Technical Trading

A reader suggested a review of the frequent, short-term trades recorded in near real time by John Lee (WeeklyTA) via his StockTwits stream, which commenced 2/22/10. StockTwits lets users “eavesdrop on traders and investors, or contribute to the conversation and build their reputation as savvy market wizards.” John Lee offers his general trading rules in his iBankCoin blog. He has provided comments on his performance record in a separate blog. While the duration of this trading record is short, it does include many trades. These trades often have multiple partial exits. Using his stream of comments on StockTwits for 2/22/10 through 4/9/10, we find that: Keep Reading

Do stocks exhibit predictable volatility behavior near their 52-week highs and lows? In their March 2010 paper entitled “How the 52-Week High and Low Affect Beta and Volatility”, Joost Driessen, Tse-Chun Lin and Otto Van Hemert analyze whether a stock’s beta, return volatility and implied volatility change as its price approaches a 52-week high or low and after its price breaches this high or low. Using price data for a broad sample of U.S. stocks for July 1963 through December 2008 and option price data for January 1996 through September 30, they find that: Keep Reading

A reader observed and asked: “There are two strategies, both of which appear to work, but which also seem contradictory to each other. Momentum says what goes up must go up further. Reversion says what goes up must come down. Both work? There must be something wrong here?!? Keep Reading

A reader asked whether Lussenheide Capital Management’s momentum timing mechanism (100-day NASDAQ Composite Index moving average crossings, with proprietary filter) beats buy and hold over the long run, noting that the company’s web site presents at “Trend Following Performance” an independently validated annualized return of over 16% for “a very simple trend following system.” The discussion of performance states: “The systems used here at…Lussenheide Capital Management Inc., uses [sic] this basic system, along with a mechanical, proprietary trading filter. Although our returns are comparable or better with those shown below, our system has more desirable characteristics, including fewer trades and less whipsaws amongst others.” The notes at the bottom of the performance table state that results exclude “fund expenses” and “advisory management fees.” Without the specifications for the proprietary filter, we can test only basic concepts directly. Using daily closes of the NASDAQ Composite Index and daily dividend-adjusted closes for various potential trading vehicles through 2/12/10, we find that: Keep Reading

Does pairs trading work for exchange-traded funds (ETF)? In their February 2010 paper entitled “Pairwise Asset Rotation Trading and Market Timing: An Anatomy to a New Trading Strategy”, Panagiotis Schizas and Dimitrios Thomakos present a market timing strategy based on transforming the predictability of relative returns/volatilities between pairs of ETFs into weekly trading signals via simple rules. They choose S&P Depository Receipts (SPY), the Financial Sector Select SPDR (XLF), PowerShares QQQ (QQQQ) and Oil Services HOLDRs (OIH) to investigate three pairs: SPY-XLF, SPY-QQQQ and SPY-OIH. For robustness, they consider weeks ending on Monday, Wednesday and Friday (for a total of nine pair-endpoint combinations). They consider five trading models based on relative pair returns, relative pair (realized) volatilities and more complex characterizations of relative pair performance. Relative return/volatility predictions derive from a rolling historical window of 104 weeks. Using daily open-high-low-close prices for SPY, XLF, QQQQ and OIH to construct weekly metrics from earliest availability through April 4, 2008, they conclude that: Keep Reading

A reader asked: “The performance on Swing-Trading.net must have 200 trades, and no losers. How is that possible?” Keep Reading

A reader requested a review of the trading methodology presented at TimothySykes.com (“Short Selling Penny Stocks”), which essentially uses price-volume analyses in attempts to detect in real time penny stocks being pumped and ride the ensuing downside (dump). Timothy Sykes, author of the An American Hedge Fund, is a former hedge fund manager and founder of BullShip Press LLC. His bio states: “Since the beginning of 2008, Timothy has been the #1 trader/investor, out of 25,000+ on Covestor.com.” Using the record of 296 trades spanning 2/1/08 through 1/22/10 (including those previously posted for October 2009, but now missing) and some recent clarifications from Timothy Sykes, we find that: Keep Reading

A reader, citing a technical indicator recommended in Mastering the Trade by John Carter, inquired about the usefulness of watching for times when certain Bollinger Bands (upper and lower bounds two standard deviations from a 20-day simple moving average) converge within a certain Keltner Channel (upper and lower bounds 1.5 times the 20-day average range from a 20-day average typical price). Breakouts from this condition are supposedly reliable for both indexes and individual securities, meaning that price continues in same direction for a while without material reversal, because the condition represents true “consolidation.” There is no specification for trend duration after these “reliable” breakouts. Using daily high, low and unadjusted closing prices for S&P Depository Receipts (SPY) for band/channel calculations, and adjusted closing prices for return calculations, over the period 1/29/93 through 1/8/10 (nearly 17 years), we find that: Keep Reading

A reader requested a review of the TimingCube market timing advisory service, which relies “on the Trend Timing Model to detect major trend changes in the broad market and to issue clear, definitive Buy and Sell signals, on average three to five times per year.” The offeror provides a history of “all ‘live’ TimingCube signals since June 18, 2001.” Using this record of 36 signals, daily S&P Depository Receipts (SPY) closes adjusted for dividends over the period 6/17/01 through 12/16/09 and daily closes of the S&P 500 Index over the period 8/30/00 through 12/16/09, we find that: Keep Reading

A reader asked: “Could you review the track record of MarketTrak? Their timing is based on neural network technology and looks at first glance reasonable. Is there any evidence that a neural network provides an edge in market timing?” Keep Reading