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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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)
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

Bollinger Bands: Buy Low and Sell High?

Are Bollinger Bands useful for specifying low and high levels of the overall U.S. stock market? In other words, can an investor beat a buy-and-hold strategy by systematically buying (selling) when the market crosses below (above) the lower (upper) Bollinger Band? To check, we examine the historical behavior of of Bollinger Bands around the 21-trading day (one month) simple moving average of S&P 500 SPDR (SPY) as a tradable proxy for the U.S. stock market. Using daily dividend-adjusted closes of SPY and contemporaneous yields for 13-week Treasury bills (T-bill) from the end of January 1993 (SPY inception) through February 2015, we find that… Keep Reading

Using RSI(2) to Trade Leveraged ETFs

A subscriber asked about the effectiveness of applying a two-period Relative Strength Index, RSI(2), to leveraged exchange-traded funds (ETF), suggesting two pairs of trade entry (oversold) and exit (overbought) settings:

  1. Buy when RSI(2) falls below 10 and sell when it subsequently rises over 90 (10-90).
  2. More conservatively, buy when RSI(2) falls below 5 and exit when it subsequently rises over 70 (5-70).

To investigate, we run simple tests on ProShares Ultra S&P 500 (SSO) with RSI(2) calculations based on the RSI template from StockCharts. Using daily dividend-adjusted SSO opens and closes during July 2006 (the first full month SSO is available) through January 2015, we find that: Keep Reading

When, Where and Why Stock Pairs Trading Works

Is stock pairs trading particularly successful under predictable conditions? In their December 2014 paper entitled “On the Determinants of Pairs Trading Profitability”, Heiko Jacobs and Martin Weber present a large-scale analysis of pairs trading, evaluating the effects on profitability of the type of news driving pair divergence, the level of available investor attention and obstacle to exploitation (limits of arbitrage). Their pairs trading approach (see the first chart below as an example) employs daily stock price data to:

  1. Calculate each month normalized total return trajectories of stocks over the past 12 months.
  2. Measure differences in trajectories for all possible stock pairs.
  3. Select the 100 pairs with minimum differences and re-normalize their prices.
  4. Whenever over the next six months a pair diverges by more than two standard deviations (per the above 12-month interval), buy the underpriced stock and sell the overpriced stock after a one-day delay.
  5. Close the positions upon price convergence within the next month with a one-day delay. If prices do not converge, close the positions after one month. A pair may trade several times during the six-month trading period.

Using stock return data from 34 countries during 2000 through 2013 (excluding small and illiquid firms) and a sample of U.S. stocks with greater than median capitalizations during 1962 through 2008 with contemporaneous news, investor attention and cost of trading proxies, they find that: Keep Reading

Upside-Downside Participation Ratio Difference as an Alpha Proxy

Is the difference between upside and downside asset participation ratios relative to a benchmark a useful metric for evaluating asset investment performance? In his June 2014 paper entitled “On the Holy Grail of ‘Upside Participation and Downside Protection'”, Edward Qian defines and investigates the performance implications of the Participation Ratio Difference (PRD) as a measure of combined upside participation and downside protection. He defines the upside (downside) participation ratio of an asset as the ratio of expected excess return for the asset to the expected excess return of its benchmark when benchmark returns are positive (negative). “Excess” means in excess of the return on cash (such that cash has zero participation rates). He defines PRD as the simple difference between positive participation ratio (P+) and negative participation ratio (P-). He then investigates the relationship between asset PRDs and one-factor (market) alphas. He then checks PRDs for the S&P 500 sectors (with the S&P 500 Index as the benchmark) and PRDs for Russell style indexes (with the Russell 3000 Index as the benchmark). Using monthly returns of the S&P 500 index and its ten sectors during October 1989 through April 2014 and monthly returns of Russell broad and value-growth style indexes during January 1979 through April 2014, he finds that: Keep Reading

Comprehensive, Long-term Test of Technical Currency Trading

Does quantitative technical analysis work reliably in currency trading? If so, where does it work best? In their May 2013 paper entitled “Forty Years, Thirty Currencies and 21,000 Trading Rules: A Large-Scale, Data-Snooping Robust Analysis of Technical Trading in the Foreign Exchange Market”, Po-Hsuan Hsu and Mark Taylor 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 also test whether technical trading effectiveness weakens over time. In testing robustness to trading frictions, they assume a fixed one-way trading cost of 0.025%. Using daily U.S. dollar exchange rates for nine developed market currencies and 21 emerging market currencies during January 1971 through July 2011, they find that:

Keep Reading

Where Technical Trading Works

In which country stock markets is technical analysis likely to work best? In the October 2014 version of her paper entitled “Technical Analysis: A Cross-Country Analysis”, Jiali Fang investigates three potential cross-country determinants of technical trading profitability:

  1. An individualism index, measuring the degree to which individuals integrate via cultural groups.
  2. Market development and integrity metrics, including stock market size, stock market age, transaction costs and measures of investor protection, anti-director rights, ownership concentration and insider trading.
  3. Information uncertainty metrics, including aggregate market turnover, volatility of cash flow growth rate and book-to-market ratio.

She considers 26 previously studied trading rules employing only past prices, classified as: variable moving average (VMA) rules, fixed-length moving average (FMA) rules and trading range break-out (TRB) rules. VMA rules are long (short) an index when a short-term moving average is above (below) a long-term moving average. FMA rules are similar to VMA rules, but hold a newly signaled position a fixed interval of 10 days. TRB rules generate buy (sell) signals when price rises above (falls below) the resistance (support) defined by prices over a specified past interval. Tests include both regressions and model strategies that are long (short) the market index as signaled and invest in the risk-free asset when there is no signal. Using cultural metrics, daily stock market index data and economic/financial variables for 50 countries during March 1994 through March 2014, she finds that: Keep Reading

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Current Momentum Winners

ETF Momentum Signal
for May 2015 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
14.5% 15.0%
Top 3 ETFs SPY
14.6% 7.8%
Strategy Overview
Current Value Allocations

ETF Value Signal
for 2nd Quarter 2015 (Final)





The asset with the highest allocation is the holding of the Best Value strategy.
Gross Compound Annual Growth Rates
(Since September 2002)
Best Value Weighted 60-40
13.7% 9.6% 8.6%
Strategy Overview
Recent Research
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