Technical Trading

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

In their 2009 book, The Ivy Portfolio: How to Invest Like the Top Endowments and Avoid Bear Markets, Mebane Faber and Eric Richardson “profile the top endowments and then examine how an investor can hope to replicate their returns while avoiding bear markets. The focus [is] on practical applications that an investor can implement immediately to take control of their investment portfolio.” Mebane Faber “is the portfolio manager at Cambria Investment Management where he manages equity and global tactical asset allocation portfolios” and a co-founder of AlphaClone, an investing research web site. Eric Richardson is Chairman and founder of Cambria Investment Management. The book has a complementary web site that links to source materials. The principal messages of the book are: Keep Reading

A reader suggested: “It would be nice to see a study of a low 2-day Relative Strength Index (RSI) as a technical indicator as suggested by TradingMarkets.com. “We focus on exchange-traded funds (ETF) to perform two simple tests of 2-day RSI trading rules as described by TradingMarkets.com in “How to Trade ETFs: The 2-Period RSI and Entry Strategies for Traders” and “The Improved R2 Strategy: 84% Correct with Just 6 Rules”. For the test of the first set of rules, we arbitrarily select the Technology Select Sector SPDR (XLK). For the test of the second set of rules, we use SPDRs (SPY) as specified. Since the in-sample rule selection period described in the latter article extends through 2006, we conduct the tests with subsequent data to avoid data snooping bias. Using daily adjusted closing prices for XLK and SPY over the period 3/20/06 through 3/17/09 and the Stockcharts.com RSI template, we find that: Keep Reading

Do investors fool themselves into thinking a low share price means a cheap price? In other words, are the simple nominal prices of stocks predictive of their future returns? In their December 2008 paper entitled “Is Share Price Relevant?”, Soosung Hwang and Chensheng Lu investigate this question by measuring the performance of portfolios formed annually by sorting listed common stocks by nominal price into five ranges: less than or equal to $5, $5 to $10, $10 to $15, $15 to $20, and more than $20. Using delisting-adjusted price data for a broad sample of NYSE/AMEX/NASDAQ common stocks over the period July 1963 through December 2006, they conclude that: Keep Reading

How do the returns from pairs trading (bets on the re-convergence of prices for similar stocks that have historically tracked but recently diverged) play out? Are there systematic ways to enhance pairs trading profitability? In the November 2008 version of their paper entitled “An Anatomy of Pairs Trading: the Role of Idiosyncratic News, Common Information and Liquidity”, Joseph Engelberg, Pengjie Gao and Ravi Jagannathan explore how news events and liquidity shocks relate to pairs trading profitability. Using daily prices for a broad set of stocks spanning January 1992 through June 2006 and related news items and trading data for January 1993 through December 2005, they conclude that: Keep Reading

In his 2008 book, Short Term Trading Strategies That Work, Larry Connors, CEO and Founder of The Connors Group, shares “more than two decades of research and trading knowledge.” He states in the introductory chapter:

“Philosophically, I live in the world of reversion to the mean when it comes to trading. What that simply means is that something stretched too far will snap back. I didn’t come up with that idea. It’s been around for decades. What I have done though is an attempt to quantify it.“

Most of the quantifying tests use daily data spanning 1995-2007 for a large sample of stocks and the S&P 500 index. Based on past reviews of hundreds of anomaly studies, here are a few observations on the analyses presented in this book: Keep Reading