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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.

Proximity to 52-week High and Short-term Momentum/Reversal

What determines whether a stock will exhibit short-term momentum or short-term reversal? In their May 2022 paper entitled “Short-term Relative-Strength Strategies, Turnover, and the Connection between Winner Returns and the 52-week High”, building upon prior research, Chen Chen, Chris Stivers and Licheng Sun investigate interactions among proximity to 52-week high, share turnover and 1-month return momentum/reversal behaviors for U.S. stocks.  Specifically, at the end of each month t, they form 125 portfolios by:

  1. Sorting stocks into fifths (quintiles) based on return during month t.
  2. Further sorting these quintiles stocks into sub-quintiles based on ratio of price at the end of month t-1 to highest price over the preceding 52 weeks.
  3. Further sorting the sub-quintiles into sub-sub-quintiles based on share turnover during month t.

They then use month t+1 value-weighted returns of the resulting 125 portfolios to evaluate short-term momentum/reversal strategies in multiple ways: buying winners and shorting losers (momentum); buying losers and shorting winners (reversal); and, winners-only or losers-only strategies based on 52-week high proximity. Using the specified trading data for a broad sample of U.S. common stocks priced at least $1 during July 1963 to December 2020, they find that:

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Quantifying the Value of SMA10 Trend Following

The 10-month simple moving average (SMA10) is a widely studied trend-following technical indicator for the U.S. stock market. How much value does it add? To investigate, we compare:

  1. SMA10 – A strategy that is each month in SPDR S&P 500 ETF Trust (SPY) when the S&P 500 Index is above its SMA10 at the end of the prior month and iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD) when below.
  2. RAND SMA10 Average – Average performance of 100 trials of a strategy that is each month randomly in SPY or LQD, but with the selection tilted toward SPY to achieve about the same percentage of time in stocks as for the SMA10 strategy.

We focus on average monthly returns, standard deviations of monthly returns, monthly return/risk (average monthly return divided by standard deviation), along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We also consider the number of SPY-LQD switches. Using monthly levels of the S&P 500 Index since October 2001 and monthly dividend-adjusted prices for SPY and LQD since July 2002 (limited by LQD), all through May 2022, we find that: Keep Reading

Combining Short-term Trading Signals

Should investors dismiss short-term signals as unexploitable due to high trading frictions? In their May 2022 paper entitled “Beyond Fama-French Factors: Alpha from Short-Term Signals”, David Blitz, Matthias Hanauer, Iman Honarvar, Rob Huisman and Pim van Vliet investigate whether investors can extract a material net alpha by applying efficient trading rules to a composite of several short-term signals from a liquid global universe of stocks. The composite signal each month normalizes and averages the following individual signals:

  1. Industry-relative 1-month reversal
  2. 1-month industry momentum
  3. Analyst earnings revisions over the last 30 days
  4. Same-calendar month average return over the past 10 years
  5. 1-month daily idiosyncratic volatility

They hypothesize that integrating signals with low correlations offers diversification benefits, thereby boosting gross returns and suppressing volatility. They each month rank stocks on individual and composite signals into fifths (quintiles) and measure alphas of equal-weighted quintile portfolios using a 6-factor (market, size, book-to-market, profitability, investment and momentum) model of stock returns. They then consider a more efficient trading strategy is each month long (short) stocks currently in the top (bottom) X% plus the stocks as selected in previous months and still among the top (bottom) Y% of stocks, with X=20/Y=20 representing conventional full quintile rotation and X=10/Y=50 representing an efficient trading rule. They assume 0.25% average 1-way trading frictions. Using daily prices and end-of-month signal data for all stocks in the MSCI World Index and regional 6-factor model returns during December 1985 through December 2021, they find that:

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Weekly Stock Market Streaks

What happens after the stock market has a streak of up or down weeks? To check, we use the S&P 500 Index (SP500) as a proxy for the U.S. stock market and calculate average weekly returns and variabilities of these returns after streaks of positive or negative weekly returns. We do not include streaks within streaks. For example, if the index has a streak of seven up weeks, only the first two weeks count as a streak of two up weeks, and only the first three weeks count as a streak of three up weeks. Using weekly SP500 closes during January 1928 through early May 2022, we find that: Keep Reading

SACEVS with SMA Filter

The “Simple Asset Class ETF Value Strategy” (SACEVS) allocates across 3-month Treasury bills (Cash, or T-bill), iShares 20+ Year Treasury Bond (TLT), iShares iBoxx $ Investment Grade Corporate Bond (LQD) and SPDR S&P 500 (SPY) according to the relative valuations of term, credit and equity risk premiums. Does applying a simple moving average (SMA) filter to SACEVS allocations improve its performance? Since many technical traders use a 10-month SMA (SMA10), we apply SMA10 filters to dividend-adjusted prices of TLT, LQD and SPY allocations. If an allocated asset is above (below) its SMA10, we allocate as specified (to Cash). This rule does not apply to any Cash allocation. We focus on gross compound annual growth rates (CAGR), maximum drawdowns (MaxDD) and annual Sharpe ratios (using average monthly T-bill yield during a year as the risk-free rate for that year) of SACEVS Best Value and SACEVS Weighted portfolios. We compare to baseline SACEVS as currently tracked and to the SMA rule applied to a 60%-40% monthly rebalanced SPY-TLT benchmark portfolio (60-40). Finally, we test sensitivity of main findings to varying the SMA lookback interval. Using SACEVS historical data, monthly dividend-adjusted closing prices for the asset class proxies and yield for Cash during July 2002 (the earliest all funds are available) through March 2022, we find that:

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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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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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