Popular Investing Research in 2025
Looking Back at a Year of Investing and Trading Research
As 2025 has come to an end, I’m sharing a curated selection of the most popular investing research from the past year, based on reader engagement in my weekly newsletters. Each week, I tracked the three most-clicked links, creating a set of “weekly winners” that reflect what readers actively chose to explore. From this group, I selected a balanced set of papers across asset classes and research themes.
The papers span asset allocation, equities, commodities, trend following, volatility, machine learning, and more, with each title linking directly to the source. I hope this collection highlights the ideas that resonated most with investors in 2025 and provides useful perspectives heading into 2026.
Thank you for reading and for your continued support. If you found this helpful, please consider liking or sharing the post. Wishing you a successful investing year in 2026.
Asset Allocation
Global Tactical Asset Allocation: Updated Results and Real-Market Implementation Using Python and IBKR (Gabriel, Pagani, and Zarattini)
Updates Meb Faber's classic tactical asset allocation through 2025 and shows that staggered rebalancing reduces timing luck. Spreading trades across weeks improves return stability, lowers drawdowns, and reduces trading costs versus single-date rebalancing.
Tactical Asset Allocation with Macroeconomic Regime Detection (Cunha Oliveira, Sandfelder, Fujita, Dong, and Cucuringu)
Uses macro regime clustering across 127 FRED-MD indicators to drive Black–Litterman ETF allocations. Regime-aware portfolios deliver higher Sharpe ratios and better downside protection than static asset-allocation strategies.
Dynamic Dragon: Integrating Regime Detection into Strategic Asset Allocation (Ni)
Introduces regime-dependent allocations to the Dragon Portfolio using cross-asset price signals, delivering higher Sharpe ratios historically and post-2020, and showing that adaptive diversification beats static all-weather portfolio construction.
Commodities
Short-Term Basis Reversal (Rossi, Zhang, and Zhu)
Finds a weekly reversal in return spreads between front- and second-month futures. Cross-sectional and time-series strategies earn Sharpe ratios above one, working best during high volatility and extending beyond commodities to equity and bond futures.
Intra-day Seasonality and Abnormal Returns in the Brent Crude Oil Futures Market (Ewald, Haugom, Ouyang, Smith-Meyer, and Stordal)
Brent crude oil futures prices exhibit statistically significant intraday patterns. These patterns can be exploited through various strategies and generate meaningful returns, even after accounting for transaction costs.
Asymmetry and Crude Oil Returns (Liu, Zhang, and Bouri)
Introduces a distribution-based asymmetry signal for crude oil, strongly predicting WTI futures returns. Rising right-tail clustering forecasts weaker next-month performance, enabling market timing with positive Sharpe ratios even after trading costs.
Crypto
Market Making in Crypto (Stoikov, Zhuang, Chen, Zhang, Wang, Li, and Shan)
Develops a market-making strategy on crypto perpetual contracts using one-minute data across 30 assets. A new “Bar Portion” signal outperforms MACD-based approaches in backtests and live trading on Hummingbot.
A Trend Factor for the Cross Section of Cryptocurrency Returns (Fieberg, Liedtke, Poddig, Walker, and Zaremba)
Introduces CTREND, a crypto trend factor combining dozens of technical indicators via an elastic net. The signal outperforms alternative strategies after costs, offering investors a robust trend-following approach in liquid cryptocurrencies.
Trading Games: Beating Passive Strategies in the Bullish Crypto Market (Palazzi)
Introduces a cointegration-based crypto pairs strategy delivering high returns and Sharpe versus buy-and-hold. Dynamic lookbacks, volatility filters, and trailing stops reduce drawdowns, keeping performance resilient across bull and bear markets.
Currencies
How to Maximize Momentum Returns in Foreign Exchange Markets? (Liu)
Enhances FX momentum by double-sorting currencies on six-month returns and higher-moment risks. Avoiding skewed, crash-prone currencies improves returns and reduces downside risk versus standard momentum strategies.
Predictable Currency Crashes (Sun, Wang, and Wang)
Shows currency crashes are predictable when rates spike, and currencies trend down. When both signals align, crash probability rises to 43% versus 8%, offering investors a simple, actionable risk-warning signal.
Equities
Volatility Decay and Arbitrage in Leveraged ETFs: Evidence from the US and Japan (Lin, Lin, Wang, Yeh)
Volatility decay in leveraged ETFs can be systematically harvested using beta-neutral hedged shorts. Across the US and Japan, strategies earn 2–6% annually with Sharpe ratios up to 2, before trading and borrowing costs.
The Intersection of Expected Returns (Sobotka)
A small set of “overlap” stocks repeatedly drives anomaly returns. Concentrating on roughly 10% of stocks captures about 40% of anomaly performance, suggesting mispricing-based alpha rather than risk premia.
Momentum at Long Holding Periods (Calluzo, Moneta, and Topaloglu)
Shows momentum rankings persist, enabling longer holding periods with lower turnover. Anticipating future ranks delivers higher returns than standard momentum and produces improved implementability for real portfolios.
Data-mined Anomalies and the Expected Market Return (Li, Platanakis, Ye, and Zhou)
Data-mined equity signals can predict market returns better than standard predictors. Selecting statistically strong, mispricing-driven signals materially improves market-timing Sharpe ratios and overall performance versus conventional signals.
Forest through the Trees: Building Cross-Sections of Stock Returns (Bryzgalova, Pelger, and Zhu)
Introduces "Asset Pricing Trees" to capture nonlinear interactions in stock characteristics. Compared to conventional univariate and double sorting, portfolios deliver up to threefold higher Sharpe ratios and 20–30% larger alphas.
Factor Investing
Causality and Factor Investing: A Primer (Lopez de Prado and Zoonekynd)
Correlation-based factor models often mislead investors and underperform out of sample. A causal framework for factor construction is proposed that reduces spurious signals, improves robustness, and delivers more reliable investment performance.
Revisiting factor momentum: A one-month lag perspective (Rönkkö and Holmi)
Factor momentum persists when measured using one-month returns rather than static tilts. Short-term factor momentum delivers significant alpha, confirming it as a robust and economically meaningful return driver.
Scaled Factor Portfolio (Jiang, Li, Ning, and Xue)
Introduces Sharpe-scaled PCA to amplify strong factor signals while shrinking noisy exposures. Across 50 anomalies, portfolios earn 12–19% annual alpha and Sharpe ratios up to 2, remaining robust after costs, and outperforming standard PCA.
The Best Defensive Strategies: Two Centuries of Evidence (Baltussen, Martens, and van der Linden)
Analyzes 222 years of data showing multi-asset defensive strategies provide improved downside protection compared to equity-only defensive factors. DAR-style correlation hedges and trend-following provide the strongest downside protection, while gold and put hedges fail long-run tests.
Machine Learning and Large Language Models
The New Quant: A Survey of Large Language Models in Financial Prediction and Trading (Fu)
Surveys over 50 studies showing how LLM-based signals from text predict asset returns. Language models often outperform traditional sentiment measures and can be integrated into systematic trading and portfolio strategies.
Predicting Extreme Returns with Fundamentals: A Machine Learning Approach (Liu and Wang)
Machine learning on fundamental data can be used to predict extreme stock winners and losers. Identifying future “rockets” and “torpedoes” enables large long–short returns by exploiting predictable tail outcomes.
Non-Linear Factor Investing in the Era of Machine Learning (Chin)
Uses genetic programming to build nonlinear combinations of 146 equity anomalies. The models exhibit low turnover, remain transparent, and produce robust out-of-sample performance, outperforming linear models.
Enhancing Trend-Following Strategies Using Machine Learning and Time Series Models (Chandrinos and Lagaros)
The authors study the Ichimoku Cloud as a trend-following signal and improve it with machine learning and time-series models. Applied to major FX pairs, models like XGBoost and Kalman filters raise returns and Sharpe ratios while reducing risk.
Options and Volatility
Sizing the Risk: Kelly, VIX, and Hybrid Approaches in Put-Writing on Index Options (Wysocki)
Tests dynamic sizing for short-dated S&P 500 put writing using Kelly, VIX-based, and hybrid rules. Adaptive sizing delivers high Sharpe ratios, significantly outperforming equities while containing tail risk effectively.
The Volatility Edge, A Dual Approach For VIX ETNs Trading (Zarattini, Mele, and Aziz)
Presents a rules-based VIX ETN strategy combining the volatility risk premium with term-structure signals. Conditioning exposure on the VIX curve delivers strong, consistent risk-adjusted performance across market regimes since 2008.
Disaggregating VIX (Degiannakis and Kafousaki)
Decomposing the VIX into trend and cycle components improves volatility forecasting. Recombined forecasts outperform standard models and deliver strong risk-adjusted returns when applied to simple VIX futures strategies.
Trend Following
Follow the Leader: Enhancing Systematic Trend-Following Using Network Momentum (Li and Ferreira)
Network momentum models consistently outperform traditional trend-following strategies, yielding higher Sharpe ratios and improved skewness of returns.
Does Trend-Following Still Work on Stocks? (Zarattini, Pagani, and Wilcox)
The paper examines a trend-following strategy for stocks, using new all-time highs as entry signals. It documents the importance of a small number of large winners in driving overall profitability.
Enhancing global equity returns with trend-following and tail risk hedging overlays (Schwalbach and Auret)
Overlaying trend-following and 10-delta SPX put option hedges on a global equity portfolio boosts performance without reducing equity exposure. Portable alpha portfolios raise CAGR and Sharpe while halving drawdowns and reducing crash risk.
The Science and Practice of Trend-following Systems (Sepp)
Analyzes binary and continuous trend-following systems, linking performance to positive long-term autocorrelation and drift. Properly tuned strategies defend in market drawdowns and improve diversification when combined with long-only equity portfolios.
Blogs
Conditional short-term trend signals (Macrosynergy)
Very.... slow... mean reversion .... and some thoughts on trading at different speeds (Rob Carver)
Can I build a scalping bot? A blogpost with numerous double digit SR (Rob Carver)
Macro trading factors: dimension reduction and statistical learning (Macrosynergy)
GitHub
FinanceDatabase A database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies, and Money Markets.
pysystemtrade Systematic trading in Python by Rob Carver.
toraniko A multi-factor equity risk model for quantitative trading.
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Solid roundup. The engagement-based curation is actually a clever filter since it sidesteps the usual academic citations game and shows what practicioners are actually digging into. I've noticed similar patterns where the most actionable papers (like the ones on regime detection) tend to get reshared way more than stuff sitting behind paywalls. One thing I'm curious about though is whether click-through rates might favor papers with punchier titles over genuine methodolgy quality, kinda like how viral content works on social meda.
In my own testing of cross sectional momentum strategies similar to Antonacci’s Dual Momentum, I have frequently observed that the second ranked asset, or a straightforward top two blend, can outperform holding only the top ranked asset, especially when volatility and drawdowns are considered. This seems to be an aspect of momentum strategies that is not often explored in the published research.