Technical Trading

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

A reader asked: “This paper tests 5,000 technical trading rules and comes up empty on all of them. Not even say 200 were good. Has this paper been vetted by anyone? Is it iron-clad? The authors write: ‘We find that many technical trading rules produce statistically significant profits before consideration is given to data snooping bias, but this profitability disappears after data snooping bias is taken into account.’ How are they doing ‘data snooping’ detection?” Keep Reading

A reader asked: “I am curious how reliable some of the factors referenced in the Stock Trader’s Almanac are, but I see no reference to it on your site. Could you review the book and/or the primary strategies in the book? I would be curious to have your perspective on how rigorous its analysis is.” Keep Reading

A reader asked: “I’ve always wondered about these computer stock trading programs you see on infomercials, such as this one (there are many others). They seem to promise ‘easy’ profits–all you have to do is buy when the program tells you to buy, and sell when it tells you to sell. Of course, if it was that simple, we’d all be rich. But how effective are they?” Keep Reading

A reader asked: “Please do an analysis of the IBD Market Pulse column. Do their ‘Market in Confirmed Uptrend’ calls really work?” Keep Reading

A reader wondered about the value of combining momentum and downside risk avoidance for tactical asset class allocation, as follows:

“One of the methods described in The Ivy Portfolio by Mebane Faber is a simple momentum-based asset class rotation system that shifts monthly into the one, two or three highest performing asset classes based on their performance over an average of the prior 3, 6 and 12 months. Instead of using just the 3, 6 and 12 month prior returns, what if we used an asset class Ulcer Performance Index (UPI): UPI = average return over prior 3, 6 and 12 months / average Ulcer Index (UI) over prior 3, 6 and 12 months. Would this modification identify which asset classes are in low-volatility uptrends and therefore the biggest bang for the buck? Would this allow us to invest comfortably in the top two asset classes, or even the top one asset class, instead of the top three as recommended by Faber?”

Calculation of UI over a rolling interval across a long sample period is cumbersome. As a substitute for UI, we use a standard deviation of downside weekly returns over past intervals for three asset classes: S&P Depository Receipts (SPY), iShares Barclays 20+ Year Treasury Bond (TLT) and iShares Russell 2000 Index (IWM) , with historical data limited to July 2002 (by TLT). Every four weeks, we allocate funds to whichever of SPY, TLT or IWM has the highest ratio of prior return to prior downside standard deviation, or to 13-week Treasury bills (T-bills) if all three past returns are less than the T-bill yield. Using weekly adjusted closes for the asset class proxies over the period 7/31/02 through 8/21/09 (369 weeks or about 89 months), we find that: Keep Reading