Technical Trading

A reader asked whether the gold-gold miner stocks arbitrage-like argument in Jay Kaeppel’s February 2010 article “Don’t Give Up On Gold Stocks Just Yet” (for which his September 2004 article “Gold Stock and Gold Bullion” is a more robust antecedent) supports frequent timing of these assets. For example, if SPDR Gold Shares (GLD) and Market Vectors Gold Miners GDX) diverge over some recent interval, do they then reliably converge quickly? To check, we examine the relative price behaviors of these funds. Using weekly dividend-adjusted closes for GLD and GDX during late May 2006 (inception for GDX) through mid-May 2016, we find that: Keep Reading

A reader requested confirmation of findings in the article “A Simple & Powerful Timing Indicator” of May 2009, which examines the strength of the (risky) NASDAQ Composite Index relative to the (conservative) S&P 500 Index as a market timing indicator. The article cites the book Technical Analysis – Power Tools For Active Investors (copyright 2005 and second printing date May 2005) as the source for the indicator. The indicator is the ratio of weekly index levels (risky-to-conservative) with respect to the ratio’s 10-week simple moving average (SMA). The associated trading rule is to move from cash (stocks) to stocks (cash) when the weekly ratio crosses above (below) its 10-week SMA. The hypothesis is that a stronger (weaker) risky index indicates risk-on (risk-off) sentiment and therefore a strong (weak) stock market. Using weekly closes for the S&P 500 Index, the NASDAQ Composite Index, the dividend-adjusted SPDR S&P 500 (SPY) and the 13-week Treasury bill (T-bill) yield as available from late November 1992 (based on inception of SPY) through mid-May 2016, we find that: Keep Reading

How does stock pairs trading performance interact with lagged pair correlation and volatility? In her May 2016 paper entitled “Demystifying Pairs Trading: The Role of Volatility and Correlation”, Stephanie Riedinger investigates how stock pair correlation and summed volatilities influence pair selection, pair return and portfolio return. Her baseline is a conventional pairs trading method that each month: (1) computes sums of daily squared normalized price differences (SSD) for all possible stock pairs over the last 12 months and selects the 20 pairs with the smallest SSDs; (2) over the next six months, buys (sells) the undervalued (overvalued) member of each of these pairs whenever renormalized prices diverge by more than two selection phase standard deviations; and, (3) closes positions when prices completely converge, prices diverge beyond four standard deviations, the trading phase ends or a traded stock is delisted. A pair may open and close several times during the trading period. At any time, six pairs portfolios trade simultaneously. She modifies this strategy to investigate correlation and volatility effects by: (1) measuring also during the selection phase return correlations and sum of volatilities based on daily closing prices for each possible stock pair; (2) allocating each pair to a correlation quintile (ranked fifth) and to a summed volatility quintile; and, (3) randomly selecting 20 twenty pairs out of each of the 25 intersections of correlation and summed volatility quintiles. She accounts for bid-ask frictions by executing all buys (sells) at the ask (bid) and by calculating daily returns at the bid. Using daily bid, ask and closing prices for all stocks included in the S&P 1500 during January 1990 (supporting initial pair trades in January 1991) through December 2014, she finds that: Keep Reading

Do simple moving averages (SMA) commonly used to identify stock market bull and bear regimes work similarly for the spot gold market? To investigate, we consider two market regime indicators: the 200-day SMA and a combination of the 50-day and 200-day SMAs. Because trading days for gold and stocks are sometimes different, we also check a 10-month SMA based on monthly closes. Using daily and monthly spot gold prices and S&P 500 Index levels during January 1973 through April 2016, we find that: Keep Reading

How well does technical trading work for spot currency exchange rates? In their April 2016 paper entitled “Technical Trading: Is it Still Beating the Foreign Exchange Market?”, Po-Hsuan Hsu, Mark Taylor and Zigan Wang test the effectiveness of a broad set of quantitative technical trading rules as applied to exchange rates of 30 currencies with the U.S. dollar over extended periods. They consider 21,195 distinct technical trading rules: 2,835 filter rules; 12,870 moving average rules; 1,890 support-resistance signals; 3,000 channel breakout rules; and, 600 oscillator rules. They employ a test methodology designed to account for data snooping in identifying reliably profitable trading rules. They focus on average return and Sharpe ratio for measuring rule effectiveness. They use empirical bid-ask spread data as available to estimate costs (averaging 0.045% one way for developed markets and 0.21% one way for emerging markets). They also test whether technical trading effectiveness weakens over time. Using daily U.S. dollar spot exchange rates and associated bid-ask spreads as available for nine developed market currencies and 21 emerging market currencies during January 1971 through mid-September 2015, they find that: Keep Reading

Does adding crash protection based on global market breadth enhance the reliability of dual momentum? In their April 2016 paper entitled “Protective Asset Allocation (PAA): A Simple Momentum-Based Alternative for Term Deposits”, Wouter Keller and Jan Willem Keuning examine a multi-class, dual-momentum portfolio allocation strategy with crash protection based on multi-market breadth. Their principal goal is consistently positive returns, at least 95% (99%) of 1-year rolling returns not below 0% (-5%). Their investment universe is 13 exchange-traded funds (ETF), 12 risky (SPY, QQQ, IWM, VGK, EWJ, EEM, IYR, GSG, GLD, HYG, LQD, TLT) and one safe (IEF). Each month, they:

1. Measure the momentum of each risky ETF as ratio of current price to simple moving average (SMA) of monthly prices over the past 3, 6, 9 or 12 months, minus one.
2. Specify the safe ETF allocation as number of risky assets with non-positive momentum divided by 12 (low crash protection), 9 (medium crash protection) or 6 (high crash protection). For example, if 3 of 12 risky assets have zero or negative momentum, the IEF allocation for high crash protection is 3/6 = 50%.
3. Allocate the balance of the portfolio to the equally weighted 1, 2, 3, 4, 5 or 6 risky assets with the highest positive momentum (reducing the number of risky assets held if not enough have positive momentum).

The interactions of four SMA measurement intervals, three crash protection levels and six risky asset groupings yield 72 combinations. They first identify the optimal combination in-sample during 1971 through 1992 and then test this combination out-of-sample since 1992. Prior to ETF inception dates, they simulate ETF prices based on underlying indexes. They assume constant one-way trading frictions 0.1%, acknowledging that this level may be too low for early years and too high for recent years. They focus on a monthly rebalanced 60% allocation to SPY and 40% allocation to IEF (60/40) as a benchmark. Using monthly simulated/actual ETF total return series during December 1969 through December 2015, they find that: Keep Reading

What is the best way to exploit short-term asset return reversal? In their November 2015 paper entitled “Market Closure and Short-Term Reversal”, Pasquale Della Corte, Robert Kosowski and Tianyu Wang examine four short-term reversal strategies that are each day long (short) assets with below-average (above-average) past returns weighted according to the degree the returns are below (above) average. Portfolio long and short sides are therefore equal in size. Specifically, they assign portfolio weights/accrue portfolio returns based on:

1. Prior close-to-open returns/next open-to-close returns (CO-OC).
2. Prior close-to-close returns/next close-to-close returns (CC-CC).
3. Prior open-to-close returns/next open-to-close returns (OC-OC).
4. Prior open-to-open returns/next open-to-open returns (OO-OO).

For calculating portfolio profitability, they assume a 50% margin requirement. They apply the strategy to each of U.S. stocks, European (French and German) stocks, Japanese stocks, UK stocks, stock index futures, interest rate (bond) futures, commodity futures and currency futures. Using daily opens and closes for stocks since January 1993 and futures since July 1982, all through December 2014, they find that: Keep Reading

Can investors avoid trend trading whipsaws by using Kalman filters to identify trends? In his February 2016 paper entitled “Trend Without Hiccups – A Kalman Filter Approach”, Eric Benhamou investigates the Kalman filter as a tool to smooth (remove the noise from) asset price series in an adaptive way that avoids most of the response lags of moving averages. He defines the Kalman filter as a recursive operation that forecasts the next value in a series based on incomplete and noisy measurements from an assumed distribution of possibilities. He considers four Kalman filter models:

1. Simple model that borrows speed and acceleration concepts from physics with four price inputs to forecast price trajectory.
2. Simple models that calculates a series of local linear trends with five price inputs, in a way very similar to Model 1.
3. More general version of Models 1 and 2 with 10 price inputs measuring both short-term (a few days) and long-term trend contributions.
4. More complex version of Model 3 with 15 price inputs that add effects of oscillations between price extremes over a specified historical interval (14 days).

To test the predictive power of these models, he uses them to identify and trade trends in E-mini S&P 500 futures contract prices. Each trade is only one contract long or short regardless of account value, uses no leverage and includes a $4 round trip commission. Using daily data for E-mini S&P 500 futures from the end of February 2015 through the end of February 2016, he finds that: Keep Reading

Can simple moving average (SMA) rules tell investors when it is prudent to leverage the U.S. stock market? In their March 2016 paper entitled “Leverage for the Long Run – A Systematic Approach to Managing Risk and Magnifying Returns in Stocks”, Michael Gayed and Charles Bilello augment conventional U.S. stock market SMA timing rules by adding leverage while in equities. Specifically, they test a Leverage Rotation Strategy (LRS) comprised of the following rules:

• When the S&P 500 Total Return Index closes above its SMA, hold the index and apply 1.25X, 2X or 3X leverage to magnify returns.
• When the S&P 500 Total Return Index closes below its SMA, switch to U.S. Treasury bills (T-bills) to manage risk.

They focus on a conventional 200-day SMA (SMA200), but include some tests with shorter measurement intervals to gauge robustness. They ignore costs of switching between stocks and T-bills. They apply targeted leverage daily with an assumed 1% annual cost of leverage, approximating current expense ratios for the largest leveraged exchange-traded funds (ETF) that track the S&P 500 Index. Using daily closes of the S&P 500 Total Return Index and T-bill yields during October 1928 through October 2015, they find that: Keep Reading

 “Pervasiveness and Robustness of SMA Effectiveness for Stocks” summarizes research finding that applying a simple moving average (SMA) trading strategy to U.S. stock portfolios produces strong risk-adjusted performance. This strategy is in stocks (cash) when price is above (below) its SMA. Is this finding valid? In his March 2016 paper entitled “Revisiting the Profitability of Market Timing with Moving Averages”, Valeriy Zakamulin challenges the validity of the research. First, he replicates the finding via simulations that incorporate one-month look-ahead bias (by including the last month of SMA calculation intervals as a strategy return). He then corrects strategy return calculations to eliminate this bias. As in the original research, he bases simulations on the following:

• Test data are monthly value-weighted returns of three sets of 10 portfolios from the data library of Kenneth French, each set formed by sorting on market capitalization, book-to-market ratio or momentum.
• Return on cash is the one-month U.S. Treasury bill yield.
• One-way stocks-cash switching cost is 0.5%.
• The sample period is January 1960 through December 2011.

Key performance metrics are net Sharpe ratio and four-factor alpha (adjusting for market, size, book-to-market and momentum factors). Using the specified data and assumptions, he finds that: Keep Reading