Technical Trading

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

“Distance Between Fast and Slow Price SMAs and Stock Returns” finds that extreme distance between a 21-trading day simple moving average (SMA) and 200-trading day SMA, as applied to individual U.S. stock price series, may be a useful return predictor. Does this finding apply to non-U.S. stock market indexes? In their December 2023 paper entitled “Market Timing with Moving Average Distance: International Evidence”, Menachem Abudy, Guy Kaplanski and Yevgeny Mugerman test ability of the distance between fast and slow SMAs to predict future returns across 92 international stock market indexes. Specifically, they each month:

• Measure moving average distance (MAD) for each index as the ratio of its 30-calendar day SMA to its 300-calendar day SMA in local currencies.
• Sort the indexes according to MAD into fifths (quintiles) or tenths (deciles).
• Reform an equal-weighted hedge portfolio that is long indexes in the top quintile or decile with MAD values above one and short indexes in the bottom quintile or decile with MAD values below one.
• Adjust portfolio returns to U.S. dollars via local currency exchange rates.

They consider the full sample of 92 indexes and three subsamples: (1) 46 countries with the highest United Nations development ratings; (2) the MSCI 25 developed markets; and, (3) the MSCI 30 emerging markets. Their benchmarks are buy-and-hold the MSCI World Index (large and mid-size firms in 23 developed countries) and the S&P Global 1200 (30 markets representing about 70% of global market capitalization). Using daily levels of 92 international stock market indexes as available since June 1980, associated U.S. dollar exchange rates and international stock factor model returns, all through November 2020, they find that: Keep Reading

Does degree of difference between fast and slow simple moving averages (SMA) for a stock price series predict future stock return? In the December 2023 revision of their paper entitled “Moving Average Distance as a Predictor of Equity Returns”, Doron Avramov, Guy Kaplanski and Avanidhar Subrahmanyam test distance between a 21-day SMA (SMA21) and 200-day SMA (SMA200) for the stock price series as a return predictor. Specifically, they each month:

• Calculate for each stock the SMA21-to-SMA200 ratio.
• Sort stocks into tenths (deciles) by this ratio.
• Calculate the standard deviation of the ratio across all stocks.
• Select stocks from the top decile with ratios greater than one plus one standard deviation for a long portfolio. Select stocks from the bottom decile with ratios less than one minus one standard deviation for a short portfolio. 
• Specify the Moving Average Distance (MAD) for a stock as 1 if it is in the long portfolio, -1 if it is in the short portfolio, and 0 otherwise. Stocks in the two portfolios are market capitalization-weighted.

They then assess the magnitude and reliability of MAD portfolio performance. They estimate breakeven trading frictions for MAD portfolios based on zero alpha relative to different factor models of stock returns. To assess uniqueness of MAD indications, they control for 18 firm characteristics and several technical indicators. Using daily prices adjusted for splits and dividends for publicly traded U.S. common stocks priced at least $5 during July 1977 through December 2018, they find that:

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Are return reversals especially strong for lottery stocks? In their October 2023 paper entitled “Maxing Out Short-term Reversals in Weekly Stock Returns”, Chen Chen, Andrew Cohen, Qiqi Liang and Licheng Sun investigate return reversals for lottery stocks, those with high recent maximum daily returns (MAX). Specifically, for their main calculations, they each week:

• For each stock, calculate MAX during the week before last.
• Sort stocks into fifths (quintiles) based on MAX values.
• Within each MAX quintile, further sort stocks based on their last-week returns.

They then use these sorts to explore interactions between effects of past MAX and effects of past returns on next-week returns. Using weekly returns for U.S. common stocks during July 1963 to December 2022, they find that: Keep Reading

Does market volatility predictably affect returns to simple moving average (SMA) trend-following strategies? In their November 2023 paper entitled “Market Volatility and the Trend Factor”, Ming Gu, Minxing Sun, Zhitao Xiong and Weike Xu investigate how stock market volatility affects multi-SMA trend factor profitability. They first assess significance of the trend factor premium, as follows:

• For each stock at the close on the last trading day of each month:
  • Compute SMAs of prices for lookback intervals of 3, 5, 10, 20, 50, 100, 200, 400, 600, 800 and 1000 trading days, and divide each SMA by the end price.
  • Starting five years into the sample period (1931), regress next-month stock returns on corresponding monthly SMA ratios over the past 60 months.
  • Average the SMA ratio regression coefficients separately over the past 12 months to estimate next-month coefficients and apply these coefficients to estimate next-month return.
• At the end of each month, sort all stocks into tenths, or deciles, based on estimated next-month returns and form a trend factor hedge portfolio that is long (short) the equal-weighted top (bottom) decile. The trend factor premium is the monthly gross return for this portfolio.

They then assess how trend factor hedge portfolio returns interact with monthly stock market return volatility (standard deviation of monthly value-weighted market returns over the past 12 months) by specifying volatility has high or low when its prior-month value is above or below the full-sample median. Using data for all listed U.S. common stocks, excluding those priced below $5 or in the lowest tenth of NYSE market capitalizations, during January 1926 through December 2022, they find that:

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