Technical Trading

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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Should qualified investors count on global macro (GM) and managed futures (MF, or alternatively CTA for commodity trading advisors) hedge funds to beat the market? In their November 2023 paper entitled “Global Macro and Managed Futures Hedge Fund Strategies: Portfolio Differentiators?”, Rodney Sullivan and Matthew Wey assess the performances of GM and MF hedge fund categories, defined as:

• GM – try to anticipate how political trends and global economic activity will affect valuations of global equities, bonds, currencies and commodities.
• MF – rely systematic trading programs based on historical prices/market trends across stocks, bonds, currencies and commodities.

For comparison, they also look at the long-short equity (LSE) hedge fund category. They decompose category returns into components driven by exposures to U.S. stock and bond market return factors, other factor premiums and unexplained alpha. They focus on how fund categories have changed since the 2008 financial crisis, emphasizing performances during market downtowns. Using index returns from Hedge Fund Research (equal-weighted) and Credit Suisse (asset-weighted) during January 1994 through December 2022, they find that:

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Is there an exploitable way to find which stocks lead and which stocks lag in returns? In their October 2023 paper entitled “Detecting Lead-Lag Relationships in Stock Returns and Portfolio Strategies”, Álvaro Cartea, Mihai Cucuringu and Qi Jin test three ways to measure and exploit linear and non-linear lead-lag relationships in individual stock returns via three steps:

1. Use daily returns for stocks over the last 60 trading days to compute all pairwise lead-lag relationships using one of two cross-correlation methods (linear relationships) or a third method based on the Levy-area of pairwise asset returns (linear and non-linear relationships).
2. Rank all stocks from strongest leaders to weakest followers for each of the three sets of pairwise relationships.
3. Each day for each set of ranks, if the sign of the average prior-day return of the strongest 20% of leader stocks is positive (negative):
   • Buy (sell) an equal-weighted portfolio of the weakest 20% of follower stocks.
   • Sell (buy) an offsetting position in SPDR S&P 500 (SPY) to approximate market neutrality.

Their universe each trading day is the top 25% of market capitalizations cross NYSE, NASDAQ and AMEX, excluding stocks with missing returns during the last 60 trading days. To test leader-follower relationship persistence, they consider also measurement frequencies of every two days, weekly, every two weeks, every three weeks and monthly. Using daily prices, market capitalizations, trading volumes and sectors of listed U.S. stocks during July 1963 through December 2022, they find that:

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