Weekly Research Recap
Latest research on investing and trading
This week’s Tuesday Roundup brings together the most useful investing ideas I’ve found over the past week, from fresh academic papers and market commentary to standout blog posts. You’ll find links throughout if you want to explore further.
Commodities
Multi-Asset Commodities Volatility Portfolio (Dottin)
Commodities alpha may be less about direction and more about volatility. By selling overpriced insurance (delta-hedged straddles), buying tail hedges, and scaling exposure with inventory signals, this strategy harvests the volatility risk premium while adapting to different regimes, delivering 8% returns at 9% volatility (Sharpe ratio of 0.9). Key takeaway: In commodities, systematically harvesting volatility, conditioned on fundamentals, may be more robust alpha than betting on price direction.
Crypto
Arbitrage trading between decentral and central cryptocurrency exchanges (Schwertfeger and Vogt)
Crypto arbitrage looks easy until you actually execute. Evidence from live trading bots shows that cross-exchange strategies can post very high gross returns (roughly 25 to 33% per month in strong periods), but most of that gets eaten by slippage, fees, limited liquidity, and execution risk. Key takeaway: The challenge isn’t finding arbitrage spreads; it’s capturing them at the price you expect.
Equity
Variance and Skewness Risk Premium and Expected Equity Returns (Ito)
Markets price downside risk. This paper shows that the downside variance risk premium is the key signal, strongly predicting returns at 3 to 6 month horizons, while upside volatility carries little information. At longer horizons, the skewness risk premium dominates with more negative skew (higher tail-risk pricing) predicting higher future returns. Key takeaway: Markets don’t reward volatility equally; they mainly reward bearing downside and tail risk.
Foreign Eyes on Wall Street: Investor Attention and U.S. Stock Reactions (Fan, Nikolsko-Rzhevskyy, and Talavera)
Foreign attention can move U.S. stocks more than most investors realize. When investors outside the U.S. actively search for company filings, those stocks tend to outperform in the short term, but the effect partly reverses, pointing to a role for temporary demand pressure. Key takeaway: Attention from global investors can potentially be traded, but mainly as a short-term signal.
A Levered ETF Anomaly Explained (Bianchi and Goldberg)
Leveraged ETFs underperform in choppy markets; that part is well known. What’s less appreciated is how large the gap can be, and why. Even when the S&P 500 was roughly flat in 2022 to 2023, 2× and 3× ETFs lost about −11% and −28%. It’s not just volatility drag from compounding; it’s amplified by how realized leverage co-moves with returns, systematically eroding performance. Key takeaway: Leveraged ETFs are path-dependent volatility trades, not long-term leverage on market returns.
Hedge Funds
Hedge Fund Awards: Do Investors and Managers Care, and Should They? (Choi, Kang, and Park)
Hedge fund awards look like signals of manager skill, but they mainly reallocate capital rather than predict performance. Winners see sizable inflows, around 0.6% of AUM per month for about a year, yet their returns don’t improve afterward. The effect seems driven by visibility and external validation, not new information. Key takeaway: Awards may boost capital, but they don’t predict alpha, and may even distort it.
Machine Learning and Large Language Models
Getting the Target Right in Return Prediction (Cakici and Zaremba)
Investors try to improve return forecasts by upgrading models, but the bigger lever is the target. Moving from raw to standardized or rank-based returns nearly triples predictive accuracy and lifts returns from 1.0% to 1.6% per month. Raw returns are noisy and market-driven; transformed return targets force models to learn relative performance. Key takeaway: Better targets in return prediction matter more than better models.
From Hypotheses to Factors: Constrained LLM Agents in Cryptocurrency Markets (Huang, Fan, Hu, and Ye)
Most investors chase better models. This paper shows the real edge may come from tighter constraints on the research process. By forcing an LLM to propose testable hypotheses under fixed rules (no data snooping), it discovers a crypto factor portfolio that holds up out-of-sample, 45% annual return, and a Sharpe of 1.55 after costs. Key takeaway: A disciplined discovery process is essential for reliable alpha.
Large Language Models for Asset Pricing: Learning from Earnings Calls (Zhang and Zhou)
This paper uses LLMs to extract features from earnings call transcripts, producing 1.7% per month with a Sharpe of 2.26 out of sample, while also predicting earnings surprises and investment decisions. Key takeaway: Earnings call language contains forward-looking information about fundamentals that markets don’t fully incorporate, but that LLM-based signals can systematically extract.
Do earnings call transcripts predict post-announcement returns? (Molinaro)
Earnings calls contain short-lived alpha. This paper shows that NLP features from transcripts predict post-announcement idiosyncratic returns with 51 to 52% hit rates and statistically significant long–short spreads, concentrated in the first few days (especially 1–3 days) after the call, with the signal fading to noise by one month. Key takeaway: Markets underreact to complex language, but the edge is small and gets priced quickly.
Macro
Could We Predict Macroeconomic Variables With Financial Conditions? (Welch)
Credit-based models used to predict macro conditions break down out of sample, often underperforming simple AR benchmarks and only adding value in crisis periods. Even improved versions require stock returns and winsorization to work (OOS R² of 41%). Key takeaway: Macro predictability is unstable; signals that look strong in-sample often fail out-of-sample.
Portfolio Construction
The economic value of forecasting and strategy gains in volatility timing (Xu, Aschakulporn, and Zhang)
Volatility timing gains come mainly from better risk estimation. Using a stochastic volatility model improves Sharpe ratios, while volatility-managed portfolios don’t create new alpha; they largely mirror optimized mean–variance allocations, even if they can outperform simpler approaches. Key takeaway: Better risk models drive alpha.
Prediction Markets
Arbitrage Analysis in Polymarket NBA Markets (Cheng, Yang, and Zou)
Some think arbitrage in prediction markets is easy. Not in Polymarket’s NBA markets. Across 75M+ order book snapshots, pricing gaps are rare and disappear within seconds. Even when cross-market trades (moneyline vs spread) offer 1% edges, position sizes are tiny, and liquidity is the real constraint. Key takeaway: The challenge isn’t spotting mispricing; it’s executing it, and capacity kills scalability.
Blogs
The Iran War Doesn’t Have to Be a Rerun of ‘That ’70s Show’ (John H. Cochrane)
Which Macro Indicators Actually Predict Market Drawdowns? (Quantseeker)
Deep Learning for Volatility Surface Repair (Jonathan Kinlay)
Curve trades with macroeconomic signals (Macrosynergy)
Geopolitical Shocks: What Moves First and Why It Matters (CFA Institute)
For The Love of The Game (Robot Wealth)
How to Build a Futures Database in Python using Norgate Data (Concretum Group)
You Can Trade (Almost) Like Mulvaney (Concretum Group)
Podcasts
Ex-Tudor Quant PM: “There Hasn’t Been a New Idea in Trading for 15 Years” (Odds on Open)
What 43 Years in the Markets Teaches You · Stephen Kalayjian (Chat with Traders)
The Last Moat | Chris Mayer and Ian Cassel on the Stock Picking Edge AI Can’t Replicate (Excess Returns)
Social Media & Industry Research
Rethinking Trend Following: Optimal Regime-Dependent Allocation (Alpha Architect)
Last Week’s Most Popular Links
Call the Zookeeper: A Unified Framework for Commodity Risk Premiums (Fan, Li, Qiao, and Zhang)
A Comparative Analysis of Overnight vs. Daytime Static and Momentum Strategies Across Sector ETFs (Salotra, Katikireddy, Anumolu, and Pinsky)
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