Technical Trading

Current price versus 10-month simple moving average (SMA) is a widely used indicator of asset and asset class trend, with current price above/below its 10-month SMA viewed as bullish/bearish. How has this indicator performed for U.S. equities in aggregate over the long run? To investigate, we employ the long-run data set of Robert Shiller to construct a very long backtest of 10-month SMA crossing signals. This data set includes monthly levels of the S&P Composite Index, calculated as average of daily closes during the month. This method of calculation deviates from that most often used for SMA signals, but arguably suppresses the effects of the turn of the month and any other monthly patterns on SMA signals. Using S&P Composite Index levels, associated dividend yields and contemporaneous long-term interest rates (comparable to yields on 10-year Treasury notes) from the Shiller data set spanning January 1871 through December 2012 (1,704 months or about 142 years), we find that: Keep Reading

Do time-series (intrinsic) momentum rules for timing stocks beat comparable simple moving average (SMA) rules? In the February 2013 version of their paper entitled “Time-Series Momentum Versus Moving Average Trading Rules”, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti compare and contrast the stock portfolio timing results of intrinsic momentum and SMA rules. They compare intrinsic momentum timing rules that buy (sell) when price moves above (below) its value 10, 50, 100 or 200 trading days ago to SMA timing rules that buy (sell) when price moves above (below) its SMA over the same look-back intervals. They focus on a long-only strategy applied to five value-weighted size (quintile) portfolios of U.S. stocks, switching to U.S. Treasury bills (T-bill) when on sell signals. As an alternative, they consider shorting stocks when on sell signals. They also test some timing rules on ten international stock markets (Australia, Canada, France, Germany, Italy, Japan, the Netherlands, Sweden, Switzerland and the UK). Using data for U.S. size portfolios from Ken French’s website during 1963 through 2011 and for international stock market indexes during 1973 through 2011, along with contemporaneous T-bill yields, they find that: Keep Reading

Do trading rules based on price relative to intermediate-term and long-term simple moving averages (SMA) outperform a buy-and-hold approach for all kinds of stocks and stock portfolios? In the January 2013 update of his paper entitled “Market Timing with Moving Averages”, Paskalis Glabadanidis examines SMA performance based on monthly returns. He uses an SMA measurement interval of 24 months for most analyses, but includes robustness tests for intervals of 6, 12, 36, 48 and 60 months. He enters (exits) a stock portfolio or individual stock whenever its monthly closing level is above (below) its SMA. When not in stocks, funds earn the contemporaneous 30-day U.S. Treasury bill yield. He applies a trading friction of 0.5% of portfolio value when entering and exiting positions. He focuses on portfolios of U.S. stocks sorted/ranked monthly by nine stock/firm characteristics: market value (size); book-to-market ratio; cash flow-to-price ratio, earnings-to-price ratio, dividend-price ratio, short-term reversal, medium-term momentum, long-term price reversal and industry classification. As robustness tests, he considers individual U.S. stocks and market and characteristic-sorted portfolios of stocks from seven non-U.S. developed countries (Australia, Canada, France, Germany, Italy, Japan and UK). Using monthly returns for value-weighted portfolios sorted by the above characteristics (from the Ken French Data Library) and for 18,397 individual U.S. stocks during 1960 through 2011, and monthly portfolio returns for the seven country markets mostly during 1975 through 2010, he finds that: Keep Reading

Are there exploitable long-term cycles in U.S. stock market returns? In the January 2013 update of his paper entitled “Secular Mean Reversion and Long-Run Predictability of the Stock Market”, Valeriy Zakamulin explores mean reversion of the S&P Composite Index over intervals ranging from two to 40 years. He then runs an out-of-sample horse race using inception-to-date data to compare three regression-based models for forecasting long-term stock market returns: (1) mean reversion over the dynamically optimal horizon; (2) the random walk (future mean return equals (evolving) historical mean return); and, (3) valuation based on Robert Shiller’s cyclically adjusted price-to-earnings ratio (P/E10). Using real (Consumer Price Index-adjusted) S&P Composite Index total annual returns and earnings over the period 1871 through 2011 (141 years), he finds that: Keep Reading

Do stock indexes behave predictably after extreme price levels, such as 52-week highs and 52-week lows? To investigate, we consider the behaviors of the Dow Jones Industrial Average (DJIA), the S&P 500 Index and the NASDAQ Composite Index over the 13 weeks after 52-week highs and lows during their available histories. Using weekly levels of these indexes from October 1928, January 1950 and February 1971, respectively, through January 2013, we find that: Keep Reading

Do mutual fund managers who employ technical analysis outperform those who do not? In their January 2013 paper entitled “Head and Shoulders above the Rest? The Performance of Institutional Portfolio Managers who Use Technical Analysis”, David Smith, Christophe Faugere and Ying Wang compare the aggregate investment performance of mutual funds that (self-reportedly) using technical analysis to that of funds not using technical analysis. Self-reported importance of technical analysis is on a five-level scale: “very important,” “important,” “utilized,” “not important” or “not utilized.” Using technical analysis importance levels and monthly returns for 10,452 actively managed U.S. equity, global equity, U.S. balanced and global balanced mutual funds during January 1993 through March 2012 (231 months), they find that: Keep Reading

Do moving average rules work for timing stocks over the long run? In his January 2013 paper entitled “The Rise and Fall of Technical Trading Rule Success”, Nicholas Taylor examines the performance of moving average trading rules as applied to components of the Dow Jones Industrial Average (DJIA) over the long run. He considers 10,800 variants of a general moving average trading rule: buy (sell) when the short-interval moving average price crosses above (below) the long-interval moving average price, with moving average measurement intervals ranging from 1 to 250 trading days. Rule variants include signal refinements that specify: a range of the ratio of short-interval to long-interval moving average prices; the number of days a signal must persist before taking action; and, the number of days for ignoring all new signals after executing a trade. He defines the return for a specific rule as the equally weighted average for applying it to all DJIA stocks. He tests both static rules and dynamically optimal sets of rules, with the latter comprised of the best rule each month from four distinct ways of measuring lagged net performance. He estimates trading frictions based on bid-ask spreads. He compares monthly performance of moving average rules to a monthly buy-and-hold benchmark based on raw return statistics and on alphas from factor (market, size and book-to-market, momentum) models of stock returns. Using daily prices of the 30 then-current DJIA stocks during October 1928 through December 2011 (82 stocks over the sample period), he finds that: Keep Reading

Andrew Abraham, founder of Abraham Investment Management, introduces his 2012 book, The Trend Following Bible: How Professional Traders Compound Wealth and Manage Risk, by stating: “I want to teach you to think like a successful trend follower. I am giving you exactly the methodologies I have used on a daily basis for the last 18 years. They are not any magical holy grail; rather, they are robust ideas that give you the ability to make low-risk trades and try to catch trends when they are present.” Using examples based on his trading experience and the results for other trend followers, he concludes that: Keep Reading

A subscriber suggested review of Empiritrage’s Volatility-Based Allocation (VBA). This strategy applies two monthly signals to an equally weighted portfolio of asset class total return proxies to determine whether to be in each proxy or cash, as follows:

• Step 1: If the 10-day simple moving average (SMA) of the S&P 500 Volatility Index (VIX) is above its 30-day SMA (risk off), substitute the risk-free asset for all asset class proxies.
• Step 2: If the 10-day simple moving average (SMA) of VIX is below its 30-day SMA (risk on), invest in each asset class proxy for which the respective two-month SMA is above the 12-month SMA, and otherwise in the risk-free asset.

Empiritrage’s simulation of VBA employs equal allocations each month to each of five asset class proxies (U.S. stocks, non-U.S. developed market stocks, emerging market stocks, real estate and long-term U.S. government bonds) or to U.S. Treasury bills (T-bills) as signaled, ignoring trading frictions, during March 1986 through August 2012. They find that VBA “dominates” an allocation based only on individual asset class proxy SMAs. However, indexes do not account for the costs of maintaining tradable assets, and the costs of switching between risk assets and cash may be material. For another perspective, we replicate VBA (with switching frictions) using the following exchange-traded funds (ETF) and estimated return on cash:

SPDR S&P 500 (SPY)
iShares MSCI EAFE Index (EFA)
iShares MSCI Emerging Markets Index (EEM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (risk-free rate)

Using daily closes for VIX since March 2003 and monthly closes for the ETFs and risk-free rate since April 2003 (limited by inception of EEM), we find that: Keep Reading

“Trend Factor and Stock Returns” describes a method of extracting information from stock price simple moving averages (SMA) that is more complicated than that used by most traders. Instead of using current price above or below an SMA as a signal, this method employs offset regressions (normalized SMAs lagged one month behind returns) to project next month’s return based on current SMAs. Does this alternative use of SMAs usefully forecast stock market returns. To investigate, we apply the methodology to predict SPDR S&P 500 (SPY) returns and to predict International Business Machines Corporation (IBM) returns. Using daily dividend-adjusted closes for SPY since the end of January 1993 and for IBM since the beginning of January 1962, both through November 2012, we find that: Keep Reading