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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 IMs 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 and one case for SMA signals, as applied to the S&P 500 Index as a proxy for the stock market and the 3-month U.S. Treasury bill (T-bill) as a proxy for cash (the risk-free rate). The three rule types are therefore:

  1. IMs Case 1 – in stocks (cash) when past index return is positive (negative).
  2. IMs Case 2 – in stocks (cash) when average monthly past index return is above (below) average monthly T-bill yield over the same interval.
  3. SMAs – in stocks (cash) when the index is above (below) the SMA.

We estimate S&P 500 Index monthly total returns using quarterly dividend yield calculated from Shiller data for March, June, September and December. This estimation does not affect index timing signals. We focus on net compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance metrics, with baseline stocks-cash switching frictions 0.2%. We use buying and holding the S&P 500 Index (B&H) as a benchmark. Using monthly closes of the S&P 500 Index during December 1927 through February 2022 (94 years), and contemporaneous monthly index dividend and T-bill yields, we find that:

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A Slinky (Short-term Reversion) Effect?

Do often frenzied investors/traders tend to overdo buying and selling, coming to their senses shortly thereafter? In other words, does the broad U.S. stock market tend to revert after short-term moves up or down? To check, we relate sequential past and future return intervals of 1, 2, 3, 5, 10, 15 and 21 trading days. Using daily closes of the S&P 500 Index over the period January 1928 through mid-March 2022, we find that: Keep Reading

SACEMS with Momentum Breadth Protection Update

“SACEMS with Momentum Breadth Crash Protection” evaluates in depth the potential of a simple momentum breadth rule to improve performance of the Simple Asset Class ETF Momentum Strategy (SACEMS). This rule forces the model to all cash when fewer than some threshold of the non-cash SACEMS assets have positive returns over a specified lookback interval. Do major findings of that evaluation still hold? To update, we repeat some of the analyses with the minor changes since made to SACEMS plus recent data. We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for the Top 1, equal-weighted (EW) Top 2 and EW Top 3 SACEMS portfolios. We look at all possible momentum breadth thresholds for the baseline SACEMS lookback interval. We then consider lookback intervals ranging from one to 12 months for a specific momentum breadth threshold. Using monthly dividend-adjusted closing prices for SACEMS assets and the T-bill yield during February 2006 through February 2022, we find that:

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Long-term SMA and TOTM Combination Strategy

“Turn-of-the-Month Effect Persistence and Robustness” indicates that average absolute returns during the turn-of-the-month (TOTM) are strong for both bull and bear markets. Does a strategy of capturing all bull market returns and TOTM returns only during bear markets perform well? To investigate, we apply four strategies to S&P Depository Receipts (SPY) as a tradable proxy for the stock market:

  1. Buy and hold SPY.
  2. Hold SPY (cash) when SPY closes above (below) its 200-day simple moving average (SMA200).
  3. Hold SPY from the close five trading days before through the close four trading days after the last trading day of each month and cash at all other times (TOTM).
  4. Hold SPY when SPY closes above its 200-day SMA and otherwise use the TOTM strategy (SMA200 or TOTM).

We explore sensitivities of these strategies to a range of one-way SPY-cash switching frictions, with baseline 0.1%. Using daily dividend-adjusted SPY from the end of January 1993 through early February 2022 and contemporaneous 3-month Treasury bill (T-bill) yields, we find that: Keep Reading

Simple Tests of an Asymmetric SMA Strategy

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 use a 10-month simple moving average (SMA10) for the typical long bull stock market and then switch to a 3-month average (SMA3) after crossing under SMA10 so that we re-enter stocks close to a V-shaped bear market bottom? To investigate, we use SPDR S&P 500 (SPY) as a proxy for the U.S. stock market and compare performance statistics for four strategies:

  1. SPY – buying and holding SPY.
  2. SMA10 – holding SPY (cash) when SPY is above (below) its prior-month SMA10.
  3. SMA3 – holding SPY (cash) when SPY is above (below) its prior month SMA3.
  4. SMA10-SMA3 – when SPY is above its prior-month SMA10, hold SPY, and when SPY is below its prior-month SMA10, hold SPY (cash) when SPY is above (below) its prior-month SMA3.

We use average daily 3-month U.S. Treasury bill (T-bill) yield as the return on cash. We assume constant 0.1% switching frictions when moving between SPY and cash. We also perform a sensitivity test to see whether SMAs of other lengths work better. Using monthly dividend-adjusted closing prices for SPY and T-bill yield during January 1993 through December 2021, we find that:

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Technical Indicator Model of Stock Returns

Do models of the cross-section of future stock returns based on technical indicators work as well as those based on fundamental factors? In their December 2021 paper entitled “Technical Indicators and Cross-Sectional Expected Returns”, Hui Zeng, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti investigate the combined abilities of 14 technical indicators to explain differences in next-month returns across stocks. These indicators involve trend-following for various lookback intervals up to 12 months based on: (1) crossover of short and long price moving averages, (2) price momentum and (3) on-balance volume. The authors apply a smoothed ordinary least squares method, which averages regression coefficients over time, to combine the technical indicators. They compare the predictive power of this 14-indicator model to that of the widely used Fama-French 3-factor (market, size, book-to-market) model of stock returns. They further measure returns to a hedge portfolio that is each month long (short) the equal-weighted or value-weighted tenth, or decile, of stocks with the highest (lowest) expected returns based on the 14-indicator model. The methodology allows calculation of initial model returns starting January 1932 for the full sample period and for three equal subperiods. Using monthly data for all listed U.S. stocks during January 1926 through December 2020 (excluding delisted firms) and contemporaneous conventional factor returns as available through December 2020, they find that:

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TLT-SPY Return Delta as Stock Market Crash Indicator

A subscriber hypothesized that a very large delta between daily iShares 20+ Year Treasury Bond (TLT) and SPDR S&P 500 (SPY) returns presages a stock market collapse, and asked for verification. To investigate, we consider two tests:

  1. Calculate correlations between daily TLT-SPY return delta and daily SPY returns over the next month (21 trading days). A stock market collapse during this interval should exhibit very negative correlations.
  2. Compute average next-day SPY returns by ranked tenth (decile) of daily TLT-SPY return deltas. Average SPY returns should be relatively very low for high deciles.

Using daily dividend-adjusted prices for TLT and SPY during late July 2002 (limited by TLT) through mid-December 2021, we find that: Keep Reading

Testing Wilshire 5000/GDP as Stock Market Predictor

Is the Buffett Indicator, the ratio of total U.S. stock market capitalization (proxied by Wilshire 5000 Total Market Full Cap, W5000) to U.S. Gross Domestic Product (GDP), a useful indicator of future U.S. stock market performance? W5000/GDP clearly has no stable average value over its available history (see the first chart below), so using the level of the ratio as a predictor is not reasonable. To investigate, we therefore consider several variables based on W5000/GDP as predictors of W5000 returns at horizons up to two years, including:

  1. Quarterly change in W5000/GDP.
  2. Average quarterly change in W5000/GDP over the past two years (eight quarters).
  3. Average quarterly change in W5000/GDP over the past five years (20 quarters).
  4. Slope of W5000/GDP over the past two years.
  5. Slope of W5000/GDP over the past five years.

We consider two kinds of tests: (1) a linear test that relates past changes in these variables to future W5000 returns up to two years; and, (2) a non-linear test that calculates average next-quarter W5000 returns by ranked fifths (quintiles) of past changes in these variables. Using quarterly levels of W5000 and quarterly GDP lagged by one quarter to ensure availability during the first quarter of 1971 (limited by W5000) through the third quarter of 2021, we find that: Keep Reading

Add Position Stop-gain to SACEMS?

Does adding a position take-profit (stop-gain) rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by harvesting some upside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-gains, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return rises above a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month maximum returns for the specified assets during February 2006 through September 2021, we find that: Keep Reading

Add Position Stop-loss to SACEMS?

Does adding a position stop-loss rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by avoiding some downside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-losses, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return falls below a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month drawdowns for the specified assets during February 2006 through September 2021, we find that: Keep Reading

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