Technical Trading

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

Adam Grimes (Chief Investment Officer of Waverly Advisors) prefaces his 2012 book, The Art and Science of Technical Analysis: Market Structure, Price Action, and Trading Strategies, by stating: “This book…offers a comprehensive approach to the problems of technically motivated, directional trading. …Trading is hard. Markets are extremely competitive. They are usually very close to efficient and most observed price movements are random. It is therefore exceedingly difficult to derive a method that makes superior risk-adjusted returns, and it is even more difficult to successfully apply such a method in actual practice. Last, it is essential to have a verifiable edge in the markets–otherwise no consistent profits are possible. This approach sets this work apart from the majority of trading books published, which suggest that simple patterns and proper psychology can lead a trader to impressive profits. Perhaps this is possible, but I have never seen it work in actual practice. …The self-directed trader will find many sections specifically addressed to the struggles he or she faces, and to the errors he or she is likely to make along the way. …[Institutional] traders will also find new perspectives on risk management, position sizing, and pattern analysis that may be able to inform their work in different areas.” Using example charts for many assets from different times over different time frames and from different markets, he concludes that: Keep Reading

What are the parameters of profitable stock pairs trading in European equity markets? In their June 2011 paper entitled “European Equity Pairs Trading: The Effect of Data Frequency on Risk and Return”, Michael Lucey and Don Walshe examine the effects of both price measurement frequency (daily, weekly or monthly) and magnitude of pair price divergence on the profitability of European stock pairs trading. In selecting and tracking stock pairs, they use normalized stock prices (current price minus two-year lagged average price divided by standard deviation of lagged prices). They first pair each stock with another exhibiting minimum total squared normalized price difference over the past two years. They then track pairs for normalized price divergence over the next six months. Whenever the divergence of a pair exceeds a threshold (ranging from 1.5 to 3.0 units), they buy the relatively undervalued stock and sell the relatively overvalued stock. They close positions when the normalized prices next converge (or at the end of the six-month tracking interval if they do not converge). They calculate gross returns, net returns and returns in excess of the contemporaneous yield on 10-Year French and German government bonds. Using daily, weekly and monthly closing prices for the most liquid stocks listed on French and German exchanges during 1998 through 2007, they find that: Keep Reading

Based on results from “Simple Sector ETF Momentum Strategy Performance”, “Does the Turn-of-the-Month Effect Work for Sectors?” and “Long-term SMA and TOTM Combination Strategy”, a subscriber proposed: “Have you ever thought of combining the three? When SPY is above a long term average, buy the best performing sector ETF using the TOTM strategy.” To investigate, we consider the 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)

We determine sector momentum based on total return over the past six months (6-1). We define bull-bear stock market state according to whether SPDR S&P 500 (SPY) is above-below its 200-day simple moving average (SMA). We define the turn-of-the-month (TOTM) as the eight-trading day interval from the close five trading days before the first trading day of a month to the close on the fourth trading day of the month. Using daily dividend-adjusted closes for the sector ETFs and SPY from 12/22/98 through 8/10/12 (164 months), we find that: Keep Reading

Do patterns formed by Japanese candlesticks, which summarize asset price behavior with a candle and two shadows indicating open-high-low-close prices over a given interval, work as intraday technical trading signals? In their August 2012 paper entitled “The Intraday Performance of Market Timing Strategies and Trading Systems Based on Japanese Candlesticks”, Matthieu Duvinage, Paolo Mazza and Mikael Petitjean investigate the power of 83 Japanese candlestick rules to predict intraday returns of the 30 components of the Dow Jones Industrial Average (DJIA) based on both stock timing metrics and optimized trading systems. They explicitly correct for data snooping bias that derives from testing a large number of rules on the same data and account for trading frictions. Using 5-minute intraday high-low-open-close prices from April 1, 2010 through April, 13 2011 for the 30 DJIA stocks (20,550 observations per stock), they find that: Keep Reading

How well do trend-following rules work when applied to the S&P 500 Index? In the March 2012 version of their paper entitled “Breaking into the Blackbox: Trend Following, Stop Losses, and the Frequency of Trading: The Case of the S&P 500”, Steve Thomas, James Seaton, Andrew Clare and Peter Smith evaluate a variety of simple daily moving average (SMA, 10 to 450 days), moving average crossover (25/50 to 150/350 days) and channel breakout (10-day to 450-day highs) trading rules as applied to the S&P 500 Index. They further investigate: (1) how measurement frequency affects rule performance; (3) effectiveness of combining the rules with stop-losses; and, (3) whether fundamental valuation metrics outperform the rules. They assume an index-cash switching cost of 0.2%. Using daily S&P 500 Index levels and monthly total returns from January 1952 through June 2011, daily S&P 500 Index total returns from July 1988 through June 2011 and contemporaneous Treasury bill yields as the return on cash, they find that: Keep Reading