Weekly Research Recap
Latest research on investing and trading
Every Tuesday, I share the most interesting investing and market insights I found during the week, including new research papers, blog posts, and podcasts. I’ve included links throughout for readers who want to explore the ideas further.
Commodities
ENSO Signals and Out-of-Sample Predictability in Soft Commodity Futures (Apte)
Using NOAA El Niño data alongside price-based features for coffee, cocoa, sugar, cotton, and orange juice futures, a simple Ridge model achieves an out-of-sample Sharpe ratio above 1 after transaction costs. Climate variables alone have little standalone predictive power, but improve risk-adjusted performance by roughly 10% when combined with market signals. Key takeaway: Public climate data may still be only partially reflected in commodity prices.
Crypto
Are Day-of-the-Week Effects in Cryptocurrencies Real? Intraday Evidence from Active and Less Active Cryptocurrencies (Aalipour, Mehdian, and Rezvanian)
Most crypto “calendar anomalies” may be statistical illusions created by daily data aggregation. This paper utilizes hourly data across 12 cryptocurrencies and finds that apparent day-of-week effects are typically driven by just a few isolated intraday hours, rather than persistent daily behavior. Bitcoin’s “Monday effect,” for example, comes largely from only two specific hours. Key takeaway: Many crypto inefficiencies are short-lived microstructure effects rather than durable alpha signals.
What Do Crypto Options Tell Us? Risk Premia Implied by BTC Option Prices (Atanasova, Miao, Segarra, and Willeboordse)
Bitcoin options may contain a genuine risk-premia structure, not just speculative flow. This paper finds that option-implied factors, especially volatility-of-volatility, help predict future BTC excess returns, while crypto variance risk premia remain highly persistent even without strong institutional hedging demand. Key takeaway: Crypto derivatives are evolving into a genuine risk-transfer market with their own priced risk factors.
Arbitrage trading between decentral and central cryptocurrency exchanges (Schwertfeger and Vogt)
This paper analyzes live high-frequency arbitrage between decentralized exchanges (DEXs) and centralized exchanges (CEXs). Four arbitrage bots generate monthly returns between 6% and 33%, but realized profits are heavily constrained by slippage, liquidity limits, and latency. Key takeaway: The edge in crypto arbitrage is increasingly coming from execution infrastructure.
Equities
Using 440k+ analyst reports from China’s A-share market, this paper finds that “Buy” and “Strong Buy” recommendations outperform comparable stocks by roughly 2.7 to 2.9% over the next 240 trading days. But much of the move starts before publication. The real edge may come from analysts identifying improving earnings and undervalued firms before the market fully reprices them. Key takeaway: Sell-side research still contain slow-moving information in less efficient markets.
Do Insider Trading Profits Result from the Superior Processing of Public Information? Evidence from Subtle Peer Firms’ Earnings Announcements (Campbell, Raleigh, and Zhao)
Most investors assume insider trading profits come from private information. This paper suggests another edge: Superior processing of subtle public signals. The authors show insiders buy their own company’s stock after positive earnings surprises from linked firms. CEOs appear especially skilled, and these trades earn annualized alphas of roughly 14 to 28%. Key takeaway: Some insider alpha come from spotting information spillovers before the market does.
Over 30 years of rolling 2-year windows, at-the-money SPY LEAPS produced average returns above 100%, versus roughly 18% for SPY itself, with realized amplification near 5x. But the tradeoff is real: Some LEAPS expired worthless, volatility was extreme, and risk-adjusted performance was not materially better than simply owning SPY. Key takeaway: LEAPS may offer convex exposure to long-run equity growth.
Machine Learning and Large Language Models
Dynamic Momentum Trading via Deep Q-Networks: An Intelligent Execution Framework for Portfolio Management (Deng, Xu, Li, Ji, and Xu)
This paper combines momentum signals, LSTM forecasts, portfolio optimization, and reinforcement learning that dynamically decides when to enter or exit trades. Across U.S. and Chinese equities, the framework improves Sharpe ratios and reduces momentum-crash drawdowns versus static strategies. Key takeaway: Adaptive execution matter as much as the signal itself in momentum investing.
Portfolio Construction
Portfolio Risk Parity with different Risk Aversion Degrees (Piantoni and Lozza)
Using S&P 500 stocks, the authors show that classic risk parity increasingly converges toward 1/N allocation as the number of holdings rises. But a Gini-based version, designed to emphasize downside and tail-risk protection rather than overall volatility, delivers stronger drawdown control and, in several tests, superior risk-adjusted performance during stressed markets. Key takeaway: The real innovation in risk parity may come from explicitly targeting tail risk, not just equalizing volatility.
Volatility
Maturity Alignment Matters: The Predictive Power of the Variance Risk Premium (Plihal and Mampouya)
Using SPY options from 2013 to 2025, the authors show that variance risk premia forecast realized volatility far better when option expiries closely match the prediction horizon. Forecast gains became strongest after SPY options moved to daily expirations. Key takeaway: Volatility signals lose information when the option maturity and forecast window are misaligned.
Blogs
How to Manage an Intraday Trend Trade (Concretum Group)
What the Front End of the VIX Curve Knows (Quantseeker)
How to build macro-aware equity indices (Macrosynergy)
Agentic Workflows for Alpha Research (Jonathan Kinlay)
Podcasts
“Market Crashes Are Good for My Strategy” - One-Man Hedge Fund PM George Livadas (Odds on Open)
When Crisis Alpha Hides in Plain Sight ft. Yoav Git & Rob Croce (Top Traders Unplugged)
He Invested Through Five Bubbles. He Wrote the Book on Them | What We Learned This Week (Excess Returns)
Last Week’s Most Popular Links
Systematic Tactical Allocation in Emerging Markets vs. U.S.: A Momentum-Based Approach (Vojtko and Dujava)
Dual Momentum Allocation Between Physical Gold and Bitcoin (Digital Gold) (Vojtko and Dujava)
Volatility Scaling in Multi-Asset Portfolios: Evidence from a Systematic Risk-Targeting Strategy (Almeida and Farias)
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