Technical Trading

How fast must traders move to operate efficiently in the high-frequency arena? In their February 2012 paper entitled “High-Frequency Technical Trading: The Importance of Speed”, Martin Scholtus and Dick van Dijk investigate execution speed sensitivity of technical trading rule performance for three highly liquid exchange-traded funds (ETF). They consider 27,424 variations of five price-based and two volume-based types of trading rules: moving average; filter; support and resistance; channel break-outs; price momentum; on-balance volume average; and, volume momentum. The baseline analysis constructs new signals every 60 seconds. They measure impact of eight execution delays (10, 20, 50, 100, 200, 500 and 1,000 milliseconds) on profitability relative to instantaneous execution. Trading frictions include bid-ask spread and impact of trading, but not transaction fees. They also measure typical levels of market activity over intervals of one day, one hour, one minute and one second. Using  complete order information for SPY, QQQQ  and IWM with millisecond timestamp accuracy during normal trading hours for January-September 2009, they find that: Keep Reading

Dollar cost averaging (DCA) is a very simple and intuitive way to buy more (less) of an asset when its price is low (high), thereby achieving some cost efficiency. Is there a simple and reliable way to enhance DCA? In their December 2011 paper entitled “Building a Better Mousetrap: Enhanced Dollar Cost Averaging”, Lee Dunham and Geoffrey Friesen examine allocation rules that retain attributes of traditional DCA but adjust to new information. Specifically, enhanced DCA (EDCA) rules adjust the amount invested in an asset according to its prior-month return. For example, one EDCA rule adds (subtracts) a fixed increment to (from) the planned monthly investment in an asset if its return for the prior month is negative (positive). Other alternatives adjust the incremental addition or reduction in monthly contribution depending on the value of the lagged monthly return. They employ both simulation and backtesting to measure the effects of EDCA. Using simulations of up to 30 years and monthly return data for six asset indexes and 100 mutual funds spanning 2000 through 2009, they find that: Keep Reading

Does the effectiveness of simple moving average (SMA) crossing signals vary with stock volatility? In the August 2011 update of their paper entitled “A New Anomaly: The Cross-Sectional Profitability of Technical Analysis”, Yufeng Han, Ke Yang and Guofu Zhou investigate the application of SMAs to portfolios of stocks sorted based on realized volatility. Specifically, each year they sort stocks into deciles by volatility (standard deviation of daily returns over the past year). For each decile, they calculate a price index, an SMA for the index and daily returns based on initial equal weighting. When a decile portfolio is above (below) its SMA, they hold the portfolio (30-day Treasury bills), with a one-day delay for switches. They compare the returns for this timing strategy to buy-and-hold by decile. They focus on a 10-day SMA, but also test 20-day, 50-day, 100-day and 200-day SMAs. Using daily returns for a broad sample of U.S. stocks spanning 1963 through 2009, they find that: Keep Reading

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