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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.

Intrinsic Momentum or SMA for Avoiding Crashes?

A subscriber suggested comparing intrinsic momentum (IM), also called absolute momentum and time series momentum, to simple moving average (SMA) as alternative signals for equity market entry and exit. To investigate across a wide variety of economic and market conditions, we measure the long run performances of entry and exit signals from IM over past intervals of one to 12 months (IM1 through IM12) and SMAs ranging from 2 to 12 months (SMA2 through SMA12. We consider two cases for IM signals: (1) in stocks (cash) when past return is positive (negative); and, (2) in stocks (cash) when average monthly past return is above (below) the average monthly risk-free rate, proxied by the 3-month U.S. Treasury bill (T-bill) yield, over the same measurement interval. The rule for SMAs is: in stocks (cash) when current level is above (below) the SMA. Using monthly T-bill yield and monthly level of the Dow Jones Industrial Average (DJIA) during January 1934 through April 2018 (over 84 years), we find that: Keep Reading

Commodity, Equity Index and Currency Popular Pairs Trading

Are technical rules applied to pairs trading attractive after correcting for data snooping bias? In their March 2018 paper entitled “Pairs Trading, Technical Analysis and Data Snooping: Mean Reversion vs Momentum”, Ioannis Psaradellis, Jason Laws, Athanasios Pantelous and Georgios Sermpinis test a variety of technical trading rules for long-short trading of 15 commodity futures, equity indexes and currency pairs (all versus the U.S. dollar) frequently used on trading websites or offered by financial market firms. Specifically, they test 18,412 trend-following/momentum and contrarian/mean-reversion rules often applied by traders to past daily pair return spreads. They consider average excess (relative to short-term interest rate) return and Sharpe ratio as key metrics for rule selection and performance measurement. They use False Discovery Rate (FDR) to control for data snooping bias, such that 90% of the equally weighted best rules in FDR-corrected portfolios significantly outperform the benchmark. Most tests are in-sample. To test robustness of findings, they: (1) account for one-way trading frictions ranging from 0.02% to 0.05% across assets; (2) consider five subperiods to test consistency over time; and, (3) perform out-of-sample tests using the first part of each subperiod to select the best rules and roughly the last year to measure performance of these rules out-of-sample. Using daily prices of specified assets and daily short-term interest rates for selected currencies during January 1990 (except ethanol starts late March 2006) through mid-December 2016, they 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 adjusted SSO opens and closes during July 2006 (the first full month SSO is available) through March 2018, we find that: Keep Reading

10-month vs. 40-week vs. 200-day SMA

A reader proposed: “I would love to see a backtest pitting a 10-month simple moving average (SMA) against a 200-day SMA for SPDR S&P 500 (SPY). I assume trading costs would go through the roof on the latter, but do performance gains offset additional costs?” Others asked about a 40-week SMA. To investigate, we use the three SMAs to time SPY since its inception and compare results. Specifically, we buy (sell) SPY at the close as it crosses above (below) the SMA, anticipating crossing signals such that trades occur at the close on the signal day (assuming calculations can occur just before the close). The baseline SMA calculation series is dividend-adjusted, but we also check use of the non-adjusted series. We assume return on cash is the 13-week U.S. Treasury bill (T-bill) yield (ignoring settlement delays). We use a baseline 0.1% one-way SPY-cash switching frictions and test sensitivity to frictions ranging from 0.0% to 0.5% (but assume dividend reinvestment is frictionless). Using monthly, weekly and daily dividend-adjusted and unadjusted closes for SPY and daily T-bill yield from the end of January 1993 through mid-March 2018, we find that:

Keep Reading

Bonds Lead Stocks?

Are bond market investors generally shrewder than their stock market counterparts, such that bond yield tops (bottoms) anticipate stock market bottoms (tops)? To investigate, we employ both a monthly lead-lag analysis and a comparison of bond yield and stock market tops and bottoms. We define “top” and “bottom” as the highest (lowest) value in a rolling window that extends from 30 months in the past to 30 months in the future (a total window of five years). Using monthly levels of Moody’s yield on seasoned Aaa corporate bonds and the Dow Jones Industrial Average (DJIA) during October 1928 through February 2018 (about 90 years) and monthly levels of the 10-year government bond interest rate and the stock market from Robert Shiller during January 1871 through February 2018 (about 148 years), we find that: Keep Reading

Industry Rotation Based on Advanced Regression Techniques

Can advanced regression techniques identify monthly cross-industry lead-lag return relationships that usefully indicate an industry rotation strategy? In their January 2018 paper entitled “Dynamic Return Dependencies Across Industries: A Machine Learning Approach”, David Rapach, Jack Strauss, Jun Tu and Guofu Zhou examine dynamic relationships between past and future returns (lead-lag) across 30 U.S. industries. To guard against overfitting the data, they employ a machine learning regression approach that combines a least absolute shrinkage and selection operator (LASSO) and ordinary least squares (OLS). Their approach allows each industry’s return to respond to lagged returns of all 30 industries. They assess economic value of findings via a long-short industry rotation hedge portfolio that is each month long (short) the fifth, or quintile, of industries with the highest (lowest) predicted returns for the next month based on inception-to-date monthly calculations. They consider three benchmark hedge portfolios based on: (1) historical past average returns of the industries; (2) an OLS-only approach; and, (3) a cross-sectional, or relative, momentum approach that is each month long (short) the quintile of industries with the highest (lowest) returns over the past 12 months. Using monthly returns  for 30 value-weighted U.S. industry groups during 1960 through 2016, they find that:

Keep Reading

Distance Between Fast and Slow Price SMAs and Equity Returns

Does the distance between fast and slow simple moving averages (SMA) of an equity price series expose the degree of surprising/informative news about the asset? In their February 2018 paper entitled “The Predictability of Equity Returns from Past Returns: A New Moving Average-Based Perspective”, Doron Avramov, Guy Kaplanski and Avanidhar Subrahmanyam investigate distance between fast and slow price series SMAs as predictors of equity (individual stocks, industry and country market) returns. They choose the 21-day SMA as fast and the 200-day SMA as slow and define the distance between them (Moving Average Distance, or MAD) as the ratio of the former to the latter. They hypothesize that future returns are a continuous function of MAD. They test their hypothesis by measuring future returns: (1) for U.S. stocks sorted into tenths (deciles) based on MAD; and, (2) for U.S. stocks, industries and country markets above and below several MAD thresholds. To assess uniqueness of MAD indications, they control for 18 firm characteristics and several past return variables across different lookback intervals. Using daily prices adjusted for splits and dividends for a broad sample of U.S. stocks priced at least $5, U.S. industry stock groups and country stock markets, and values of U.S. Treasury bill (T-bill) yields and control variables, during June 1977 through October 2015, they find that: Keep Reading

Chemical Activity Barometer as Stock Market Trend Indicator

A subscriber proposed: “It would be interesting to do an analysis of the Chemical Activity Barometer [CAB] to see if it has predictive value for the stock market. Either [look] at stock prices when [CAB makes] a two percent pivot down [from a preceding 6-month high] as a sell signal and one percent pivot up as a buy signal…[or when CAB falls] below its x month moving average.” The American Chemistry Council claims that CAB “determines turning points and likely future trends of the wider U.S. economy” and leads other commonly used economic indicators. To investigate its usefulness for U.S. stock market timing, we consider the two proposed strategies, plus two benchmarks, as follows:

  1. CAB SMAx Timing – hold stocks (the risk-free asset) when monthly CAB is above (below) its simple moving average (SMA). We consider SMA measurement intervals ranging from two months (SMA2) to 12 months (SMA12).
  2. CAB Pivot Timing – hold stocks (the risk-free asset) when monthly CAB most recently crosses 1% above (2% below) its maximum value over the preceding six months. We look at a few alternative pivot thresholds.
  3. Buy and Hold (B&H) – buy and hold the S&P Composite Index.
  4. Index SMA10 – hold stocks (the risk-free asset) when the S&P Composite Index is above (below) its 10-month SMA (SMA10), assuming signal execution the last month of the SMA measurement interval.

Since CAB data extends back to 1912, we use Robert Shiller’s S&P Composite Index to represent the U.S. stock market. For the risk-free rate, we use the 3-month U.S. Treasury bill (T-bill) yield since 1934. Prior to 1934, we use Shiller’s long interest rate minus 1.59% (the average 10-year term premium since 1934). We assume a constant 0.25% friction for switching between stocks and T-bills as signaled. We focus on number of switches, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. Using monthly data for CAB, the S&P Composite Stock Index, estimated dividends for the stocks in this index (for calculation of total returns) and estimated long interest rate during January 1912 through December 2017 (about 106 years), and the monthly T-bill yield since January 1934, we find that: Keep Reading

Combining Market, Unemployment and Interest Rate Trends

In reaction to “Combine Market Trend and Economic Trend Signals?”, a subscriber suggested adding an interest rate trend signal to those for the U.S. stock market and U.S. unemployment rate for the purpose of timing the S&P 500 Index (SP500). To investigate, we look at combining:

We consider scenarios when the SP500 trend is positive, the UR trend is positive, the T-bill trend is positive, at least one trend is positive (>=1), at least two trends are positive (>=2) or all three trends are positive (All). For total return calculations, we adjust the SP500 monthly with estimated dividends from the Shiller dataset. When not in the index, we assume return on cash from the broker is the specified T-bill yield. We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance metrics. We use the average monthly T-bill yield during a year as the risk-free rate for that year in Sharpe ratio calculations. While we do not apply any stocks-cash switching frictions, we do calculate the number of switches for each scenario. Using the specified monthly data through October 2017, we find that: Keep Reading

Combine Market Trend and Economic Trend Signals?

A subscriber requested review of an analysis concluding that combining economic trend and market trend signals enhances market timing performance. Specifically, per the example in the referenced analysis, we look at combining:

  • The 10-month simple moving average (SMA10) for the broad U.S. stock market. The trend is positive (negative) when the market is above (below) its SMA10.
  • The 12-month simple moving average (SMA12) for the U.S. unemployment rate (UR). The trend is positive (negative) when UR is below (above) its SMA12.

We consider scenarios when the stock market trend is positive, the UR trend is positive, either trend is positive or both trends are positive. We consider two samples: (1) dividend-adjusted SPDR S&P 500 (SPY) since inception at the end of January 1993 (24 years); and, (2) the S&P 500 Index (SP500) since January 1950, adjusted monthly by estimated dividends from the Shiller dataset, as a longer-term robustness test (67 years). Per the referenced analysis, we use the seasonally adjusted civilian UR, which comes ultimately from the Bureau of Labor Statistics (BLS). BLS generally releases UR monthly within a few days after the end of the measured month. When not in the stock market, we assume return on cash from the broker is the yield on 3-month U.S. Treasury bills (T-bill). We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance metrics. We use the average monthly T-bill yield during a year as the risk-free rate for that year in Sharpe ratio calculations. While we do not apply any stocks-cash switching frictions, we do calculate the number of switches for each scenario. Using the specified monthly data through October 2017, we find that: Keep Reading

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