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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.

SweetSpot: Market-beating Reversion of Unloved Niches?

A reader suggested reviewing the detailed track record of SweetSpot Investments LLC, consisting of 29 closed trades over the past 12 years. The basic SweetSpot strategy posits market-beating three-year reversion of the three least popular “sectors” out of 100 formed from 500 non-diversified mutual funds and exchange-traded funds (ETF). Popularity is a function of fund assets and prior-year fund flows and returns. From a practical perspective, this strategy results in a steady-state portfolio of nine “sector” funds, each year selling the three oldest holdings and adding three new ones. Since 2009, the strategy includes as a hedge a short position in a market index fund or a position in an inverse market index fund “whenever the market’s intermediate-term trend falls below its long term trend.” The detailed track record includes no trades since that change in strategy. Using results from 29 SweetSpot trades from the end of 1998 through the beginning of 2011, we find that: Keep Reading

Combining Return Reversal and Industry Momentum

Does a strategy of combining monthly individual stock return reversal with monthly industry momentum enhance results compared to the separate strategies. In their August 2011 paper entitled “One-month Individual Stock Return Reversals and Industry Return Momentum”, Marc Simpson, Emiliano Giudici and John Emery examine the relationship between individual stock return reversals and industry momentum by considering three strategies: (1) a conventional reversal strategy that each month buys (shorts) individual stock losers (winners); (2) a simple industry momentum strategy that each month buys (shorts) the previous month’s winning (losing) industry portfolio; and, (3) a combined reversal-industry momentum strategy that buys (shorts) the losing (winning) stocks within the previous month’s winning (losing) industry portfolio. Using monthly returns, SIC codes and the Fama-French definitions for ten industries over the period January 1931 through December 2010 (960 months) , they find that: Keep Reading

Effects and Prediction of Extreme Returns

Are financial market returns from extreme outlier days mostly good or bad for investors? Is the occurrence of such days usefully predictable? In his August 2011 paper entitled “Where the Black Swans Hide & The 10 Best Days Myth”, Mebane Faber examines the effects and predictability of daily market return outliers. Using daily returns for the broad U.S. stock market for September 1928 through December 2010 and shorter samples through 2010 for 15 other country stock markets (as in “The (Worldwide) Futility of Market Timing?”), he finds that: Keep Reading

Purified Short-term Stock Reversal

As described in “Monthly Stock Return Reversal Update”, evidence for a conventional monthly stock return reversal effect since 1990 is weak. Is there a way to enhance the effect? In their August 2011 paper entitled “Short-Term Residual Reversal”, David Blitz, Joop Huij, Simon Lansdorp and Marno Verbeek present a short-term reversal strategy based on deviations of individual stock returns from a rolling 36-month Fama-French three-factor (market, size and book-to-market) model (residuals) rather than raw returns. They standardize (suppress noisiness of) these residual returns by dividing them by their standard deviations over past 36 months. The past winner (loser) portfolio of the residual reversal strategy consists of the tenth of stocks with the highest (lowest) standardized residual returns. Using monthly returns and risk factors for common U.S. stocks priced above $1 with market capitalizations above the NYSE median over the period January 1926 through December 2008, and estimates of institution trading frictions for 1991-1993, they find that: Keep Reading

Technical Trend-following: Fighting the Last War?

When do simple moving averages (SMA) serve as useful trading rules? Do they exploit some hidden pattern in asset price behavior? In their July 2011 paper entitled “The Trend is not Your Friend! Why Empirical Timing Success is Determined by the Underlying’s Price Characteristics and Market Efficiency is Irrelevant “, flagged by a subscriber, Peter Scholz and Ursula Walther investigate the relationship between the performance of technical trend-following rules and the characteristics (statistics) of the target asset return series. They use timing rules based on SMAs of different intervals (5, 10, 20, 38, 50, 100 and 200 trading days) as examples of trend-following rules. They consider the effects on SMA rule performance of variations in four asset price series statstics: the first-order trend (drift); return autocorrelation (return persistence); volatility of returns; and, volatility autocorrelation (volatility persistence/clustering). Analyses are long-only and ignore trading frictions, dividends, return on cash and buffering tactics such as stop-loss. They use a robust array of risk and performance measures to compare SMA rule performance to a buy-and-hold approach. Using both simulated price series and ten years of daily prices (2000-2009) for 35 country stock market indexes, they find that: Keep Reading

Monthly Stock Return Reversal Update

Is the monthly stock return reversal effect currently exploitable? In the August 2011 version of their paper entitled “New Evidence on Short-Term Reversals in Monthly Stock Returns: Overreaction or Illiquidity?”, Chris Stivers and Licheng Sun investigate the persistence, size-sensitivity and seasonality of monthly stock return reversal in the context of three competing explanations: (1) investor overreaction to news (exploitable); (2) market illiquidity (perhaps unexploitable); and, (3) large stocks lead small stocks (exploitable). They evaluate simple value-weighted and equal-weighted prior-month loser-minus-winner (LMW) strategies based on a sort of prior-month returns, and five more complex equal-weighted LMW strategies based on double-sorts of prior-month returns and market capitalizations. Using monthly return and market capitalization data for a broad sample of U.S. stocks and 30 industries over the period February 1926 through December 2010, they find that: Keep Reading

Weekly Stock Market Streaks

What happens after the stock market exhibits a streak of up or down weeks? To check, we use the S&P 500 Index as a proxy for the U.S. stock market and calculate average weekly returns and variabilities of these returns after streaks of positive or negative weekly returns. Using weekly closes of the S&P 500 Index for January 1950 through mid-August 2011, we find that: Keep Reading

Power of Skewness and Kurtosis to Predict Stock Returns

Many studies rely on the first moment (mean) of historical asset return distributions and/or the second moment (variance or standard deviation) to predict future returns. Are the third (skewness, indicating left-right tail asymmetry) and fourth (kurtosis, indicating fat-tailedness) moments of return distributions useful for predicting returns? In the July 2011 update of their paper  entitled “Do Realized Skewness and Kurtosis Predict the Cross-Section of Equity Returns?”, Diego Amaya, Peter Christoffersen, Kris Jacobs and Aurelio Vasquez investigate whether decile sorts of individual stocks based on variance, skewness and kurtosis of intraday stock returns over the past week significantly predict returns the next week. Using past-week averages of daily realized volatility, skewness, and kurtosis computed from prices at five-minute intervals, and associated firm characteristics, for a broad sample of U.S. stocks over the period January 1993 through September 2008 (over two million firm-week observations), they find that: Keep Reading

Complex Mean Reversion and Swing Trading Stock Index Strategy

A reader inquired about the complex strategy for trading stock index proxies and futures described in the March 2010 paper “MR Swing: A quantitative System for Mean‐reversion and Swing Trading in Market Regimes” by David Abrams and Scott Walker. This strategy posits that:

  • The stock market switches between bull and bear states, with the bull or bear state in effect when current index level is above or below a channel generated by 200-day simple moving averages (SMA) of daily highs and lows. The channel buffers whipsaws.
  • Different (not symmetrically opposite) trading approaches work best during these two states. Specifically, swing trading (short-term mean reversion) works in the bull (bear) state.
  • Mean reversion and swing trading signal calculations must incorporate stock market volatility.
  • The swing trading and mean reversion components must not produce serious drawdowns when the 200-day SMA indicator whipsaws between bull and bear states.

Using fairly recent daily data for the S&P 500 Index, SPDR S&P 500 (SPY), exchange-traded fund (ETF) proxies for several other stock market indexes and index futures, they find that: Keep Reading

Effectiveness of Very Long Moving Averages

The typical long-term moving average used for technical analysis is 200 trading days. Do moving averages measured over even longer intervals have value? In the December 2010 version of their paper entitled “Technical Analysis with a Long Term Perspective: Trading Strategies and Market Timing Ability”, Dusan Isakov and Didier Marti investigate the performance of stock market trading rules based on simple moving averages (SMA) with measurement intervals up to four years. Their general assumption is that a short-term SMA crossing over (under) a long-term SMA predicts an up (down) trend, with a buffer useful for filtering noise when the values of the two SMAs are close. They consider a basic investment strategy of going long (short) the stock market at the next close after a filtered buy (sell) signal and otherwise staying in cash at the risk free rate. They test this strategy on a universe of 1,886 simple rules involving combinations of: 23 short-term SMAs with measurement intervals of one to 100 days; 48 long-term SMAs with measurement intervals of five to 1000 days; and, a buffer of 1% to filter noise. They then evaluate four complex rules for out-of-sample testing based on combining inception-to-date or rolling historical outputs of the 1,886 simple rules. They also investigate the effects of leverage implemented through debt or index options. Using daily closing levels of the S&P 500 Index to compute moving averages and daily returns and contemporaneous lending and borrowing rates and index option prices over the period 1990 through 2008 (with 1990 through 1993 reserved for initial estimation), they find that: Keep Reading

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