Weekly Research Recap
Latest research on investing and trading
It’s Tuesday, and time for this week’s curated roundup of the most actionable investing insights from the last seven days, spanning academic research, industry reports, blogs, and social media, with links throughout.
Crypto
From Network Fundamentals to Macro-Financial Integration: The Evolving Predictability of Bitcoin Returns (Palazzi, Junior, and Klotzle)
Research shows that Bitcoin has become more correlated and financially integrated with equities. This paper documents that the return predictability of Bitcoin expanded from mainly blockchain signals to include stronger macro-financial drivers after 2019, with spillovers from traditional markets intensifying during stress episodes. Key takeaway: Bitcoin is not a reliable safe haven; on-chain valuation metrics consistently matter, but macro risk dominates when it matters most.
Decentralized Finance (DeFi): A Review and Research Agenda (Momtaz)
This comprehensive survey paper on DeFi reviews nearly 90 academic studies. For example, it shows that tokens can help launch and coordinate platforms, but power in trading, mining, and governance often becomes concentrated again. Key takeaway: DeFi changes how finance is organized, but it does not remove risk or incentives; it simply shifts who bears them.
Equities
Price-Path Convexity and Short-Horizon Return Predictability (Gulen and Woeppel)
Price-path convexity, the curvature of recent prices, strongly and negatively predicts short-horizon returns. At the aggregate level, a 1σ rise in convexity lowers next-month returns by 0.40%. Cross-sectionally, a low-minus-high convexity strategy earns 0.84% per month. The effect persists after factor controls and is linked to return overextrapolation. Key takeaway: When price paths become too convex, short-term returns tend to disappoint, suggesting an opportunity to fade overextrapolated moves.
Tail Risk Around FOMC Announcements (Jacobs, Ke, and Pan)
Using 7-day option-implied moments measured two days before pre-scheduled FOMC meetings, the authors show that left-tail risk is priced around announcements. More negative abnormal skewness and higher kurtosis predict stronger post-FOMC excess returns for up to four days. The effect is larger during expansionary monetary shocks and high-uncertainty regimes. Key takeaway: When crash risk rises into Fed meetings, investors demand and earn a temporary tail-risk premium afterward.
Idiosyncratic volatility (Feldman, Kang, and Zhao)
Conventional idiosyncratic volatility (IV), residual variance from factor models, misclassifies systematic zero-beta risk as diversifiable noise. In Fama–French 100 portfolios, true IV averages 13.6% annually versus 15–17% using the standard measure; the true/misspecified ratio averages 77–78% in typical scenarios. Key takeaway: Much of the “IV puzzle” may be measurement error; rethink IV-based strategies using a correctly defined risk decomposition.
Assessing Factors and the CAPM in 2026 (Welch)
Surveying more than 250 top U.S. finance academics, the paper finds a 5% expected equity premium. Of 20 factors, only momentum, profitability, value, and market beta receive positive future return forecasts; most others cluster at zero. Roughly one-third still back the CAPM. Key takeaway: Academics largely reject the idea that most factor premia compensate for risk; only beta and value retain a risk label, while momentum and profitability are seen as behavioral.
Voice Beyond Words: Evidence That Managerial Tone Predicts Returns When Text Does Not (Pope)
Even when the sentiment of earnings transcripts is neutral, this paper shows executive vocal delivery still predicts returns in Russell 3000 earnings calls. Sorting CEO/CFO Q&A speech into voice-based quintiles yields 40–70 bps excess returns over 10–30 days, with a +0.60% top–bottom spread over 20 days. Text alone loses power under neutrality. Key takeaway: When words and text are neutral, tone still predicts returns; investors should mine managerial voice, not just transcripts.
Hedge Funds
Partisan Hedge Funds (Chen, Huang, Sun, and Teo)
Hedge funds that load up on stocks tied to the sitting president’s economic agenda trail those that avoid them by 4.44% per year after risk adjustment. Politically aligned managers hold more of these stocks, talk more positively about them, and perform even worse after polarization shocks like mass shootings or major protests. The effect is strongest among highly partisan managers. Key takeaway: Letting politics guide stock selection can meaningfully hurt returns, even for professional investors.
Options & Volatility
A Parsimonious and Interpretable Factor Model of the Implied Volatility Surface (Wan, Wei, and Zhu)
This paper develops a five-factor model of the implied volatility surface, estimated from 7.2 million SPX options. It sharply reduces in-sample implied volatility fitting errors relative to standard polynomial benchmarks and delivers the lowest out-of-sample surface forecast errors. The extracted factors also outperform the VIX in predicting realized volatility. Key takeaway: A small, economically interpretable set of IV factors captures tail risk and improves both surface fitting and volatility forecasting.
Prediction Markets
Who Profits from Prediction Markets? Execution, not Information (Della Vedova)
Analyzing millions of Polymarket trades, the paper shows that predicting outcomes is not what drives profits. Retail traders are accurate in direction 51.3% of the time, yet lose 2.0% on invested capital on average ($79M in total), while high-frequency bots hover at chance (49.9%) but earn +0.56%, totaling $134M. A roughly 2.5-cent per-contract execution advantage explains the difference. Key takeaway: Speed, pricing, and liquidity provision, not superior forecasts, separate winners from losers, on average.
Blogs
Estimating the Term Structure of Corporate Bond Risk Premia (New York Fed)
More Bets, Better Bets (Quantitativo)
State-Space Models for Market Microstructure: Can Mamba Replace Transformers in High-Frequency Finance? (Jonathan Kinlay)
Geopolitical Risk and Portfolio Oversight (CFA Institute)
Systematic Allocation in International Equity Regimes (Quantpedia)
Three Levers That Drive VC Returns (CFA Institute)
The Winter of our Pairs Trading Discontent: Problems, limitations, frustrations (Robot Wealth)
Podcasts
Richard Craib - Crowd-Sourced Alpha with Numerai (Flirting with Models)
Alpha Comes From a Differentiated View - Ex-Point72 Prop Research Head Kirk McKeown on Edge in 2026 (Odds on Open)
3 AI Stock Winners & 3 Write-Offs - Prof. Damodaran (Meb Faber)
Social Media & Industry Research
The Concentration Game: Understanding Portfolio Effects of U.S. Equity Market Concentration (D.E. Shaw)
Should Trend Follow Carry: Lessons from Bonds, Gold, and 2022 (Research Affiliates)
Exploiting Myopia: The Returns to Long-Term Investing (Alpha Architect)
How To Be A World-Class Agentic Engineer (SystematicLongShort)
Last Week’s Most Popular Links
Enhancing Asset Allocation and Portfolio Rebalancing Through Dynamic Theme Detection (Rubio)
Large Language Models and Generative Factor Discovery in Crypto Markets (Sun, Wang, and Zhang)
Expectation Bias and Short-term Momentum (Gao, Ma, and Yuan)
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