Technical Trading

A reader asked about the gold-gold miner stocks arbitrage-like argument in Jay Kaeppel’s 2/2/10 article “Don’t Give Up On Gold Stocks Just Yet”, for which his 9/21/04 article “Gold Stock and Gold Bullion” is a more robust antecedent. Does the relationship between gold and gold miner stocks support more frequent switching than indicated in these articles? For example, if SPDR Gold Shares (GLD) and Market Vectors Gold Miners GDX) diverge over some recent interval, do they then tend to converge? To check, we construct a simple long-only pair trading strategy and test it with available data. Using weekly dividend-adjusted closes for GLD and GDX since late May 2006 (inception for GDX) through mid-June 2011, we find that: More…

A reader requested: “Please test the following hypothesis [presented by Simon Maierhofer, co-founder of ETFguide.com] in the article ‘Gold’s Bluff – Is a 30 Percent Drop Next?’: Ironically, gold is more than just a hedge against market turmoil. Gold is actually one of the most accurate indicators of the stock market’s long-term direction. The Dow Jones measured in gold is a forward looking indicator.” For this evaluation, we test relationships between the spot price of gold and the level of the Dow Jones Industrial Average (DJIA). Using monthly data for the spot price of gold in dollars per ounce and DJIA over the period January 1971 through May 2011 (485 months), we find that: More…

When the stock market close is strong (weak) relative to the daily range, does it indicate pent-up buying (selling) demand? Should a trader follow the trend of the close the next day, position for a reversal or look for a better indicator? To investigate, we compare the position of the daily close for a broad market index relative to same-day high and low to the next-day return for the index. We also compare the five-day and ten-day average relative closes to the index return for the next five and ten days, respectively. Using daily high, low and close levels of the S&P 500 Index for the period 7/15/83 (the earliest without obvious errors available) through 6/15/11 (7,043 trading days), we find that: More…

Are there predictable long-term cycles in stock market returns? In the June 2011 version of his paper entitled “Very Long-Term Mean Reversion and Predictability of the U.S. Stock Market Returns”, Valeriy Zakamulin investigates mean reversion for the S&P Composite Index and for ten size and value-growth portfolios of U.S. stocks over intervals ranging from two to 40 years. Using nominal and real S&P Composite Index annual returns over the period 1871 through 2009 and nominal and real annual returns on portfolios of U.S. stocks sorted by size and book-to-market ratio over the period 1927 through 2009, he finds that: More…

The typical long-term moving average used for technical analysis is 200 trading days. Do moving averages measured over even longer intervals have value? In the December 2010 version of their paper entitled “Technical Analysis with a Long Term Perspective: Trading Strategies and Market Timing Ability”, Dusan Isakov and Didier Marti investigate the performance of stock market trading rules based on simple moving averages (SMA) with measurement intervals up to four years. Their general assumption is that a short-term SMA crossing over (under) a long-term SMA predicts an up (down) trend, with a buffer useful for filtering noise when the values of the two SMAs are close. They consider a basic investment strategy of going long (short) the stock market at the next close after a filtered buy (sell) signal and otherwise staying in cash at the risk free rate. They test this strategy on a universe of 1,886 simple rules involving combinations of: 23 short-term SMAs with measurement intervals of one to 100 days; 48 long-term SMAs with measurement intervals of five to 1000 days; and, a buffer of 1% to filter noise. They then evaluate four complex rules for out-of-sample testing based on combining inception-to-date or rolling historical outputs of the 1,886 simple rules. They also investigate the effects of leverage implemented through debt or index options. Using daily closing levels of the S&P 500 Index to compute moving averages and daily returns and contemporaneous lending and borrowing rates and index option prices over the period 1990 through 2008 (with 1990 through 1993 reserved for initial estimation), they find that: More…

Can investors systematically benefit from the perspective that trading is the exchange of one asset for another, not the buying and selling of a single asset? In his paper entitled “Optimal Rotational Strategies Using Combined Technical and Fundamental Analysis”, third-place winner for the 2011 Wagner Award presented by the National Association of Active Investment Managers, Tony Cooper presents methods and tools designed to exploit the precept that valuations are relative. An organizing concept for these methods and tools is the Binary Decision Chart (BDC), which in one form addresses simultaneous analysis of two competing investments for the purpose of switching or weighting and in an extended form addresses combining technical analysis (based on observed price action) and fundamental analysis (indicator-based prediction). BDCs are cumulative return charts, but the horizontal axis may be a technical or fundamental indicator rather than time. More specifically, using various asset price series and indicators, he illustrates the following methods/tools: More…

Is the variation of individual stock returns with liquidity a sound investment foundation? In the April 2011 version of their paper entitled “Liquidity as an Investment Style”, Roger Ibbotson, Zhiwu Chen and Wendy Hu examine the viability and distinctiveness of a liquidity investment style and investigate the portfolio-level performance of liquidity in combination with size, value and momentum investment styles. They define liquidity as annual turnover, number of shares traded divided by number of shares outstanding, a metric fairly independent of market capitalization. They hypothesize that stocks with relatively low (high) turnover tend to be near the bottom (top) of their ranges of expectation. Their liquidity style thus overweights (underweights) stocks with lower (higher) annual turnover. Using monthly data for the 3,500 U.S. stocks with the largest market capitalizations (with some screening for price, market capitalization, stock type and data availability) over the period 1972-2010, they find that: More…

“The Industry 52-week High Effect” summarizes findings that the 52-week high effect, the future outperformance (underperformance) of stocks currently near their respective 52-week highs (lows), is stronger and more consistent for 20 industries than for individual stocks. Do findings apply to equity sectors that are somewhat broader than the 20 industries? Specifically, might such a strategy outperform past six-month return when applied to the following nine sector exchange-traded funds (ETF) defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR), all of which have trading data back to December 1998:

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

To check, we consider three strategies based on closeness of each sector ETF to its 12-month high, defined as ratio of monthly close to highest monthly close over the prior 12 months. The three strategies are to: (1) allocate all funds each month to the sector ETF closest to its 12-month high at the end of the preceding month (12MH-1); (2) allocate all funds each month to the sector ETF closest to its 12-month high at the end of the month before the preceding month (12MH-1;1); and, (3) allocate all funds each quarter to the sector ETF closest to its 12-month high at the end of the month before the end of the quarter (12MH-3;1). Strategy (2) addresses the concern that a sector ETF surging toward a 12-month might experience some reversion the next month, and strategy (3) addresses the concern (based on the methodology in “The Industry 52-week High Effect”) that the effect materializes over several months. For comparison, we include the strategy of monthly allocation to the sector ETF with the highest total return over the past six months (6-1). Using monthly dividend-adjusted closing prices for the nine sector ETFs and S&P Depository Receipts (SPY) over the period December 1998 through March 2011 (148 months), we find that: More…

Are 52-week highs and lows useful equity price momentum indicators at the industry level? In their March 2011 paper entitled “Industry Information and the 52-Week High Effect”, Xin Hong, Bradford Jordan and Mark Liu compare the 52-week high effect for industries to that for individual stocks. This effect consists of the future outperformance (underperformance) of stocks currently near their respective 52-week highs (lows). Using monthly closes and rolling 52-week (intraday) highs for all stocks listed on NYSE, AMEX and NASDAQ and 20 value-weighted industry indexes constructed from SIC codes for these firms over the period July 1963 through 2009, they find that: More…

Do technical indicators add value to fundamental indicators in assessing broad stock market valuation? In their March 2011 paper entitled “Forecasting the Equity Risk Premium: The Role of Technical Indicators”, Christopher Neely, David Rapach, Jun Tu and Guofu Zhou examine the powers of technical and fundamental indicators to predict stock market returns. They consider 12 variations of three stock market index technical indicators: (1) relative values of two moving averages (1 month versus 3, 6, 9 and 12 months); (2) return momentum (past 3, 6, 9 and 12 months); and, (3) relative values of two on-balance volume moving averages (1 month versus 3, 6, 9 and 12 months). They consider 14 fundamental indicators ranging from stock market valuation ratios to Treasury yields, yield spreads and the default spread. They compare mean squared equity risk premium forecast errors for technical and fundamental indicators to that for the historical average premium. They also compare the average utility gain for a mean-variance investor who allocates monthly between stocks and Treasury bills based on either technical or fundamental market forecasts to that for an investor who uses the historical average premium. Finally, they generate equity risk premium forecasts based on a rolling principal component analysis that encapsulates the predictive powers of the 26 technical and fundamental indicators into three or four variables. Using monthly price and volume data for the dividend-adjusted S&P 500 Index and monthly readings of the 14 U.S. fundamental indicators as available over the period 1927 through 2008 (1926-1959 for in-sample optimization and 1960–2008 for out-of-sample testing), along with NBER business expansion and contraction dates, they find that: More…