Technical Trading

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

In 2007, a reader inquired about the usefulness of James Altucher’s QQQQ Crash Trade Trigger. As stated by James Altucher: “The basic idea is that if the QQQQs, the volatile ETF representing the Nasdaq 100 index, make a sharp move down, then mean reversion will eventually kick in and bring the index back up. In other words, whenever people are panicking, that’s the time to get in. The move is not always a huge move but it’s been reliable.” Using QQQQ daily open, low and closing prices for 3/10/99 (the earliest available) through 8/19/09, we find that: Keep Reading

A reader observed and asked: “In the last few weeks, there have been several times when the Dow Jones Industrial Average (DJIA) was down a lot, and Asian stock markets followed it down the next day. How reliably do Asian stock markets follow sharp drops in the U.S. stock market?” To investigate, we first examine the overall relationship between the U.S. stock market (represented, as suggested, by the DJIA) and Asian stock markets (Hang Seng and Nikkei 225). Then, we focus on what happens in Asian stock markets the day after sharp drops in the U.S. market. Using daily closing levels of the DJIA, the Hang Seng index and the Nikkei 225 index for 12/31/86-8/14/09 (roughly 5800 trading days), we find that: Keep Reading

In his 2009 book, Quantitative Strategies for Achieving Alpha, flagged by Jeff Partlow, author Richard Tortoriello “seeks to determine empirically the major fundamental and market-based drivers of future stock market returns” by testing over 1,200 alternative investment strategies. He believes “that the quantitative approaches outlined in this book can provide a proven way to generate investment ideas for the qualitative investor as well as a discipline that can help improve investment results.” Richard Tortoriello is an equity research analyst with Standard & Poor’s. The principal elements of the book are: Keep Reading

A reader wondered about the value of combining momentum and simple moving average signals for asset class allocation, as follows:

• Each month calculate the average momentum of each asset class over the prior 3, 6 and 12 months
• Hold the top positions as long as they are also trading above their 10-month SMA (otherwise go to cash)

We test these rules using exchange-traded funds (ETF) as easily tradable asset class proxies. However, many ETFs have very short histories, greatly restricting any such test. We use S&P Depository Receipts (SPY), iShares Barclays 20+ Year Treasury Bond (TLT) and iShares Russell 2000 Index (IWM) as available asset classes, with historical data limited to July 2002 (by TLT). We use the 13-week Treasury bill (T-bill) yield as a proxy for the return on cash. Each month, we allocate funds to the one asset class with the highest average momentum over the prior 3, 6 and 12 months, unless the momentum leader is below its lagged 10-month SMA, in which case we put all funds into T-bills. Using monthly values for SPY, TLT, IWM and the T-bill yield over the period July 2002 through April 2009 (82 months), we find that: Keep Reading