Technical Trading

A reader requested a test of the following hypothesis from the article “Gold’s Bluff – Is a 30 Percent Drop Next?” [no longer available]: “Ironically, gold is more than just a hedge against market turmoil. Gold is actually one of the most accurate indicators of the stock market’s long-term direction. The Dow Jones measured in gold is a forward looking indicator.” To test this assertion, we examine relationships between the spot price of gold and the level of the Dow Jones Industrial Average (DJIA). Using monthly data for the spot price of gold in dollars per ounce and DJIA over the period January 1971 through September 2025, we find that: Keep Reading

Can machine learning exploit many stock similarity factors to produce exceptional statistical arbitrage (pairs trading) performance? In their August 2025 paper entitled “Attention Factors for Statistical Arbitrage”, Elliot Epstein, Rose Wang, Jaewon Choi and Markus Pelger present the Attention Factor Model, which employs machine learning to:

1. Identify similar stocks based on both past returns and firm fundamentals (similarity factors).
2. Generate signals for temporary price divergences between similar stocks.
3. Set weighting/trading rules to exploit such price divergences.

Their model considers many similarity factors and the time series behaviors of these factors to maximize portfolio Sharpe ratio after transaction costs. They retrain the model each year on a rolling window of eight years of data, using the last two years of the first set of training data to select tuning parameters. We consider model variations that identify 1, 3, 5, 8, 10, 15, 30 or 100 similarity factors. They assume total costs of 0.05% one-way trading frictions and 0.01% shorting costs. They consider results of prior research as a benchmark. Using daily returns and 39 firm characteristics for the 500 largest U.S. stocks by month during January 1990 through December 2021, with model testing during January 1998 through December 2022, they 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 ten asset class exchange-traded funds (ETF), plus Cash:

• Invesco DB Commodity Index Tracking (DBC)
• iShares MSCI Emerging Markets Index (EEM)
• iShares JPMorgan Emerging Markets Bond Fund (EMB)
• iShares MSCI EAFE Index (EFA)
• SPDR Gold Shares (GLD)
• iShares Russell 2000 Index (IWM)
• iShares iBoxx $ Investment Grade Corporate Bond (LQD)
• 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 September 2025, we find that:

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“Compendium of Live ETF Factor/Niche Premium Capture Tests” summarizes results for its eponymous title. Here we add a live test of the short-term reversal effect among U.S. stocks. Specifically, we examine the performance of the now dead Vesper U.S. Large Cap Short-Term Reversal Strategy ETF (UTRN), designed to track the performance of a portfolio of 25 of the 500 largest U.S.-listed stocks most likely benefit from the short-term reversal effect. We use SPDR S&P 500 ETF Trust (SPY) as the benchmark. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for UTRN and SPY during September 2018 (UTRN inception) through March 2025 (UTRN death), we find that:

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How do hedge fund managers think about fundamental analysis versus technical analysis in managing their stock portfolios? In his July 2025 paper entitled “Portfolio Construction: Blending Fundamental and Technical Analysis”, Gregory Blotnick describes the interplay between fundamental and technical analyses in long/short equity portfolio construction from the perspective of a hedge fund with a high velocity of ideas. He includes case studies and technical screening exercises to illustrate the roles of momentum, valuation metrics and relative strength in idea generation, risk management and capital allocation. Based on his experience and examples, he concludes that: Keep Reading

Do the trading activities of especially informed equity and equity option traders predict stock returns? In the June 2025 revision of their paper entitled “An Information Factor: What Are Skilled Investors Buying and Selling?”, Matthew Ma, Xiumin Martin, Matthew Ringgenberg and Guofu Zhou construct an information factor (INFO) using the trades of corporate insiders, short sellers and option traders. Specifically, they each month for each stock calculate:

• To inform the long side of the INFO factor portfolio, net insider purchases (purchases minus sales).
• To inform the short side of the INFO factor portfolio:
  • Short interest (number of shares shorted divided by shares outstanding).
  • Option trading (total option volume divided by total stock volume).
• For each of these three metrics, assign a rank from 1 to 100, with higher rank indicating higher level of positive private information.
• Average the three ranks to compute an information score.
• Reform 10 equal-weighted (decile) portfolios of stocks sorted by information score, with the INFO factor portfolio long the top decile and short the bottom.
• Hold the portfolios for one month.

They assess the impact of stock trading frictions by assuming costs equal to half the respective effective bid-ask spreads. Using insider trading, short interest and option/stock trading volumes during January 1996 through December 2019, they find that: Keep Reading

How does the performance of the U.S. stock market compare to that of the aggregated stock markets in the rest of the world over the long run? Is there alternating leadership? To investigate, we use the S&P 500 Index (SP500) as a proxy for the U.S. stock market and the World ex USA Index in U.S. dollars as a proxy for the rest-of-world  equity market(ROW). We consider three ways to relate U.S. and ROW equity returns:

1. Basic return statistics/cumulative performances.
2. Lead-lag analysis between U.S. and ROW annual returns to see whether there is some cycle in the relationship (with the U.S. stock market compared to itself as a control).
3. Sequences of end-of-year high water marks for U.S. and ROW equity markets.

Using annual SP500 and ROW levels during December 1969 (limited by ROW) through December 2024, we find that: Keep Reading

Is trend-following generally an attractive strategy for crypto-assets? In their April 2025 paper entitled “Catching Crypto Trends; A Tactical Approach for Bitcoin and Altcoins”, Carlo Zarattini, Alberto Pagani and Andrea Barbon test a long-only trend-following strategy on Bitcoin. They then extend the strategy to all cryptocurrencies listed for at least one year since 2015 with median daily trading volume of at least $2 million over the preceding 30 days. Their base strategy employs a daily ensemble of short-term and long-term trend signals based on the maximum and minimum closes over the last 5, 10, 20, 30, 60, 90, 150, 250 or 360 days, and the midpoints between them, as follows:

• For each lookback interval and each asset, open a position whenever daily closing price crosses above the maximum for the lookback interval.
• Close each open position based on a daily trailing stop that is the higher of the prior-day trailing stop and the midpoint of maximum and minimum closes over the associated lookback interval.
• Resize each open position daily to 25% target annualized volatility (25% divided by annualized 90-day standard deviation of returns), with leverage capped at 200%.
• Reform each day an equal-weighted ensemble portfolio of open positions for all lookback intervals.

They consider transaction costs of 0.10%, 0.25% and 0.50% and propose a way to mitigate impact of these costs. They also analyze whether crypto-asset trend-following returns diversify trend-following returns for traditional asset classes. Using survivorship bias-free open, high, low, close and volume data aggregated across exchanges for 21,616 individual crypto-assets during January 2010 through mid-March 2025, they find that:

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What are the implications of stock trading volume spikes for near-term returns? In their February 2025 paper entitled “Volume Shocks and Overnight Returns”, Álvaro Cartea, Mihai Cucuringu, Qi Jin and Mungo Wilson study the effects of stock trading volume shocks during normal trading hours on subsequent overnight and next-day returns. For each stock each day, they identify volume shocks as unusually high or low values of daily volume during normal hours (open-to-close) divided by the exponential moving average of daily volume with 60-day half-life, minus one. They then sort stocks by this metric into fifths, or quintiles, and calculate subsequent overnight (close-to-open) and next-day (open-to-close) gross annualized returns and Sharpe ratios for equal-weighted or value-weighted quintile portfolios. To ensure exploitability, they then employ five linear and machine learning models (trained on data through 2015) to forecast volume shocks and construct long-only portfolios to capture the overnight returns associated with prior-day volume spikes. Using daily trading volume and trading day/overnight price data for all NYSE/AMEX/NASDAQ common stocks during January 2000 through December 2022, they find that:

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What is the state of formal research on cryptocurrency investment strategies? In his April 2025 paper entitled “Quantitative Alpha in Crypto Markets: A Systematic Review of Factor Models, Arbitrage Strategies, and Machine Learning Applications”, William Mann synthesizes over two dozen peer-reviewed studies on systematic cryptocurrency trading strategies spanning 2018-2025. He categorizes studies as:

1. Arbitrage and statistical arbitrage (spot-futures, cross-exchange, pairs trading).
2. Factor-based investing (factor models, trend-following, diversification).
3. Sentiment and behavioral modeling (news sentiment, social sentiment).
4. Volatility forecasting (autoregression, machine learning).
5. Algorithmic trading and price prediction (machine learning, deep learning, specialized metrics).

He includes implementation aids in the form of modular Python code for backtesting and a bibliography of published research. Based on the body of relevant formal research, he concludes that:

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