This week’s briefing covers the investment research and market ideas I found most valuable over the past seven days, across academic research, industry publications, independent blogs, and social media. Links to all original sources are included.
Equities
Expected market risk premiums in the international cross-section (Berkman and Malloch)
Options markets contain a surprisingly strong signal for international equity allocation. Across international markets, option-implied expected market risk premia predict next-month returns: A 1%-point higher risk premium corresponds to roughly 1.1% higher subsequent excess return in the cross-section. Key takeaway: Local index options help predict next-month index returns.
Calendar Effects of Asset Pricing Factors (Fan, Li, and Miao)
Factor returns have calendar effects, but almost exactly opposite to the market. Across 153 U.S. equity factors, premia are weaker overnight, on Fridays, in January, around month-end, and near major macro announcements. Key takeaway: Market seasonality and factor seasonality tend to run in opposite directions.
The economics of dispersion trading have changed dramatically. In 2010–14, option-implied correlation exceeded subsequent realized correlation by 10.7 points on average. By 2022–26, that spread had flipped to -1.9 points. Key takeaway: The historical premium for selling correlation across S&P 500 stocks has largely vanished.
Two Heads Are Better Than One: t-Statistics and Monotonicity in the Factor Zoo (Fan)
A high t-stat isn’t enough to identify robust equity factors. Across 150 U.S. characteristics, t-stats and monotonicity, the share of months returns rise across factor quintiles, are nearly unrelated (correlation −0.08). Combining both better predicts out-of-sample returns and alphas. Key takeaway: Factor robustness is better judged by both statistical strength and the shape of returns across portfolios.
ETFs
Volatility and Returns to Leveraged ETFs (Bessembinder)
Leveraged ETFs don’t necessarily suffer from high volatility. What matters is serial correlation in the underlying returns. When returns tend to persist, more volatility can boost expected leveraged returns; when they reverse, it hurts. Without serial correlation, volatility itself has no effect. Key takeaway: Serial correlation, not volatility alone, determines the expected impact of daily rebalancing.
Twenty Years of Leveraged and Inverse Exchange-Traded Products: What Have We Learned? (Chang and Madhavan)
Twenty years after leveraged ETFs were launched, the authors review what investors have learned. Daily leverage works as advertised, but long-run returns are highly path-dependent. Hidden financing costs can create additional performance drag. Key takeaway: With leveraged ETFs, the return path and hidden costs matter greatly for realized returns.
Machine Learning & Large Language Models
Agentic Quantitative Trading: A Survey of Workflows, Systems, and Evaluation (Hua et al.)
This survey paper covers how agentic AI is being applied to quantitative trading. Across 20 systems, signal discovery is nearly universal, but only 2 consider all five core areas: Factor research, signals, portfolios, execution, and risk management. Key takeaway: Strong AI forecasts and backtests do not reliably translate into live trading performance as risk control, liquidity, and execution costs matter greatly.
Option-Implied Signals and Crash Risk: Predictability and Machine-Learning Evidence from U.S. Equity Options (Li and Wang)
Machine learning is uncovering option signals that traditional measures miss. Using U.S. equity options from 2015–2026, XGBoost beats linear models for next-month return prediction in the recent 2023–26 regime: 1.29% vs. 0.07% OOS R². The most important predictors also change across market regimes. Key takeaway: Option-market predictability is regime-dependent, with nonlinear ML adding value in the most recent period.
Options
Volatility Salience: Evidence from the Options Market (Ding, Li, Shi, and Zhai)
Investors appear to overpay for options when a stock’s recent volatility stands out relative to market volatility. Across U.S. equity options from 1996–2025, greater “volatility salience” predicts lower subsequent delta-hedged returns. The effect is strongest in OTM options: High-minus-low spreads reach −1.11% for calls and −1.83% for puts. Key takeaway: Unusually attention-grabbing volatility appears to predict option overpricing.
OPIN and the Cross Section of Equity Option Returns (Chen, Hu, and Yang)
Informed options trading appears to be priced. A new measure of informed trading intensity (OPIN) strongly predicts delta-hedged option returns: Options with more informed activity subsequently earn lower returns. For ATM calls, the high-minus-low OPIN spread is about −2.3% over 52 days. Key takeaway: Option order flow contains information about subsequent option returns.
Portfolio Construction
The Marginal Cost of Diversification (Sanford)
Diversification can eventually increase portfolio risk. In U.S. equities, nearly half of stock additions that reduce idiosyncratic risk also increase systematic risk. As portfolios grow, the idiosyncratic benefit shrinks faster than the systematic-risk effect. Key takeaway: More holdings do not necessarily mean less total risk; what matters is how each addition changes factor exposure.
Prediction Markets
The Cross-Section of Price Changes in Prediction Markets (Hou, Li, and Zheng)
Prediction markets exhibit short-term reversal. Across nearly 97,000 Kalshi contracts, recent 1–6 day price moves tend to reverse, with the strongest effect at the 1-day horizon: Contracts with the largest prior increases underperform those with the largest declines by 0.77 cents the next day. Key takeaway: Prediction-market prices show short-run mean reversion.
Blogs
Does Short-Term Mean Reversion Work Across Asset Classes? (Quantseeker)
Can ChatGPT Forecast Stock Price Movements? (Alpha Architect)
Podcasts
David Booth: What True Wealth Actually Means (Rational Reminder)
Jack Schwager: 40 Years of Market Wizards (Capital Horizons)
The Art of Simple Investing | Tips from Ben Carlson (Bogleheads)
Social Media & Industry Research
September Setup: The Asymmetry Has Changed (Citadel)
Academic Alpha (AQR)
Last Week’s Most Popular Links
On the Anatomy of Trend (Kjaer)
Prior Sentiment and Returns Around Earnings Announcements (Kazemi and Makridis)
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The pairing of t-statistics and monotonicity stood out. A significant spread and a consistent relationship across the sorted portfolios aren’t quite the same question. I’d be interested in whether the combination still helps when transaction costs and turnover are held to the same standard across the candidate factors.
Nice roundup. Always good to see other research driven takes on positioning heading into this kind of volatility.