Weekly Research Recap
Latest research on investing and trading
This week’s Tuesday Roundup features the most valuable investing insights I came across over the last seven days, spanning new academic research, market analysis, high-quality blog posts, and thoughtful online discussions. Links are included throughout for further reading.
Commodities
Call the Zookeeper: A Unified Framework for Commodity Risk Premiums (Fan, Li, Qiao, and Zhang)
Past research has documented many types of risk premiums in commodity markets. This paper tests 81 predictors spanning commodity, macro, and sentiment data, then builds a dynamic long-short factor using 37 commodity-specific characteristics in a forecast model to predict next-month returns and rank commodities. The best-performing long/short portfolio delivered an annualized return of 5.8% with a Sharpe ratio of 0.64. Key takeaway: Combining signals tends to outperform single-factor commodity investing.
Crypto
Cross-Sectional Dispersion and the State Dependence of Cryptocurrency Momentum (Makgolo and Zhang)
Many investors believe that momentum crashes when volatility spikes, but in crypto, the better warning sign may be return dispersion. This paper finds that the dispersion of returns across coins predicts momentum breakdowns better than Bitcoin volatility. In extreme dispersion regimes, momentum weakened sharply. A dispersion-scaled strategy increased Sharpe from 0.63 to 0.80 and cut max drawdown from -42.5% to -17.1%. Key takeaway: For crypto momentum, watch coin dispersion, not just Bitcoin volatility.
Size-Momentum Puzzle in Cryptocurrencies (Xu and Wu)
Momentum in crypto markets depends on size. The authors show that small coins mean-revert sharply, with recent losers rebounding strongly, while large coins display momentum. Using 2018 to 2025 data, the smallest crypto quintile showed a 4.9% weekly reversal spread, while the largest delivered +1.0% weekly momentum, rising further over longer holding periods. Key takeaway: One momentum model does not fit all of crypto; small coins tend to reverse, while large coins trend.
Equities
Short-Term Reversal Persists Globally-If Properly Measured (Stosik and Zaremba)
Standard short-term reversals have weakened considerably over the years. This paper argues that the bigger issue is measurement. Recent losers often rebound once returns are judged relative to industry peers rather than raw price moves. Across 64 countries, the standard reversal signal earned just +0.05% per month, while the industry-adjusted version returned +0.53% monthly with a Sharpe of 0.74. Key takeaway: Short-term mean reversion still exists, but within industries.
Integrating Geopolitical Risk Into Low Volatility Factor Construction (Kallali, Binh, Roseneberg, Tilly, and Sekine)
Low-volatility strategies typically rely only on price data, but this paper finds geopolitical signals can improve them using firm-level news sentiment and regime indicators. The baseline model returned 10.4% annually with a Sharpe of 0.70, while the best geopolitical version delivered 12.8% with similar volatility and a 0.84 Sharpe; a more aggressive version reached 14.5% with a 0.91 Sharpe. Key takeaway: Defensive investing may improve when conditioned on geopolitical regimes.
A Comparative Analysis of Overnight vs. Daytime Static and Momentum Strategies Across Sector ETFs (Salotra, Katikireddy, Anumolu, and Pinsky)
Overnight vs. intraday returns for sector ETFs differ substantially. Across 10 U.S. sector ETFs (1999–2025), overnight returns were stronger than daytime returns, while long overnight plus intraday reversal often performed best. The top strategy turned $100 into over $31,000 in XLK before costs, though much of the edge faded after transaction costs. Key takeaway: Sector ETFs show distinct overnight and intraday return patterns, but monetizing them in practice is challenging.
Save The Date: Analyst/Investor Days as a Trading Signal (Cabrera, Kolokolova, and Zhang)
Analyst/Investor Days may be a stronger signal than most investors realize. Across 1,000+ U.S. firms, announcing an Investor Day led to abnormal returns and a run-up into the event. A long-short strategy buying announcing firms and shorting matched peers earned a Sharpe ratio of 1.06. A machine-learning timing version improved this to a Sharpe of 1.22. Key takeaway: Corporate communication events offer scalable event-driven alpha.
FX
Cross-sectional currency momentum and order flow (Sakemoto and Suda)
Cross-sectional FX momentum has struggled recently, but this paper shows that combining momentum with short-term FX swap order flows can restore returns. Currencies with strong recent performance and positive swap buying pressure earned 3.8% annually (Sharpe 0.57) versus -1.0% for standard momentum. In high-volatility periods, returns rose to 8–10%. Key takeaway: FX trends are strongest when confirmed by hedging flows.
Machine Learning and Large Language Models
ChatGPT as a Time Capsule: The Limits of Price Discovery (Lehner and Lopez-Lira)
Markets may be slower to price public narratives than many assume. This study uses older ChatGPT model snapshots to rank 7,000 U.S. stocks based only on what was knowable at the time. Firms with stronger outlooks earned 1.22% higher next-month returns per standard deviation, while a top-minus-bottom spread reached 2.55% monthly in-sample. The scores also predicted stronger sales growth and analyst upgrades. Key takeaway: LLMs may help process public information that investors aggregate only gradually.
Cross-Stock Predictability via LLM-Augmented Semantic Networks (Huang, Fan, Hu, and Ye)
This paper uncovers alpha from convergence trades between economically linked firms. Using 10-K filings, the authors build stock networks, then use an LLM to classify real firm relationships and remove noisy links. When linked stocks diverge, the strategy bets on mean reversion. A long-short S&P 500 strategy improved Sharpe from 0.74 to 0.82 and cut max drawdown from -10.5% to -7.9%. Key takeaway: LLMs can be used to identify relative-value opportunities across connected firms.
Prediction Markets
Beating the Earnings Game: Why Do Prediction Markets Outperform Professional Analysts? (Rabetti, Shao, and Zhang)
Prediction markets may be coming for Wall Street analysts. Studying 469 Polymarket contracts, this paper finds crowd-implied probabilities beat analyst consensus in forecasting earnings surprises: 78.5% vs 43.7% hit rate. The edge appears linked to continuous updating, diverse participants, and fewer institutional forecast biases. Key takeaway: Decentralized forecasts may become a serious new information signal for investors.
Prediction Market Accuracy: Crowd Wisdom or Informed Minority? (Gomez Cram, Guo, Jensen, and Kung)
Prediction markets are usually sold as collective intelligence in action. This paper suggests the opposite: Prices are made accurate by a small group of informed traders. Using complete Polymarket trading records, the authors estimate that roughly 3% of accounts account for most price discovery, react fastest to news, and earn persistent profits. The crowd supplies liquidity and losses. Key takeaway: What looks like crowd wisdom in prediction markets may simply reflect the decisions of a small informed minority.
Quant Finance
This is a very comprehensive review of quant finance topics, spanning topics such as probability theory, econometrics, time-series modeling, portfolio construction, derivatives pricing, tail-risk methods, Monte Carlo simulation, and machine learning.
Blogs
When Big Gets Small: Trading the Lower Tier of Large Caps and Upper Mid Caps (Quantpedia)
Backtests Lie: Building a Stress-Test Framework for Trading Signals (Vertox)
Podcasts
Sam Hartzmark: The Dividend Mistake Most Investors Still Make (Meb Faber)
How to Trade Futures | Rob Carver (Capital Horizons)
“Concentrated Strategies Will Do Extremely Well” - Sean Emory on Outperforming the Index (Odds on Open)
The $17M+ Prop Firm Roundtable - How The World’s BEST Traders Really Win (Words of Rizdom)
Social Media & Industry Research
The Skip-Month Mystery: What Last Month’s Returns Are Really Telling You (Alpha Architect)
The Inflation Diversification Problem (Man Group)
The MOST Important Thing: Unlocking Investment Wisdom with Antti Ilmanen (AQR)
Last Week’s Most Popular Links
Spot-Based Basis and Basis Momentum in Commodity Futures Markets (Luo and Xue)
Survival of the Fittest: A Three-Factor Model in the Currency Market (Liu, Wang, and Zhao)
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