Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for June 2020 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for June 2020 (Final)
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Technical Trading

Does technical trading work, or not? Rationalists dismiss it; behavioralists investigate it. Is there any verdict? These blog entries relate to technical trading.

Technical Trading Thoroughly Tested on Emerging Currencies

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

Review of The Mutual Fund Strategist Timing (Revised to Append Comment)

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

Amplifying Momentum with Volume and Accounting Indicators

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

Use Short-term Signals to Inform Rebalancing?

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

John Lee (WeeklyTA): StockTwits Wizard?

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

How the 52-Week High and Low Affect Beta and Volatility

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

Reaction, Momentum and Reversion

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

Lussenheide’s Basic Timing Strategy

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

ETF Pair Trading Based on Relative Returns/Volatilities

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

Impossibly Good?

A reader asked: “The performance on Swing-Trading.net must have 200 trades, and no losers. How is that possible?” Keep Reading

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