Momentum#
Momentum strategies exploit the tendency for assets that have recently outperformed to continue outperforming, and for recent laggards to continue lagging. This phenomenon is one of the most robust and well-documented anomalies in finance, persisting across asset classes, geographies, and time periods spanning over a century. Researchers distinguish between time-series momentum — where an asset’s own past return predicts its future return — and cross-sectional momentum, which ranks assets relative to each other. Both forms have been documented in equities, currencies, futures, commodities, and cryptocurrencies.
The papers and posts collected here span the full range of momentum research: foundational academic work establishing the statistical evidence, practitioner-focused studies on crypto and equity market implementation, and applied frameworks for combining momentum with volatility targeting, regime filters, and risk-adjusted ranking. Key topics include dual momentum, lookback period selection, the relationship between momentum and behavioral biases, and the question of whether momentum is truly a risk premium or an exploitable inefficiency.
Related topics include Trend Following, which shares conceptual roots but focuses on time-series signals and longer-horizon systematic programs, Equity Factors for momentum within factor model contexts, and Mean Reversion, which represents the opposite force and is often combined with momentum for balanced portfolio construction.
Momentum and trend following trading strategies for currencies and bitcoin#
Momentum trading strategies are thoroughly described in the academic literature and used in many trading strategies by hedge funds, asset managers, and proprietary traders. Baz et al. (2015) describe a momentum strategy for different asset classes in great detail from a practitioner’s point of view. Using a geometric Brownian Motion for the dynamics of the returns of financial instruments, we extensively explain the motivation and background behind each step of a momentum trading strategy. Constants and parameters that are used for the practical implementation are derived in a theoretical setting and deviations from those used in Baz et al. (2015) are shown. The trading signal is computed as a mixture of exponential moving averages with different time horizons. We give a statistical justification for the optimal selection of time horizons. Furthermore, we test our approach on global currency markets, including G10 currencies, emerging market currencies, and cryptocurrencies. Both a time series portfolio and a cross-sectional portfolio are considered. We find that the strategy works best for traditional fiat currencies when considering a time series based momentum strategy. For cryptocurrencies, a cross-sectional approach is more suitable. The momentum strategy exhibits higher Sharpe ratios for more volatile currencies. Thus, emerging market currencies and cryptocurrencies have better performances than the G10 currencies. This is the first comprehensive study showing both the underlying statistical reasons of how such trading strategies are constructed in the industry as well as empirical results using a large universe of currencies, including cryptocurrencies.
Momentum trading in cryptocurrencies: short-term returns and diversification benefits#
This paper studies J/K momentum strategies in cryptocurrency markets, examining whether buying recent winners and selling recent losers generates risk-adjusted returns. Using DCC correlation models to analyze portfolio diversification, the authors document positive momentum payoffs in crypto markets and explore how these strategies interact with traditional asset portfolios.
Pure Momentum in Cryptocurrency Markets#
Momentum is one of the most widespread, persistent, and puz- zling phenomenon in asset pricing. The prevailing explanation for momentum is that investors under-react to new information, and thus asset prices tend to drift over time. We use a unique fea- ture of cryptocurrency markets: the fact that they are open 24/7, and report returns over the last 24 hours. Thus, the one-day re- turn is subject to predictable fluctuations based on the removal of lagged information. We show that investors respond positively to changes in reported returns that are unrelated to any new release of information, or change in the asset fundamentals. We call this behavioral anomaly “Pure Momentum”.
Optimizing the Persistence of Price Momentum: Which Trends Are Your Friends?#
The traditional wisdom that price momentum which ranks stocks’ raw trailing returns is crash-prone fails to differentiate the various drivers of stocks’ past performances. As such, we compare the persistence of different sources of stocks’ price momentum discerned from applying factor-based performance attribution to their trailing 12-month returns. Our empirical analysis shows that beta- and country-driven price trends were not robust while style and industry momentum persisted both over the intermediate and, more strongly, short-term. Stock-specific momentum persisted over the intermediate term but strongly reverted over the short term; it was subsumed as a stand-alone strategy by both industry and style momentum and should be downweighed when optimizing a momentum signal for persistence. Our results suggest that style momentum is mostly a proxy for static factor tilts while industry and stock-specific momentum appear a separate anomaly that is strongest conditional on low-volatility market regimes. Their premium may reflect investor underreaction to economic shifts to which stocks’ exposures are imperfectly captured by binary industry classifications. Our results corroborate a strand of the extant literature through the novel lens of exactly decomposing the cross-section of stocks’ price momentum; contradicting findings are explained by methodological differences.
All Days Are Not Created Equal: Understanding Momentum by Learning to Weight Past Returns#
This paper introduces a flexible framework for weighting past returns when constructing momentum signals, challenging the standard equal-weighting of each month in a formation window. The authors learn optimal past-return weights from data, showing that simple modifications to return weighting can significantly improve momentum signal quality and out-of-sample performance.
Beat the Market: An Effective Intraday Momentum Strategy for S&P500 ETF (SPY)#
This paper investigates the profitability of a simple yet effective intraday momentum strategy applied to SPY, one of the most liquid ETFs tracking the S&P 500. Unlike academic literature that typically limits trading to the last 30 minutes of the trading session, this model initiates trend-following positions as soon as there is an indication of abnormal demand/supply imbalance in the intraday price action. The strategy introduces dynamic trailing stops to mitigate downside risks while allowing for unlimited upside potential. From 2007 to early 2024, the resulting intraday momentum portfolio achieved a total return of 1,985% (net of costs), an annualized return of 19.6%, and a Sharpe Ratio of 1.33.
By Carlo Zarattini, Andrew Aziz, and Andrea Barbon.
Mentioned by Pasta Capital in this discussion.
Risk-adjusted Momentum Strategy Construction and Industry Heterogeneity Analysis Based on STARR Indicator#
This paper proposes a risk-adjusted momentum strategy using the STARR (Stable Tail-adjusted Return Ratio) indicator — a metric that replaces standard deviation in the Sharpe ratio with Conditional Value at Risk (CVaR) to better capture downside tail risk. The study constructs both Sharpe-based and STARR-based momentum factors across industry-level ETFs from the S&P 500 and Nikkei 225, applying mean-variance optimisation to monthly data spanning 2010–2025.
The STARR-based strategy demonstrates superior downside risk control compared to conventional Sharpe-based momentum, particularly during extreme market conditions such as the COVID-19 crisis. Performance varies significantly across sectors and volatility regimes, confirming meaningful industry heterogeneity in momentum returns. The strategy maintains robustness through alternative parameter configurations and cross-market validation between U.S. and Japanese equities, suggesting that incorporating downside-sensitive metrics like CVaR into momentum signal construction can enhance risk-adjusted returns and improve portfolio stability in diverse market environments.
By Xupeng Zhang (McGill University).
A Tale of Two Anomalies: The Implications of Investor Attention for Price and Earnings Momentum#
This paper studies how investor attention amplifies both price momentum and earnings momentum, finding that assets receiving less investor attention tend to exhibit stronger momentum effects. The results connect momentum to the gradual information diffusion hypothesis, where attention constraints slow the market’s incorporation of fundamental information.
Time Series Momentum#
Tobias J. Moskowitz, Yao Hua Ooi, and Lasse Heje Pedersen (Journal of Financial Economics, 2012) document a remarkably broad time-series momentum effect across 58 liquid futures and forward contracts spanning equity indices, sovereign bonds, currencies, and commodities. Using a security’s own past return rather than its return relative to peers, they show that return persistence is strong from one to twelve months and then partially reverses at longer horizons.
Our summary: this is the canonical modern TSMOM paper. It matters because it separates “trend following” from the usual cross-sectional winner-minus-loser construction and shows that a security’s own lagged return has predictive power across very different asset classes. The paper also argues that most of the effect comes from positive auto-covariance rather than lead-lag structure across assets, and it links the profits to speculators effectively riding the trend while hedgers absorb the other side. If you need one foundational citation for multi-asset trend following, this is it.
Key metrics: the paper reports positive 12-month time-series momentum profits for all 58 contracts in the sample. A diversified TSMOM portfolio earns a Sharpe ratio greater than 1 on an annual basis, roughly 2.5x the Sharpe ratio of the equity market, with little correlation to passive asset-class benchmarks or standard pricing factors.
Time Series Momentum and Volatility Scaling#
This paper decomposes the returns of time-series momentum strategies to show that volatility scaling — adjusting position sizes inversely to recent volatility — is the primary source of their superior risk-adjusted performance. The authors demonstrate that the constant-volatility version of TSMOM outperforms the raw version, providing theoretical and empirical support for volatility-targeted implementation.
Is Momentum Really Momentum?#
Robert Novy-Marx (Journal of Financial Economics, 2012) argues that the classic momentum effect is driven much more by intermediate-horizon returns than by very recent continuation. In his formulation, what investors usually call “momentum” often looks more like an echo from returns earned roughly 12 to 7 months before portfolio formation than a clean near-term persistence effect.
Our summary: this paper matters because it reframes momentum from a single monolithic anomaly into a term-structure question. If intermediate-horizon returns do the heavy lifting while the most recent month is polluted by reversal, then naive short-lookback momentum rules are badly specified from the start. That is exactly why this paper remains important background for anyone experimenting with very short-horizon trend signals: it explains why some “momentum” setups fail even when the broader anomaly is real.
Key metrics: Novy-Marx finds that strategies based on firms’ performance 12 to 7 months before formation are more profitable than strategies based on the recent 6 to 2 months, especially among the largest and most liquid stocks. He also shows that similar intermediate-horizon patterns appear in international equity indices, commodities, and currencies.
Time Series Momentum: Is It There?#
Dashan Huang, Jiangyuan Li, Liyao Wang, and Guofu Zhou (Journal of Financial Economics, 2020) reexamine the Moskowitz-Ooi-Pedersen evidence using the same broad futures data set but a more skeptical statistical lens. They argue that pooled regressions overstate the strength of TSMOM because of fixed-effect issues, persistent predictors, and the interaction between volatility scaling and heterogeneous asset means.
Our summary: this is the main empirical challenge paper in the TSMOM debate. The authors’ point is not that trend-following strategies cannot make money, but that the specific claim of statistically reliable return predictability from past 12-month returns is much weaker than it first appears once you examine assets individually and use bootstrap-adjusted inference. Even if one ultimately disagrees with the conclusion, this paper is essential because it raises the methodological bar for what counts as convincing TSMOM evidence.
Key metrics: in asset-by-asset regressions, 47 of 55 assets have t-statistics below 1.65 and only 3 assets deliver significant out-of-sample R^2 at the 10% level. In pooled regressions the headline t-statistic is 4.34, but the paper reports bootstrap critical values of 12.53 and 4.83, implying the pooled result is not statistically significant under their more conservative tests.
Bitcoin Intraday Time-Series Momentum#
Dehua Shen, Andrew Urquhart, and Pengfei Wang study whether Bitcoin exhibits an intraday time-series momentum effect despite trading continuously around the clock. Because Bitcoin has no natural exchange open and close like equities, the paper defines effective trading sessions using volume spikes and then tests whether returns early in the trading day predict returns later in the same day.
Our summary: this is the closest academic match for sub-daily BTC trend-following questions. The paper shows that intraday momentum in Bitcoin is not just a stylized-fact curiosity; it has practical trading content and seems strongest when market activity is elevated. Equally important, the authors test competing mechanisms and conclude the effect is more consistent with liquidity provision, disposition effects, and aversion to overnight risk than with a simple late-information story.
Key metrics: the first half-hour of Bitcoin trading significantly predicts the last half-hour in both in-sample and out-of-sample tests. The effect is strongest during sessions with the highest trading volume or volatility, and the authors report substantial economic gains from intraday momentum-based trading, especially during Bitcoin downturns.
Value and Momentum Everywhere#
AQR’s landmark cross-asset study documenting value and momentum premia across 8 major asset classes including equities, bonds, currencies, and commodities. The paper shows that momentum and value are pervasively positive across all asset classes and that they are negatively correlated with each other, making them natural complements in a diversified systematic portfolio.
By Clifford S. Asness, Tobias J. Moskowitz, and Lasse Heje Pedersen.
Cryptocurrencies and Momentum#
This paper tests classic cross-sectional momentum strategies across a large universe of 143 cryptocurrencies from 2014 to 2018. The authors find that for the broad universe, winner-minus-loser returns are close to zero and generally insignificant, challenging the straightforward application of equity momentum strategies to crypto markets. Some trimmed-sample specifications produce negative rather than positive momentum returns.
Dynamic Time Series Momentum of Cryptocurrencies#
Oliver Borgards (North American Journal of Economics and Finance, 2021) investigates momentum effects across twenty cryptocurrencies relative to US equities using a dynamic modelling framework that evaluates momentum periods following formation periods at both daily and intraday frequencies. The paper documents that formation periods frequently precede momentum periods across all time scales, and that cryptocurrencies exhibit notably stronger and longer momentum periods than equities.
Our summary: this paper provides strong evidence that time-series momentum in crypto is not only present but qualitatively different from the equity version — stronger, longer-lasting, and more exploitable. The author attributes the amplification to the difficulty of valuing intrinsic worth in crypto assets, which attracts more noise traders and delays mean-reversion. A momentum-based trading strategy outperforms buy-and-hold for both asset classes, but only the cryptocurrency strategy delivers superior risk-adjusted returns and diminished downside exposure. The identification of critical price thresholds where volatility spikes temporarily and triggers directional price movements is a useful practical detail for signal design.
Key metrics: the paper reports that momentum strategies outperform buy-and-hold for both cryptocurrencies and the stock market index, but only crypto momentum strategies generate higher risk-adjusted returns and lower downside exposure than a passive investment. Formation periods are followed by momentum periods at both daily and intraday frequencies across all twenty cryptocurrencies studied.
A Trend Factor for the Cross Section of Cryptocurrency Returns#
Christian Fieberg, Gerrit Liedtke, Thorsten Poddig, Thomas Walker, and Adam Zaremba (Journal of Financial and Quantitative Analysis) propose CTREND, a new trend factor for cryptocurrency returns that aggregates price and volume information across different time horizons. Using data on more than 3,000 coins and machine learning methods to exploit information from various technical indicators, they show that the resulting signal reliably predicts cryptocurrency returns, cannot be subsumed by known factors, and remains robust across different subperiods, market states, and alternative research designs.
Our summary: this is one of the most thorough cross-sectional crypto trend studies to date. The key practical finding is that the signal survives transaction costs and persists in big and liquid coins — answering the perennial concern that crypto momentum results depend on trading micro-caps that cannot actually be traded. By using ML to combine multiple technical indicators across horizons rather than relying on a single lookback, the paper also demonstrates that the crypto trend premium has a richer information structure than simple price momentum. An asset pricing model that incorporates CTREND outperforms competing factor models, making it a strong candidate for any crypto factor framework.
Key metrics: the CTREND factor generates statistically significant returns that survive transaction costs and remain robust in large and liquid coins. The effect persists across different subperiods and market states, and an asset pricing model incorporating CTREND provides a superior explanation of cryptocurrency returns compared to competing factor models.
Nonlinear Time Series Momentum#
We document a persistent nonlinear relationship between price trends and risk-adjusted returns across markets and asset classes that is consistent with asset pricing theory. Nonlinearities in time series momentum are consistent with past returns reflecting information about conditional expected returns, in line with investors using conditioning information to form efficient portfolios. Machine learning techniques are useful in uncovering these relationships and yield economically and statistically significant out-of-sample improvements in time series momentum strategies.
Simple Crypto Momentum Strategy: Buying Corrections#
A straightforward crypto trading approach shared by Pavel demonstrating that simple strategies can work effectively in cryptocurrency markets. The strategy involves: buying corrections on the highest momentum coins, holding positions for one day, and applying a basic regime filter to avoid unfavorable market conditions.
The equity curve shown suggests consistent performance with this systematic approach. The strategy exemplifies how basic momentum and mean-reversion concepts can be combined—buying dips in strong trending assets while using regime filtering to stay out during adverse market conditions.
By Pavel (Robuxio).
A Delta-Neutral Cross-Sectional Momentum Strategy in Crypto#
Research Article #73 from Trading Research Hub. Explores a delta-neutral cross-sectional momentum strategy applied to cryptocurrency markets. Cross-sectional momentum ranks assets against each other based on their relative performance, going long the strongest performers and short the weakest to create a market-neutral portfolio.
The strategy aims to capture the momentum premium in crypto while eliminating directional market exposure. By maintaining delta neutrality, the approach seeks to profit from the spread between winners and losers regardless of overall market direction, making it particularly relevant for volatile crypto markets where directional risk can overwhelm individual alpha signals.
By Pedma.
Systematic Momentum in Crypto: 249% Returns with 82% Less Volatility#
Research Article #61 from Trading Research Hub. Presents a systematic momentum strategy that delivered 249% returns with 82% less volatility than the broader crypto market. The article addresses the core challenge of crypto investing: extreme drawdowns exceeding 90% and volatility above 80% that make buy-and-hold approaches impractical for most investors.
The strategy uses a systematic momentum approach with volatility targeting to deliver steady growth while avoiding the worst of crypto’s drawdown episodes. The article demonstrates how disciplined risk management can capture a significant portion of crypto’s upside while making the return stream suitable for investors who cannot tolerate the asset class’s native risk profile.
By Pedma.
Applying Faber’s Relative Strength Framework to Cryptocurrency Markets#
Research Article #57 from Trading Research Hub. Applies the relative strength investing framework from Mebane Faber’s 2010 paper “Relative Strength Strategies for Investing” to cryptocurrency markets. The study tests whether the momentum-based ranking and allocation rules that have worked in traditional markets translate to the unique dynamics of crypto assets.
By Pedma.
Harvesting Risk Premia Through Dual Momentum: Reviewing Antonacci’s Framework#
Research Article #23 from Trading Research Hub. An in-depth review of Gary Antonacci’s 2012 paper on dual momentum, which combines relative momentum (comparing assets against each other) with absolute momentum (an asset’s own past performance) to harvest risk premia across asset classes.
The article explains how momentum has been documented across equities, currencies, bonds, and other asset classes, while acknowledging that the reasons for its persistence remain debated between rational (risk premia) and behavioral (herding, anchoring) explanations. The dual momentum framework is analyzed for its applicability to cryptocurrency portfolio construction and systematic trading strategies.
By Pedma.
A Simple Monthly Momentum Strategy for US Stocks#
A beginner-friendly systematic trading strategy using monthly momentum on Nasdaq 100 stocks. The approach focuses on robustness over complexity, framing the strategy as premium harvesting of beta rather than alpha mining — fewer moving parts mean fewer things that can break compared to overoptimized, datamined strategies.
The exact rules: trade only when NDX is in an uptrend (Close > MA200), filter the Nasdaq 100 universe for stocks also in uptrend with positive 6-month returns, rank by 6-month return once a month, buy the top 5 equally weighted at 20% each, hold for one month, then repeat. No stop loss, no profit target. The author positions this as a solid core sleeve for a systematic portfolio that can be improved by combining it with uncorrelated strategies like long mean reversion or by adding better filters and ranking criteria.
By Petr Podhajsky.
Gold Momentum Strategy#
A simple dual-momentum approach for timing gold investments. The strategy combines 12-month total return signals from both gold (GLD) and 10-year U.S. Treasuries (IEF) to filter out weak periods and reduce drawdowns.
The trading rules are straightforward: when both gold and Treasury returns are positive at month-end, go long gold; if either is negative, stay in cash. This deliberate simplicity—with minimal parameters—reduces the risk of overfitting. Based on research from Allocate Smartly’s “Gold Cross-Asset Momentum,” historical backtesting over 50 years shows the strategy has generally reduced exposure during losing phases and outperformed a basic gold-only momentum approach.
By QuantifiedStrategies.com.
Backtesting a Community-Sourced Momentum Strategy#
Strategy Backtest #14 from Trading Research Hub. Tests a momentum-based trading strategy through systematic backtesting. Part of an ongoing series designed to make strategy research accessible by collecting backtests of different approaches in one place, saving readers hours of independent research time.
The article tests specific strategy rules from the community, providing honest performance analysis including both the promising and concerning aspects of the results. It emphasizes the importance of working with practitioners who have proven live track records, and the distinction between historical backtest performance and actual trading outcomes.
By Pedma.
Systematic Regime Detection for Momentum Strategy Timing#
An article on the systematic approach to regime targeting for momentum trading strategies. The article argues that trading against the general market direction is one of the most common causes of losses, and presents a framework for identifying favorable market regimes.
The article covers how to define and detect market regimes, how to adjust strategy exposure based on the current regime, and how to avoid trading during unfavorable conditions. The practical framework helps momentum traders align their positions with the broader market trend while knowing when to reduce exposure or “go fishing.”
By Pedma.
NDX Momentum#
A momentum-based rotational strategy focused on Nasdaq-listed stocks. The system ranks stocks by their momentum scores and rotates into the strongest performers while applying a market regime filter to reduce exposure during unfavorable conditions. The post details the ranking methodology, rebalancing frequency, and historical performance characteristics of this systematic approach to capturing momentum in technology-heavy equities.
By Peter, CrackingMarkets.
US Stock Momentum Trading System for Retail Traders#
A comprehensive deep research report on building a US stock momentum trading system suitable for retail traders. The post was generated using ChatGPT Pro’s Deep Research functionality and covers extensive academic and practical sources on momentum strategies. It addresses universe selection, momentum measurement, portfolio construction, rebalancing schedules, and risk management, providing a thorough starting point for retail traders looking to implement systematic momentum strategies.
By Peter, CrackingMarkets.
RealTest Code for SPX Momentum Rotational System#
Complete RealTest code for an SPX momentum rotational system, backtested using Norgate Data with commissions and delisted stocks included. The system checks a market regime filter based on the balance between stocks with positive and negative longer-term momentum, then opens up to 10 positions ranked by momentum. The post provides the full implementation code for site supporters.
By Peter, CrackingMarkets.
Dynamic Asset Allocation for Practitioners, Part 3: Risk-Adjusted Momentum#
A comprehensive study testing 13 different risk-adjusted momentum indicators across 16,116 simulation variations to determine whether accounting for volatility and drawdowns improves momentum-based asset allocation. The metrics tested include Sharpe ratio, Omega ratio, Sortino ratio, Calmar ratio, DVR (Sharpe x R-squared of equity curve), VaR, CVaR, max loss ratio, average drawdown ratio, high-low differential, Ulcer Performance Index, gain-to-pain ratio, and fractal efficiency.
The key finding is that all 13 methods delivered virtually identical median results with an average pairwise correlation of 0.945, suggesting investors inherently exhibit risk awareness when expressing momentum preferences. However, risk-adjusted approaches showed much smaller dispersion than pure price momentum, and aggregating all approaches into a combined index delivered superior risk-adjusted performance with a Sharpe ratio of 1.29 — outperforming any single metric alone. The research demonstrates that diversifying across multiple highly correlated risk-adjusted momentum indicators provides measurable risk-reduction benefits without sacrificing returns.
By ReSolve Asset Management.
Dynamic Asset Allocation for Practitioners Part 2: Risk-Adjusted Momentum#
The second installment in a series on dynamic asset allocation, investigating how 13 risk-adjusted momentum metrics compare to raw momentum for portfolio construction. The research tests across a 10-asset universe spanning commodities, gold, US/European/Japanese/emerging market stocks, international REITs, and treasuries from 1995 onward, using multiple portfolio concentrations (2–5 holdings) and systematic asset removal to minimise selection bias.
The DVR and return-to-max-loss ratio delivered the highest Sharpe ratios, while the Ulcer Performance Index achieved the best return-to-maximum-drawdown performance. A counterintuitive finding: risk-adjusted momentum portfolios exhibited lower Sharpe ratios than raw momentum systems from Part 1, suggesting that disaggregating risk management from momentum signals may produce better results. The gain-to-pain ratio demonstrated the greatest consistency across different asset universes and concentrations.
By Adam Butler, GestaltU.
Risk-Adjusted Momentum Oscillator (RAMO)#
An open-source TradingView indicator that combines traditional momentum calculation with real-time risk assessment, addressing a gap in conventional momentum indicators by incorporating drawdown metrics into momentum calculations. The core innovation is a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions using the formula: Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor), with a floor of 0.05 to prevent complete signal suppression.
The indicator supports three momentum calculation modes (rate of change, price momentum, log returns) and employs adaptive EMA smoothing where the alpha parameter adjusts based on volatility percentile — faster response during volatile periods, stability during calm markets. Z-score normalisation clamps values to [-3.5, 3.5] for outlier handling. Additional features include momentum acceleration (second derivative) for early trend change detection, linear regression prediction for leading signals, and volume-based exhaustion detection for identifying potential reversals on declining volume. The risk environment classification (low/medium/high based on drawdown depth) filters long signals during high-risk conditions.
By EdgeTools.
Systematic Allocation in International Equity Regimes#
A research article examining systematic tactical allocation between US equities and international markets (EAFE and Emerging Markets) using momentum-based signals. The study uses a 56-year dataset (1969–2025) to test whether a signals-based framework can systematically time exposure to EAFE equities amid changing macroeconomic conditions.
Two strategies are tested: a pure spread momentum approach using rate-of-change signals across 6, 12, 24, and 36-month lookback periods, and a trend-conditioned approach applying simple moving average filters for allocation signals. The research finds intermediate-term windows (12–36 months) generate optimal risk-adjusted returns by balancing signal responsiveness against noise, and that systematic overlays can generate alpha orthogonal to equity-market betas — particularly during major secular market transitions.
By Cyril Dujava, Quantpedia. Mentioned by Radovan Vojtko in this discussion.
A 46-Year Backtest on a Carhart Momentum Strategy: Why Tiny Edges Compound#
A LinkedIn post reporting a 46-year (1980–2026) backtest of a cross-sectional Carhart momentum strategy run on a 100-stock S&P 500 universe with monthly rebalancing. The strategy produces a 24.7% CAGR versus the S&P 500’s 9.6%, an information ratio of 0.81, and positive returns in 55% of 389 monthly rebalances. The central argument is that a statistically tiny edge — a mean information coefficient (IC) of just 0.013, effectively noise in isolation — compounds into meaningful alpha when applied across many independent bets. Invoking Grinold’s Fundamental Law of Active Management (IR ≈ IC × √Breadth), the author shows that 0.013 × √389 ≈ 0.26 per period scales into a strong information ratio, framing systematic investing as a contest won by breadth and discipline rather than by heroic single forecasts.
The momentum factor that this strategy harvests is the same one Mark Carhart added to the Fama-French three-factor model in 1997, building on the cross-sectional winners-minus-losers anomaly documented by Jegadeesh and Titman (1993). Its strengths are well established: momentum is one of the most robust and pervasive anomalies in finance, persisting across asset classes, geographies, and over a century of data, and — as the post illustrates — it offers high breadth, making it well suited to systematic, diversified implementation. Its weaknesses are equally well documented. The factor lacks a clean theoretical foundation (Carhart treated it as an empirical, atheoretical add-on); it suffers rare but catastrophic crashes (e.g. roughly -73% over three months in 2009) that occur during sharp market rebounds and make returns highly non-normal; it carries high turnover, so transaction costs can erode or even eliminate paper profits; and its premium has decayed sharply, averaging only ~2% annually over 2014–2024 versus ~10% in the 1990s, with crowding via factor ETFs frequently blamed. Practitioner remedies include residual/idiosyncratic momentum, volatility scaling, and dynamic crash-risk management.
The post’s own discussion surfaces the key caveats. The Grinold framework assumes bets are independent, frictionless, and have constant IC — assumptions that rarely hold. One commenter (Alexander Hübbert) notes that consecutive 12-month momentum signals overlap by eleven months, so the 389 monthly rebalances are far from 389 independent bets, which inflates the effective breadth and overstates the implied information ratio; the 100 S&P 500 names are also highly correlated rather than orthogonal. Other commenters question multi-factor strategies’ recent real-world performance and raise survivorship-bias concerns about a backtest restricted to current/historical large-cap constituents. Together these points are a useful reminder that a backtested IR of 0.81 on overlapping, correlated, frictionless bets is an optimistic upper bound rather than a deliverable live result.
By Gilberto Pellerano. Shared in this LinkedIn discussion.
Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations#
Baltas and Kosowski (Imperial College / Oxford-Man Institute, 2019) study three methodological levers in the standard time-series momentum (TSMOM) strategy used by managed futures funds — the volatility estimator that scales positions, the trading rule that detects price trends, and the treatment of pairwise correlations across portfolio constituents — and show that each can materially improve net-of-cost performance over the naive Moskowitz–Ooi–Pedersen (2012) and Hurst–Ooi–Pedersen (2013) baseline. The paper is a methodological follow-up that complements “Demystifying Managed Futures”: instead of asking “what factor drives CTA returns?”, it asks “how should you actually build a TSMOM strategy if you have to pay transaction costs and your assets are correlated?”
The first two contributions are turnover reductions. Replacing the standard close-to-close volatility estimator with the Yang–Zhang (2000) range-based estimator (which uses open-high-low-close prices) reduces strategy turnover by 17% (t-stat 5.33) without statistically significant performance degradation. Replacing the binary sign trading rule with a continuous trading signal derived from the t-statistic of past daily log-returns over 12 months further reduces turnover by 24% (t-stat 8.49). Combined, these two changes cut turnover by 36% (t-stat 13.19) while leaving Sharpe ratios statistically indistinguishable from the standard TSMOM portfolio. The third contribution is a “correlation-adjusted” TSMOM variant that dynamically scales overall portfolio leverage based on the contemporaneous average pairwise signed correlation of constituents, reducing leverage in high-correlation regimes (and raising it in low-correlation regimes). This adjustment safeguards against downside risk and is most valuable in the post-2008 period, when futures-market correlations rose sharply.
Our summary: this is the natural sequel to Hurst-Ooi-Pedersen and the methodological reference for anyone implementing TSMOM. The paper is most useful for three reasons. First, it shows the Sharpe-1.8 gross headline number of the “Demystifying Managed Futures” paper is fragile to standard implementation choices and that careful volatility estimation and trading-rule design recover most of the gap that transaction costs open up. Second, it provides one of the few formal frameworks for handling the rising-correlation problem that has plagued CTAs since 2008 — relevant to the diagnosis offered in Babu et al. (2020) “You Can’t Always Trend When You Want”. Third, it introduces a transaction-cost model for futures-based strategies that separately accounts for roll-over and rebalancing costs, which has since become a standard reference for CTA cost modelling.
Data: 56 futures contracts across four asset classes — 25 commodities, 14 developed-country equity indices, 8 currency pairs, and 9 government bond futures — over December 1974 to February 2013, with the cross-section growing over time. No public source code is released; the volatility estimator and trading rule are fully specified in the paper and Yang–Zhang (2000) is well documented in the literature. Implementation is reproducible given an equivalent futures dataset, though specific results depend on continuous-futures roll methodology, which is not exhaustively documented.
Key metrics: gross annualised performance of the baseline TSMOM strategy is 16.12% over the sample, with transaction costs representing about 10% of that figure. The improved specification reduces transaction costs to roughly 105 basis points (from a higher baseline) without a statistically significant fall in Sharpe ratio. The combined Yang–Zhang + continuous-signal variant cuts turnover by 36.23% (t-stat 13.19); the correlation-adjusted variant outperforms the naive implementation, with the outperformance most pronounced in the 2009–2013 post-crisis sub-period when the default specification exhibits a negative after-cost Sharpe ratio. 49 of the 56 futures contracts have a positive unconditional mean TSMOM excess return.
By Nick Baltas and Robert Kosowski.
Momentum Factor Investing: Evidence and Evolution#
Baltussen, Dom, Van Vliet, and Vidojevic (Erasmus School of Economics / Northern Trust Asset Management, October 2025) present a comprehensive review and empirical analysis of the momentum factor across domestic and global stock markets spanning up to 159 years of data (1866-2024). The paper establishes momentum as an “eternal feature of financial markets” by demonstrating its robustness across multiple sample periods, 46 countries, and over 4,000 different portfolio construction specifications. Beyond traditional price momentum, the authors document that momentum is fundamentally multi-dimensional — present in firm fundamentals (earnings surprises, analyst revisions, news sentiment), firm-specific residual returns, anchor-based measures (52-week high), industry and network peer returns, and factor returns themselves.
The paper’s central contribution is showing that combining these diverse momentum signals into a multi-dimensional composite produces superior risk-adjusted returns that cannot be explained by traditional price momentum alone. The authors also address crash risk — momentum’s primary weakness — demonstrating that risk-managed approaches using volatility scaling substantially reduce maximum drawdowns while preserving or enhancing returns. On the question of post-publication decay, the paper finds no statistically significant evidence that momentum profits have diminished since the original Jegadeesh and Titman (1993) publication, further supported by 61 years of pre-CRSP out-of-sample data where arbitrage was impossible. The behavioral explanation — investor underreaction and gradual information diffusion — is favored over risk-based explanations.
Data uses CRSP (1927-2024), a pre-CRSP hand-collected dataset (1866-1926), and JKP international data covering 46 countries (1990-2024). No source code is provided, but the methodology is fully specified and reproducible with standard academic datasets.
Key metrics for the winner-minus-loser long-short portfolio: annualised return of 9.24% VW / 9.75% EW over the full CRSP sample (1927-2024) with t-statistics of 4.31 and 4.66. Over the deepest 159-year sample (1866-2024), returns average 8.83% VW / 8.95% EW with t-statistics around 5. The median Sharpe ratio across 4,096 portfolio design choices equals 0.61 (range 0.38-0.94). Global momentum premium: 7.77% (world), 7.57% (ex-US), 6.20% (emerging markets), positive in all 31 countries with continuous data. Maximum drawdown for standard price momentum is -88.41% VW. Risk-managed momentum (RM_MOM) increases annualised return to 17.87% while reducing max drawdown to -49.37%. The multi-dimensional EW_ALL composite achieves 9.65% EW annualised with a t-statistic of 8.22 and lower volatility (11.62%) versus standard price momentum (20.71%).
Mentioned by Darren (@ReformedTrader) in this discussion.
Real-Time Profitability of Published Anomalies: An Out-of-Sample Test#
Jing-Zhi Huang and Zhijian Huang test whether published stock market anomalies remain profitable out-of-sample in real time. The paper constructs a long-only strategy that each year recursively selects the best past-performing anomaly from a pool of published anomalies over a given training period — effectively applying momentum to anomalies themselves.
The key finding is that this recursive best-performer strategy outperforms the equity market even after transaction costs, suggesting that published anomalies exhibit performance persistence. The result is relevant to meta-strategy research: if individual anomalies show momentum, then dynamically rotating capital toward recently profitable strategies has an empirical basis. The paper also addresses data-snooping bias, a critical concern when selecting among many anomalies. Published in the Quarterly Journal of Finance, 2014.
Time-Series and Cross-Sectional Momentum in Anomaly Returns#
Wang, Yan, and Zheng document strong evidence of both time-series and cross-sectional momentum in the long-short returns of a comprehensive sample of stock market anomalies. Strategies that exploit this persistence — going long recently profitable anomalies and short recently unprofitable ones — deliver significant abnormal returns that are robust to the standard stock-level momentum effect and cannot be explained by traditional asset-pricing models.
The momentum in anomaly returns is more pronounced when arbitrage capital is scarcer or market liquidity is lower, consistent with limits-to-arbitrage explanations. This paper provides the direct empirical foundation for the meta-strategy approach of rotating across trading strategies based on recent performance. Published in European Financial Management, Volume 27, 2021.
Factor Momentum and the Momentum Factor#
Ehsani and Linnainmaa show that momentum in individual stock returns emanates from momentum in factor returns. Most factors are positively autocorrelated: the average factor earns a monthly return of 6 basis points following a year of losses and 51 basis points following a positive year. A factor momentum strategy that goes long recent winner factors and short recent loser factors is highly profitable and subsumes the standard stock momentum factor.
The central implication is that momentum may not be a distinct risk factor — it times other factors. This reframes the meta-strategy question: allocating to recently winning strategies may work because the underlying factors those strategies capture are themselves persistent. Published in the Journal of Finance, 2022.
Two Centuries of Multi-Asset Momentum#
Geczy and Samonov extend momentum tests to the longest available histories of global financial asset returns — 215 years (1800–2014) — covering equities, bonds, currencies, commodities, sectors, and individual stocks. The paper confirms the significance of the momentum premium both within and across asset classes over this extended sample, making it one of the most comprehensive out-of-sample validations of momentum as a pervasive market phenomenon.
The breadth of the evidence — spanning multiple countries, asset classes, and two centuries of data — strengthens the case that momentum is a robust empirical regularity rather than a sample-specific artifact. The multi-asset perspective is directly relevant to meta-strategy construction, where the question is whether momentum operates at the strategy level just as it does across individual securities and asset classes.
Is There Momentum in Factor Premia? Evidence from International Equity Markets#
Zaremba and Shemer examine whether momentum exists in the returns of factor premia — market, value, size, momentum, low-risk, and quality — across 24 international equity markets from 1990 to 2016. They find strong evidence that top-performing factors continue to outperform the worst-performing factors, both within individual equity markets and in a cross-country framework.
However, the paper also shows that momentum in factor premia is largely explained by the classic stock-level momentum effect, raising questions about whether factor momentum is a distinct phenomenon or a manifestation of the same underlying force. This is a cautionary result for meta-strategy builders who assume factor-level momentum provides independent diversification. Published in Research in International Business and Finance, Volume 46, 2018.
Factor Performance 2010-2019: A Lost Decade?#
Blitz examines whether the 2010–2019 decade represents a structural break in factor investing, documenting that value, size, and low-volatility factors all underperformed during this period while momentum remained profitable but with elevated crash risk. The paper investigates potential explanations including crowding, changes in market structure, and the prolonged low-interest-rate environment.
The practical implications are significant for meta-strategy builders: factor performance exhibits extended regime dependence, and a momentum overlay that rotates into recently winning factors would have concentrated into the few factors that worked (momentum, quality) while avoiding the many that didn’t. The paper serves as a cautionary case study on the risks of performance chasing at the factor level and the importance of understanding whether decay is temporary or permanent. Published in the Journal of Portfolio Management, 2020.
The Intramonth Momentum Cycle#
Nathan, Suominen, and Tasa show that U.S. equity momentum returns are not earned evenly through the month but concentrate in a six-trading-day window — the days nine to four before month-end — which the authors label the pre-turn-of-the-month (PreTOM) period. Across 1980–2025, $1 invested in the value-weighted Winners-Minus-Losers (WML) portfolio only during these six days each month grows to $18.78, versus just $2.37 for a dollar invested only during the rest of the month; the full-sample WML strategy grows to $44.46. Equivalently, 29% of trading days produce 78% of momentum’s cumulative return.
The paper traces this concentration to predictable month-end cash demand: investors raise liquidity by selling, and the selling falls disproportionately on losers. The effect is entirely loser-driven — bottom-decile losers underperform the market by 7.9 basis points per day during PreTOM, while winners show no comparable concentration. To explain why losers are sold, the authors introduce the concept of asset dispensability: stocks with embedded tax losses, low foregone dividend income, and salient underperformance are the natural candidates to liquidate when cash is needed, and a direct dispensability measure predicts the concentration better than past returns themselves. Because the underperformance is transitory price pressure rather than fundamentals, roughly 70% of it reverses within a week. The pattern replicates across 19 developed markets, and settlement-cycle reforms (U.S. T+2-to-T+1 and synchronized European T+3-to-T+2 transitions) each shift loser-selling pressure one trading day closer to month-end, providing clean identification that the timing is mechanically tied to settlement. The implication is that much of the momentum premium reflects predictable liquidity demand rather than under-reaction or priced risk.
Key metrics: the baseline panel Loser × PreTOM coefficient is −7.15 bps/day (t = −3.08); the WML PreTOM-minus-Rest difference is −5.46 bps/day (t = −2.80, value-weighted); the cross-market pooled contrast is +11.85 bps/day (t = 2.04, p = 0.042) in 12 of 15 countries; and the Carhart UMD factor earns +5.51 bps/day during PreTOM versus +1.36 bps/day otherwise. During dash-for-cash episodes (September 2008, October 2008, March 2020) losers underperformed winners by 10–18 percentage points; in March 2020 past-loser funds returned −25% against −11% for winner funds while the UMD factor earned +9.4%.
The study is purely empirical, built on CRSP daily decile portfolios (sorted on cumulative returns over months −12 to −2 using NYSE breakpoints), TAQ order-flow data, and Morningstar/CRSP mutual-fund flow data, with an Internet Appendix providing robustness tests. No public code repository is referenced.
Mentioned by Piotr Pomorski (@PtrPomorski) in this discussion, where the paper was shared as new research on the calendar timing of momentum returns.
Corrections Within Momentum: A Simple Robust Entry Rule#
A concise practitioner sketch of a strategy that combines a momentum filter with a mean-reversion entry. The rule has just three conditions: measure momentum on an asset, enter during a correction using a limit order, and exit after a fixed number of days. The design philosophy is to keep the logic simple and robust across asset classes rather than over-fitting a complex signal.
The author lays out the intuition behind each parameter. Stronger momentum makes the signal more reliable, and a deeper correction makes the entry stronger — deeper corrections increase exposure to the mean-reversion effect, while shallower corrections lean more on the underlying momentum factor. In other words, the strategy trades the pullback within an established uptrend, sizing conviction by how far price has retraced. An accompanying equity curve is shown on a universe of the top 50 coins by volume, constructed survivorship-bias-free.
Mentioned by Pavel Kýček (CEO at ROBUXIO) in this LinkedIn discussion, where he frames it as an example of building portfolios on simple, robust ideas and shows the resulting crypto equity curve.
By Pavel Kýček.
A System Behind a +40% Week — Six Years of Trading Experience Compressed Into One Article#
An X Article by Michael (@MichaelZTrading), an English rendering of an April 2026 piece credited to @kovainvest, that writes out a complete discretionary momentum-breakout process in the William O’Neil / Mark Minervini / Stan Weinstein lineage — stock selection, screening, entry, stop-loss, pyramiding, selling, exposure control and journaling — as a single non-negotiable checklist. The framing device is a letter to the author’s earlier self, who lost 40% in two months despite having read the books; the argument is that the missing piece was never theory but a process that removes in-the-moment discretion. The “+40% week” of the title is the account’s return in the week before publication, shown as an X portfolio-performance card at the top of the article; no track record beyond that single figure is given.
The process. Stock selection is a four-gate filter, all gates mandatory: fundamentals (quarterly EPS growth of 25%+ year-over-year, ideally accelerating, and revenue growth of 20%+ — the C and A of CAN SLIM), technicals (RS Rating 80+ and preferably 90+, price above the 50-day moving average, 10-day above 20-day, within 15–20% of the 52-week high), institutional sponsorship (a rising count of fund holders in 13F filings), and market environment (a confirmed index uptrend with no cluster of distribution days, on the O’Neil premise that three-quarters of stocks move with the market). The weekly screener is disclosed verbatim — price above $10, average daily range above 4%, RS above 90, price above the 50 EMA, 10 EMA above 20 EMA, more than 70% above the 52-week low — and typically returns 50–80 names, which three rounds of chart review cut to a 10–15 name watchlist by discarding “ugly” charts, flagging cup-with-handle, flat and VCP bases, and keeping names where the 10/20 EMA cross or the base contraction is about to complete. The author says the screener has been backtested “hundreds of times on QuantConnect” but reports no statistics from it.
Entry, stop and sizing rules. The primary setup is Minervini’s Volatility Contraction Pattern, defined strictly as three simultaneous contractions: successive pullbacks shrinking from 25–35% to 15–20% to 5–10%, volume falling well below the 50-day average with several near-dead sessions, and 3–5 consecutive days of sub-1% closes. The trigger is the consolidation high plus 10 cents on a day whose volume is 40–50% above the 50-day average; the buy zone runs from the pivot to 5% above it, beyond which the trade is passed. The preferred entry, though, is the pocket pivot — an up day whose volume exceeds the largest down-day volume of the prior ten sessions — taken late in the contraction and timed on a 60- or 15-minute chart, which the author says accounts for all of his most profitable trades and, in the $RKLB example, got him in roughly 8% below the daily-chart breakout. Stops are a 7–8% hard ceiling with the actual stop the tightest of: just below the pivot, 2–3% under the entry-day low, below the 21 EMA, or 2× ATR; a time stop exits anything going nowhere after 2–3 weeks; and a low-volume intraday pierce that closes back above the pivot is a shakeout to be re-bought, whereas a heavy-volume close below is a failure. Adds are pyramided 50/30/20 at the breakout, +2–3% and +2–5% (or the next pocket pivot), never into a loser, never more than 5% above the pivot and never when the index is weakening, with a 25% cap per name and 3–5 names in total.
Exits and exposure. Offensive selling uses O’Neil’s 8-week rule (a 20% gain within three weeks of breakout is held at least eight weeks), climax-run diagnostics (25–30%+ in 2–3 weeks, widening ranges, gap-ups on heavy volume, price escaping the trend channel — illustrated with $MU and $SNDK, where he trimmed half and trailed the rest against the 10 EMA), and churning (heavy volume with no progress or weak closes). Defensive selling triggers on a heavy-volume break of the 50-day / 10-week average, a broken uptrend line, five or more distribution days in 4–5 weeks, or a post-earnings gap below the 50 EMA, with the instruction to place the order the moment the signal fires. Portfolio exposure is regime-gated: above the 50 EMA with no distribution cluster, up to 4–5 positions; below it with 5+ distribution days in four weeks, total exposure under 30% in the one or two strongest names. Three consecutive stop-outs halve position size; five stop the trader for a week. The piece closes with a trading-journal template and the claim that the rules take an hour to understand and three years to execute.
Our reading. This is a well-organised statement of the growth-stock breakout canon rather than new evidence, and its value is as a fully specified checklist that can be turned into testable hypotheses. Every number is asserted rather than measured: the “decades of CAN SLIM history” behind the 7–8% stop, the 40–50% volume threshold, the 75% index co-movement figure and the 8-week rule are all inherited from O’Neil and Minervini, and none comes with a hit rate, expectancy, drawdown or sample. The two trade examples and the headline week are anecdotes selected after the fact, and the QuantConnect backtests are mentioned without a single result. The process is also less systematic than its presentation suggests: the screener is mechanical, but the three chart-review passes (“ugly shape”, “clean base”, “about to complete its contraction”), the multi-timeframe pocket-pivot timing and the climax-run judgement are where selection actually happens, and they are not reducible to rules — the checklist disciplines a discretionary trader rather than replacing one. There are internal tensions worth noticing: a fixed 7–8% stop on a universe filtered for an average daily range above 4% is one-and-a-half to two days of ordinary noise, which the “take the tightest stop” rule makes worse, and the shakeout re-entry clause is a standing invitation to be whipsawed twice. The regime rules, the losing-streak circuit breaker, the time stop and the insistence on doing nothing in signal-less weeks are the parts most likely to survive a serious test.
Related concepts. Each gate in the selection filter maps onto a documented effect. An RS Rating above 80 with price within 15–20% of the 52-week high is cross-sectional momentum plus the 52-week-high anchor, both of which Baltussen et al. show to be robust across 159 years and 46 countries in “Momentum Factor Investing: Evidence and Evolution” (above), and their finding that momentum is also present in earnings surprises and analyst revisions is the academic counterpart of the EPS-acceleration gate. The “M” gate — trading breakouts only in a confirmed index uptrend and cutting exposure when distribution days pile up — is a hand-rolled version of the regime conditioning in Goulding, Harvey and Mazzoleni’s Momentum Turning Points and Breaking Bad Trends, where Correction and Rebound states are exactly the periods in which trend entries perform worst, and of the timing work in “Systematic Regime Detection for Momentum Strategy Timing” (above). The VCP’s three-way contraction followed by a volume-confirmed range expansion has a direct quantitative analogue in 100 Trading Indicators Tested Across 4,100+ Strategies, where bar range relative to ATR and volume were the only filters that consistently improved thousands of breakout systems out of sample while oscillators consistently hurt them; the continuation edge after a break of a significant high is what Do Breakouts of Strong Swings Work? measures across 40 futures markets. The pocket-pivot logic — buying late-contraction days inside an established uptrend rather than the visible breakout — sits between the breakout school and the pullback entries in “Corrections Within Momentum” and “Simple Crypto Momentum Strategy: Buying Corrections” (both above), and the observation that waiting for the daily candle costs 8% of entry price is the same trade-off those entries exploit.
Validation elsewhere. The exit and sizing rules are where the collection’s evidence pushes back hardest. Fixed-percentage stops are examined in The Hidden Costs and Return Erosion of Stop Losses in Systematic Trading, which quantifies how much return stops give up for the risk they remove, and volatility-scaled alternatives are argued for in Vol-Based Stops and Why Volatility-Adjusted Sizing Matters More Than You Think — the article’s 2× ATR stop is the right instinct, but taking the tightest of four stops throws it away. The entry-day-low and time-stop rules, by contrast, are the kind of practitioner rule that Testing the Low-of-Day Stop Loss Rule in Crypto Trend-Following subjects to a backtest, and the Bilbo Box Breakout write-up, whose 21 EMA gate, volume gate and 10-day time stop are nearly the same rules on a different instrument, found that every premium-based stop and profit target it tried made results worse. The 50/30/20 pyramid is directly contradicted by Pyramiding Behaves Very Differently Across Trading Models, whose argument is that adding into a breakout raises the cost basis precisely when the remaining move is shortest and the payoff depends most on a rare outlier; the article’s own “never add more than 5% above the pivot” clause is a tacit concession. The elevated crash risk of concentrated momentum books documented in “Factor Performance 2010-2019: A Lost Decade?” (above) is the reason the exposure-cutting rules matter more than the entry rules, and Why Published Trading Edges Still Work Decades Later supplies the argument for why a process published by O’Neil in 1988 can still carry an edge — the behavioural cost of holding through a 30% base pullback and of sitting in cash for weeks is one most participants will not pay. For a systematic counterpart to this discretionary process, “US Stock Momentum Trading System for Retail Traders” (above) covers the same universe-selection, ranking and rebalancing questions with rules that can actually be backtested.
Published by Michael (@MichaelZTrading) as an X Article on September 17, 2026, crediting @kovainvest as the original author (dated April 17, 2026); it had drawn roughly 360K views within a day.
By @kovainvest, English edition by Michael (@MichaelZTrading).
Backtesting Kullamagi’s Episodic Pivot Setup in Crypto Markets#
Research Article #44 from Trading Research Hub. A deep-dive backtesting Kristjan Kullamagi’s Episodic Pivot (EP) trading setup, a strategy the famous swing trader claims made him millions in profits. The setup was originally discovered by Pradeep Bonde (Stockbee) and involves identifying stocks that gap up significantly on catalytic events.
The article systematically defines the EP setup rules, applies them to crypto market data, and evaluates the strategy’s performance through rigorous backtesting. This approach of taking a successful discretionary trader’s methodology and translating it into systematic rules demonstrates how qualitative trading insights can be quantified and validated.
By Pedma.
Building and Using Crypto Momentum Scanners for Systematic Trading#
Research Article #31 from Trading Research Hub. Explains the construction and use of crypto momentum scanners for systematic trading. The article answers the frequently asked question of how to build and use tools that systematically identify short-term momentum across a universe of cryptocurrency assets.
The article covers the practical application of momentum scanners for gauging market sentiment and informing position decisions. By monitoring which assets are trending strongly in the short term, traders can gain a better perspective on overall market conditions and adjust their positioning accordingly, supporting both automated and semi-automated trading approaches.
By Pedma.
Bimodality Everywhere: International Evidence of Deep Momentum#
Chulwoo Han and Chang Qin examine the prevalence of bimodality in momentum returns across 45 countries and evaluate Deep Momentum, a machine learning strategy designed to exploit the bimodal structure of momentum return distributions. The study finds that momentum portfolios do not produce normally distributed returns: past winners either soar or crash, with very few outcomes in between. This bimodal pattern is documented across a wide range of equity markets, is persistent over time, and is negatively correlated with momentum profits, individualism, and disposition effects, while positively correlated with turnover and volatility.
Our summary: this paper connects the well-known momentum crash literature to a concrete distributional explanation. Bimodality is not just a curiosity—it is a systematic property of momentum returns that varies across countries, and that variation predicts where traditional momentum will struggle. The Deep Momentum strategy, which conditions on the shape of the return distribution rather than just past returns, consistently outperforms both standard momentum and naïve machine learning approaches. The finding that a global model pooled across all 45 countries outperforms country-specific models is also practically important: it suggests that sharing information across markets is worth the complexity, particularly for countries with shorter data histories.
Data and code: the study covers equity markets across 45 countries. No public code repository was identified at the time of writing. The paper is available on SSRN.
Key metrics: Deep Momentum outperforms traditional momentum and naïve machine learning strategies consistently across the 45-country sample. Outperformance is strongest in countries with higher bimodality, larger sample sizes, and higher volatility. The global pooled model beats country-specific models for both global and country-level portfolio construction. Results are presented country-by-country rather than as a single aggregate Sharpe ratio or annualized return figure.
Mentioned by Ivan Blanco (@iblanco_finance) in this discussion: “Momentum stocks don’t just win or lose, they polarize. The distribution shape tells you more than the mean.”