Hi there! This week’s research recap brings you the key investing insights and resources from the past seven days, with direct links to all sources.
Crypto
State-Dependent Market (In)Efficiency in Cryptocurrency Markets (Barak, Razmi, and Mousavi)
Cryptocurrency return predictability is regime-dependent. This paper tests strategies based on “Directional Change events”, where a new trend is confirmed only once price moves a fixed percentage from the last extreme. A machine-learning model adaptively selects the optimal threshold each day. From 2021–2024, this approach delivered 446% out-of-sample return with a Sharpe of 1.34 (pre-costs), more than double the best static rule (0.59). By leveraging on-chain and trend features, the model distinguishes mean-reverting from trending regimes and adjusts optimal thresholds accordingly.
Currencies
Long-Run Interest Rate Differentials and the Profitability of Currency Carry (Kaebi and Martins)
The authors decompose 10-year yields into their cyclical and persistent components, showing that carry profits come almost entirely from the latter. Sorting currencies on this signal outperforms standard carry, with lower turnover and reduced crash risk. Overall, the findings suggest that structural yield trends provide a more reliable foundation for FX carry strategies.
Equities
Expected Stock Returns in Bullish Times (Estrada)
Current conditions echo 1999: Lofty valuations and unrealistic growth assumptions. A return decomposition shows earnings growth and P/E expansion are negatively correlated (ρ ≈ –0.50). In 1999, sustaining the prior 17.9% returns required >10% earnings growth and P/Es near 35, but the 2000s delivered a return of –0.7%. In 2025, sustaining 13.3% returns needs equally implausible assumptions. Takeaway: Investors should expect mean reversion, not another decade of euphoria.
The Information Content of The Implied Volatility Surface: How to More Efficiently Use Option Information to Predict Stock Returns? (Han, Liu, and Tang)
By applying Partial Least Squares to the entire option volatility surface, the authors extract a measure of stock-specific downside jump risk that strongly predicts stock returns. A decile-sorted long–short portfolio earns 18.4% annually (Sharpe 1.29, pre-costs and assuming availability of shorts), robust across maturities and thresholds. Hence, the term structure of option prices embeds forward-looking crash risk that can potentially be systematically exploited.
Green Window Dressing (Parise and Rubin)
ESG mutual funds engage in “green window dressing” around quarter-end disclosures. They temporarily boost ESG exposure before filing, then unwind after, which increases Morningstar sustainability scores and frees capital for higher-return assets. ESG stocks themselves rise 0.20% abnormally pre-disclosure, only to reverse afterward. Investors relying on ESG ratings should recognize they may reflect cosmetic trading rather than genuine, persistent sustainability exposure.
The “Actual Retail Price” of Equity Trades (Schwarz, Barber, Huang, Jorion, and Odean)
Despite the common suspicion that payment for order flow (PFOF) harms execution quality, this study finds the opposite in some cases: PFOF brokers like TD Ameritrade delivered the lowest round-trip costs (~7 bps), while commission brokers such as IBKR Pro faced the highest (~46 bps). The disparities stem from market makers quoting systematically different prices to different brokers for identical trades, not from PFOF itself. In general, execution quality reflects how costly a broker’s order flow is for the market maker to handle, not whether the broker accepts PFOF.
Machine Learning and Large Language Models
Large Language Models and Futures Price Factors in China (Cheng, Liu, and Zhou)
GPT-generated factors for Chinese futures deliver robust out-of-sample performance beyond the model’s training cutoff. Dynamic multi-factor long–short portfolios and long-only portfolios achieve Sharpe ratios above 3.0. Alphas are consistently positive and highly significant. Overall, the findings show GPT can autonomously create factors that match or exceed traditional models.
Extracting the Structure of Press Releases for Predicting Earnings Announcement Returns (Wu, Akin, Martineau, Gregoire, and Veneris)
Text in earnings press releases (”soft information”) is as powerful as earnings surprises (”hard information”) in explaining announcement-day stock returns. Using 138k releases (2005–2023), FinBERT clearly outperforms bag-of-words models in predictive accuracy and explanatory power. Hence, the narrative tone, captured best by FinBERT, moves markets as much as the numbers.
Macro
Estimating the Sign and Magnitude of the Inflation Risk Premium (Kim and Ronn)
This paper defines the inflation risk premium (IRP) as the gap between market-implied inflation (from TIPS breakevens or inflation swaps) and realized inflation or survey expectations. At the 1-year horizon, the IRP is negative, between 0.5% and 1%, indicating market measures understate near-term inflation. The IRP steadily converges toward zero for 5–10 year horizons. As a result, investors should note that market-based inflation measures embed a systematic downward bias in short-term inflation expectations.
Portfolio Management
Strategic Style Allocation: Absolute or Relative? (van Vliet)
The author shows that optimal style allocation hinges on investor goals. From 1963 to 2025, low-volatility stocks delivered the highest Sharpe ratio (0.57) and reduced drawdowns, making them best for absolute-return objectives. In contrast, momentum, value, and profitability factors achieved stronger information ratios, appealing to those aiming for benchmark outperformance. Factor timing rarely pays off without hit rates above 60%. The author concludes that integrating multiple factors in one portfolio maximizes efficiency while curbing timing risk.
The Stock-Bond Correlation: A Tale of Two Days in the U.S. Treasury Market (Hu, Jin, and Pan)
This paper develops a daily stock–bond correlation from intraday (5-minute) returns on S&P 500 E-mini and 10-year Treasury futures. It identifies “bond safety days” (ρ ≈ –0.63) when Treasuries hedge equities, and “bond risk days” (ρ ≈ +0.19) when Treasuries amplify market stress. On safety days, convenience yields widen and term premia fall; on risk days, premia rise. For investors, tracking this intraday correlation could help distinguish between risk-on/off environments, improving portfolio hedging and tactical allocation.
The reasons why maximum diversification is better than minimum risk, including in terms of risk (Torrente and Uberti)
The authors test minimum risk vs. maximum diversification portfolio construction across multiple markets (S&P 500 sectors, DAX, MIB, ESX, FTSE) and risk measures (variance, MAD, VaR, CVaR). Out-of-sample, diversification dominates, producing more stable allocations, lower turnover costs, and often lower realized risk than formal risk minimization. Moreover, Sharpe ratios are consistently higher for maximum diversification, especially with short estimation windows where minimum risk fails.
Blogs
A scorecard for global equity allocation (Macrosynergy)
Employing volatility of volatility in long-term volatility forecasts (Outcast Beta)
Timing Leveraged Equity Exposure in a TAA Model (QuantSeeker)
Medium
Claude Sonnet 4.5 Is Finally Here (Jim Clyde Monge)
Anchoring VWAP Like Institutions (Cristian Velasquez)
Podcasts
21st Century Investing Strategies From Dmitry Balyasny (Masters in Business)
In Search of the Next Fund Manager w/ Market Wizards Author Jack Schwager & Emanuel Balarie (Chat with Traders)
Convexity Maven Harley Bassman: How To Survive The Next Rate Cycle (ReSolve Asset Management)
Social Media / Industry Research
Hedge funds and high-frequency traders are converging (FT Alphaville)
Fundamental Growth (Research Affiliates)
How top hedge funds can pay traders $100mn (FT, via Giuseppe Paleologo)
Last Week’s Most Popular Links
Predicting Extreme Returns with Fundamentals: A Machine Learning Approach (Liu and Wang)
The stock market impact of volatility hedging: Evidence from end-of-day trading by VIX ETPs (Bangsgaard and Kokholm)
Good Carry Trades and Market Dynamics (Iwanaga and Sakemoto)
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