Technical Trading

Is there a reliable way to improve the performance of conventional moving average signals? In the October 2011 and November 2011 versions of their papers entitled “An Improved Moving Average Technical Trading Rule” and “An Improved Moving Average Technical Trading Rule II”, Fotis Papailias and Dimitrios Thomakos investigate a modification of the conventional moving average crossover trading strategy that add a dynamic trailing stop (long-only variation) or a dynamic trailing stop-and-reverse (long-short variation). In order to stay long after a moving average buy signal, the modification requires that the asset price must remain at least as high as the entry price. Specifically:

1. Price crossing above a moving average, or a short-interval moving average crossing above a long-interval moving average, signals initial entry.
2. After going long, switching to cash or a short position occurs only if the price falls below the reference entry price (ignoring conventional moving average sell signals).
3. While long, the reference entry price changes when the crossover signals a sell/switch and then a subsequent buy/re-switch.

Entry and exit/switching times for the modified strategy therefore differ over time from those of a conventional moving average crossover strategy. In comparing modified and conventional strategy performance characteristics, they consider: simple, exponential and weighted moving averages; price crossovers of 5, 20, 50, 100 and 200-day moving averages; and, (5,20), (10,20), (20,50), (20,100) and (50,200) pairs of short-interval and long-interval moving average crossovers. They conservatively assume a delay of one trading day in signal implementation. Using daily prices for broad stock indexes, a variety of exchange-traded funds (ETF) and several currency exchange rates as available, they find that: Keep Reading

Does short-term (one-month) stock return reversal persist? If so, is there a best way to refine and exploit it? In their March 2012 paper entitled “Short-Term Return Reversal: the Long and the Short of It”, Zhi Da, Qianqiu Liu and Ernst Schaumburg decompose the total short-term reversal into an across-industry component (long prior-month loser industries and short prior-month winner industries) and a within- industry component (long prior-month loser and short prior-month winner stocks within each industry). They then further decompose the within-industry return reversal into three components related to: (1) variation in three-factor (market, size, book-to-market) expected stock returns; (2) underreaction/overreaction to within-industry cash flow news (relative to analyst forecasts); and, (3) a residual component attributable to discount rate news/liquidity shocks. Using monthly data for a broad sample of relatively large and liquid stocks accounting for about 75% of U.S. equity market capitalization over the period January 1982 through March 2009, they conclude that: Keep Reading

Can the forward-looking aspect of the S&P 500 Volatility Index (VIX) amplify technical analysis? In their September 2011 paper entitled “Using VIX Data to Enhance Technical Trading Signals”, James Kozyra and Camillo Lento apply nine simple technical trading rules (three each moving average crossovers, filters and trading range breakouts) to VIX to generate daily trading signals for the S&P 500 Index, the NASDAQ index and the Dow Jones Industrial Average. They reason that a relatively high (low) level of VIX indicates strong (weak) future stock index returns, so technical rules that separate daily levels of VIX into high and low regimes should aid trading. They compare results for VIX rule signals to those for signals generated by applying the rules to the indexes themselves. In all 27 cases (nine rules times three indexes), rule implementation assumes going long (short) an index on the day after buy (sell) signals. Estimated trading friction accounts for the bid-ask spread and a broker fee at the time of each trade. Using daily closes for VIX and the three indexes for January 1999 through July 2009, they find that: Keep Reading

Do U.S. stock market returns during the first and last hours of normal trading days reliably indicate what comes next? To investigate, we analyze average SPDR S&P 500 (SPY) returns during 9:30-10:30, 9:30-15:00, 9:30-16:00 and 15:00-16:00 for normal trading days during 2007 (bullish year) and 2008 (bearish year). Using a sample of SPY one-minute prices spanning 2007-2008, we find that: Keep Reading

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

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

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

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

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

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