Technical Trading

A subscriber suggested review of the February 2017 paper “Bubbles for Fama”, in which Robin Greenwood, Andrei Shleifer and Yang You assess Eugene Fama’s claim that stock prices do not exhibit bubbles. They define a bubble candidate as a value-weighted U.S. industry or international sector that rises over 100% in both raw and net of market returns over the prior two years, as well as 50% or more raw return over the prior five years. They define a crash as a 40% drawdown within a two-year interval. They also look at characteristics of industry/sector portfolios identified bubble candidates, including level and change in volatility, level and change in turnover, firm age, return on new versus old companies, stock issuance, book-to-market ratio, sales growth, price-earnings ratio and price acceleration (abruptness of price run-up). They evaluate timing strategies that switch from an industry portfolio to either the market portfolio or cash (with risk-free yield) based on a price run-up signal, or a signal that combines price run-up and other characteristics. Their benchmark is buying and holding the industry portfolio. Using value-weighted returns for 48 U.S. industries (based on SIC code) during January 1926 through March 2014 and for 11 international sectors (based on GICS codes) during October 1985 through December 2014, they find that:

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A subscriber asked for confirmation that the strategy described at “Meb Faber’s 12-Month High Switch” (the strategy) is attractive. This strategy at each monthly close:

• Calculates the 12-month high for: SPDR S&P 500 ETF Trust (SPY), iShares MSCI EAFE ETF (EFA), Vanguard Real Estate Index Fund ETF Shares (VNQ), SPDR Gold Shares (GLD) and Invesco Optimum Yield Diversified Commodity Strategy No K-1 ETF (PDBC) based on dividend-adjusted prices.
• Allocates 20% of the portfolio to each asset currently within 5% of its 12-month high.
• Allocates any remaining funds to iShares 7-10 Year Treasury Bond ETF (IEF) if IEF is within 5% of its 12-month high, or otherwise to cash with return estimated by the yield on 3-month U.S. Treasury bills (T-bill).
• Rebalances to equal weights each month and applies trading frictions of 0.1% per 1-way trade for any switches in holdings.

To investigate, we replicate the strategy, substituting Invesco DB Commodity Index Tracking Fund (DBC) for PDBC to get a longer sample period that includes the 2008-2009 financial crisis. We use an equal-weighted (EW), monthly rebalanced combination of SPY, EFA, VNQ, GLD and DBC as a benchmark. We apply 0.1% frictions to ETF switches but not to simple rebalances, which are generally small. Using monthly dividend-adjusted prices for SPY, EFA, VNQ, GLD and DBC during February 2006 (DBC inception) through June 2024, we find that: Keep Reading

Is industry trend-following an attractive strategy over the long run? In their June 2024 paper entitled “A Century of Profitable Industry Trends”, Carlo Zarattini and Gary Antonacci evaluate the long-term performance of a long-only industry trend-following (Timing Industry) strategy, modeled as follows:

• Entry – buy an industry when its daily closing price crosses above the upper band of either its 20-day Keltner Channel (with a multiplier of 2 for the high-low price range component) or its 20-day Donchian Channel.
• Sizing – each day for each open position, calculate 14-day past return volatility as an estimate of its future volatility and resize all open positions so that they contribute equally to overall portfolio volatility, limiting overall portfolio leverage to 200%.
• Exit – each day for each open position, close the position if it crosses below a stop loss represented by the lower band of either its 40-day Keltner Channel (again with a multiplier of 2 for the high-low price range component) or its 40-day Donchian Channel. However, do not ever lower the stop loss. When a position closes, reinvest proceeds into 1-month U.S. Treasury bills.

For a long-term test, they apply these rules to nearly 98 years of daily returns for 48 hypothetical annually rebalanced, capitalization-weighted industry portfolios constructed by assigning a Standard Industrial Classification (SIC) Code to each stock traded on NYSE, AMEX and NASDAQ. For a recent and more realistic test, they apply these rules to 31 sector exchange-traded funds (ETF) offered by State Street Global Advisors. Utilizing daily returns for the 48 industry portfolios since July 1926 and for the 31 sector ETFs as available (inceptions January 2005 to June 2018), all through March 2024, they find that:

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Is the Buffett Indicator, the ratio of total U.S. stock market capitalization (proxied by Wilshire 5000 Total Market Index 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 the level of the ratio is not a useful predictor. We therefore consider the following variables based on W5000/GDP as predictors of W5000 returns at horizons up to two years:

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 relating past changes in these variables to future W5000 returns up to two years; and, (2) a non-linear test calculating average next-quarter W5000 returns by ranked fifths (quintiles) of past changes in these variables. Using quarterly levels of W5000 (with extension), Shiller’s P/E10 lagged by one quarter and quarterly GDP lagged by one quarter during the first quarter of 1971 through the first quarter of 2024, we find that: Keep Reading

A subscriber asked: “Some pundits have noted that U.S. stocks have greatly outperformed foreign stocks in recent years. What does the performance of U.S. stocks vs. foreign stocks over the last N years say about future performance?” To investigate, we use the S&P 500 Index (SP500) as a proxy for the U.S. stock market and the ACWI ex USA Index as a proxy for the rest-of-world (ROW) equity market. We consider three ways to relate U.S. and ROW equity returns:

1. Lead-lag analysis between U.S. and ROW annual returns to see whether there is some cycle in the relationship.
2. Multi-year correlations between U.S. and next-period ROW returns, with periods ranging from one to five years.
3. Sequences of end-of-year high water marks for U.S. and ROW equity markets.

For the first two analyses, we relate the U.S. stock market to itself as a control (to assess whether ROW market behavior is distinct). Using monthly levels of the S&P 500 Index and the ACWI ex USA Index during December 1987 (limited by the latter) through April 2024, we find that: Keep Reading

Is momentum better measured by a granular fitted line or beginning-to-end return? In their March 2024 paper entitled “Trended Momentum”, Charlie Cai, Peng Li and Kevin Keasey investigate use of an analytically/visually clear linear stock price trend to enhance conventional momentum. They measure price trend clarity (TC) as R-squared for a regression of daily price versus date over the past 12 months. Specifically, they each month:

• Sort stocks into fifths (quintiles) based on conventional momentum, return from 12 months ago to one month ago.
• Further sort the top momentum quintile into finer quintiles based on TC.
• Form  equal-weighted or value-weighted portfolios of resulting sorts and compute their gross returns and 3-factor (market, size, book-to-market) alphas over the next six months.

To confirm use of TC to measure clarity of price trend, they separately conduct an experiment that relates analytical TC to trend clarity perceived by sample of 128 individuals each evaluating 10 pairs of stock charts. Their sample includes daily price data for U.S. common stocks from January 1927 through December 2020. Analyses requiring earnings start in 1964, while those involving investor sentiment span 1967 through 2018. They groom all variables to exclude outliers. In further analyses, they employ global stock price data. Using the specified methodology and data, they find that:

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Can day traders get rich with an Opening Range Breakout (ORB) strategy that buys (sells) unusually active stocks with positive (negative) opens that break out to new highs (lows) during the first five minutes of the trading day? In their February 2024 paper entitled “A Profitable Day Trading Strategy For The U.S. Equity Market”, Carlo Zarattini, Andrea Barbon and Andrew Aziz test a 5-minute ORB applied to stocks with unusually high daily trading volume (Stocks in Play). Rules for this strategy start with screening listed U.S. stocks for:

1. Opening price above $5.
2. Average daily trading volume at least 1,000,000 shares during the last 14 trading days.
3. Average True Range (ATR) over the last 14 days more than $0.50.
4. Opening range interval volume relative to the last 14 days (Relative Volume) at least 100% and among the 20 with the highest Relative Volumes.

Each day for each stock surviving this screen, they place a stop order to buy (sell) if the stock moves up (down) in the first five minutes and then again reaches the high (low) of this range after the first five minutes. For each executed trade, they set a stop-loss order at 10% ATR distance from the executed entry price. If the stop loss does not trigger intraday, they close the trade at the market close. They size each trade such that the loss on a triggered stop-loss would be 1% of capital deployed and impose a 4X leverage constraint. They assume $25,000 starting capital and impose $0.0035 per share commission (per Interactive Brokers Pro Tiered as of December 31, 2023). Using the specified data for all U.S.-listed stocks (over 7,000) during January 2016 through December 2023, they find that:

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“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 SPDR S&P 500 ETF Trust (SPY) as a tradable proxy for the stock market:

1. SPY – buy and hold SPY.
2. SMA200 – hold SPY (cash) when SPY closes above (below) its 200-day simple moving average (SMA200) the prior day.
3. TOTM – 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. SMA200 or TOTM – hold SPY when SPY closes above its 200-day SMA the prior day and otherwise use the TOTM strategy.

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 January 2024 and contemporaneous 3-month Treasury bill (T-bill) yields as the return on cash, we find that: Keep Reading

What are 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:

• Invesco 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 December 2023, we find that:

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“Distance Between Fast and Slow Price SMAs and Stock Returns” finds that extreme distance between a 21-trading day simple moving average (SMA21) and 200-trading day simple moving average (SMA200), as applied to individual U.S. stock price series, may be a useful stock return predictor. “Distance Between Fast and Slow Price SMAs and Country Stock Index Returns” finds that extreme distance between a 30-calendar day simple moving average and 300-calendar day simple moving average, as applied to country stock market indexes, may be a useful index return predictor. Do these findings apply the time series for the S&P 500 Index (SP500)? To investigate, we test relationships between the SMA21-SMA200 ratio for SP500, measured at month-ends, to SP500 future monthly returns. Using daily SP500 closing levels from the end of December 1927 through November 2023, we find that: Keep Reading