Each week, I review recently published investment research and highlight some of the most interesting findings from academic papers, industry publications, and investment blogs. Links to all sources are provided.
Asset Allocation
Simple Portfolios that Deliver Consistent Decent Returns (Eden Wallberg and Karolin)
Does better portfolio construction require more complexity? This study of ETF strategies over 2005–2025 finds that an equal-weighted portfolio of stocks, bonds, and gold delivers a Sharpe ratio of 0.68 versus 0.52 for traditional 60/40. Risk estimation and volatility control add value, while more sophisticated return forecasts struggle to improve performance. The findings suggest that asset selection and risk management may matter more than predicting future returns.
Commodities
Identifying Strong and Priced Risk Factors in Commodity Futures Markets (Cai, Qin, and Webb)
Does a profitable trading factor necessarily represent a priced source of risk? Studying commodity futures from 1993–2026, the authors find that signals based on changes in CFTC Commitments of Traders (COT) positions predict returns but do not command robust systematic risk premia. In contrast, momentum, skewness, basis, basis momentum, and hedging pressure earn significant risk premia. Return predictability does not necessarily imply a priced risk factor.
Equities
Stock Market Cycles (Baele and Bekaert)
What does a typical bull or bear market actually look like? New research on US stocks finds that since 1960, bear markets lasted a median of 74 trading days, with a 16.2% decline, versus 294 days and a 37% gain for bull markets. Bear markets accounted for just 23% of trading days. Importantly, market cycles are more complex than simply rising or falling prices, with crashes, recoveries, and corrections occurring within broader trends.
Daily mean Reversion in Global Equity Indices: Serial Dependence, Tail Risk, and Exploitation (Bertola)
This study of six global equity indices (2002–2025) finds that buying after down days and shorting after up days for one day produced an average 27.0% CAGR and a Sharpe ratio of 1.64. Restricting shorts to markets below their 200-day moving average increased the Sharpe ratio to 1.74 and reduced maximum drawdown from 26.8% to 19.0%.
Factor momentum and stock momentum in the UK: Spanning and its limits (Foye)
Is stock momentum really just factor momentum? The author studies 153 UK equity factors (1986–2025) and finds that conventional stock momentum loses its statistically significant alpha after controlling for factor momentum. However, residual momentum and several valuation factors retain significant alphas, suggesting that factor momentum does not explain all return anomalies.
Factor Investing
Crowded Factors and Volatility Timing: When Risk Scaling Works (Feng)
Can factor crowding improve volatility targeting? This paper measures crowding of six equity factors using past returns, return persistence, and VIX sensitivity. In out-of-sample tests, crowding-aware volatility management achieves a Sharpe ratio of 1.03, versus 0.96 for standard volatility scaling and 0.85 without scaling. The findings suggest that factor crowding provides useful information for managing risk.
Machine Learning & Large Language Models
When Price Charts Rhyme: Historical Visual Retrieval for Stock Return Prediction (Chen, Liu, Wu, and Zhou)
Can AI identify profitable patterns in stock charts? This paper finds that matching current price charts with similar historical patterns helps predict future returns. A fine-tuned vision model achieves a gross Sharpe ratio of 6.37 in an equal-weighted long-short strategy, versus 4.46 for a CNN trained directly on chart images. Predictability is strongest among smaller, less liquid stocks with high retail trading activity.
Macro
Stagflation and Asset Management (Bera, Harvey, and Hooper)
Traditional stock-bond diversification struggles during high inflation. Using 107 years of US data finds that a 60/40 portfolio earned average annualized real returns of 9.0% during contained inflation, compared with −1.3% during overheating and −0.2% during stagflation. During stagflation, gold (+9.4%) and multi-asset trend following (+13.5%) performed considerably better, highlighting the potential value of alternative diversifiers.
Volatility
Expected and Realized Returns on Volatility (Hu and Jacobs)
Using data from 2004 to 2022, this paper finds that expected VIX futures returns, estimated using a mean-reverting VIX model and current futures prices, predict subsequent realized returns. Following volatility spikes, both expected and realized returns on long volatility positions become more negative. The signal also negatively predicts S&P 500 returns, although the statistical evidence is weaker. Its predictive power extends beyond conventional volatility risk measures.
Blogs
Sector Rotation: Does Portfolio Construction Matter? (Quantseeker)
The Log-Linear Regression: Usage for Long-Term Financial Asset Analysis (Portfolio Optimizer)
Stock Price Bubble Dissection Requires a look at Fundamentals and Speculation (Alpha Architect)
Podcasts
Hedge Fund Manager David Orr: Investing is 99% Talent (Odds on Open)
How an Discretionary Ag Trader “Reads the Soybean Leaves” with Stephen Davis (RCM Alternatives)
How Leveraged ETFs Really Work (Marco Sammon & Chris Murray) (Rational Reminder)
Social Media & Industry Research
A Conversation with Ken Griffin (Citadel)
What If Gold Had a Yield? (Man Group)
Last Week’s Most Popular Links
Weak Factors for Commodity Futures Portfolios (Nakagawa and Sakemoto)
Regime-Aware Interpretable Ranking for Sector Rotation (Koa et al.)
Revisiting Momentum Crashes: Volatility Timing across Developed and Emerging Markets (Feng, Huang, and Su)
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The finding that an equal-weighted mix of stocks, bonds and gold hit a 0.68 Sharpe versus 0.52 for 60/40 is a useful reminder that risk management seemed to matter more than return forecasts in that study.