Technical Trading

Does technical trading work, or not? Rationalists dismiss it; behavioralists investigate it. Is there any verdict? These blog entries relate to technical trading.

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U.S. Stock Market Death Crosses and Golden Crosses

A subscriber requested tests exploring whether a recent death cross for the Dow Jones Industrial Average (DJIA) portends an index crash. To investigate, we consider two ways of evaluating DJIA performance after death crosses and conversely defined golden crosses:

  1. Behavior of the index during the 126 trading days (six months) after death and golden crosses.
  2. Behavior of the index between converse crosses (death cross-to-golden cross, and golden cross-to-death cross).

We focus on distributions of average returns and maximum drawdowns during specified periods. We also check robustness by repeating DJIA tests on the S&P 500 Index. Using daily DJIA closes during October 1928 through mid-August 2015 and daily S&P 500 Index closes during January 1950 through mid-August 2015, we find that: Keep Reading

DJIA-Gold Ratio as a Stock Market Indicator

A reader requested a test of the following hypothesis from 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.” To test this assertion, we examine 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 July 2015 (535 months), we find that: Keep Reading

Combining Annual Fundamental and Monthly Trend Screens

Stock return anomaly studies based on firm accounting variables generally employ annually reformed portfolios that are long (short) the tenth of stocks expected to perform well (poorly). Does adding monthly portfolio updates based on technical stock price trend measurements boost anomaly portfolio performance? In the June 2015 version of their paper entitled “Anomalies Enhanced: The Use of Higher Frequency Information”, Yufeng Han, Dayong Huang and Guofu Zhou test eight equal-weighted long-short portfolios that combine annual screening based on a predictive accounting variable with monthly screening based on a simple moving average (SMA)-based stock price trend rule. The eight accounting variables (screened in June based on prior December data) are: (1) book-to-market ratio; (2) gross profitability; (3) operating profitability; (4) asset growth; (5) investment growth; (6) net stock issuance; (7) accruals; and, (8) net operating assets. The price trend screen excludes from the long (short) side of the portfolio any stock for which 50-day SMA is less than (greater than) 200-day SMA at the end of the prior month. Using accounting and daily price data for a broad sample of U.S. stocks during July 1965 through December 2013, they find that: Keep Reading

Best Stock Pairs Trading Method?

What is the best stock pairs trading method? In their June 2015 paper entitled “The Profitability of Pairs Trading Strategies: Distance, Cointegration, and Copula Methods”, Hossein Rad, Rand Kwong Yew Low and Robert Faff compare performances of three pairs trading methods as applied to U.S. stocks.

  1. Distance – Select the 20 stock pairs with the smallest sum of squared differences in initially normalized dividend-adjusted prices during a 12-month formation period. Then re-normalize prices of selected pairs and initiate equal long-short trades when prices diverge by at least two formation-period standard deviations during a subsequent six-month trading period. Close trades when prices converge or, if not, at the end of the trading period. Re-open trades if prices diverge again withing the trading period.
  2. Cointegration – Sort stock pairs based on sum of squared differences in initially normalized dividend-adjusted prices during a 12-month formation period. Then determine which pairs are cointegrated (exhibit a reliable mean-reverting relationship) during the formation period, and select the 20 cointegrated pairs with the smallest sum of squared differences. Over the subsequent six-month trading period, trade pair divergences and convergences based on cointegration statistics, with long and short position sizes also determined by these statistics.
  3. Copula – Select the 20 stock pairs with the smallest sum of squared differences in initially normalized dividend-adjusted prices during a 12-month formation period. Then construct best-fit copulas for each pair and use copula statistics to determine when pair prices diverge and converge during a subsequent six-month trading period, opening and closing equal long-short trades accordingly.

They iterate each method monthly, so each always involves six overlapping portfolios. They assume round trip broker fees start at 0.7% in 1962 and gradually decline to 0.09% in recent years. They estimate impact of trading on price as 0.3% during 1962-1988 and 0.2% since. They assume zero cost of shorting. They calculate returns based on both employed capital (funding only actual trades) and committed capital (funding 20 concurrent positions per portfolio, with no return on cash). Monthly return for each method is the equally weighted average for the six overlapping portfolios. Using daily dividend-adjusted prices for a broad sample of relatively liquid U.S. common stocks during 1962 through 2014, they find that: Keep Reading

Best Moving Average Weighting Scheme for Market Timing?

What is the best scheme over the long run for identifying U.S. stock market trends? In the May 2015 version of his paper entitled “Market Timing With a Robust Moving Average”, Valeriy Zakamulin isolates the most robust moving average weighting scheme for a U.S. stock market index based on monthly data. He tests 300 weighting schemes. For all schemes, test portfolios are in stocks (a risk-free asset) when the last index price is above (below) the moving average. His principal performance metric is the Sharpe ratio. He defines robust as: (1) being insensitive to outliers; and, (2) generating consistent performance across all observed market environments. He specifies the range of observed market environments as 30 subperiods, each 10 years in length (with 5-year overlaps). He assumes that there is no optimal trend measurement look-back interval and therefore considers 15 intervals (4 to 18 months). He therefore generates 450 ranks by Sharpe ratio for each of the 300 weighting schemes and defines the most robust as the one with the highest median rank. Using monthly estimates of the Standard and Poor’s Composite Total Return Index and the risk-free rate during January 1860 through December 2014, he finds that: Keep Reading

Trend Indicator Similarities

What is the best way to do asset price trend analysis? Two recent papers address this question. In the May 2015 version of their paper entitled “Which Trend is Your Friend?”, Ari Levine and Lasse Pedersen compare time series (intrinsic or absolute) momentum, moving average (fast and slow) crossovers and other trend indicators to determine the best way to identify a price trend. In the May 2015 version of their paper entitled “Uncovering Trend Rules”, Paul Beekhuizen and Winfried Hallerbach describe how to determine the underlying historical weighting schemes (a combination of continuation and reversion) of price moving averages and combinations of price moving averages. Using both theoretical analyses and examples, these papers conclude that: Keep Reading

Optimal Intrinsic Momentum and SMA Intervals Across Asset Classes

“Intrinsic Momentum or SMA for Avoiding Crashes?” compares a 10-month simple moving average (SMA10) and various length intrinsic momentum (IM) measurement intervals for timing the Dow Jones Industrial Average (DJIA) over the long run. SMA10 is likely close to optimal for the DJIA sample per “Is There a Best SMA Calculation Interval for Long-term Crossing Signals?”. A few IM measurement intervals are competitive with SMA10. What are the optimal IM and SMA intervals for different asset class proxies? To investigate, we use data from “Simple Asset Class ETF Momentum Strategy” for the following eight asset class exchange-traded funds (ETF), plus Cash:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

For IM tests, we invest in each ETF (Cash) when its return over the past one to 12 months is positive (negative). For SMA tests, we invest in each ETF (Cash) when its price is above (below) its average monthly price over the past two to 12 months. Since SMA rules use price levels and IM rules use returns, IM measurement interval N corresponds to SMA measurement interval N+1. For example, a 6-month IM measurement uses the same start and stop points as a 7-month SMA measurement. The key metric for comparing different IM and SMA measurement intervals is compound annual growth rate (CAGR) from earliest ETF data availabilities through the end of the sample period. Using monthly dividend-adjusted closing prices for the asset class proxies and the yield for Cash over the period July 2002 (or inception if not available then) through March 2015 (no more than 153 months), we find that: Keep Reading

Optimal SMA Calculation Interval for Long-term Crossing Signals?

Is a 10-month simple moving average (SMA10) the best SMA for long-term crossing signals? If not, is there some other optimal SMA calculation interval? To check, we compare performance statistics for SMA crossing signals generated by calculation intervals ranging from 2 trailing months (SMA2) to 48 trailing months (SMA48), as applied to the S&P 500 Index. Using monthly S&P 500 Index closes, monthly S&P 500 Composite Index dividend data from Robert Shiller and monthly average yields for 3-month Treasury bills (T-bills) during January 1950 through March 2015, we find that: Keep Reading

Long-term Tests of Simple X% Rules

A subscriber requested long-term tests of simple versions of the strategy described by Jason Kelly in The 3% Signal: The Investing Technique that Will Change Your Life. We start with a general strategy targeting an X% quarterly increase in a stock fund, as follows:

  1. Initiate X% rules with either 80%-20% or 60%-40% allocations to a stock fund and a bond fund.
  2. If over the next quarter the stock fund increases by more than X%, transfer the excess from the stock fund to the bond fund.
  3. If over the next quarter the stock fund increases by less than X%, make up the shortfall by transferring money from the bond fund to the stock fund.
  4. If at the end of any quarter the bond fund does not have enough money to make up a shortfall in the stock fund: either draw the bond fund down to 0 and add cash to make up the rest of the shortfall; or, draw the bond fund down to 0 and bear the rest of the shortfall in the stock fund.
  5. Consider two benchmarks: a 100% allocation to the stock fund (B&H); and, 60%-40% allocations to the stock and bond funds, rebalanced quarterly (60-40). Whenever adding cash to the bond fund per Step 4, add equal amounts to the benchmarks.

We consider for X% a range of 2% to 4% in increments of 0.5%. We employ stock and bond mutual funds with long histories: Fidelity Magellan (FMAGX) and Fidelity Investment Grade Bond (FBNDX). We assume there are no trading frictions when adding or withdrawing money from these funds. Using quarterly returns for these funds from the first quarter of 1972 (limited by FBNDX) through the first quarter of 2015 (43.25 years), we find that:

Keep Reading

Market Timing with Moving Averages Over the Very Long Run

Which moving average rules and measurement (lookback) intervals work best? In the March 2015 version of his paper entitled “Market Timing with Moving Averages: Anatomy and Performance of Trading Rules” Valeriy Zakamulin compares market timing rules based on different kinds of moving averages, including simple momentum. He first compares the mathematics of these rules to identify similarities and differences. He then conducts very long run out-of-sample tests of a few trading rules with distinct weighting schemes to measure their market timing effectiveness. He tries both an expanding window (inception-to-date) and rolling windows to discover optimal lookback intervals. He uses Sharpe ratio as his principal performance metric. He estimates one-way trading friction as a constant 0.25%. Using monthly returns for the S&P Composite Index and for the risk-free asset during January 1860 through December 2009, he finds that: Keep Reading

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