Weekly Research Recap
Latest research on investing and trading
Hi there! Below is a new roundup of great investing research from the past week. Each paper is linked to its original source for easy access.
Commodities
The Reaction of Corn Futures Markets to US and Brazilian Crop Reports (Silveira, Silva, Mattos, Junior, and Capitani)
Crop reports from the US and Brazil inform markets about expected corn production, but not all reports have the same impact. The authors find that US reports prompt sharp price movements and trading spikes in both US and Brazilian futures markets while Brazilian reports, on the other hand, have a more muted effect.
Crypto
Stablecoins and the US Treasury Market (Yadav and Malone)
Stablecoins are growing rapidly as a means to move digital dollars quickly and cheaply. To keep their value stable, they rely heavily on US government bonds, which are seen as safe and easy to trade. This paper warns that the relationship is risky: Stablecoins depend on a bond market that doesn't operate around the clock and sometimes freezes up. Hence, investors should realize that stablecoins aren't risk-free money substitutes.
Bitcoin Price Volatility: Effects of Retail Traders, Illegal Users, and Sentiment (John and Li)
Bitcoin is notoriously volatile, limiting its use as a stable currency. This paper finds that retail investors on platforms like Robinhood fuel ongoing volatility, while privacy-seeking trades, tracked through Monero activity, spark sudden price jumps. Online search trends also affect both types of movement. The key takeaway: Bitcoin’s swings are driven by both speculative hype and hidden, anonymous activity.
Equities
Investor sentiment and stock returns: Wisdom of crowds or power of words? Evidence from Seeking Alpha and Wall Street Journal (Lachana and Schröder)
Many investors rely on media to gauge market mood, but it's unclear whether traditional news or social platforms are better at capturing that sentiment. This paper shows that short-term stock returns are more strongly predicted by sentiment from Seeking Alpha articles and comments than by those from the Wall Street Journal. The takeaway for investors is that social media platforms provide more timely and impactful signals of market sentiment than traditional financial news outlets.
Is there an inflation premium in the cross-section of stock returns? (Bruno, Goltz, and Luyten)
Many investors seek protection from inflation. This paper examines whether certain stocks can reliably serve that role. While some equities respond to inflation shocks, particularly those tied to headline and energy inflation, these relationships are unstable and hard to estimate. As a result, any risk premium tied to inflation hedging with stocks is difficult to pin down. The bottom line: Stock-based inflation hedges are rare and unreliable.
ESG
ESG Mania and Institutional Trading (Demirer, Rognone, and Zhang)
Investors may believe that putting money into ESG stocks helps make a positive impact. This paper challenges that idea by showing that institutional investors often invest in ESG stocks to follow trends, boost reputation, or attract new money, not to promote sustainability. These institutions tend to move in groups and their trades don’t improve stock prices or performance. Hence, ESG investing isn’t always about values, but more about image.
Does ESG Information Deliver Investment Value? A High-Dimensional Portfolio Perspective (Bruno, Goltz, and Naly)
Can ESG data and signals help build better portfolios? This paper tests that idea using over 200 ESG metrics and advanced portfolio techniques. The result: While ESG data improves risk-adjusted returns in-sample, results out-of-sample show no significant improvement.
Fixed Income
From Risk-Neutral to Risk-Contaminated Expectations in Swap Markets: The Emergence of a "Hairy Premium" (Georgievska and Saunders)
Investors often assume that interest rate swaps are fairly priced, with neither side gaining on average. However this paper shows that fixed-rate receivers in long-term swaps have consistently earned significant returns, averaging about 2.7% annually, mainly due to biased market expectations and structural limitations that prevent proper pricing.
Machine Learning and Large Language Models
High-Dimensional Learning in Finance (Fallahgoul)
Recent research has challenged the idea that more complex, over-parameterized models consistently improve performance. This paper shows that a widely used technique breaks key theoretical assumptions when implemented in practice. It also finds that, given the weak signals in financial data, even ideal complex models would need decades of training data. Overall, the findings suggest that complex models may simply be repackaging simpler patterns.
Towards Competent AI for Fundamental Analysis in Finance: A Benchmark Dataset and Evaluation (Wu, Wang, Zou, Wang, and Shao)
Many investors hope AI can automate deep company analysis, but today’s models often fall short. This study shows that while advanced AI tools can extract facts and offer logical commentary from financial reports, they consistently struggle with getting the math right, miscalculating key ratios and growth rates. To support progress, the authors release a new dataset and code repository for testing AI on real financial statements.
Options
Skewness Premium for Short-Term Exposure to Squared Market Returns (Wallmeier)
This paper proposes an options trading strategy that isolates the S&P 500 skewness risk premium, which amounts to around 5% annually and persists over daily, monthly, and quarterly horizons. Overall, the author provides clear evidence that investors require compensation for short-term downside risk.
Volatility
Predicting Realized Variance Out of Sample: Can Anything Beat The Benchmark? (Pollok)
This paper compares several volatility forecasting approaches, including simple time-series models, machine learning methods like LASSO, and factor-based models. While none clearly outperform the benchmark in terms of accuracy, models like LASSO lead to better returns when used for trading straddles. This suggests that more sophisticated forecasts can enhance trading performance even if their statistical improvements are modest.
Volatility: A Dead Ringer for Downside Risk (Estrada)
Volatility is often criticized for treating gains and losses the same, which can seem misleading to investors focused on downside risk. However, this paper shows that volatility closely mirrors several measures that capture only losses. Investors can therefore continue using volatility as a simple, familiar way to gauge risk, confident that it generally reflects the kind of losses they care about most.
Blogs
Cross-country relative duration strategies with macro factors (Macrosynergy)
Weekly Research Insights (QuantSeeker)
Pre-Announcement Drift for BoE, BoJ, SNB: Do Markets Move Before the Word Is Out? (Quantpedia)
Supervised Portfolios: A Supervised Machine Learning Approach to Portfolio Optimization (Portfolio Optimizer)
A new strategy to fight inflation (John H. Cochrane)
FinTwit and LinkedIn
The All Weather Investor (Alan Dunne)
A Strategy for (Micro)Strategy (Stylus Capital, by Rob Carver)
Bill Ackman's Key to Long Term Investment Success (@BillAckman)
The Hidden Value of Streaky Returns in Stock Portfolios (AQR)
(So) What If You Miss the Market’s N Best Days? (@CliffordAsness)
Systematic Strategies & Quant Trading 2025 (HedgeNordic)
GitHub
Podcasts
Ralph Sueppel on Quant Trading Macro the Right Way (Macro Hive)
Pivotal Perspectives: Decoding the Modern Hedge Fund Landscape with Jon Caplis (RCM Alternative)
The Two Tribes of Trend ft. Richard Brennan (Top Traders Unplugged)
$66 Billion. 17 Straight Outperforming Years | The Overlooked Strategy David Giroux Used To Do It (Excess Returns)
In Gold We Trust 2025 - The Big Long Live Debate (ReSolve Asset Management)
Last Week’s Most Popular Links
A Protocol for Causal Factor Investing (Lopez de Prado and Zoonekynd)
Commodity Inflation Risk Premium: A Powerful Characteristic for Commodity and Stock Market Returns (Hou, Platanakis, Ye, and Zhou)
Asymmetries at the core of short-term return predictability (Breckenfelder, Buchwalter, and Tedongap)
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