Technical Trading

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

A reader asked: “Should the moving average crossover threshold be symmetrical, or does it make sense to try getting back in close to the bottom?” In other words, should we perhaps use a 200-day simple moving average (SMA) to stick with the typical long bull market grind upward and then switch to a 50-day SMA signal after crossing under the 200-day SMA so that we re-enter closer to a V-shaped bear market bottom? Using daily closes for the S&P 500 Index commencing May 1959, the 3-month Treasury bill (T-bill) yield commencing January 1960 and S&P Depository Receipts (SPY), adjusted for dividends, commencing January 1993, all through September 2012, we 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

Is it possible to determine when long-term stock market reversion is imminent? In their August 2012 paper entitled “Long-Term Return Reversal: Evidence from International Market Indices”, Mirela Malina and Graham Bornholt compare the performances of a conventional contrarian strategy that considers only long-term past returns to that of a “late-stage” contrarian strategy that buys (sells) long-term losers (winners) with relatively good (poor) recent returns, as applied to country stock market indexes. Specifically, their conventional contrarian strategy each month buys (sells) the quarter of indexes with the worst (best) returns over the past 36, 48 or 60 months and holds positions for 3, 6, 9 or 12 months (such that portfolios overlap), with a 12-month gap between ranking and holding intervals to avoid intermediate-term momentum effects. The late-stage contrarian strategy each month sorts indexes based on returns over the past 36, 48, or 60 months to identify the quarter with the worst (best) returns and then splits these winner and loser groups into halves based on returns over the past 3, 6, 9, or 12 months. The strategy then buys (sells) the long-term loser/short-term winner (long-term winner/short-term loser) indexes and holds positions for 3, 6, 9 or 12 months, with a one-month gap between ranking and holding intervals to ensure executability. Using monthly total (dividend-reinvested) returns for 18 developed and 26 emerging market indexes in U.S. dollars during January 1970 (or the earliest availability) through January 2011 (193 to 493 monthly observations across countries), they find that: Keep Reading

A reader commented and asked: “Several of my friends swear by the McClellan Summation Index for timing medium term bull/bear moves. Have you any evaluation of its usefulness?” The McClellan Summation Index derives from the McClellan Oscillator, a technical indicator developed in 1969 by Sherman and Marian McClellan, for which the daily input is the number of stocks that closed higher (advances) minus the number that closed lower (declines). The McClellan Oscillator smooths and seeks to concentrate the information in this daily breadth input stream via the difference of two exponential moving averages. The McClellan Summation Index is a running total of the daily values of the McClellan Oscillator. McClellan Financial Publications describes how to calculate the McClellan Oscillator. Advances and Declines is a public source of the historical numbers of advances and declines for U.S. exchanges. Using the daily numbers of NYSE advances and declines for March 1965 through most of June 2012 and daily dividend-adjusted closes of SPDR S&P 500 (SPY) from the end of January 1993 through most of June 2012 (about 19.5 years), we find that: Keep Reading