Every week I sift through academic papers, industry research, market commentary, and investing discussions to find the ideas that matter. Here are this week's highlights, with links throughout.
Crypto
Crypto Futures Risk Mitigation: Dynamic Rebalancing, Carry and ARP Strategies (Georgiou, Eigenheer, and Sapuric)
Crypto futures often offer unusually high carry, but capturing it is harder than it looks. This paper moves beyond documenting the anomaly by testing implementable long-spot/short-futures strategies with dynamic rebalancing and option overlays. Key takeaway: The persistence of a risk premium doesn't guarantee it's easy to harvest.
Equities
Disagreement on Volatility (Yang and Zhu)
Investor disagreement isn't just about expected returns, but also about future risk. This paper measures disagreement about future stock volatility and finds that a long–short portfolio buying the lowest-disagreement stocks and shorting the highest-disagreement stocks earns about a 1% monthly return spread. Key takeaway: Risk disagreement is priced.
The Costs and Benefits of Leveraged ETFs (Murray and Sammon)
Leveraged ETFs aren’t inherently good or bad. This paper shows their success depends on what they leverage. Broad index LETFs can create investor value, while single-stock LETFs face much higher volatility drag and financing costs. Key takeaway: A 2× ETF on an index is economically very different from a 2× ETF on a single stock.
Short Interest Data is Short (Welborn)
Official short interest is widely used to gauge bearish positioning. This paper suggests it misses part of the picture. Comparing FINRA data with securities-lending records, lending exceeded reported short interest on 47 to 61% of reporting dates. Key takeaway: Public short-interest data probably understate true short exposure.
FX
Covered Interest Parity: The Long Run Evidence (Accominotti, Cen, Chambers, and Degorce)
Covered interest parity (CIP) didn't just break after 2008. Using data from 19 currencies over 1963–2025, this paper shows violations were common for decades. The real exception was 2000–06. Key takeaway: Even textbook arbitrage can persist for years when financial intermediaries face balance-sheet constraints.
Machine Learning and Large Language Models
A Seat at the Table: Turning AI Macroeconomic Views into Portfolios with a Factor Framework (Elkamhi and Lee)
Everyone is asking how AI will change investing. This paper argues AI's biggest role may not be stock picking, but helping investors form disciplined, probabilistic macro views. Those views are translated into candidate portfolios through a transparent, rule-based framework while humans retain final control. Key takeaway: The greatest value of AI in asset allocation is augmenting human judgment, not replacing it.
Options
The Factor Structure of 0DTE Option Returns (Dorion, Orlowski, and Song)
0DTE options now account for a huge share of SPX trading, but are they mispriced? This paper shows that six systematic risk factors explain most intraday 0DTE option returns. Compensation comes from bearing gamma and jump-skewness risk, while any remaining alpha disappears after realistic transaction costs. Key takeaway: Apparent 0DTE mispricing largely reflects systematic risk exposures.
Portfolio Construction
Is Correlation Neglect Bad for Portfolio Diversification? (Chen)
Investors are often told to pay more attention to correlations. This paper argues the real challenge is noisy correlation estimates. Preserving only the direction of co-movement produces more robust out-of-sample portfolios. Key takeaway: Robust diversification comes from preserving the direction of correlations, not their noisy magnitudes.
Private Equity
Buyout Performance with Assets Valued at Market (Ennis and Rasmussen)
This paper challenges one of private equity's biggest narratives. Using market prices instead of sponsor-reported NAVs, the authors estimate a beta of roughly 1.5–1.6 and find no statistically significant alpha. Key takeaway: Smoothed valuations can make private equity appear less risky, and more skillful, than market prices suggest.
Volatility
Beyond Current News: A Momentum-Augmented Model of Future Volatility (Zakamulin)
This paper uses 12-month momentum to forecast next-month volatility and finds a robust U-shaped relationship. Future volatility is highest after prolonged declines, lowest after moderate positive trends, and rises again after exceptionally strong rallies. Key takeaway: Momentum contains information about future risk, not just future returns.
Blogs
Stablecoins and the Future of Treasury Markets (CFA Institute)
Raise rates? Warsh’s conundrums (John H. Cochrane)
Conformal Prediction in Quantitative Finance (Vertox Quant)
A Window into Bond Investors’ Uncertainty About R‑Star (New York Fed, Guillaume Roussellet)
Podcasts
Stacie Mintz – Turning Qualitative Fundamentals into Quantitative Factors (Flirting with Models)
The 6-Year Test 10,000 Tickers Failed: with Eric Crittenden of Standpoint Funds (RCM Alternatives)
Private Equity Unwind with Dan Rasmussen, Founder & Managing Partner of Verdad Advisers (Planet MicroCap)
Social Media & Industry Research
Quick Take: Follow the Earnings (Acadian Asset Management)
Flying below the radar: Insider trading by executives below the top (Alpha Architect)
The frenzied serenity of the stock market (Acadian Asset Management, Owen Lamont)
Last Week’s Most Popular Links
The Science and Practice of Trend-Following Systems (Sepp and Lucic)
Learning to Trade I - Greek, Parameter and Statistical Hedging (Buehler)
Visual Recovery (Hu and Wu)
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The correlation-estimation paper caught my attention. Keeping only the direction of co-movement is a useful reminder that a more detailed estimate isn't necessarily a more dependable input. I'd be curious how the approach behaves when the direction itself changes across market regimes.