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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.

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

Simple Tests of Sy Harding’s Seasonal Timing Strategy

Does the technically adjusted Seasonal Timing Strategy popularized some years ago in Sy Harding’s Street Smart Report Online (now unavailable due to Mr. Harding’s death) generate attractive performance? This strategy combines “the market’s best average calendar entry [October 16] and exit [April 20] days with a technical indicator, the Moving Average Convergence Divergence (MACD).” According to Street Smart Report Online, applying this strategy to a Dow Jones Industrial Average (DJIA) index fund generated a cumulative return of 213% during 1999 through 2012, compared to 93% for the DJIA itself. To check over a longer sample period with an alternative market proxy, we apply the strategy to SPDR S&P 500 (SPY) since its inception and consider several alternatives, as follows:

  1. SPY – buy and hold SPY.
  2. Seasonal-MACD – seasonal timing per specified dates with MACD refinement, holding cash when not in SPY.
  3. Seasonal Only – seasonal timing per the same dates without MACD refinement, again holding cash when not in SPY.
  4. SMA200 – hold SPY (cash) when the S&P 500 Index is above (below) its 200-day simple moving average at the prior daily close. 

For all strategies, we use the yield on short-term U.S. Treasury bills (T-bills) as the return on cash. Using daily closes for the S&P 500 Index, dividend-adjusted closes for SPY and T-bill yield during 1/29/93 (SPY inception) through 10/1/21, 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?” [no longer available]: “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 August 2021, we find that: Keep Reading

Comparing the Sahm Indicator and the Yield Curve

In response to “Combining SMA10 and Sahm Indicator”, a subscriber asked for a comparison of signals generated by the Sahm Recession Indicator (Sahm) and by yield curve inversion. The former signals a recession when the 3-month simple moving average (SMA) of the U.S. unemployment rate is at least 0.5% higher than its low during the last 12 months. The latter signals a recession when the yield on the 3-month U.S. Treasury bill (T-bill) rises above the yield on the 10-year U.S. Treasury note (T-note). To investigate, we calculate average monthly returns and standard deviations of monthly returns for the S&P 500 Index (SP500):

  • When Sahm does not indicate a recession and, separately, when it does.
  • When the yield curve does not indicate a recession and, separately, when it does.
  • When SP500 is below its 10-month SMA (SMA10) and, separately, when it is above (for additional perspective).

Using end-of-month levels of SP500 since March 1959, Sahm levels since inception in December 1959 (history vintage 8/6/2021) and T-bill and T-note yields since December 1959, all through July 2021, we find that:

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Optimal Intrinsic Momentum and SMA Intervals Across Asset Classes

What are the optimal intrinsic/absolute/time series momentum (IM) and simple moving average (SMA) lookback intervals for different asset class proxies? To investigate, we use data for the following eight asset class exchange-traded funds (ETF), plus Cash:

  • PowerShares DB Commodity Index Tracking (DBC)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
  • iShares MSCI EAFE Index (EFA)
  • SPDR Gold Shares (GLD)
  • iShares Russell 2000 Index (IWM)
  • SPDR S&P 500 (SPY)
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • Vanguard REIT ETF (VNQ)
  • 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 at the ends of the last two to 12 months. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key metrics for comparing different IM and SMA lookback intervals since earliest ETF data availabilities based on the longest IM lookback interval. 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 by then) through July 2021, we find that:

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Combining SMA10 and Sahm Indicator

A subscriber asked about a stock market timing strategy that combines the market 10-month simple moving average (SMA10) and the Sahm Recession Indicator (Sahm), which signals the start of a recession when the 3-month SMA of the U.S. unemployment rate is at least 0.5% higher than its low during the last 12 months. Specifically, the strategy:

  • Holds the S&P 500 Index (SP500) unless it is below its SMA10 and Sahm first signals a recession.
  • Subsequently holds cash until SP500 crosses above its SMA10.

To investigate, we compare three alternative strategies:

  1. SP500 – buy and hold the index.
  2. SMA10 – hold the index only while it is above its SMA10 and otherwise hold cash.
  3. SMA10+Sahm – combined signals as specified above.

We focus on average monthly return, standard deviation of monthly returns, monthly reward/risk (average return divided by standard deviation), compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. Using end-of-month levels of SP500 since March 1959, Shiller’s monthly SP500 dividends (to estimate SP500 total returns) since January 1960, Sahm since inception in December 1959 (history vintage 8/6/2021) and T-bill yield since December 1959, all through July 2021, we find that:

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