2005 through December 2013, he finds that: Keep Reading

Sensitivities of U.S. Stock Market Trend Following Rules

How sensitive in a recent sample are outcomes from simple trend following rules to the length of the measurement interval used to detect a trend. To investigate, we consider two simple types of trend following rules as applied to the U.S. market:

  1. Hold a risky asset when its price is above its x-month simple moving average (SMAx) and cash when below, with x ranging from two to 12.
  2. Hold a risky asset when its x-month return, absolute or intrinsic momentum (IMx), is positive and cash when negative, with x ranging from one to 12.

Specifically, we apply these 23 rule variations to time the S&P 500 Index since the inception of SPDR S&P 500 (SPY) as an easy and flexible way to trade the index over the available sample period and two subperiods, the decade of the 2000s and the last five years. We use the yield on 3-month U.S. Treasury bills (T-bills) to approximate return on cash. We use buying and holding SPY as a benchmark for the active rules. Using monthly closing levels of the S&P 500 Index since April 1992 and dividend-adjusted prices for SPY and T-bill yields since January 1993, all through March 2014, we find that: Keep Reading

DJIA-Gold Ratio as a Stock Market Indicator

A reader requested a test of the following hypothesis [presented by Simon Maierhofer, co-founder of ETFguide] from the article “Gold’s Bluff – Is a 30 Percent Drop Next?”: “Ironically, gold is more than just a hedge against market turmoil. Gold is actually one of the most accurate indicators of the stock market’s long-term direction. The Dow Jones measured in gold is a forward looking indicator.” To test this assertion, we examine relationships between the spot price of gold and the level of the Dow Jones Industrial Average (DJIA). Using monthly data for the spot price of gold in dollars per ounce (London 3:00 PM fix) and DJIA over the period January 1971 through March 2014 (519 months), we find that: Keep Reading

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