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.
Asset Allocation
Oil–VIX States as Conditional Signals for Cross-Asset Allocation (Aylsworth, Poechhacker, Schwaiger, and Werbach)
Oil prices alone are an incomplete allocation signal. This paper argues that it's the combination of oil and market volatility (VIX) that matters. High oil with low volatility resembles a late-cycle expansion, while high oil with high volatility signals stress. Key takeaway: The same oil price can imply very different portfolio risks.
Crypto
The Party’s Over: How the Bitcoin ETF Killed Crypto’s Cool Factor (Krause)
This paper argues that Bitcoin ETFs didn't just institutionalize crypto; they changed its character. After the ETF launch, Bitcoin's volatility and equity beta declined, while Dogecoin's Sharpe ratio fell from 0.30 to 0.01 and Google search interest dropped 63%. Key takeaway: As Bitcoin becomes institutionalized, the retail speculation that once fueled crypto booms is likely to become less dominant.
Equities
Returns to “Do-Nothing” Portfolios (Bessembinder)
The last decade rewarded concentration in mega-cap stocks. However, history tells a different story. From 1971 to 2025, concentrated portfolios generally underperformed broader portfolios, while randomly selected narrow portfolios lagged the index more often than they beat it. Key takeaway: Recent mega-cap dominance is the exception, not the rule.
The CAPE that Cried Wolf (Palazzo)
CAPE may not have failed; its denominator changed. The author argues that accounting changes have depressed reported earnings, making traditional CAPE overstate valuations. Adjusting for these distortions restores much of its long-run predictive power. Key takeaway: Before dismissing CAPE, consider whether the accounting changed.
Main Street in Wall Street (Ghezzi)
Wall Street may know more about Main Street than we think. Filtering out high-frequency market noise, stock prices can be used to recover investors’ real-time expectations of economic activity, often before traditional macro data catches up. Key takeaway: Markets are a leading macro indicator, not just a pricing mechanism.
Skewness Managed Portfolios (Gong, Lynch, and Ogden)
Many classic factor premiums are driven by just a handful of extreme winners. The authors show that tilting anomaly portfolios toward stocks with higher expected skewness on the long side and lower expected skewness on the short side improves returns by 5.5% annually across 18 anomalies while also boosting Sharpe ratios. Key takeaway: Expected skewness appears to add value beyond traditional factor signals.
AI Premium (Borri, Liu, and Tsyvinski)
AI adoption may now be priced as a systematic risk factor. Using 380 trillion AI tokens across 400+ LLMs, the authors construct an AI factor from realized AI consumption. Firms with the highest AI exposure subsequently outperformed the least exposed by 64 bps per week, even after standard factor adjustments. Key takeaway: Markets seem to price AI exposure as a distinct source of risk.
Machine Learning and Large Language Models
What NLP Sentiment Can’t Hear -How Vocal Delivery Improves Earnings-Call NLP Sentiment (Pope)
Adding vocal delivery to NLP sentiment improves return predictability. The strongest effect is on the downside: Negative language delivered with vocal strain underperformed similar negative language delivered with greater control by about 1% over the next 20 trading days. Key takeaway: Voice appears to make text-based sentiment more informative, not by replacing NLP, but by helping investors interpret it.
Portfolio Optimization
Adapting the Black-Litterman Framework to Fixed-Income Portfolios (Tandle)
The author adapts the Black–Litterman framework to fixed-income investing by combining market-implied returns with macroeconomic views. While the model improved returns in some risk regimes, it generally failed to beat a simple equal-weight portfolio on a risk-adjusted basis and required much higher turnover. Key takeaway: Sophisticated optimization isn't automatically superior to simple diversification.
Blogs
Buying the Dip Isn’t Free (Quantseeker)
Rolling, rolling, rolling.... updating statistical estimates yes or no (Rob Carver)
One of These Things (Is Not Like the Others). Or is it? Pooling rule p&l estimates across instruments. (Rob Carver)
Systematic FX trading with regression learning and transaction cost analysis (Macrosynergy)
Guardrails Make the Researcher: What an AI Agent Got Right (And Wrong) Replicating Nine Equity Anomalies (Quantpedia)
Podcasts
Peter Hecht – Portable Alpha: Solving the Funding Problem of Alternatives (Flirting with Models)
Ex-Citadel Quant on Trading the Most Asymmetric Market - Neel Somani (Odds on Open)
Social Media & Industry Research
Beta-arbitrage returns and stock market bubbles (Ptuomov)
Reads you may have missed (Man Group)
Catching Angels Before They Fall (Man Group)
Credit’s Systematic Shift (Acadian Asset Management)
Last Week’s Most Popular Links
Boundaries of Time Series Momentum (Suominen and Hjalmarsson)
Beyond Growth Rates: Macro Trends and Price Cycles (Favero, Melone, Myers, and Tamoni)
To Cluster Or Not To Cluster That is the Question... (Rob Carver)
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