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 week, including new research papers, blogs, and podcasts. Links are included throughout for readers who want to explore the ideas in more detail.
Asset Allocation
Systematic Tactical Allocation in Emerging Markets vs. U.S.: A Momentum-Based Approach (Vojtko and Dujava)
Simple 6 to 12 month trend signals between EM and U.S. equities help transform a historically weak EM-vs-US spread into a profitable long-short allocation strategy with Sharpe ratios above 0.5. The strongest performance is achieved by blending multiple trend horizons rather than optimizing a single parameter. Key takeaway: Global allocation may depend less on forecasting macro turning points and more on adapting to persistent relative-performance trends.
Commodities
On the Comovement of Contango and Backwardation Across Futures Commodity Markets (Luisi, Roccazzella, and Triantafyllou)
Contango and backwardation tend to move together across energy, metals, and agricultural futures markets, especially during crises. Gold is the outlier: When other commodity curves become more contangoed, gold often moves the opposite way. Key takeaway: Macro shocks increasingly dominate commodity term structures, meaning diversification across commodities may be far weaker than expected.
Dual Momentum Allocation Between Physical Gold and Bitcoin (Digital Gold) (Vojtko and Dujava)
A simple momentum strategy rotating weekly between GLD and Bitcoin based on relative and absolute strength improves risk-adjusted performance versus buy-and-hold. The strongest variant deliver 80% annualized returns with a Sharpe of 1.64. Key takeaway: There is potential alpha in systematically adapting to when investors prefer digital versus physical stores of value.
Equities
The Crash Risk in Individual Stocks Embedded in Skewness Swap Returns (Pederzoli)
Investors appear willing to pay heavily for protection against crashes in individual stocks. Skewness-based option strategies earn roughly 20% monthly average returns with Sharpe ratios near 0.5, though punctuated by rare but severe losses during market stress. The effect strengthened materially after 2008 as deep OTM put protection became more expensive. Key takeaway: Stock-specific tail risk appears to command a large positive risk premium in option markets.
Economic Uncertainty and the Beta Anomaly in G10 Countries (Atilgan, Demirtas, Gunaydin, and Tosun)
Across G10 equity markets, low-beta stocks outperform high-beta stocks primarily during periods of low economic uncertainty. In calmer environments, the performance gap between low- and high-beta portfolios is often around 1% per month. During high-uncertainty periods, the relationship weakens substantially. Key takeaway: The beta anomaly appears highly regime dependent.
The Difficulty of Market Timing: Proximity Matters More Than You Think (Lars N. Kestner)
Getting the direction right isn’t enough in market timing; precision matters. Using 22 years of SPY data, this paper shows that even investors who correctly identify every major turning point lose most of the edge if trades are early or late by more than a few weeks. Timing errors of 8 to 16 weeks quickly converge toward buy-and-hold performance, while only trades within roughly 4 weeks retain meaningful outperformance. Key takeaway: Market timing is less about being right and more about being precisely right; small timing errors can eliminate most of the edge.
Why the Standard Sharpe Ratio Misleads for Market-timing Strategies with Many Zero Return Days (Lars N. Kestner)
Market-timing strategies often look worse on paper than they actually are. This paper shows that the standard Sharpe ratio mechanically declines as time out of the market increases, even when in-market performance is unchanged. Zero-return days create a mixture distribution that depresses Sharpe and increases kurtosis. But when measured only on invested days, Sharpe remains stable. Key takeaway: Standard Sharpe ratios can penalize low-exposure strategies for being inactive, not for having worse returns.
Machine Learning and Large Language Models
On the predictability of ETF returns with technical predictors (Gong and Muller)
A random forest model trained only on global stock technical indicators generates long-short ETF return spreads of 0.76% per month (t=2.76), with volatility and momentum signals carrying most of the predictive power. The edge is strongest in less efficient, lower-liquidity markets like China and fade quickly at longer horizons. Key takeaway: Cross-sectional patterns in individual stocks appear to carry over into ETF returns.
A Comparative Analysis of Regressor Machine Learning Models in Forecasting SPDR S&P 500 ETF Trust (SPY) Movements (Lee and Haldankar)
This paper tests five machine-learning regressors on short-term SPY prediction and finds that adding technical indicators consistently lifts directional accuracy from roughly random levels (46 to 49%) to about 54%. Random Forest benefited the most from added features, while Prophet achieved the highest hit rate at 54.6%. Key takeaway: In short-term market forecasting, feature engineering may matter more than model complexity.
A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective (Zhang and Zhang)
Most investors think LLMs will easily unlock alpha in markets. The reality may be far messier. This review paper argues that many impressive LLM trading results may overstate real-world performance due to data leakage, short sample periods, and illiquidity. In some cases, strategies with extremely high reported Sharpe ratios deteriorate sharply once realistic trading frictions are considered. Key takeaway: The biggest challenge for AI in investing may not be prediction; it’s surviving contact with real markets.
Portfolio Construction
Volatility Scaling in Multi-Asset Portfolios: Evidence from a Systematic Risk-Targeting Strategy (Almeida and Farias)
A simple multi-asset vol-scaled portfolio nearly doubles the Sharpe ratio of a 60/40 benchmark during calm market regimes (0.75 vs 0.41) by mechanically increasing exposure when realized volatility is low. But the tradeoff is severe during crises: De-leveraging leads to underperformance during sharp V-shaped recoveries like 2020. Key takeaway: Volatility scaling works best when markets stay calm but struggles during violent rebounds.
Blogs
Don’t Be Too Smart About History (Quantseeker)
Reinforcement Learning for Optimal Execution (Jonathan Kinlay)
Bad Month for Your Strategy? Should You Change It? (Alvarez Quant Trading)
The Anatomy of a Successful Trend Program (Concretum Group)
Book Review: Wrong Number (CFA Institute)
Podcasts
John Gu – Crypto Market Making & The Cold Start Problem (Flirting with Models)
Martyn Tinsley - 1 of 2 - Building Robust Trading Strategies - The Masterclass (The Algorithmic Advantage)
“If it is easy and obvious, there is no edge in it” - TD Quant Matt Schrager (Odds on Open)
Crisis Alpha, Cocoa Trends, and Correlated Trendlessness: Inside Aspect’s Strategy with Chris Reeve (RCM Alternatives)
Social Media & Industry Research
Why Momentum Investing Has Been Struggling—And What Volatility Has to Do With It (Alpha Architect)
Reads You May Have Missed (Man Group)
Last Week’s Most Popular Links
Variance and Skewness Risk Premium and Expected Equity Returns (Ito)
Multi-Asset Commodities Volatility Portfolio (Dottin)
Getting the Target Right in Return Prediction (Cakici and Zaremba)
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