Weekly Research Recap
Latest research on investing and trading
Each Tuesday, I share the most interesting market and investing insights I came across during the past seven days, including new research papers, blogs, and podcasts. Links are included throughout for readers who want to explore the ideas in more detail.
Crypto
Demand-Side Fee Flows and Return Predictability on Ethereum (Dunnes and Eckberg)
Ethereum may have a fundamental valuation signal after all. This paper builds a valuation metric from Ethereum’s protocol fees relative to token supply and finds that periods of unusually strong fee activity predict higher ETH returns over the next 1–2 months. The effect appears only after the 2024 Dencun upgrade, with out-of-sample R² reaching 14.4% at a 45-day horizon. Key takeaway: Protocol fundamentals seem to contain predictive information for crypto returns.
The “Actual Retail Price” of Crypto Trades (Rösch, Shohfi, Stanco, and Walz)
Retail crypto trading may be far more expensive than most investors realize. This paper executed nearly 500 real trades across Coinbase, Kraken, Crypto.com, and Robinhood, finding all-in trading costs of 253 to 834 bps on major crypto exchanges, while Robinhood and crypto ETFs often provided much cheaper execution. Key takeaway: For retail crypto investors, choosing the right platform can save more than picking the right coin.
Equities
War Risk and Time-Varying Return Predictability of the Dividend-Price Ratio (Yu, Xiao, Zhou)
The dividend-price ratio predicts future U.S. stock returns primarily during periods of active war. Outside high-war periods, it adds little forecasting value. The authors argue that war speeds the correction of market mispricing, making valuation signals more informative. Key takeaway: Even classic valuation signals aren’t universally useful, their predictive power depends on the market regime.
When Do Anomalies Deserve Capital? Macro-Regime Memory and Dynamic Cross-Sectional Factor Allocation (Taheri Hosseinkhani)
Macro timing is one of investing’s oldest ideas. This paper applies it in a different way. Instead of timing the market, it dynamically allocates capital across factor families like value, momentum, quality, and liquidity using macro regimes. Across 212 equity signals, the approach achieves an out-of-sample Sharpe ratio of 1.51. Key takeaway: In factor investing, the timing of factor allocation can matter as much as the factors themselves.
On Volatility-managed Portfolios: The Role of Conditional Factor Correlations (Barroso, Maio, and Wang)
Only 3 of 9 individual equity factors deliver robust gains from volatility scaling. However, volatility scaling remains effective for diversified factor portfolios. It’s not just about timing volatility, it automatically reduces exposure when factor correlations rise, and the paper shows much of its edge comes from this mechanism. Key takeaway: In multi-factor investing, volatility scaling helps manage the impact of rising factor correlations, not just volatility itself.
Fixed Income
Understanding Corporate Bond Excess Returns (Cui, Lu, and Song)
The excess returns on corporate bonds aren’t as credit-driven as many investors assume. This paper matches every corporate bond with a synthetic Treasury having identical cash flows, isolating returns unique to credit markets. It finds that roughly 41% of the excess return measured over T-bills is actually compensation for interest-rate risk. Key takeaway: Separate duration from corporate-bond-specific risk before evaluating corporate bond strategies.
FX
The Shape of Carry: Construction, Leverage, and Crash Risk Across Currency and Bond Markets (Gouws)
Carry delivers a modest 0.5 Sharpe across currencies and bonds but comes with severe negative skew and crash risk. Proper portfolio construction makes the premium more investable: Diversify carry sources and size positions by risk. Key takeaway: Proper risk management is key to harvesting the carry premium.
Machine Learning and Large Language Models
Bimodality Everywhere: International Evidence of Deep Momentum (Han and Qin)
This paper enhances traditional momentum by predicting the entire return distribution rather than a single outcome, then ranking stocks by their expected returns. Across 45 countries, this improved the average annualized Sharpe ratio from 0.98 for traditional momentum to 2.49. Key takeaway: Rather than relying only on the most likely outcome, use the entire predicted probability distribution when constructing momentum portfolios.
Portfolio Construction
Investors often assume sophisticated optimization beats simple diversification. This paper suggests otherwise. Across nine equity factors (1976–2025), recursive mean-variance optimization and dynamic factor timing generally failed to outperform a simple equal-weighted (1/N) portfolio out of sample. Key takeaway: Simple diversification remains remarkably difficult to beat once estimation risk is taken into account.
Risk Management
A Brief History of Financial Risk (Hartzmark and Solomon)
For most of market history, investors didn’t think about risk the way we do today. This paper shows that before Markowitz, risk wasn’t volatility, it was largely the chance of losing money over the long run, while stocks were often viewed as speculation rather than investments. Modern portfolio theory fundamentally changed how investors think about risk. Key takeaway: Today's definition of risk is relatively recent, and it fundamentally changed investing.
Blogs
Beyond Diversification: Factor Risk in Sovereign Portfolios (CFA Institute)
Can AI Do Financial Research? (Quantpedia)
Podcasts
~100% Returns in 2025, No Losing Year Since 2008: Erik Smolinski on Edge for Retail Trader Edge (Odds on Open)
The Creator of Claude Code on The Hottest Piece of Software in the World (Odd Lots)
Why Trend Following Is Changing Faster Than Ever ft. Yoav Git (Top Traders Unplugged)
Social Media & Industry Research
The Intramonth Momentum Cycle (Alpha Architect)
Two Accounting Anomalies: One May Be Risk, the Other Is Mispricing (Alpha Architect)
A More Fragile World (Citadel)
Last Week’s Most Popular Links
Winners Glide, Losers Stumble: A Behavioral Reversal Tilt to Momentum (Wen and Feng)
Betting on the Leverage Effect (Doshi, Jacobs, and Sert)
Do AIs Make Good Traders, and Do They Make Good Traders Better? (Bell, Haghani, and White)
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