Technical Trading

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):

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

We use State Street SPDR S&P 500 ETF Trust (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 April 2026, we 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 2025, we find that: Keep Reading

Is there a way to enhance time series momentum by considering both trend regime and expected risk-adjusted performance during each state? In his March 2026 paper entitled “Rethinking Trend Following: Optimal Regime-Dependent Allocation”, Valeriy Zakamulin describes and tests his optimal regime-dependent allocation (OPT) strategy, which:

1. Determines the trend following regime based on either 2-regime (Bull/Bear) or 4-regime (Bull/Correction/Bear/Rebound) models.
2. Assigns asset exposures that historically maximize gross Sharpe ratio during each regime, with 100% long for the regime with the highest estimated Sharpe ratio and exposures scaled down across other regimes according to their relative signal-to-noise ratios. Regimes with sufficiently negative estimates may reach 100% short, but regimes with weak/very noisy signals have weights close to zero.

Backtests employ data:

• Since July 1926 for value-weighted U.S. equity indexes for the overall market, the largest and smallest fifths of stocks, and the fifths of stocks with the highest and lowest book-to-market ratios.
• Since January 1975 (1977 for Canada) for value-weighted developed market equity indexes of 14 other countries.
• For robustness, since July 1963 for 18 U.S. stock portfolios formed on multiple factors/firm characteristics.

Sharpe ratio estimates derive from expanding windows of historical training data initially through 1968 for the long U.S. samples, December 2003 for the other country samples and 1997 for the U.S. factor/firm characteristics portfolios. Using the specified datasets through December 2025, he finds that:

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A subscriber suggested adding a simple moving average (SMA) timing rule to “Top 5 or Top 10 NASDAQ 100 Momentum Stocks?” in order to suppress relatively deep maximum drawdowns (MaxDD). To investigate, we consider SMAs for Invesco QQQ Trust (QQQ) ranging from two months to 24 months. We then hold Top 5 or Top 10 (3-month U.S. Treasury bills, T-bills) when prior-month QQQ is above (below) its SMA. As key performance metrics, we use gross compound annual growth rate (CAGR), MaxDD and Sharpe ratio with average monthly yield on T-bills during a year as the risk-free rate for that year. Using monthly Top 5 and Top 10 portfolio returns and T-bill yields since January 2008 and end-of-month dividend-adjusted QQQ prices since February 2006, all through January 2026, we find that: Keep Reading

Are there cryptocurrencies that are so alike that they generally track each other and reliably revert whenever they diverge? In their December 2025 paper entitled “Pairs Trading in Crypto”, Sasha Stoikov, Dora Xu, Shijie Shao, Yourui Wang, Tongshu Zhang and Jinxuan Hu show how to identify cryptocurrency pairs with stable relationships and execute mean reversion strategies in real time. Specifically, they:

• Identify pairs to trade by combining correlation behaviors, structural metadata and stability diagnostics.
• Generate entry thresholds, exit rules and risk controls (stop-loss, pair suspension and pair abandonment) for long-short trades that exploit overvaluation and undervaluation of pairs based on rolling window divergences.

Starting with hourly data for a sample of 543 cryptocurrency perpetual futures contract series (147,153 potential pairs), with 800 days through February 2025 as a training set and March through September 2025 as a test set (plus some short live tests during late November and early December 2025), they find that:

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A subscriber asked for verification of the finding in “Is Buying Stocks at an All-Time High a Good Idea?” that it is not only a good idea, but a great one, including comparison to a moving average crossover rule. To investigate, we use the S&P 500 Index as a proxy for the U.S. stock market and test a strategy that holds SPDR S&P 500 ETF Trust (SPY) when the S&P 500 Index stands at an all-time high at the end of last month and otherwise holds Vanguard Long-Term Treasury Fund Investor Shares (VUSTX). We compare results to buying and holding SPY, buying and holding VUSTX, and holding SPY (VUSTX) when the S&P 500 Index is above (below) its 10-month simple moving average (SMA10) at the end of last month. We assume 0.1% switching frictions. We compute average net monthly return, standard deviation of monthly returns, net monthly Sharpe ratio (with monthly T-bill yield as the risk-free rate), net compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key strategy performance metrics. We calculate the number of switches for each scenario to indicate sensitivities to switching frictions and taxes. Using monthly closes for the S&P 500 Index, SPY and VUSTX during January 1993 (inception of SPY) through January 2026, we find that:

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Does adding a position take-profit (stop-gain) rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by harvesting some upside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-gains, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return rises above a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month maximum returns for the specified assets during February 2006 through January 2026, we find that: Keep Reading

Does adding a position stop-loss rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by avoiding some downside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-losses, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return falls below a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month drawdowns for the specified assets during February 2006 through January 2026, we find that: Keep Reading

Is there a way to amplify the MAX overpricing anomaly (measured as the average of the five highest daily returns for a stock over the past month), which is driven by the desire of some investors for a lottery-like payoff? In their January 2026 paper entitled “MAX on Steroids: A New Measure of Investor Attraction to Lottery Stocks”, Baris Ince, Turan Bali and Han Ozsoylev introduce MAXᵝ as a variable that distills lottery-seeking behavior by removing the systematic return component from MAX. Specifically, they each month:

1. Sort stocks into tenths (deciles) based on their market betas as measured by a rolling window of 252 daily returns.
2. Within each beta-sorted decile, sort stocks based on MAX.

They then focus on excess returns (relative to U.S. Treasury bills) and factor model alphas of the value-weighted extreme deciles of MAX aggregated across beta deciles, plus a hedge portfolio that is long (short) the stocks in the highest (lowest) decile. Using daily returns and ownership data for U.S. listed common stocks, excluding utility/financial stocks and stocks priced under $5, during January 1968 through December 2022, they find that:

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Do recent daily returns for a stock reliably predict its near-term performance? In their January 2026 paper entitled “A Unified Framework for Anomalies based on Daily Returns”, Nusret Cakici, Christian Fieberg, Gabor Neszveda, Robert Bianchi and Adam Zaremba relate the distribution of last-month (21 trading days) daily returns to next-month return without imposing functional forms, via elastic-net regression. They re-estimate the relationship annually using inception-to-date training data. Their approach considers two aspects of past returns:

1. A chronological component that captures the sequence of returns, typically associated with short-term price pressure and liquidity effects.
2. A rank-based component that captures how extreme returns are, commonly linked to behavioral distortions.

They combine these two components into a single Daily Return Information (DRI) signal and compute returns for its corresponding long-short factor, the Daily Return Information Factor (DRIF). The DRIF portfolio is each month long (short) the tenth, or decile, of stocks with the strongest (weakest) expected returns based on DRI. Using monthly firm characteristics and associated daily returns for a broad sample of U.S. stocks, excluding those priced under $5 at end of month and those in the bottom 1% of NYSE market capitalizations, during January 1937 through December 2024, they find that:

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