Technical Trading

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

A reader requested a review of the trading advisory service offered at StocksandBulls.com. The FAQs there state that the site “contains the information that is required to successfully trade the stock market on a regular basis with our ‘home growned’ robust trading system. From recommended buy and sell signals, all generated one day in advance to showing the end results, all is posted in total transparency.” The FAQs further state that the methodology used is “capable of detecting logical and profitable buy and sell signals quite accurately on stocks, without any guesswork left for users to do.” Using the trading record on site from inception on 2/13/06 through 12/14/09, we find that: Keep Reading

A reader asked: “Can you elaborate on high frequency trading order executions and possibly on corresponding techniques?” Keep Reading

What fundamental and technical factors are optimum for stock selection, and how well do they work? In the October 2008 draft of their paper entitled “Case Closed”, flagged by a reader, Robert Haugen and Nardin Baker present a model of future stock returns based on multiple regressions of 12 factors they find most significant in predicting monthly returns. Using monthly data for a sample of U.S. stocks over the 45-year period 1963-2007, they conclude that: Keep Reading