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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.

Require a Subsequent Confirming Signal?

A subscriber asked about a tactic that requires a subsequent confirming signal before triggering a strategy action. To investigate we use the 10-month simple moving average (SMA10) as applied to the S&P 500 Index and exploited via SPDR S&P 500 (SPY) over the available history of SPY. Specifically, we compare performances of the following three strategies:

  1. SPY:SMA10 Baseline – buy (sell) SPY when the S&P 500 Index crosses above (below) its SMA10.
  2. SPY:SMA10 Confirmed – buy (sell) SPY when the S&P 500 Index crosses above (below) its SMA10 and holds the crossing action the next month (suppressing whipsaws).
  3. SPY:SMA10 Tranched – each month allocated half of funds to SPY:SMA10 Baseline and the other half to SPY:SMA10 Confirmed.

We assume that trades execute immediately at monthly closes coincident with signals (requiring slight anticipation of signals). We assume that cash earns the yield on 3-month U.S. Treasury bills and trading frictions for switching between SPY and cash are 0.1%. We focus on average return, standard deviation of returns, return/risk (average return divided by standard deviation of returns), compound annual growth rate (CAGR) and maximum drawdown (MaxDD), all based on monthly data. Using monthly S&P 500 Index closes since March 1992 and monthly dividend-adjusted closes for SPY since January 1993, both through August 2022, we find that: Keep Reading

SPY Breakout/Breakdown Signal Usefulness

A subscriber asked about a tactic employed in some strategies wherein a new X-day high (breakout) triggers a buy and a new Y-day low (breakdown) triggers a sell. Specific X:Y values requested are 55:20, 100:20, 200:20, 100:50 and 200:100. To investigate, we apply this tactic to SPDR S&P 500 (SPY) over its entire history, using unadjusted daily closes to identify breakouts/breakdowns and dividend-adjusted prices to calculate returns. We focus on percentage of days in and out of SPY and average daily return, standard deviation of daily returns and daily return/risk (average daily return divided by standard deviation of daily returns) when in SPY and out of SPY. We also look at number of switches into and out of SPY. We consider execution of trades at the same daily close as the signals and at the next open. Using daily unadjusted and dividend-adjusted SPY opening and closing prices from the end of January 1993 through August 2022, we find that: Keep Reading

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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