Welcome to this week’s Tuesday roundup, your curated selection of the most actionable investing ideas from the past week, spanning academic research, industry reports, blog posts, and insightful discussions across social media, with links throughout.
Asset Allocation
From Strategic Policy to Tactical Trades: Dynamic Asset Allocation with Predictable Returns and Trading Costs (Kolm and Ritter)
This paper develops a dynamic framework for tactical asset allocation that jointly accounts for expected returns, portfolio risk, trading costs, and how today’s positions affect future decisions. It derives when optimal trades should be smaller or larger than standard one-period TAA and how quickly portfolios should adjust. Key takeaway: Optimal tactical trades depend on both today’s opportunity and the cost of repositioning tomorrow.
Commodities
Correlation and Commodity Risk Premia (Fan and Zhang)
Commodity momentum struggles when commodities start moving together. Across 29 futures markets, scaling down momentum exposure during high-correlation regimes reduces drawdowns and improves risk-adjusted returns. Combining correlation, volatility, and stop-loss rules raises net Sharpe from 0.42 to 0.67. Key takeaway: Cross-commodity correlation can be a useful signal for timing momentum exposure.
Crypto
Bitcoin option expiration, gamma exposure, and intraday price reversals (Weiss, Gaudiosi, Zhou, and Webb)
Bitcoin options appear to move the underlying around expiration. The paper finds that when ATM open interest is high and dealer gamma exposure is negative, Bitcoin tends to fall before the 8:00 UTC expiry and then fully reverse within two hours. Key takeaway: Bitcoin option positioning can create systematic, short-lived price pressure around expiration.
Equities
Financial-Statement Signals and the Cross-Section of Stock Returns (Dong)
The accounting “factor zoo” may be much smaller than it looks. This survey catalogs 155 financial-statement signals, but argues that many reflect overlapping economic ideas such as valuation, profitability, accruals, investment, financing, and fundamental momentum. Key takeaway: Don’t treat every accounting signal as an independent factor. Combine signals by economic concept to avoid repeatedly loading on the same information.
The Implied Equity Term Structure (Baele, Driessen, and Jankauskas)
This paper asks how the price of equity risk changes with the horizon of corporate cash flows. Using U.S. stocks since 1980, the authors find that longer-dated cash flows generally command higher expected returns. But the pattern reverses for value firms and companies with high credit risk. Key takeaway: Equity risk is priced differently across horizons, and the shape depends strongly on firm characteristics.
Rethinking Predictability: Distributional Forecasts of the Market Risk Premium (Sinha)
Expected-return forecasts may miss much of what matters for investors. Using S&P 500 data, this paper finds limited ability to predict next-month excess returns, yet forecasts of the full return distribution contain useful risk information. Distribution-based allocation cuts max drawdown from 56.9% to as low as 16.0%. Key takeaway: Forecasting uncertainty can be more useful for controlling downside risk than forecasting returns.
Factor Time (Bowles, Reed, Ringgenberg, and Thornock)
Fama–French factors may be more time-sensitive than they look. Standard portfolios are refreshed annually, leaving accounting information increasingly stale. Monthly updates materially change value and investment factors, but the greater improvement comes from modeling the returns generated by these updates separately. Key takeaway: How often factor portfolios are updated significantly affects measured alpha and performance.
Timing the Market: A Skewed Perspective (Chan)
“Time in the market beats timing the market” is not universal. Across six developed markets over 25 years, waiting for a 20% dip to buy beat monthly investing in Japan, the UK, and Germany, but produced 12.6% less final wealth in the U.S. Key takeaway: The cost of waiting to buy a dip depends on the market drift. The stronger the long-run return, the more expensive it is to sit in cash waiting for a better entry.
Blogs
Gold and macro factors (Macrosynergy)
Are Covered Calls Just Expensive De-Risking? (Quantseeker)
Not another one! My fifth book... (Rob Carver)
A Sharpe of 2.1 From Nothing: The Second Number Your Agent Doesn’t Log (Jonathan Kinlay)
Podcasts
How Top Prop Traders Find and Scale Their Edge · Jeff Holden (Chat With Traders)
Professional Trading Roundtable: “A 50% Market Crash Is Mathematically Inevitable!” (Words of Rizdom)
Social Media & Industry Research
What Daily Stock Returns Tell Us About the Economy (Alpha Architect)
What I Learned About Asset Pricing (Pedro Santa Clara)
Investing in private equity: A decision framework for drawdown versus semiliquid funds (Vanguard)
Last Week’s Most Popular Links
Agentic Quantitative Trading: A Survey of Workflows, Systems, and Evaluation (Hua et al.)
Expected market risk premiums in the international cross-section (Berkman and Malloch)
Two Heads Are Better Than One: t-Statistics and Monotonicity in the Factor Zoo (Fan)
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The factor-update question makes me want to separate faster information from faster trading. Have you tried an experiment that refreshes the estimates frequently but only trades when the change is larger than a cost threshold? This is something I do often with stat-arb, maybe useful here too.