Weekly Research Recap
Latest research on investing and trading
Every Tuesday, I curate a selection of market insights, research papers, podcasts, and investing ideas that stood out during the week, along with links for further reading.
Commodities
A hidden Markov model for statistical arbitrage in international crude oil futures markets (Fanelli, Fontana, and Rotondi)
By combining Brent, WTI, and the newer Shanghai crude oil futures in a hidden Markov regime-switching framework, the authors show that temporary dislocations between global oil benchmarks remain surprisingly tradable. The strongest models delivered roughly 6–8% annualized excess returns after transaction costs, with Sharpe ratios around 1 to 1.3 out of sample. Key takeaway: New markets may create fresh inefficiencies before arbitrage capital fully compresses them.
Crypto
Cryptocurrency Return Forecasting Using Technical Analysis: Out-of-Sample Evidence, Economic Value, and Macro-Conditional Predictability (Magner, Hardy, Lavin, and Sanhueza)
Testing 84 technical signals across the 10 largest cryptocurrencies from 2017 to 2026, this paper finds that trend and momentum signals in BTC, BNB, SOL, and XRP delivered the strongest out-of-sample performance after costs, with surviving strategies showing net Sharpe ratios around 0.8 to 1.1. Predictability strengthens during high BTC volatility, crypto sentiment shifts, and gold inflows. Key takeaway: Alpha in crypto is regime-dependent.
Equities
Factoring in the Low-Volatility Factor (Soebhag, Baltussen, and van Vliet)
Low-volatility investing manages sizeable AUM, yet most asset pricing models still ignore it. This paper argues that the disconnect stems from unrealistic assumptions about frictionless long-short investing. Once trading costs and factor asymmetry are considered, low-vol materially improves standard factor models because its returns survive implementation frictions better than many competing factors. Key takeaway: Low-vol investing becomes even more valuable once real-world frictions are considered.
Many “growth vs defensive” rotation models rely on rigid market regimes. Instead, this paper adjusts exposure between growth/tech and defensive/value ETFs using continuous signals tied to rates, VIX stress, drawdowns, and growth crowding. From 2017 to 2026, the smooth-allocation model achieved a 1.01 Sharpe ratio with materially smaller drawdowns than 100% growth exposure. Key takeaway: Style timing may work better as a continuous risk-management process than a binary regime switch.
Machine Learning and Large Language Models
Battle of transformers: Adversarial attacks on financial sentiment models (Can Turetken and Leippold)
Financial sentiment models increasingly drive trading, yet this paper shows how fragile they remain. GPT-4o-generated paraphrases fooled FinBERT and FinGPT in 20–54% of cases, cutting accuracy by up to 26 percentage points. The main weakness? Overreliance on trigger words and weak numerical reasoning. Key takeaway: Many financial NLP models still pattern-match language better than they understand finance, making them vulnerable to manipulation.
The Volatility Premium of Machine Learning: Decomposing Signal from Mechanism in Directional Trading Strategies (Gonzalez Maiz Jimenez, Lopez-Herrera, and Reyes-Santiago)
Using 216k+ out-of-sample forecasts across 48 U.S. stocks, this paper shows that, under perfect fills, random signals produce Sharpe ratios nearly identical to those of ML models. But as fills became more realistic, ML adds substantial value, especially during high-volatility regimes associated with stronger investor overreaction. Key takeaway: Execution quality may matter as much as the model itself.
Options
Navigating IV: Options Trading Strategy in Earnings Season (Lu)
Across 5,000+ option trades from 2020 to 2025 on 30 high-volume tech stocks, selling 5–10% OTM strangles around earnings produced strong risk-adjusted performance, while long-volatility strategies were broadly unprofitable. Key takeaway: Systematically harvesting post-earnings IV crush appears more effective than betting on volatility expansion.
Retail Option Imbalance and the Cross-Section of Stock Returns (Liu)
Sorting stocks by retail call-option buying pressure generates a long-short strategy with a Sharpe ratio of 1.76 (before costs) and a daily alpha of roughly 27 bps. The effect peaks after 2 trading days, then gradually reverses. Key takeaway: Retail option flow may create short-lived price overshooting.
This is a comprehensive review paper, walking through option pricing, Greeks, hedging, volatility modeling, and American option valuation, while also highlighting where theory breaks down in practice through transaction costs, discrete hedging, and volatility dynamics.
Uncovering the asymmetric information content of high-frequency options (Alexiou, Bevilacqua, Hizmeri)
The sign of high-frequency option returns matters. Negative jumps in OTM calls and positive jumps in OTM puts strongly predict future volatility, variance risk premia, and even equity returns. In out-of-sample volatility timing tests, these signals generate economic gains worth up to 206 bps annually. Key takeaway: Measures of downside risk in options contain information that aggregate volatility measures miss.
Blogs
Timing VX Futures with the Front-End VIX Curve (Quantseeker)
Active Dual Momentum GTAA Strategy (Quantpedia)
The Metamorphosis (Robot Wealth)
Warsh’s Challenges: Monetary Policy (John H. Cochrane)
Podcasts
The Only 4-Sharpe Crypto Fund — Leigh Drogen, Starkiller Capital (Odds on Open)
He Studied 100 Years of Bubbles. He Exposed Private Equity’s Volatility Illusion | The Weekly Wrap (Excess Returns)
Patrick Welton’s Journey from Stanford Oncologist to one of Trend Following’s Quiet Legends (RCM Alternatives)
Social Media & Industry Research
When Everyone Trades the Same Factor Playbook (Alpha Architect)
Total Portfolio Approach: A Quant Lens (AQR)
The Hidden Macro Bets in Quant Portfolios (Man Group)
Last Week’s Most Popular Links
Agentic Workflows for Alpha Research (Jonathan Kinlay)
ENSO Signals and Out-of-Sample Predictability in Soft Commodity Futures (Apte)
Dynamic Momentum Trading via Deep Q-Networks: An Intelligent Execution Framework for Portfolio Management (Deng, Xu, Li, Ji, and Xu)
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