Technical Trading

Can technical (price) analysis usefully identify major bubbles and anti-bubbles in financial markets? In the May 2009 version of their paper entitled “A Consistent Model of ‘Explosive’ Financial Bubbles With Mean-Reversing Residuals”, Lin Li, Ruoen Ren and Didier Sornette model bubbles. In their March 2010 paper entitled “Diagnosis and Prediction of Market Rebounds in Financial Markets”, Wanfeng Yan, Ryan Woodard and Didier Sornette similarly model anti-bubbles. Using daily levels of various financial market indexes, they conclude that: Keep Reading

Does the momentum anomaly hold for industries/asset classes over the long run? In his April 2010 draft paper entitled “Relative Strength Strategies for Investing”, Mebane Faber quantifies the effects on gross returns of applying simple momentum/trend following  rules to U.S. equity industry and global asset class portfolios. His “intent is to describe some simple methods that an everyday investor can use to implement momentum models in trading.” Momentum rankings derive from trailing total returns over intervals ranging from one to twelve months, as well as a combination of multiple intervals. Using monthly levels of ten value-weighted U.S. equity industries spanning July 1926 through December 2009 and of global asset classes spanning 1973-2009, he concludes that: Keep Reading

Are “proven” technical trading rules reliable profit-makers, or artifacts of data snooping bias? In their April 2010 paper entitled “Illusory Profitability of Technical Analysis in Emerging Foreign Exchange Markets”, Pei Kuang, Michael Schröder and Qingwei Wang apply several tests to evaluate the decisiveness of data snooping bias in the past profitability of technical trading rules for ten emerging foreign exchange markets. These environments are arguably less intensively mined than many other financial markets. Using spot exchange rates in the selected markets over the period January 1994 to July 2007 to test 25,998 commonly used simple, pattern and complex trading rules (see the table below), they find that: Keep Reading

A reader requested: “Please analyze The Mutual Fund Strategist, written by Holly Hooper-Fournier. They seem to have done quite well.” While the web site for this newsletter does not explicitly describe its timing method, it does explain that: “All of our timing models are oriented towards the intermediate trend…a period between several weeks and several months in duration. Focusing on the intermediate trend allows us to control risk effectively without subjecting your invested capital to the wide price swings that are associated with major trend following systems, such as a 200-day moving average.” The newsletter makes publicly available a record of “MFStrategist US Growth” timing signals since 2/16/01, as verified by TimerTrac.com. Using these 48 signals, daily dividend-adjusted closing prices for S&P 500 SPDR (SPY) and the daily short-term Interest Rate Composite over the period May 2000 through March 2010, we find that: Keep Reading

Can investors enhance momentum returns for individual stocks with combination strategies that incorporate other technical and accounting indicators? In the April 2010 draft of their paper entitled “Technical, Fundamental, and Combined Information for Separating Winners from Losers”, Cheng-Few Lee and Wei-Kang Shih investigate combined momentum strategies based on past stock returns, past trading volume and sets of fundamental (accounting) indicators. They consider two distinct sets of fundamentals: Piotroski’s FSCORE for value stocks and Mohanram’s GSCORE for growth stocks. Their combined strategy is long (short) past winners (losers) with weak (strong) past relationship between returns and trading volume and high (low) fundamental scores. Using stock return/volume and firm fundamentals data for a broad sample of NYSE and AMEX non-financial stocks spanning 1982-2007 (26 years), they find that: Keep Reading

Can long-term investors who periodically rebalance their portfolios materially enhance performance by using short-term signals to “permit” rebalancing? In their April 2010 paper entitled “To Trade or Not to Trade? Informed Trading with High-Frequency Signals for Long-Term Investors”, Roni Israelov and Michael Katz test the effects of such a tactical “informed trading” twist on long-run portfolio performance, focusing on the net Sharpe ratio as the bottom line discriminator. As an example, they apply one-week reversal signals to rebalancing a value-momentum portfolio that selects high value (book-to-market), high momentum (12-month past return) country indexes from among developed markets. Using book-to-market and return data for 18 developed markets over the period 1980-2009 (30 years), they conclude that: Keep Reading

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