Technical Trading

In response to the U.S. stock market timing backtest in “Usefulness of P/E10 as Stock Market Return Predictor”, a subscriber suggested combining a 10-month simple moving average (SMA10) technical signal with a P/E10 (or Cyclically Adjusted Price-Earnings ratio, CAPE) fundamental signal. Specifically, we test:

• SMA10 – bullish/in stocks (bearish/in cash) when prior-month stock index level is above (below) its SMA10.
• SMA10 AND Binary 20-year – in stocks only when both SMA10 and P/E10 Binary 20-year signals are bullish, and otherwise in cash. The latter rule is bullish when last-month P/E10 is below its rolling 20-year monthly average.
• SMA10 OR Binary 20-year – in stocks when one or both of the two signals are bullish, and otherwise in cash.
• NEITHER SMA10 NOR Binary 20-year – in stocks only when neither signal is bullish, and otherwise in cash.

We use Robert Shiller’s S&P Composite Index to represent stocks. We consider buying and holding the S&P Composite Index and the standalone P/E10 Binary 20-year strategy as benchmarks. Using monthly data from Robert Shiller, including S&P Composite Index level, associated dividends, 10-year government bond yields and values of P/E10 as available during January 1871 through September 2022, we find that:

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Does breadth of equity sector performance predict overall stock market return? To investigate, we relate next-month stock market return to sector breadth (number of sectors with positive past returns) over lookback intervals ranging from 1 to 12 months. We consider the following nine sector exchange-traded funds (ETF) offered as Standard & Poor’s Depository Receipts (SPDR):

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

We use SPDR S&P 500 (SPY) to represent the overall stock market and also relate next-month SPY return to the sign of past SPY return. Using monthly dividend-adjusted returns for SPY and the sector ETFs during December 1998 through October 2022, we find that: Keep Reading

Are days with strong stock market return reversals predictive of the future trend? To check, we we use daily opens, lows, highs and closes of the S&P 500 Index to define the following:

• Reversal – days with both (1) lows more than 1% below the open and (2) highs more than 1% above the open.
• Reversal Down – Reversal days for which the close is less than 0.25% above the low.
• Reversal Up – Reversal days for which the close is less than 0.25% below the high.

These thresholds are arbitrary but generate enough signals for some testing. For each group we plot cumulative index returns over the next 21 trading days. Using S&P 500 Index daily levels as specified during January 1962 (limited by availability of daily lows and highs) through September 2022 (15,293 trading days), we find that: Keep Reading

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

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

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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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

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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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

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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