Weekly Research Recap
Latest research on investing and trading
Here’s this week’s Tuesday roundup, a selection of the most practical investing ideas from recent academic studies, industry research, blogs, and insightful discussions across social platforms, with links included throughout.
Commodities
Intraday Stylized Facts and the Shape of Volatility Build-Up in ICE Brent Crude Oil Futures (Haugom, Ewald, Chen, and Smith-Meyer)
Using tick-level Brent futures data (2006–2025), the paper documents a range of intraday regularities: 1-minute returns display short-term reversals (first-lag autocorrelation of about −0.1), while volatility is persistent and follows a pronounced daily cycle. Volatility builds up unevenly over the day, rising more rapidly after market openings and information releases, and does so more strongly and rapidly in near-dated contracts. Key takeaway: Intraday volatility in Brent is predictable, making execution timing, dynamic risk control, and time-aware volatility modeling essential.
Crypto
Cryptocurrencies: Asset Classification, Trading and Portfolio Management (Gottwald, Sun, Chan-Lau, and Mitra)
Cryptocurrencies exhibit commodity-like behavior, including pronounced fat tails and frequent extreme moves. Momentum strategies are strongly regime-dependent, delivering gains mainly during periods of market stress and elevated volatility. Combining crypto with commodities improves portfolio efficiency and Sharpe ratios relative to commodities alone. Key takeaway: Crypto behaves like a speculative, commodity-like risk factor best used as a diversifier rather than a standalone allocation.
Equities
The 52-Week High Momentum Strategy: Evidence in Chinese Stock Market (Lan, Truong, and Zhang)
The 52-week high strategy emerges as a robust momentum signal in Chinese equities while traditional momentum is negative and industry momentum is weak. Its predictive power remains after controls and dominates in cross-sectional tests, but weakens beyond a 12-month holding period. Key takeaway: Price proximity to the 52-week high is a behaviorally driven momentum signal that seems to hold up in Chinese equities.
Geopolitical Risk and Equity Returns: Evidence From Global Markets (Coqueret and Zhou)
Geopolitical risk is priced immediately in equities: A one-unit shock lowers market returns by about 1% on impact, with stronger effects in emerging markets. At the firm level, exposures vary widely, with many stocks reacting immediately while others, particularly in emerging economies, adjust with a lag. Key takeaway: While geopolitical risk is an important risk factor, its effect in equities is primarily contemporaneous, with limited predictive power.
Fixed Income
The Co-Pricing Factor Zoo (Dickerson, Julliard, and Mueller)
Sparse models fail to explain corporate bond returns, particularly when pricing bonds and equities jointly. Risk premia are driven by a broad, dense set of factors, not a few dominant ones. After accounting for duration risk, bond-specific factors add little beyond equity and macro factors, while combining many signals delivers strong performance (Sharpe of 1.5 to 1.8). Key takeaway: Once duration risk is handled, equity and macro factors price corporate bond returns, but you need a broad set of them.
FX
Dividend Flows and the Foreign Exchange Rate (Zheng)
Dividend payments, though known in advance, generate predictable FX pressure: Currencies with large payouts depreciate by 4.7 bps within 2 days and 6.5 bps within about a week. A short-term strategy earns 30 bps/month in alpha, unexplained by standard FX factors. The effect is driven by benchmark investors’ dividend repatriation and is stronger when intermediary constraints bind. Key takeaway: Even predictable, mechanical flows can move currencies and create small but consistent trading opportunities.
Machine Learning and Large Language Models
Do LLMs Make Markets More Efficient? (Lu, Xu, and Vulicevic)
LLM access speeds up how quickly markets react to news. When LLMs are available, next-day return predictability from news sentiment falls by about 46 to 61%, with no reversal. During outages, predictability, and the profits of a simple news-sentiment strategy, roughly double to triple. Key takeaway: LLMs accelerate price discovery, but outages create brief, exploitable delays.
Multiple Testing
The False Discovery Rate in Finance: Identification Failure and Search-Adjusted Estimation (Lopez de Prado and Fabozzi)
Published factor results are typically the most favorable outcomes from many tested specifications, not single-trial estimates. The paper shows this search-and-selection process breaks standard inference and leads to systematically underestimated false discovery rates (FDRs). Under realistic assumptions, search-adjusted FDR can exceed 70 to 80%, even when nominal significance appears strong. Key takeaway: Most reported factors likely reflect selection bias; robust inference requires out-of-sample validation or explicit modeling of the search process.
Options
Time variation of size premium in the options market (Nguyen)
Delta-hedged options on large firms outperform small-firm options by 1.0 to 1.4% per month, but this spread disappears in recessions and down markets. The effect is strongest in expansions, consistent with demand-based pricing: Options on small, lottery-like firms become overpriced when investor demand is high, while flight-to-quality reduces this mispricing in downturns. Key takeaway: Time-varying demand for lottery-like options, not risk, drives the inverse size effect in options.
Prediction Markets
From Iran to Taylor Swift: Informed Trading in Prediction Markets (Mitts and Ofir)
Prediction markets appear efficient, but a small subset of traders consistently outperforms by trading just before major events. Accounts flagged for unusual timing, size, and concentration win about 70% of the time and capture roughly $143M in profits, far above random outcomes, suggesting informational advantages rather than skill alone. Key takeaway: Prediction markets aggregate information but also reward those with private information.
Blogs
How imputation helps statistical learning for macro trading signals (Macrosynergy)
VIX vs. Policy Uncertainty: What They Signal for Risk (CFA Institute)
One Year Later: Is ChatGPT Finally Worth Using for Quantitative Analysis? (Quantpedia)
Uncertainty (Quantitativo)
GitHub
Podcasts
Faheem Osman – Commodity QIS: An Under-Appreciated Source of Systematic Returns? (Flirting with Models)
“Positions Can Be LESS Risky at Higher Prices” - Derek Pilecki on Finding Edge in Financials (Odds on Open)
Private Equity’s Low Volatility Isn’t Real (Meb Faber Show)
The ICT Trader Who Made $2 Million After SWITCHING To Orderflow - Yush (Words of Rizdom)
Social Media & Industry Research
A Practical Approach to Weighting Signals (Factset Insights)
Mean Reversion in Play: Carry is BACK?! (Alpha Architect)
Winning the Long Game with RAFI (Research Affiliates)
Last Week’s Most Popular Links
Rethinking Trend Following: Optimal Regime-Dependent Allocation (Zakamulin)
Momentum and Reversal on the Short-Term Horizon: Evidence from Commodity Markets (Ding, Kang, Yu, and Zhao)
Bimodality Everywhere: International Evidence of Deep Momentum (Han and Qin)
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